diff --git a/kan/.ipynb_checkpoints/KANLayer-checkpoint.py b/kan/.ipynb_checkpoints/KANLayer-checkpoint.py index acd22f84..4e671934 100644 --- a/kan/.ipynb_checkpoints/KANLayer-checkpoint.py +++ b/kan/.ipynb_checkpoints/KANLayer-checkpoint.py @@ -63,7 +63,7 @@ class KANLayer(nn.Module): unlock already locked activation functions """ - def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base=1.0, scale_sp=1.0, base_fun=torch.nn.SiLU(), grid_eps=0.02, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, save_plot_data = True, device='cpu', sparse_init=False): + def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.5, scale_base_mu=0.0, scale_base_sigma=1.0, scale_sp=1.0, base_fun=torch.nn.SiLU(), grid_eps=0.02, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, save_plot_data = True, device='cpu', sparse_init=False): '''' initialize a KANLayer @@ -119,7 +119,7 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base= grid = torch.linspace(grid_range[0], grid_range[1], steps=num + 1)[None,:].expand(self.in_dim, num+1) grid = extend_grid(grid, k_extend=k) self.grid = torch.nn.Parameter(grid).requires_grad_(False) - noises = (torch.rand(self.num+1, self.in_dim, self.out_dim) - 1 / 2) * noise_scale / num + noises = (torch.rand(self.num+1, self.in_dim, self.out_dim) - 1/2) * noise_scale / num # shape: (size, coef) self.coef = torch.nn.Parameter(curve2coef(self.grid[:,k:-k].permute(1,0), noises, self.grid, k)) #if isinstance(scale_base, float): @@ -128,7 +128,9 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base= else: mask = 1. - self.scale_base = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_base * mask).requires_grad_(sb_trainable) # make scale trainable + self.scale_base = torch.nn.Parameter(scale_base_mu * 1 / np.sqrt(in_dim) + \ + scale_base_sigma * (torch.rand(in_dim, out_dim)*2-1) * 1/np.sqrt(in_dim)) + #self.scale_base = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_base * mask).requires_grad_(sb_trainable) # make scale trainable #else: #self.scale_base = torch.nn.Parameter(scale_base.to(device)).requires_grad_(sb_trainable) self.scale_sp = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_sp * mask).requires_grad_(sp_trainable) # make scale trainable @@ -193,7 +195,7 @@ def forward(self, x): y = torch.sum(y, dim=1) # shape (batch, out_dim) return y, preacts, postacts, postspline - def update_grid_from_samples(self, x): + def update_grid_from_samples(self, x, mode='sample'): ''' update grid from samples @@ -216,21 +218,32 @@ def update_grid_from_samples(self, x): tensor([[-1.0000, -0.6000, -0.2000, 0.2000, 0.6000, 1.0000]]) tensor([[-3.0002, -1.7882, -0.5763, 0.6357, 1.8476, 3.0002]]) ''' + batch = x.shape[0] #x = torch.einsum('ij,k->ikj', x, torch.ones(self.out_dim, ).to(self.device)).reshape(batch, self.size).permute(1, 0) x_pos = torch.sort(x, dim=0)[0] y_eval = coef2curve(x_pos, self.grid, self.coef, self.k) num_interval = self.grid.shape[1] - 1 - 2*self.k - ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1] - grid_adaptive = x_pos[ids, :].permute(1,0) - margin = 0.01 - h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval - grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device) - grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive + + def get_grid(num_interval): + ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1] + grid_adaptive = x_pos[ids, :].permute(1,0) + h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval + grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device) + grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive + return grid + + grid = get_grid(num_interval) + + if mode == 'grid': + sample_grid = get_grid(2*num_interval) + x_pos = sample_grid.permute(1,0) + y_eval = coef2curve(x_pos, self.grid, self.coef, self.k) + self.grid.data = extend_grid(grid, k_extend=self.k) self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k) - def initialize_grid_from_parent(self, parent, x): + def initialize_grid_from_parent(self, parent, x, mode='sample'): ''' update grid from a parent KANLayer & samples @@ -258,19 +271,31 @@ def initialize_grid_from_parent(self, parent, x): tensor([[-1.0000, -0.8000, -0.6000, -0.4000, -0.2000, 0.0000, 0.2000, 0.4000, 0.6000, 0.8000, 1.0000]]) ''' + batch = x.shape[0] - # preacts: shape (batch, in_dim) => shape (size, batch) (size = out_dim * in_dim) - #x_eval = torch.einsum('ij,k->ikj', x, torch.ones(self.out_dim, ).to(self.device)).reshape(batch, self.size).permute(1, 0) - x_eval = x - pgrid = parent.grid # (in_dim, G+2*k+1) - pk = parent.k - y_eval = coef2curve(x_eval, pgrid, parent.coef, pk) - h = (pgrid[:,[-pk]] - pgrid[:,[pk]])/self.num - grid = pgrid[:,[pk]] + torch.arange(self.num+1,) * h + x_pos = torch.sort(x, dim=0)[0] + y_eval = coef2curve(x_pos, parent.grid, parent.coef, parent.k) + num_interval = self.grid.shape[1] - 1 - 2*self.k + + def get_grid(num_interval): + ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1] + grid_adaptive = x_pos[ids, :].permute(1,0) + h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval + grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device) + grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive + return grid + + grid = get_grid(num_interval) + + if mode == 'grid': + sample_grid = get_grid(2*num_interval) + x_pos = sample_grid.permute(1,0) + y_eval = coef2curve(x_pos, parent.grid, parent.coef, parent.k) + grid = extend_grid(grid, k_extend=self.k) self.grid.data = grid - self.coef.data = curve2coef(x_eval, y_eval, self.grid, self.k) + self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k) def get_subset(self, in_id, out_id): ''' diff --git a/kan/.ipynb_checkpoints/MultKAN-checkpoint.py b/kan/.ipynb_checkpoints/MultKAN-checkpoint.py index ead5b444..796f6ad6 100644 --- a/kan/.ipynb_checkpoints/MultKAN-checkpoint.py +++ b/kan/.ipynb_checkpoints/MultKAN-checkpoint.py @@ -24,7 +24,7 @@ class MultKAN(nn.Module): # include mult_ops = [] - def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=1.0, scale_base_mu=0.0, scale_base_sigma=1.0, base_fun='silu', symbolic_enabled=True, affine_trainable=False, grid_eps=1.0, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, seed=1, save_act=True, sparse_init=False, auto_save=True, first_init=True, ckpt_path='./model', state_id=0, round=0, device='cpu'): + def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=0.3, scale_base_mu=0.0, scale_base_sigma=1.0, base_fun='silu', symbolic_enabled=True, affine_trainable=False, grid_eps=0.02, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, seed=1, save_act=True, sparse_init=False, auto_save=True, first_init=True, ckpt_path='./model', state_id=0, round=0, device='cpu'): super(MultKAN, self).__init__() @@ -60,6 +60,8 @@ def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=1.0, sca base_fun = torch.nn.SiLU() elif base_fun == 'identity': base_fun = torch.nn.Identity() + elif base_fun == 'zero': + base_fun = lambda x: x*0. self.grid_eps = grid_eps self.grid_range = grid_range @@ -67,9 +69,7 @@ def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=1.0, sca for l in range(self.depth): # splines - scale_base = scale_base_mu * 1 / np.sqrt(width_in[l]) + \ - scale_base_sigma * (torch.randn(width_in[l], width_out[l + 1]) * 2 - 1) * 1/np.sqrt(width_in[l]) - sp_batch = KANLayer(in_dim=width_in[l], out_dim=width_out[l+1], num=grid, k=k, noise_scale=noise_scale, scale_base=scale_base, scale_sp=1., base_fun=base_fun, grid_eps=grid_eps, grid_range=grid_range, sp_trainable=sp_trainable, sb_trainable=sb_trainable, sparse_init=sparse_init) + sp_batch = KANLayer(in_dim=width_in[l], out_dim=width_out[l+1], num=grid, k=k, noise_scale=noise_scale, scale_base_mu=scale_base_mu, scale_base_sigma=scale_base_sigma, scale_sp=1., base_fun=base_fun, grid_eps=grid_eps, grid_range=grid_range, sp_trainable=sp_trainable, sb_trainable=sb_trainable, sparse_init=sparse_init) self.act_fun.append(sp_batch) self.node_bias = [] @@ -185,7 +185,7 @@ def initialize_from_another_model(self, another_model, x): for l in range(self.depth): self.symbolic_fun[l] = another_model.symbolic_fun[l] - return self.to(device) + return self.to(self.device) def log_history(self, method_name): @@ -221,7 +221,8 @@ def refine(self, new_grid): auto_save=True, first_init=False, state_id=self.state_id, - round=self.round) + round=self.round, + device=self.device) model_new.initialize_from_another_model(self, self.cache_data) model_new.cache_data = self.cache_data diff --git a/kan/.ipynb_checkpoints/spline-checkpoint.py b/kan/.ipynb_checkpoints/spline-checkpoint.py index 6a14510c..705c7d61 100644 --- a/kan/.ipynb_checkpoints/spline-checkpoint.py +++ b/kan/.ipynb_checkpoints/spline-checkpoint.py @@ -120,7 +120,7 @@ def coef2curve(x_eval, grid, coef, k, device="cpu"): return y_eval -def curve2coef(x_eval, y_eval, grid, k): +def curve2coef(x_eval, y_eval, grid, k, lamb=1e-8): ''' converting B-spline curves to B-spline coefficients using least squares. @@ -163,10 +163,20 @@ def curve2coef(x_eval, y_eval, grid, k): mat = mat.permute(1,0,2)[:,None,:,:].expand(in_dim, out_dim, batch, n_coef) # (in_dim, out_dim, batch, n_coef) # coef shape: (in_dim, outdim, G+k) y_eval = y_eval.permute(1,2,0).unsqueeze(dim=3) # y_eval: (in_dim, out_dim, batch, 1) - #print(mat) device = mat.device - coef = torch.linalg.lstsq(mat, y_eval, - driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0] + + + #coef = torch.linalg.lstsq(mat, y_eval, + #driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0] + + XtX = torch.einsum('ijmn,ijnp->ijmp', mat.permute(0,1,3,2), mat) + Xty = torch.einsum('ijmn,ijnp->ijmp', mat.permute(0,1,3,2), y_eval) + n1, n2, n = XtX.shape[0], XtX.shape[1], XtX.shape[2] + identity = torch.eye(n,n)[None, None, :, :].expand(n1, n2, n, n).to(device) + A = XtX + lamb * identity + B = Xty + coef = (A.pinverse() @ B)[:,:,:,0] + return coef diff --git a/kan/KANLayer.py b/kan/KANLayer.py index acd22f84..4e671934 100644 --- a/kan/KANLayer.py +++ b/kan/KANLayer.py @@ -63,7 +63,7 @@ class KANLayer(nn.Module): unlock already locked activation functions """ - def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base=1.0, scale_sp=1.0, base_fun=torch.nn.SiLU(), grid_eps=0.02, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, save_plot_data = True, device='cpu', sparse_init=False): + def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.5, scale_base_mu=0.0, scale_base_sigma=1.0, scale_sp=1.0, base_fun=torch.nn.SiLU(), grid_eps=0.02, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, save_plot_data = True, device='cpu', sparse_init=False): '''' initialize a KANLayer @@ -119,7 +119,7 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base= grid = torch.linspace(grid_range[0], grid_range[1], steps=num + 1)[None,:].expand(self.in_dim, num+1) grid = extend_grid(grid, k_extend=k) self.grid = torch.nn.Parameter(grid).requires_grad_(False) - noises = (torch.rand(self.num+1, self.in_dim, self.out_dim) - 1 / 2) * noise_scale / num + noises = (torch.rand(self.num+1, self.in_dim, self.out_dim) - 1/2) * noise_scale / num # shape: (size, coef) self.coef = torch.nn.Parameter(curve2coef(self.grid[:,k:-k].permute(1,0), noises, self.grid, k)) #if isinstance(scale_base, float): @@ -128,7 +128,9 @@ def __init__(self, in_dim=3, out_dim=2, num=5, k=3, noise_scale=0.1, scale_base= else: mask = 1. - self.scale_base = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_base * mask).requires_grad_(sb_trainable) # make scale trainable + self.scale_base = torch.nn.Parameter(scale_base_mu * 1 / np.sqrt(in_dim) + \ + scale_base_sigma * (torch.rand(in_dim, out_dim)*2-1) * 1/np.sqrt(in_dim)) + #self.scale_base = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_base * mask).requires_grad_(sb_trainable) # make scale trainable #else: #self.scale_base = torch.nn.Parameter(scale_base.to(device)).requires_grad_(sb_trainable) self.scale_sp = torch.nn.Parameter(torch.ones(in_dim, out_dim) * scale_sp * mask).requires_grad_(sp_trainable) # make scale trainable @@ -193,7 +195,7 @@ def forward(self, x): y = torch.sum(y, dim=1) # shape (batch, out_dim) return y, preacts, postacts, postspline - def update_grid_from_samples(self, x): + def update_grid_from_samples(self, x, mode='sample'): ''' update grid from samples @@ -216,21 +218,32 @@ def update_grid_from_samples(self, x): tensor([[-1.0000, -0.6000, -0.2000, 0.2000, 0.6000, 1.0000]]) tensor([[-3.0002, -1.7882, -0.5763, 0.6357, 1.8476, 3.0002]]) ''' + batch = x.shape[0] #x = torch.einsum('ij,k->ikj', x, torch.ones(self.out_dim, ).to(self.device)).reshape(batch, self.size).permute(1, 0) x_pos = torch.sort(x, dim=0)[0] y_eval = coef2curve(x_pos, self.grid, self.coef, self.k) num_interval = self.grid.shape[1] - 1 - 2*self.k - ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1] - grid_adaptive = x_pos[ids, :].permute(1,0) - margin = 0.01 - h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval - grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device) - grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive + + def get_grid(num_interval): + ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1] + grid_adaptive = x_pos[ids, :].permute(1,0) + h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval + grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device) + grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive + return grid + + grid = get_grid(num_interval) + + if mode == 'grid': + sample_grid = get_grid(2*num_interval) + x_pos = sample_grid.permute(1,0) + y_eval = coef2curve(x_pos, self.grid, self.coef, self.k) + self.grid.data = extend_grid(grid, k_extend=self.k) self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k) - def initialize_grid_from_parent(self, parent, x): + def initialize_grid_from_parent(self, parent, x, mode='sample'): ''' update grid from a parent KANLayer & samples @@ -258,19 +271,31 @@ def initialize_grid_from_parent(self, parent, x): tensor([[-1.0000, -0.8000, -0.6000, -0.4000, -0.2000, 0.0000, 0.2000, 0.4000, 0.6000, 0.8000, 1.0000]]) ''' + batch = x.shape[0] - # preacts: shape (batch, in_dim) => shape (size, batch) (size = out_dim * in_dim) - #x_eval = torch.einsum('ij,k->ikj', x, torch.ones(self.out_dim, ).to(self.device)).reshape(batch, self.size).permute(1, 0) - x_eval = x - pgrid = parent.grid # (in_dim, G+2*k+1) - pk = parent.k - y_eval = coef2curve(x_eval, pgrid, parent.coef, pk) - h = (pgrid[:,[-pk]] - pgrid[:,[pk]])/self.num - grid = pgrid[:,[pk]] + torch.arange(self.num+1,) * h + x_pos = torch.sort(x, dim=0)[0] + y_eval = coef2curve(x_pos, parent.grid, parent.coef, parent.k) + num_interval = self.grid.shape[1] - 1 - 2*self.k + + def get_grid(num_interval): + ids = [int(batch / num_interval * i) for i in range(num_interval)] + [-1] + grid_adaptive = x_pos[ids, :].permute(1,0) + h = (grid_adaptive[:,[-1]] - grid_adaptive[:,[0]])/num_interval + grid_uniform = grid_adaptive[:,[0]] + h * torch.arange(num_interval+1,)[None, :].to(x.device) + grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive + return grid + + grid = get_grid(num_interval) + + if mode == 'grid': + sample_grid = get_grid(2*num_interval) + x_pos = sample_grid.permute(1,0) + y_eval = coef2curve(x_pos, parent.grid, parent.coef, parent.k) + grid = extend_grid(grid, k_extend=self.k) self.grid.data = grid - self.coef.data = curve2coef(x_eval, y_eval, self.grid, self.k) + self.coef.data = curve2coef(x_pos, y_eval, self.grid, self.k) def get_subset(self, in_id, out_id): ''' diff --git a/kan/MultKAN.py b/kan/MultKAN.py index ead5b444..796f6ad6 100644 --- a/kan/MultKAN.py +++ b/kan/MultKAN.py @@ -24,7 +24,7 @@ class MultKAN(nn.Module): # include mult_ops = [] - def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=1.0, scale_base_mu=0.0, scale_base_sigma=1.0, base_fun='silu', symbolic_enabled=True, affine_trainable=False, grid_eps=1.0, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, seed=1, save_act=True, sparse_init=False, auto_save=True, first_init=True, ckpt_path='./model', state_id=0, round=0, device='cpu'): + def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=0.3, scale_base_mu=0.0, scale_base_sigma=1.0, base_fun='silu', symbolic_enabled=True, affine_trainable=False, grid_eps=0.02, grid_range=[-1, 1], sp_trainable=True, sb_trainable=True, seed=1, save_act=True, sparse_init=False, auto_save=True, first_init=True, ckpt_path='./model', state_id=0, round=0, device='cpu'): super(MultKAN, self).__init__() @@ -60,6 +60,8 @@ def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=1.0, sca base_fun = torch.nn.SiLU() elif base_fun == 'identity': base_fun = torch.nn.Identity() + elif base_fun == 'zero': + base_fun = lambda x: x*0. self.grid_eps = grid_eps self.grid_range = grid_range @@ -67,9 +69,7 @@ def __init__(self, width=None, grid=3, k=3, mult_arity = 2, noise_scale=1.0, sca for l in range(self.depth): # splines - scale_base = scale_base_mu * 1 / np.sqrt(width_in[l]) + \ - scale_base_sigma * (torch.randn(width_in[l], width_out[l + 1]) * 2 - 1) * 1/np.sqrt(width_in[l]) - sp_batch = KANLayer(in_dim=width_in[l], out_dim=width_out[l+1], num=grid, k=k, noise_scale=noise_scale, scale_base=scale_base, scale_sp=1., base_fun=base_fun, grid_eps=grid_eps, grid_range=grid_range, sp_trainable=sp_trainable, sb_trainable=sb_trainable, sparse_init=sparse_init) + sp_batch = KANLayer(in_dim=width_in[l], out_dim=width_out[l+1], num=grid, k=k, noise_scale=noise_scale, scale_base_mu=scale_base_mu, scale_base_sigma=scale_base_sigma, scale_sp=1., base_fun=base_fun, grid_eps=grid_eps, grid_range=grid_range, sp_trainable=sp_trainable, sb_trainable=sb_trainable, sparse_init=sparse_init) self.act_fun.append(sp_batch) self.node_bias = [] @@ -185,7 +185,7 @@ def initialize_from_another_model(self, another_model, x): for l in range(self.depth): self.symbolic_fun[l] = another_model.symbolic_fun[l] - return self.to(device) + return self.to(self.device) def log_history(self, method_name): @@ -221,7 +221,8 @@ def refine(self, new_grid): auto_save=True, first_init=False, state_id=self.state_id, - round=self.round) + round=self.round, + device=self.device) model_new.initialize_from_another_model(self, self.cache_data) model_new.cache_data = self.cache_data diff --git a/kan/spline.py b/kan/spline.py index 6a14510c..705c7d61 100644 --- a/kan/spline.py +++ b/kan/spline.py @@ -120,7 +120,7 @@ def coef2curve(x_eval, grid, coef, k, device="cpu"): return y_eval -def curve2coef(x_eval, y_eval, grid, k): +def curve2coef(x_eval, y_eval, grid, k, lamb=1e-8): ''' converting B-spline curves to B-spline coefficients using least squares. @@ -163,10 +163,20 @@ def curve2coef(x_eval, y_eval, grid, k): mat = mat.permute(1,0,2)[:,None,:,:].expand(in_dim, out_dim, batch, n_coef) # (in_dim, out_dim, batch, n_coef) # coef shape: (in_dim, outdim, G+k) y_eval = y_eval.permute(1,2,0).unsqueeze(dim=3) # y_eval: (in_dim, out_dim, batch, 1) - #print(mat) device = mat.device - coef = torch.linalg.lstsq(mat, y_eval, - driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0] + + + #coef = torch.linalg.lstsq(mat, y_eval, + #driver='gelsy' if device == 'cpu' else 'gels').solution[:,:,:,0] + + XtX = torch.einsum('ijmn,ijnp->ijmp', mat.permute(0,1,3,2), mat) + Xty = torch.einsum('ijmn,ijnp->ijmp', mat.permute(0,1,3,2), y_eval) + n1, n2, n = XtX.shape[0], XtX.shape[1], XtX.shape[2] + identity = torch.eye(n,n)[None, None, :, :].expand(n1, n2, n, n).to(device) + A = XtX + lamb * identity + B = Xty + coef = (A.pinverse() @ B)[:,:,:,0] + return coef diff --git a/tutorials/.ipynb_checkpoints/API_9_video-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/API_9_video-checkpoint.ipynb index 27c39d53..c5a723c9 100644 --- a/tutorials/.ipynb_checkpoints/API_9_video-checkpoint.ipynb +++ b/tutorials/.ipynb_checkpoints/API_9_video-checkpoint.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "2075ef56", "metadata": { "tags": [] @@ -31,7 +31,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.90e-01 | test_loss: 3.15e-01 | reg: 1.18e+01 | : 100%|█| 5/5 [00:09<00:00, 1.98s/it" + "| train_loss: 2.89e-01 | test_loss: 2.96e-01 | reg: 1.31e+01 | : 100%|█| 5/5 [00:09<00:00, 1.94s/it" ] }, { @@ -57,7 +57,7 @@ "print(device)\n", "\n", "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", - "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=2, device=device)\n", + "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1, device=device)\n", "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n", "dataset = create_dataset(f, n_var=4, train_num=3000, device=device)\n", "\n", diff --git a/tutorials/.ipynb_checkpoints/Example_10_relativity-addition-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_10_relativity-addition-checkpoint.ipynb new file mode 100644 index 00000000..50d49d0e --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_10_relativity-addition-checkpoint.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 10: Relativitistic Velocity Addition" + ] + }, + { + "cell_type": "markdown", + "id": "6465ec94", + "metadata": {}, + "source": [ + "In this example, we will symbolically regress $f(u,v)=\\frac{u+v}{1+uv}$. In relavitity, we know the rapidity trick $f(u,v)={\\rm tanh}({\\rm arctanh}\\ u+{\\rm arctanh}\\ v)$. Can we rediscover rapidity trick with KAN?" + ] + }, + { + "cell_type": "markdown", + "id": "94056ef6", + "metadata": {}, + "source": [ + "Intialize model and create dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a59179d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + } + ], + "source": [ + "from kan import *\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "# initialize KAN with G=3\n", + "model = KAN(width=[2,1,1], grid=10, k=3, device=device)\n", + "\n", + "# create dataset\n", + "f = lambda x: (x[:,[0]]+x[:,[1]])/(1+x[:,[0]]*x[:,[1]])\n", + "dataset = create_dataset(f, n_var=2, ranges=[-0.9,0.9], device=device)" + ] + }, + { + "cell_type": "markdown", + "id": "cb1f817e", + "metadata": {}, + "source": [ + "Train KAN and plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a87b97b0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 2.28e-03 | test_loss: 2.31e-03 | reg: 6.50e+00 | : 100%|█| 20/20 [00:03<00:00, 5.88it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3f1cfc9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot(beta=10)" + ] + }, + { + "cell_type": "markdown", + "id": "5ca6421a", + "metadata": {}, + "source": [ + "Retrain the model, the loss remains similar, meaning that the locking does not degrade model behavior, justifying our hypothesis that these two activation functions are the same. Let's now determine what this function is using $\\texttt{suggest_symbolic}$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2ccb7048", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " function fitting r2 r2 loss complexity complexity loss total loss\n", + "0 arctanh 0.999992 -15.786788 4 4 -15.786788\n", + "1 tan 0.999825 -12.397871 3 3 -12.397871\n", + "2 arccos 0.998852 -9.753944 4 4 -9.753944\n", + "3 arcsin 0.998852 -9.753944 4 4 -9.753944\n", + "4 sqrt 0.982166 -5.808383 2 2 -5.808383\n" + ] + }, + { + "data": { + "text/plain": [ + "('arctanh',\n", + " ((x)>,\n", + " (x)>,\n", + " 4,\n", + " (x, y_th)>),\n", + " 0.999992311000824,\n", + " 4)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.suggest_symbolic(0,1,0,weight_simple=0.0)" + ] + }, + { + "cell_type": "markdown", + "id": "0092be41", + "metadata": {}, + "source": [ + "We can see that ${\\rm arctanh}$ is at the top of the suggestion list! So we can set both to arctanh, retrain the model, and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1bb96fe1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is 0.9999759197235107\n", + "saving model version 0.2\n", + "r2 is 0.999992311000824\n", + "saving model version 0.3\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(1.0000, device='cuda:0')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fix_symbolic(0,0,0,'arctanh')\n", + "model.fix_symbolic(0,1,0,'arctanh')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "83b852a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 7.94e-04 | test_loss: 9.43e-04 | reg: 4.12e+00 | : 100%|█| 20/20 [00:04<00:00, 4.34it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=20, update_grid=False);" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9ccd0923", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot(beta=10)" + ] + }, + { + "cell_type": "markdown", + "id": "4b98a727", + "metadata": {}, + "source": [ + "We will see that ${\\rm tanh}$ is at the top of the suggestion list! So we can set it to ${\\rm tanh}$, retrain the model to machine precision, plot it and finally get the symbolic formula." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "99ad38b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " function fitting r2 r2 loss complexity complexity loss total loss\n", + "0 tanh 0.999998 -16.336284 3 3 -16.336284\n", + "1 arctan 0.999435 -10.764618 4 4 -10.764618\n", + "2 cos 0.995899 -7.926177 2 2 -7.926177\n", + "3 sin 0.995899 -7.926177 2 2 -7.926177\n", + "4 gaussian 0.994457 -7.492519 3 3 -7.492519\n" + ] + }, + { + "data": { + "text/plain": [ + "('tanh',\n", + " ((x)>,\n", + " (x)>,\n", + " 3,\n", + " (x, y_th)>),\n", + " 0.9999979138374329,\n", + " 3)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.suggest_symbolic(1,0,0,weight_simple=0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "af24c80d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is 0.9999979138374329\n", + "saving model version 0.5\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(1.0000, device='cuda:0')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fix_symbolic(1,0,0,'tanh')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "01936f17", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.97e-06 | test_loss: 2.06e-06 | reg: 0.00e+00 | : 100%|█| 2000/2000 [00:21<00:00, 93.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.6\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"Adam\", lr=1e-3, steps=2000, update_grid=False, singularity_avoiding=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "76bcc188", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b62b0246", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\tanh{\\left(\\operatorname{atanh}{\\left(x_{1} \\right)} + \\operatorname{atanh}{\\left(x_{2} \\right)} \\right)}$" + ], + "text/plain": [ + "tanh(atanh(x_1) + atanh(x_2))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "formula = model.symbolic_formula()[0][0]\n", + "nsimplify(ex_round(formula, 4))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_11_encouraing_linear-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_11_encouraing_linear-checkpoint.ipynb new file mode 100644 index 00000000..ee212632 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_11_encouraing_linear-checkpoint.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "095b0666", + "metadata": {}, + "source": [ + "# Example 11: Encouraging linearity\n", + "\n", + "In cases where we don't know how deep we should set KANs to be, one strategy is to try from small models, grudually making models wider/deeper until we find the minimal model that performs the task quite well. Another strategy is to start from a big enough model and prune it down. This jupyter notebook demonstrates cases where we go for the second strategy. Besides sparsity along width, we also want activation functions to be linear ('shortcut' along depth)." + ] + }, + { + "cell_type": "markdown", + "id": "ef047a0f", + "metadata": {}, + "source": [ + "There are two relevant tricks: \n", + "\n", + "(1) set the base function 'base_fun' to be linear; \n", + "\n", + "(2) penalize spline coefficients. When spline coefficients are zero, the activation function is linear." + ] + }, + { + "cell_type": "markdown", + "id": "91301ca0", + "metadata": {}, + "source": [ + "$f(x)={\\rm sin}(\\pi x)$. Although we know a [1,1] KAN suffices, we suppose we don't know that and use a [1,1,1,1] KAN instead." + ] + }, + { + "cell_type": "markdown", + "id": "77f9e16d", + "metadata": {}, + "source": [ + "without trick" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c881665b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.74e-04 | test_loss: 3.84e-04 | reg: 8.88e+00 | : 100%|█| 20/20 [00:05<00:00, 3.79it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from kan import *\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "# create dataset f(x,y) = sin(pi*x). This task can be achieved by a [1,1] KAN\n", + "f = lambda x: torch.sin(torch.pi*x[:,[0]])\n", + "dataset = create_dataset(f, n_var=1, device=device)\n", + "\n", + "model = KAN(width=[1,1,1,1], grid=5, k=3, seed=0, noise_scale=0.1, device=device)\n", + "\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "201ceacf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAHiCAYAAAAkiYF/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvVElEQVR4nO3de1SVdb7H8c+z90bYpIjgDcdKQTzhnDmZ1Xi0M42OjMyklqVNos6xpotZSaUlY5rdC5cVoE2W19LJrjjZEi+VOd1l1JjpVCszGQsQJZF9lIvB3s/v/DHKQTNFeWDD5v1ay7VcbHZ+Xcvdm9/zey6WMcYIAAAHuYI9AAAg9BAXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAc5wn2AEBrYIxRWVmZKioq1L59e8XGxsqyrGCPBbRYrFyAk/D5fMrOzlZiYqK6dOmi3r17q0uXLkpMTFR2drZ8Pl+wRwRaJIsnUQIntnHjRo0ZM0ZVVVWS/rV6OeroqiUyMlI5OTlKSUkJyoxAS0VcgBPYuHGjRowYIWOMbNv+0e9zuVyyLEu5ubkEBqiHuADH8fl86tmzp6qrq08alqNcLpe8Xq+KiooUHR3d9AMCrQB7LsBxnn/+eVVVVTUoLJJk27aqqqq0YsWKJp4MaD1YuQD1GGOUmJiogoICnc5Hw7IsxcfHa+fOnZxFBoi4AMfYv3+/unTp0qj3x8bGOjgR0DpxWAyop6KiolHvP3TokEOTAK0bcQHqad++faPe36FDB4cmAVo34gLUExsbq4SEhNPeN7EsSwkJCYqJiWmiyYDWhbgA9ViWpalTp57Re9PS0tjMB45gQx84Dte5AI3HygU4TnR0tHJycmRZllyuk39Ejl6hv3r1asIC1ENcgBNISUlRbm6uvF6vLMv6weGuo1/zer1at26dhg8fHqRJgZaJuAA/IiUlRUVFRcrKylJ8fPwxr8XHxysrK0vFxcWEBTgB9lyABjDGaPPmzRo2bJg2bdqkoUOHsnkPnAQrF6ABLMuq21OJjo4mLMApEBcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERfgFGzb1oEDB/Ttt99KkkpKSlRZWRnkqYCWjcccAz/i8OHDeuedd7RixQpt3bpVpaWlqqioUMeOHdW7d28NHz5ckyZNUlJSEk+mBI5DXIATKCgo0IwZM5Sbm6sePXpo6NChuuCCCxQVFaWysjJt27ZNmzdvVm1traZNm6a0tDRFRkYGe2ygxSAuwHG++OILpaamqrCwUHfeeaduvPFGRUVFKT8/X36/XxEREerfv79KSkqUmZmp5cuXa/z48XryyScJDHAEcQHqKSsr0xVXXKEdO3ZoyZIlGjlypNxutwoKCjRw4ED5fD717t1beXl5io6Olt/v17JlyzRjxgxNnz5ds2fPlsvFVibgCfYAQEuycOFCbdu2TfPnz9eoUaOOCUVtba38fr/8fr8kybIshYWF6frrr1dhYaHmz5+vkSNHasCAAcEaH2gx+BELOKK0tFTLly/XoEGDNGHChAavQDwej9LS0tS1a1ctXrxYHAwAiAtQZ+vWrSosLNTEiRMVERGhQCBwzK+jjDE/eK1z58666qqr9Pbbb8vn8wXvLwG0EBwWA47Iz89Xu3btNGDAAKWnp+uzzz6re626urru2pZ9+/Zp3Lhx8nj+/+MzZcoUXXLJJVqwYIGKi4vVqVOnZp8faEmIC3BEaWmpIiIi1LFjR+Xl5emDDz444fdVV1dr06ZNx3xtxIgRGjx4sGzbZuUCiLgAdcLDw2Xbtvx+v1wu1w/2XGzbrvv98a9ZlqWamhpJUlhYWNMPC7RwxAU4IiEhQZWVlSoqKtLcuXNVXl5e91pJSYnS0tJUWVmpbt26acGCBWrfvn3d60lJSXr33XcVERGhbt26BWN8oEUhLsARAwcOVLt27bRhwwZlZGQcszopKCio22OJjIxUcnLyMfsqfr9f69atU1JSkuLi4pp9dqCl4Wwx4Ih+/fpp0KBBeumll7Rr164Gn1JsjFFeXp7eeustpaamKjw8vIknBVo+4gIcER4ervT0dPl8PqWnp+vgwYOnDIwxRiUlJZoxY4YSExM1bty4ZpoWaNmIC1DPkCFDdPfdd2v9+vW6+eabVVxcLGOM3G63unfvrri4OHXt2lUul0vGGH311VeaNGmSdu/erczMTE5BBo7g3mLAcb7//ntlZGRo3rx5OuecczRlyhQNHz5c4eHhcrvdCgQCqqio0Jo1a7Ro0SKFhYXp2Wef1bBhw4I9OtBiEBfgBAKBQN3G/vbt2+X1ehUXF6ezzjpLhw4d0p49e+R2uzV69GjNnDlTffr0CfbIQItCXICTqKqq0rZt2/T+++9ry5YtWrt2rcaOHavk5GQNGTJEffr0kdvtDvaYQItDXIAG+uSTT3ThhRdq+/bt3PkYOAU29IHTwOOMgYYhLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcx8PCgAYyxsi2bblcLp7rApwCKxfgNLhcfGSAhvAEewCgsWzb1t69exUIBII9imNcLpe6d+8ut9sd7FGAM8JhMbR65eXlGjdunPr27RvsURyzY8cOrVq1Sp07dw72KMAZYeWCVs8Yo7i4OGVmZjp22Mq2bRUWFmrr1q36+uuvZdu24uPjdfHFF6tXr15NuqIwxmjy5MmybbvJ/gygqREXhATLsmRZVqPjYozRN998o8zMTL322msqLS2t+5+8ZVmKjY3V5Zdfrrvuukt9+/Ztko19ooJQwO4kcEQgENAbb7yhlJQUPfXUU9q7d686duyo888/X/3791dsbKzKysq0bNkypaSk6NVXXw2pfR7ASaxcAEm1tbV69tlnNXv2bB06dEixsbG66aab9Pvf/15nn322JKmkpEQvv/yynnrqKRUWFuqGG25QaWmpbr75Znk8fJSA+li5oM2rra1VVlaW0tPTdejQIfXv31+vv/66HnzwQf3bv/2bIiMjFRkZqYSEBP3xj3/U2rVrddFFF6myslJ//OMf9dxzz3EoCzgOcUGbFggEtHjxYt1///06fPiwhgwZopycHA0aNOiE+zcul0sXXHCBXn75ZQ0aNEjV1dVKT0/X22+/LU68BP4fcUGbZYzR6tWrdc899+jw4cO69NJLtWLFCp177rkn3ai3LEvnnnuuli1bpvPOO08+n0+33367du/eTWCAI4gL2iRjjPLy8nTHHXfo0KFDOv/887Vs2TL16NGjQWeAWZalxMREPfXUU4qOjtZXX32lWbNmqaamphmmB1o+4oI2xxijoqIiTZkyRXv37tXZZ5+tpUuXqlevXqd1arFlWfrlL3+pGTNmyOVyafXq1XrttddYvQAiLmiDqqqqNG3aNH366afq0KGDsrOz1b9//zO6ZsXlcumWW27RpZdeqtraWj300EMqKSlpgqmB1oW4oE0JBALKzMzUmjVr5Ha7NXPmTI0cObJRF0O2b99eDz30kKKiorRz505lZ2dz9hjaPOKCNsMYo/Xr12vevHkKBAIaO3aspk6d2uhbuViWpYEDB2rSpEmSpGXLlumzzz7j8BjaNOKCNsEYo127dmnatGmqqKjQf/zHf2jevHnyer2O/PfdbrfuuOMO9ezZUwcOHNDjjz/O1fto04gL2oTKykpNnz5du3btUkxMjObPn9/gM8Ma6txzz9Utt9wiy7K0Zs0abd26ldUL2izigpAXCAQ0f/58rVu3Th6PR/fee68uueQSx286aVmWrrvuOvXp00cVFRXKysqS3+939M8AWgvigpBmjNFf//pXzZs3T7Zta+zYsbrxxhub7ImSXbp00ZQpU2RZltavX6/t27ezekGbRFwQsowx2rNnj6ZNm6aDBw8qKSlJGRkZioiIaLI/07IspaamKiEhQZWVlXrqqafYe0GbRFwQsmpqajRnzhx99tln6tChg5544gn17NmzSZ7BUl+XLl10ww03yLIs5ebm6vPPP2f1gjaHuCAkGWP0yiuvaNWqVXK5XEpLS1NycnKTh0X61+pl/Pjx6tmzpw4ePKjFixcTF7Q5xAUhxxijr776Svfee69qamp06aWXavr06U36aOLj9ejRQxMmTJAk5eTk6Jtvvmm2PxtoCYgLQk51dbVmzpypwsJCdenSRfPmzVNUVFSzzmBZliZNmqTY2FiVlpbqhRdeYPWCNoW4IKQYY/Tcc88pNzdXbrdbs2bNOuP7hjVWQkKCRo4cKUn685//rAMHDjT7DECwEBeElM8//1yPPPKI/H6/fvvb3+r6669vstOOT8XlcumGG26Q1+vVrl27tH79elYvaDOIC0JGdXW1Zs2apb179youLk6PPfaYY7d3OROWZenCCy/U4MGDZdu2li1bpu+//z5o8wDNibggJBhjtHLlSm3YsKHucFhSUlJQDofV165du7rVU15enrZt28bqBW0CcUHI8Hg86ty5s37zm99o0qRJQQ+L9K/Vy69//WslJibq8OHDeu6554gL2gTigpBw9L5eb731lrKzs4N6OOx4nTp10rhx4yRJubm5+vbbb4M8EdD0iAtChmVZ6tevn3r37t0iVi1HWZala665Rp06dVJpaan+8pe/sHpByCMuQDNISEjQsGHDJEkvvPCCKisrgzwR0LSIC9AM3G63Jk2aJI/Ho88++0wfffQRqxeENOICNAPLsvSLX/xC/fr1U21trVauXCnbtoM9FtBkiAvQTNq3b69rrrlGkvTmm2+ysY+QRlyAZmJZlsaMGaOYmBjt379fr7/+OofGELKIC9CM4uPjNXToUEnSSy+9pKqqqiBPBDQN4gI0I5fLpd///vfyeDz69NNPtXXrVlYvCEnEBWhGRzf2ExMTVVNTw634EbKIC9DMOnbsqDFjxkiSNmzYoL179wZ5IsB5xAVoZpZlaezYserQoYNKSkq4FT9CEnEBguC8887T4MGDZYzRqlWrVFNTE+yRAEcRFyAIPB6PJkyYIJfLpb/97W/6/PPPgz0S4CjiAgSBZVlKTk5Wz549VVVVpVdeeYVDYwgpxAUIki5dumjEiBGSpNdff10+ny+4AwEOIi5AkFiWpXHjxik8PFwFBQV69913Wb0gZBAXIEgsy9KAAQN0/vnnKxAIaNWqVdzMEiGDuABB5PV6625muXnzZu3evTu4AwEOIS5AEFmWpVGjRik2NlYHDhzQG2+8EeyRAEcQFyDIevXqVXczy5dffpmbWSIkEBcgyFwul8aPHy+3261PP/1U27dvD/ZIQKMRFyDILMvSpZdeqoSEBH3//fdas2YNZ42h1fMEewDACcYY2bYty7KCPcoZiYqK0i233KKKigpdeeWVmjdvXrBHAhqFuKDVc7lc2rdvn9LS0oI9SqMYY2RZlrKzs1VUVCSXiwMLaL0sw/obrZxt29q/f39IXSPicrkUGxsrt9sd7FGAM0JcAACO47AY0ED1fw5rrXs7QHPhoC7QQPn5+XK73crPzw/2KECLR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1yABjDGqLy8XJJUXl4ung4OnBxxAU7C5/MpOztbiYmJSk5OljFGycnJSkxMVHZ2tnw+X7BHBFoky/AjGHBCGzdu1JgxY1RVVSVJx6xWLMuSJEVGRionJ0cpKSlBmRFoqYgLcAIbN27UiBEjZIyRbds/+n0ul0uWZSk3N5fAAPUQF+A4Pp9PPXv2VHV19UnDcpTL5ZLX61VRUZGio6ObfkCgFWDPBTjO888/r6qqqgaFRZJs21ZVVZVWrFjRxJMBrQcrF6AeY4wSExNVUFBwWmeEWZal+Ph47dy5s24/BmjLiAtQz/79+9WlS5dGvT82NtbBiYDWicNiQD0VFRWNev+hQ4ccmgRo3YgLUE/79u0b9f4OHTo4NAnQuhEXoJ7Y2FglJCSc9r6JZVlKSEhQTExME00GtC7EBajHsixNnTr1jN6blpbGZj5wBBv6wHG4zgVoPFYuwHGio6OVk5Mjy7Lkcp38I3L0Cv3Vq1cTFqAe4gKcQEpKinJzc+X1emVZ1g8Odx39mtfr1bp16zR8+PAgTQq0TMQF+BEpKSkqKipSVlaW4uPjj3ktPj5eWVlZKi4uJizACbDnAjSAMUabN2/WsGHDtGnTJg0dOpTNe+AkWLkADWBZVt2eSnR0NGEBToG4AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AKdg27YOHDigb7/9VpJUUlKiysrKIE8FtGw85hj4EYcPH9Y777yjFStWaOvWrSotLVVFRYU6duyo3r17a/jw4Zo0aZKSkpJ4MiVwHOICnEBBQYFmzJih3Nxc9ejRQ0OHDtUFF1ygqKgolZWVadu2bdq8ebNqa2s1bdo0paWlKTIyMthjAy0GcQGO88UXXyg1NVWFhYW68847deONNyoqKkr5+fny+/2KiIhQ//79VVJSoszMTC1fvlzjx4/Xk08+SWCAI4gLUE9ZWZmuuOIK7dixQ0uWLNHIkSPldrtVUFCggQMHyufzqXfv3srLy1N0dLT8fr+WLVumGTNmaPr06Zo9e7ZcLrYyAU+wBwBakoULF2rbtm2aP3++Ro0adUwoamtr5ff75ff7JUmWZSksLEzXX3+9CgsLNX/+fI0cOVIDBgwI1vhAi8GPWMARpaWlWr58uQYNGqQJEyY0eAXi8XiUlpamrl27avHixeJgAEBcgDpbt25VYWGhJk6cqIiICAUCgWN+HWWM+cFrnTt31lVXXaW3335bPp8veH8JoIXgsBhwRH5+vtq1a6cBAwYoPT1dn332Wd1r1dXVdde27Nu3T+PGjZPH8/8fnylTpuiSSy7RggULVFxcrE6dOjX7/EBLQlyAI0pLSxUREaGOHTsqLy9PH3zwwQm/r7q6Wps2bTrmayNGjNDgwYNl2zYrF0DEBagTHh4u27bl9/vlcrl+sOdi23bd749/zbIs1dTUSJLCwsKaflighSMuwBEJCQmqrKxUUVGR5s6dq/Ly8rrXSkpKlJaWpsrKSnXr1k0LFixQ+/bt615PSkrSu+++q4iICHXr1i0Y4wMtCnEBjhg4cKDatWunDRs2KCMj45jVSUFBQd0eS2RkpJKTk4/ZV/H7/Vq3bp2SkpIUFxfX7LMDLQ1niwFH9OvXT4MGDdJLL72kXbt2NfiUYmOM8vLy9NZbbyk1NVXh4eFNPCnQ8hEX4Ijw8HClp6fL5/MpPT1dBw8ePGVgjDEqKSnRjBkzlJiYqHHjxjXTtEDLRlyAeoYMGaK7775b69ev180336zi4mIZY+R2u9W9e3fFxcWpa9eucrlcMsboq6++0qRJk7R7925lZmZyCjJwBPcWA47z/fffKyMjQ/PmzdM555yjKVOmaPjw4QoPD5fb7VYgEFBFRYXWrFmjRYsWKSwsTM8++6yGDRsW7NGBFoO4ACcQCATqNva3b98ur9eruLg4nXXWWTp06JD27Nkjt9ut0aNHa+bMmerTp0+wRwZaFOICnERVVZW2bdum999/X1u2bNHatWs1duxYJScna8iQIerTp4/cbnewxwRaHOICNNAnn3yiCy+8UNu3b+fOx8ApsKEPnAYeZww0DHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxPM8FaCBjjGzblsvl4tb7wCmwcgFOg8vFRwZoCE+wBwAay7Zt7d27V4FAINijOMblcql79+48QhmtFofF0OqVl5dr3Lhx6tu3b7BHccyOHTu0atUqde7cOdijAGeElQtaPWOM4uLilJmZ+aOHrQ4fPqzt27dr69atqqioUEJCgi655BKdffbZLW7/xBijyZMny7btYI8CnDHigpBgWZYsy/pBXIwx2rZtm+bMmaP33ntPhw8frvv+Hj166KabbtJtt92mjh07tpjIEBWEAnYnEbJs29brr7+u0aNH680335Tf71efPn10wQUXqFOnTiouLtYDDzyg8ePH69tvvxVHiAHnEBeEJGOM3nzzTd10003au3evzjnnHC1ZskQff/yx3n//fb377ru67rrr5PF4tHHjRqWmpuqbb74hMIBDiAtCjjFGO3bs0K233qoDBw4oMTFRr732miZOnKiYmBh5vV4lJSXpT3/6kzIyMhQZGam8vDzdcMMN2r9/P4EBHEBcEHKqq6uVnp6u3bt3q3PnzlqyZIkGDBhwzJ6KZVkKDw/Xrbfeqscee0zh4eH661//qrvvvrtuXwbAmSMuCCnGGL344ovasGGDPB6P7rvvPg0ePPhHN+s9Ho8mT56sO+64Q5Zl6cUXX9SiRYvYVAcaibggpJSUlGju3Lny+/1KTk7Wtddee8qr6sPCwjRz5kyNGDFCfr9fDz/8sLZs2cLhMaARiAtChjFGixYt0q5duxQVFaU5c+bI6/U26L3t27fXE088ofj4eB04cEB33XWXysvLm3hiIHQRF4SMwsJCLVu2TJL0u9/9ThdddFGDr12xLEvx8fHKyMhQRESE/va3v+mJJ54IqVvKAM2JuCAkGGO0cuVKFRcXq1OnTpo6depp32TSsixdfvnl+u///m8ZY/T000/rww8/5PAYcAaIC0LGJZdcossuu0ypqalKSko6oyvuPR6PZs+erfPOO08HDx7UPffco//93/9tgmmB0EZcEBIsy9KQIUP02muv6dFHHz3jW+MfvS3Mgw8+qPDwcOXl5enpp5/m7DHgNBEXhJTw8HB16NChUfcJsyxLo0aN0tVXXy3btpWVlaW///3vHB4DTgNxAU4gLCxMc+bM0TnnnKOysjLNnj1bVVVVwR4LaDWIC3ACR88emz17tjwej95++239+c9/ZvUCNBBxAX6EZVlKTU3Vb37zGwUCAT366KP6+uuvCQzQAMQFOAmv16sHH3xQXbp0UVFRke6//37V1NQEeyygxSMuwElYlqWf/exnmjZtmlwul1avXq033niD1QtwCsQFOAWXy6XJkydr0KBBqqmp0Zw5c1RcXExggJMgLkADREVF6ZFHHlFUVJS++uorPfLII/L7/cEeC2ixiAvQAJZlafDgwbrllltkWZZWrlypdevWsXoBfgRxARrI7XZr2rRpuuiii1RdXa177rmHw2PAjyAuwGmIiYlRRkaGoqKi9OWXX2rOnDmcPQacAHEBToNlWfrFL36h22+/XS6XSy+++KJefPFFVi/AcYgLcJrcbrfuvPNO/fKXv1RNTY1mzZrFvceA4xAX4AxERUUpMzNTP/nJT7R3715NnTpV3333HYEBjiAuwBmwLEv//u//rrlz58rr9WrLli1KT0/X4cOHgz0a0CIQF+AMWZalq6++WmlpabIsS6tWrdKTTz7J9S+AiAvQKB6PRzNnztTo0aPl9/uVkZGhVatW8XAxtHnEBWik9u3bKzs7WwMHDlRVVZWmT5+ujRs3sv+CNo24AI1kWZbi4uK0ZMkS9e3bVwcOHNDkyZP18ccfExi0WcQFcIBlWUpKStLSpUvVo0cPFRcX67rrrtP//M//EBi0ScQFcIhlWRo0aJAWLVqk2NhYff3117r22mtVUFBAYNDmEBfAQZZlKSUlRQsWLFCHDh30j3/8Q9ddd5327NlDYNCmEBfAYS6XS2PHjq27BubDDz/U5MmTVVZWRmDQZhAXoAm43W5df/31uvfee9WuXTtt2LBBaWlpqqioIDBoE4gL0EQ8Ho/uuOMO3XHHHXK5XHr11Vc1e/Zs7qKMNoG4AE0oPDxc9957ryZNmiRjjJ599lllZWVxFT9CHnEBmpjX69W8efM0YsQI1dbW6uGHH9Yrr7zCVfwIacQFaGKWZaljx47605/+pIsvvlhVVVW688479d5777H/gpBFXIBmYFmWfvKTn2jx4sXq3bu39u/fr8mTJ+vLL78kMAhJxAVoJkdv0//MM88oJiZGX3/9tW666Sbt27ePwCDkEBegGVmWpV/96ld6/PHH5fV69dFHH+n2229XRUVFsEcDHEVcgGbmcrk0YcIE3X333XK73Vq9erUeeughTlFGSCEuQBB4PB7dfffdmjhxoowxWrBggZYsWcIZZAgZxAUIkqOnKP/qV79STU2NZs2apbVr17L/gpBAXIAgsSxLMTExWrhwoX7605/q4MGDuu2227R169ZgjwY0GnEBgsiyLMXHx2vx4sXq0aOH9uzZo+XLlysQCAR7NKBRPMEeAHCCMUa2bcuyrGCPckYuuugiPfXUU3rzzTd13333aebMmcEeCWgUy3CAF62cz+dTamqqevXqFexRGuVoIN1utwoKCvTCCy+oc+fOwR4LOCPEBa2ebdvav39/SJ1p5XK5FBsbK7fbHexRgDNCXAAAjmPPBWig+j+Htda9HaC5cLYY0ED5+flyu93Kz88P9ihAi0dcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcgAYwxqi8vFySVF5eLp4ODpwccQFOwufzKTs7W4mJiUpOTpYxRsnJyUpMTFR2drZ8Pl+wRwRaJMvwIxhwQhs3btSYMWNUVVUlScesVizLkiRFRkYqJydHKSkpQZkRaKmIC3ACGzdu1IgRI2SMkW3bP/p9LpdLlmUpNzeXwAD1EBfgOD6fTz179lR1dfVJw3KUy+WS1+tVUVGRoqOjm35AoBVgzwU4zvPPP6+qqqoGhUWSbNtWVVWVVqxY0cSTAa0HKxegHmOMEhMTVVBQcFpnhFmWpfj4eO3cubNuPwZoy4gLUM/+/fvVpUuXRr0/NjbWwYmA1onDYkA9FRUVjXr/oUOHHJoEaN2IC1BP+/btG/X+Dh06ODQJ0LoRF6Ce2NhYJSQknPa+iWVZSkhIUExMTBNNBrQuxAWox7IsTZ069Yzem5aWxmY+cAQb+sBxuM4FaDxWLsBxoqOjlZOTI8uy5HKd/CNy9Ar91atXExagHuICnEBKSopyc3Pl9XplWdYPDncd/ZrX69W6des0fPjwIE0KtEzEBfgRKSkpKioqUlZWluLj4495LT4+XllZWSouLiYswAmw5wI0gDFGmzdv1rBhw7Rp0yYNHTqUzXvgJFi5AA1gWVbdnkp0dDRhAU6BuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuACnYNu2Dhw4oG+//VaSVFJSosrKyiBPBbRsPOYY+BGHDx/WO++8oxUrVmjr1q0qLS1VRUWFOnbsqN69e2v48OGaNGmSkpKSeDIlcBziApxAQUGBZsyYodzcXPXo0UNDhw7VBRdcoKioKJWVlWnbtm3avHmzamtrNW3aNKWlpSkyMjLYYwMtBnEBjvPFF18oNTVVhYWFuvPOO3XjjTcqKipK+fn58vv9ioiIUP/+/VVSUqLMzEwtX75c48eP15NPPklggCOIC1BPWVmZrrjiCu3YsUNLlizRyJEj5Xa7VVBQoIEDB8rn86l3797Ky8tTdHS0/H6/li1bphkzZmj69OmaPXu2XC62MgFPsAcAWpKFCxdq27Ztmj9/vkaNGnVMKGpra+X3++X3+yVJlmUpLCxM119/vQoLCzV//nyNHDlSAwYMCNb4QIvBj1jAEaWlpVq+fLkGDRqkCRMmNHgF4vF4lJaWpq5du2rx4sXiYABAXIA6W7duVWFhoSZOnKiIiAgFAoFjfh1ljPnBa507d9ZVV12lt99+Wz6fL3h/CaCF4LAYcER+fr7atWunAQMGKD09XZ999lnda9XV1XXXtuzbt0/jxo2Tx/P/H58pU6bokksu0YIFC1RcXKxOnTo1+/xAS0JcgCNKS0sVERGhjh07Ki8vTx988MEJv6+6ulqbNm065msjRozQ4MGDZds2KxdAxAWoEx4eLtu25ff75XK5frDnYtt23e+Pf82yLNXU1EiSwsLCmn5YoIUjLsARCQkJqqysVFFRkebOnavy8vK610pKSpSWlqbKykp169ZNCxYsUPv27eteT0pK0rvvvquIiAh169YtGOMDLQpxAY4YOHCg2rVrpw0bNigjI+OY1UlBQUHdHktkZKSSk5OP2Vfx+/1at26dkpKSFBcX1+yzAy0NZ4sBR/Tr10+DBg3SSy+9pF27djX4lGJjjPLy8vTWW28pNTVV4eHhTTwp0PIRF+CI8PBwpaeny+fzKT09XQcPHjxlYIwxKikp0YwZM5SYmKhx48Y107RAy0ZcgHqGDBmiu+++W+vXr9fNN9+s4uJiGWPkdrvVvXt3xcXFqWvXrnK5XDLG6KuvvtKkSZO0e/duZWZmcgoycAT3FgOO8/333ysjI0Pz5s3TOeecoylTpmj48OEKDw+X2+1WIBBQRUWF1qxZo0WLFiksLEzPPvushg0bFuzRgRaDuAAnEAgE6jb2t2/fLq/Xq7i4OJ111lk6dOiQ9uzZI7fbrdGjR2vmzJnq06dPsEcGWhTiApxEVVWVtm3bpvfff19btmzR2rVrNXbsWCUnJ2vIkCHq06eP3G53sMcEWhziAjTQJ598ogsvvFDbt2/nzsfAKbChD5wGHmcMNAxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA4HhYGNJAxRrZty+Vy8VwX4BQ8wR4AaCzbtrV3714FAoEm/7OMMc0SFpfLpe7du/MIZbRarFzQ6pWXl2vcuHHq27dvsEdpFNu2Jf0rLDt27NCqVavUuXPnIE8FnBlWLmj1jDGKi4tTZmamXK7Wu424bNkyffnll7rtttv00EMP1cUGaI2IC0KCZVmyLKvVxuWbb77Ro48+qm+++UYVFRXigAJau9b5SQRCSCAQ0BNPPKFvvvlGnTp10o033thqIwkcxb9gIIiMMcrLy9OKFSskSTfccIPOP//8IE8FNB5xAYKourpaDz74oA4dOqTExETdfvvtrFoQEvhXDASJMUavvvqqNm/eLI/Ho5kzZ6p79+7BHgtwBHEBgsAYoz179uixxx6T3+/X0KFDdfXVV3NxJkIGcQGCwLZtPfnkk9q5c6eioqJ03333yev1BnsswDHEBWhmxhht2bJFy5YtkyT94Q9/0M9//nNWLQgpxAVoZhUVFZozZ44OHjyovn37avr06dzmBSGHuADNyBijZcuW6b333lNYWJjmzJmjuLi4YI8FOI64AM3EGKPPP/9cc+fOlW3buvzyy3XllVdyOAwhibgAzaS6ulqzZs3Svn37FBcXpwceeEDh4eHBHgtoEsQFaAa2bWvp0qVav369PB6PZs2apfPOO49VC0IWcQGamDFGn3zyiR555BEFAgFddtllmjRpEmFBSCMuQBMyxqisrEx33HGHvvvuO51zzjnKyMjgmhaEPOICNKGamhrNmTNHW7ZsUUREhDIyMtS3b19WLQh5xAVoIrZta/HixXUXS95888266qqrCAvaBOICNAFjjNauXat7771XtbW1Gj58uO69916FhYUFezSgWRAXwGHGGH344Ye65ZZbdPDgQf30pz/VU089pY4dOwZ7NKDZEBfAQcYY5efn6w9/+INKSkrUs2dPLVmyRL179+ZwGNoU4gI4xBijf/zjH5o4caJ27dqlzp07a9GiRbr44osJC9oc4gI44Oi1LOPHj9eOHTvUqVMnLVy4UL/+9a8JC9ok4gI0kjFGH3zwga655hrt2LFDMTExeuaZZzR69GgeWYw2i3/5QCPYtq21a9cqNTVV//znP9WlSxctXrxYV111FWFBm8a/fuAM+f1+Pffcc7r22mtVUlKis88+WytXrtQVV1xBWNDmeYI9ANDaGGN0+PBhPf7445o7d66qq6vVr18/LV26lM174AjiApwGY4zKy8uVnp6uFStWyO/367/+67+0aNEibusC1ENcgAYyxuif//ynbr31Vr311luyLEtjxoxRdna2unfvTliAejgwDDSAMUYff/yxrrzySr355psKCwtTWlqali5dSliAE2DlApyC3+/Xq6++qunTp2vfvn3q2LGjHnroId14440KCwsjLMAJEBfgRxhjVFVVpXnz5umJJ55QVVWVzj33XC1YsEC//e1vOSMMOAniApyAMUYlJSWaPn26cnJyFAgE9J//+Z9auHChfvazn7FaAU6BH72A4xy9lcuVV16pV155RZI0fvx45eTkEBaggVi5APX4/X795S9/0bRp07Rnzx6dddZZSk9P15133imv10tYgAYiLoD+tVqprKzU448/Xre/0rNnTz3xxBO68sor5Xa7gz0i0KoQF7R5xhgVFRVp+vTpev311xUIBPTzn/9cTz/9tPr3789qBTgD7LmgTbNtWx999JGuuOIK5eTkyLIsTZgwQatXryYsQCOwckGbZIxRbW2tVq5cqVmzZum7775Thw4dNHPmTE2dOpX9FaCRiAvaHGOMDhw4oPvvv19Lly7V999/r969eyszM1OXXXYZ+yuAA4gL2hRjjD799FPdfvvt+uCDDyRJw4YNU3Z2ts477zxWK4BD2HNBm3D0MNiLL76oUaNG6f3331d4eLhuv/12vfzyy4QFcBgrF4Q8Y4z279+vhx9+WEuWLNHhw4fVo0cPZWRk6He/+508Hg9hARxGXBDSbNtWXl6e7rrrLuXl5UmSfvGLXygrK0vnn38+UQGaCHFBSDLGqKKiQosWLdLcuXNVVlYmr9eryZMn65577lFMTAxhAZoQcUHIsW1b+fn5mj17tjZt2qRAIKCEhAQ99thjuvzyyzkMBjQD4oKQUlZWpmeeeUbz58/X/v37FRYWpjFjxujhhx9WfHw8UQGaCXFBSDDGaP369XrggQeUn58vY4x69eqlOXPm6JprrlF4eDhhAZoRcUHI+Pvf/678/Hx5vV5dc801uueee9S7d2+iAgQBcUHIuOGGG/T1119r3LhxGjp0qMLCwmSMkTEm2KOdFtu2W93MwPEsw79itHI+n0+pqanq1auXAoFASNy+paCgQC+88II6d+4c7FGAM0Jc0OrZtq39+/fLtu1gj+IYl8ul2NjYkAgl2ibiAgBwHHsuQAPV/zmMkwSAk+PGlUAD5efny+12Kz8/P9ijAC0ecQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AA1gjFF5ebkkqby8/JhHHgP4IeICnITP51N2drYSExOVnJwsY4ySk5OVmJio7Oxs+Xy+YI8ItEiW4Ucw4IQ2btyoMWPGqKqqSpKOWa1YliVJioyMVE5OjlJSUoIyI9BSERfgBDZu3KgRI0bIGCPbtn/0+1wulyzLUm5uLoEB6iEuwHF8Pp969uyp6urqk4blKJfLJa/Xq6KiIkVHRzf9gEArwJ4LcJznn39eVVVVDQqLJNm2raqqKq1YsaKJJwNaD1YuQD3GGCUmJqqgoOC0zgizLEvx8fHauXNn3X4M0JYRF6Ce/fv3q0uXLo16f2xsrIMTAa0Th8WAeioqKhr1/kOHDjk0CdC6ERegnvbt2zfq/R06dHBoEqB1Iy5APbGxsUpISDjtfRPLspSQkKCYmJgmmgxoXYgLUI9lWZo6deoZvTctLY3NfOAINvSB43CdC9B4rFyA40RHRysnJ0eWZcnlOvlH5OgV+qtXryYsQD3EBTiBlJQU5ebmyuv1yrKsHxzuOvo1r9erdevWafjw4UGaFGiZiAvwI1JSUlRUVKSsrCzFx8cf81p8fLyysrJUXFxMWIATYM8FaABjjA4cOKBDhw6pQ4cOiomJYfMeOAniAgBwHIfFAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBw3P8BieRdtZ3ZE5kAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "13c725a5", + "metadata": {}, + "source": [ + "with tricks" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a22ffff3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 8.89e-03 | test_loss: 8.40e-03 | reg: 1.83e+01 | : 100%|█| 20/20 [00:04<00:00, 4.20it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from kan import *\n", + "\n", + "# create dataset f(x,y) = sin(pi*x). This task can be achieved by a [1,1] KAN\n", + "f = lambda x: torch.sin(torch.pi*x[:,[0]])\n", + "dataset = create_dataset(f, n_var=1, device=device)\n", + "\n", + "# set base_fun to be linear\n", + "model = KAN(width=[1,1,1,1], grid=5, k=3, seed=0, base_fun='identity', noise_scale=0.1, device=device)\n", + "\n", + "# penality spline coefficients\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=1e-4, lamb_coef=10.0);" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c82c8db5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAHiCAYAAAAkiYF/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvX0lEQVR4nO3de3BUZZ7/8c/pTtLpECCQcF0UCWSpsNZwUZZFnP3Bkk1mBRQFJQQQEZGLEgcUIoo746yWoMjVWeRSgCg3FRgcAjKAyAC7MgSiKyqCZFlJCEQgLblx6Zzn98dAJiBCICd00nm/qqii0un4pWp63nmep/scyxhjBACAg1yBHgAAEHyICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjgsJ9ABATWCM0alTp1RYWKjIyEhFR0fLsqxAjwVUW6xcgGvw+XyaNWuW4uLi1KhRI7Vq1UqNGjVSXFycZs2aJZ/PF+gRgWrJ4k6UwNVt2rRJ/fr1U3FxsaS/rl4uubRqiYiI0OrVq5WUlBSQGYHqirgAV7Fp0yb16tVLxhjZtv2z3+dyuWRZltLT0wkMUA5xAa7g8/nUokULlZSUXDMsl7hcLnm9XmVnZysqKqrqBwRqAM5cgCu88847Ki4urlBYJMm2bRUXF2vp0qVVPBlQc7ByAcoxxiguLk5ZWVm6kZeGZVmKjY3VoUOHeBcZIOICXObkyZNq1KhRpZ4fHR3t4ERAzcS2GFBOYWFhpZ5fUFDg0CRAzUZcgHIiIyMr9fy6des6NAlQsxEXoJzo6Gi1bt36hs9NLMtS69at1bBhwyqaDKhZiAtQjmVZGjt27E09NzU1lcN84CIO9IEr8DkXoPJYuQBXiIqK0urVq2VZllyua79ELn1Cf82aNYQFKIe4AFeRlJSk9PR0eb1eWZb1k+2uS1/zer3asGGDEhMTAzQpUD0RF+BnJCUlKTs7WzNnzlRsbOxlj8XGxmrmzJnKyckhLMBVcOYCVIAxRtu2bVPPnj21detW9ejRg8N74BpYuQAVYFlW2ZlKVFQUYQGug7gAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgA12Hbtk6fPq3vv/9ekpSbm6uioqIATwVUb9zmGPgZZ8+e1SeffKKlS5dqz549ysvLU2FhoerXr69WrVopMTFRQ4cOVXx8PHemBK5AXICryMrK0sSJE5Wenq7mzZurR48e6tixo+rVq6dTp04pIyND27Zt04ULFzR+/HilpqYqIiIi0GMD1QZxAa7w9ddfa+DAgTp69KjGjRunESNGqF69esrMzJTf71d4eLg6dOig3NxczZgxQ4sXL1ZKSoqmT59OYICLiAtQzqlTp/TAAw/o22+/1cKFC9W7d2+53W5lZWWpS5cu8vl8atWqlXbv3q2oqCj5/X4tWrRIEydO1LPPPqvJkyfL5eIoEwgJ9ABAdTJ37lxlZGRo9uzZ6tOnz2WhuHDhgvx+v/x+vyTJsiyFhoZq+PDhOnr0qGbPnq3evXurU6dOgRofqDb4FQu4KC8vT4sXL1bXrl01aNCgCq9AQkJClJqaqsaNG2vBggViMwAgLkCZPXv26OjRoxo8eLDCw8NVWlp62Z9LjDE/eSwmJkYPPfSQtmzZIp/PF7h/BFBNsC0GXJSZmamwsDB16tRJaWlp2r9/f9ljJSUlZZ9tOXHihJKTkxUS8reXz+jRo9WtWzfNmTNHOTk5atCgwS2fH6hOiAtwUV5ensLDw1W/fn3t3r1bO3fuvOr3lZSUaOvWrZd9rVevXrrnnntk2zYrF0DEBSjj8Xhk27b8fr9cLtdPzlxs2y77+5WPWZal8+fPS5JCQ0OrfligmiMuwEWtW7dWUVGRsrOzNXXqVOXn55c9lpubq9TUVBUVFalJkyaaM2eOIiMjyx6Pj4/X9u3bFR4eriZNmgRifKBaIS7ARV26dFFYWJg+/vhjTZky5bLVSVZWVtkZS0REhBISEi47V/H7/dqwYYPi4+PVrFmzWz47UN3wbjHgonbt2qlr165auXKlDh8+XOG3FBtjtHv3bm3evFkDBw6Ux+Op4kmB6o+4ABd5PB6lpaXJ5/MpLS1NZ86cuW5gjDHKzc3VxIkTFRcXp+Tk5Fs0LVC9ERegnO7du2vChAnauHGjRo0apZycHBlj5Ha71bRpUzVr1kyNGzeWy+WSMUYHDx7U0KFDdeTIEc2YMYO3IAMXcW0x4Arnzp3TlClT9MYbb+j222/X6NGjlZiYKI/HI7fbrdLSUhUWFmrdunWaP3++QkNDNW/ePPXs2TPQowPVBnEBrqK0tLTsYH/v3r3yer1q1qyZ6tSpo4KCAh07dkxut1t9+/bVpEmT1KZNm0CPDFQrxAW4huLiYmVkZGjHjh367LPPtH79evXv318JCQnq3r272rRpI7fbHegxgWqHuAAVtG/fPt11113au3cvVz4GroMDfQCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMdxszCggowxMsbIsixZlhXocYBqjZULcAOIClAxIYEeAKgs27Z1/PhxlZaWBnoUx7hcLjVt2lRutzvQowA3hW0x1Hj5+fkaMGCA2rZtqwsXLigkJKTGrzAOHDigFStWKCYmJtCjADeFlQtqPGOMmjdvrpdeeklpaWlKSEjQgw8+WGMDY4zRyJEjZdt2oEcBbhpxQVAwxujVV1/V0qVL9dFHH8nv9+uRRx6Ry1XzjhWJCoJBzXvlAVdhWZYeffRRtW3bVvn5+Ro9erSWLVsWVOcwQE1CXBAULMtShw4dtGrVKt1555368ccf9fTTT2vJkiUEBggA4oKgcuedd2rlypXq0KGDCgoKNG7cOC1YsEB+vz/QowG1CnFBULEsS/Hx8Vq5cqXuvvtuFRYW6rnnntPcuXMJDHALERcEHcuyFBcXpxUrVuif/umfVFxcrOeff16zZs3ShQsXAj0eUCsQFwQly7IUGxur5cuX695771VJSYkmT56sadOm6fz584EeDwh6xAVBy7IstWzZUsuWLVOPHj107tw5vfzyy5oyZYrOnTsX6PGAoEZcENQsy1KLFi307rvvKjExUefPn9err76qV155RWfPng30eEDQIi4IepZlqVmzZlqyZInuu+8++f1+vf766/rtb3+rkpKSQI8HBCXiglrBsiw1adJEixcv1v333y+/36/p06dr8uTJBAaoAsQFtYZlWYqJidHChQvVr18/lZaWavbs2XrhhRdUXFwc6PGAoEJcUKtYlqWGDRtq3rx5euSRR2Tbtt566y09//zzKioqCvR4QNAgLqh1LMtSVFSU5s6dq+TkZNm2rblz5yotLU1FRUXiLhRA5REX1EqWZal+/fr6/e9/r0GDBskYo3nz5mnChAkEBnAAcUGtdSkwc+bM0eDBg2WM0fz58/Xcc88RGKCSiAtqNcuyVK9ePc2ePVtDhgyRJC1cuFDjx49XYWEhgQFuEnFBrWdZlurWratZs2Zp6NChkqRFixYRGKASiAugvwVm5syZGjZsmCRp8eLFGjdunAoKCggMcIOIC3CRZVmKjIzU9OnTNXz4cEnSkiVL9Otf/5rAADeIuADlXArMm2++qSeeeEKStHTpUqWmpurMmTMEBqgg4gJcwbIs1alTR9OmTdOTTz4py7L03nvvaezYsQQGqCDiAlzFpcC88cYbGjlypCzL0rJly/T000/rxx9/JDDAdRAX4GdYlqWIiAhNnTpVo0aNksvl0vLly/XUU0/J5/MRGOAaiAtwDeUDM2bMGLlcLq1cuVJjxowhMMA1EBfgOizLktfr1Wuvvaann35aLpdL77//vkaNGqX8/HwCA1wFcQEq4FJgXn31VaWmpsrtduvDDz/UqFGjdPr0aQIDXIG4ABV0KTCvvPKKnnnmGbndbq1evVojR44kMMAViAtwAyzLUnh4uH73u99p3LhxcrvdWrt2rUaMGKFTp04RGOAi4gLcoEuBefnllzV+/Hi53W6tW7dOI0aM0MmTJwkMICkk0AMANdGlwPz2t7+Vy+XSm2++qY8++ki2bWvhwoWKiYmRZVmBHhMIGFYuQCWEh4fr3//93zVhwgSFhIRo/fr1euKJJ1jBoNYjLkAlhYeH66WXXtLEiRMJDHARcQEc4PF4NHny5MsCM3z4cAKDWou4AA65MjDp6ekaPny4fvjhBwKDWoe4AA66FJi0tLSywDzxxBMEBrUOcQEc5vF49OKLL5YFZv369Xr88ceVl5dHYFBrEBegClwKzPPPP6/Q0FBt2LBBjz32mE6cOEFgUCsQF6CKeDwevfDCC3rhhRcUFhamTZs26dFHH9WxY8cIDIIecQGqkMfj0aRJkzR58mR5PB5t2bJFQ4YMUXZ2NoFBUCMuQBULCwvTxIkT9dvf/lbh4eH69NNPlZKSoiNHjhAYBC3iAtwCYWFhGj9+vF555RV5vV7t2rVLAwcO1OHDhwkMghJxAW6R0NBQpaamaurUqapTp47+8pe/aMCAATpw4ACBQdAhLsAtFBISolGjRmn69OmKjIxUZmamkpOTtX//fgKDoEJcgFssJCREjz/+uObMmaP69evryy+/1IABA/T5558TGAQN4gIEgNvt1pAhQzR37lw1aNBABw4c0IABA7Rnz55AjwY4grgAAeJyufTII49owYIFio6O1nfffaeBAwfqv/7rv1jBoMbjZmFAALlcLvXt21ehoaEaMWKEjhw5ohUrVsi27UCPBlQKKxcEBWOMbNuukX8k6b777tPixYs1bNiwsrtbAjWZZVh/o4bz+XxKTk5Wq1atAj1Kpfn9foWEhOjw4cNavny5YmJiAj0ScFOIC2o827Z18uTJoNpKcrlcio6OltvtDvQowE0hLgAAx3GgD1RQ+d/DLMsK4CRA9cepIVBBmZmZcrlcyszMDPQoQLVHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXIAKMMYoPz9fkpSfny/uDg5cG3EBrsHn82nWrFmKi4tTQkKCJCkhIUFxcXGaNWuWfD5fYAcEqinL8CsYcFWbNm1Sv379VFxcLEmXrVYsy5IkRUREaPXq1UpKSgrIjEB1RVyAq9i0aZN69eolY4xs2/7Z73O5XLIsS+np6QQGKIe4AFfw+Xxq0aKFSkpKrhmWS1wul7xer7KzsxUVFVX1AwI1AGcuwBXeeecdFRcXVygskmTbtoqLi7V06dIqngyoOVi5AOUYYxQXF6esrKwbekeYZVmKjY3VoUOHys5jgNqMuADlnDx5Uo0aNarU86Ojox2cCKiZ2BYDyiksLKzU8wsKChyaBKjZiAtQTmRkZKWeX7duXYcmAWo24gKUEx0drdatW9/wuYllWWrdurUaNmxYRZMBNQtxAcqxLEtjx469qeempqZymA9cxIE+cAU+5wJUHisX4ApRUVFavXq1LMuSy3Xtl8ilT+ivWbOGsADlEBfgKpKSkpSeni6v1yvLsn6y3XXpa16vVxs2bFBiYmKAJgWqJ+IC/IykpCRlZ2dr5syZio2Nveyx2NhYzZw5Uzk5OYQFuArOXIAKMMZo27Zt6tmzp7Zu3aoePXpweA9cAysXoAIsyyo7U4mKiiIswHUQFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRF+A6bNvW6dOn9f3330uScnNzVVRUFOCpgOqN2xwDP+Ps2bP65JNPtHTpUu3Zs0d5eXkqLCxU/fr11apVKyUmJmro0KGKj4/nzpTAFYgLcBVZWVmaOHGi0tPT1bx5c/Xo0UMdO3ZUvXr1dOrUKWVkZGjbtm26cOGCxo8fr9TUVEVERAR6bKDaIC7AFb7++msNHDhQR48e1bhx4zRixAjVq1dPmZmZ8vv9Cg8PV4cOHZSbm6sZM2Zo8eLFSklJ0fTp0wkMcBFxAco5deqUHnjgAX377bdauHChevfuLbfbraysLHXp0kU+n0+tWrXS7t27FRUVJb/fr0WLFmnixIl69tlnNXnyZLlcHGUCIYEeAKhO5s6dq4yMDM2ePVt9+vS5LBQXLlyQ3++X3++XJFmWpdDQUA0fPlxHjx7V7Nmz1bt3b3Xq1ClQ4wPVBr9iARfl5eVp8eLF6tq1qwYNGlThFUhISIhSU1PVuHFjLViwQGwGAMQFKLNnzx4dPXpUgwcPVnh4uEpLSy/7c4kx5iePxcTE6KGHHtKWLVvk8/kC948Aqgm2xYCLMjMzFRYWpk6dOiktLU379+8ve6ykpKTssy0nTpxQcnKyQkL+9vIZPXq0unXrpjlz5ignJ0cNGjS45fMD1QlxAS7Ky8tTeHi46tevr927d2vnzp1X/b6SkhJt3br1sq/16tVL99xzj2zbZuUCiLgAZTwej2zblt/vl8vl+smZi23bZX+/8jHLsnT+/HlJUmhoaNUPC1RzxAW4qHXr1ioqKlJ2dramTp2q/Pz8ssdyc3OVmpqqoqIiNWnSRHPmzFFkZGTZ4/Hx8dq+fbvCw8PVpEmTQIwPVCvEBbioS5cuCgsL08cff6wpU6ZctjrJysoqO2OJiIhQQkLCZecqfr9fGzZsUHx8vJo1a3bLZweqG94tBlzUrl07de3aVStXrtThw4cr/JZiY4x2796tzZs3a+DAgfJ4PFU8KVD9ERfgIo/Ho7S0NPl8PqWlpenMmTPXDYwxRrm5uZo4caLi4uKUnJx8i6YFqjfiApTTvXt3TZgwQRs3btSoUaOUk5MjY4zcbreaNm2qZs2aqXHjxnK5XDLG6ODBgxo6dKiOHDmiGTNm8BZk4CKuLQZc4dy5c5oyZYreeOMN3X777Ro9erQSExPl8XjkdrtVWlqqwsJCrVu3TvPnz1doaKjmzZunnj17Bnp0oNogLsBVlJaWlh3s7927V16vV82aNVOdOnVUUFCgY8eOye12q2/fvpo0aZLatGkT6JGBaoW4ANdQXFysjIwM7dixQ5999pnWr1+v/v37KyEhQd27d1ebNm3kdrsDPSZQ7RAXoIL27dunu+66S3v37uXKx8B1cKAPAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHczwWoIGOMjDGyLEuWZQV6HKBaY+UC3ACiAlRMSKAHACrLtm0dP35cpaWlgR7FMS6XS02bNuUWyqix2BZDjZefn68BAwaobdu2gR7FMQcOHNCKFSsUExMT6FGAm8LKBTWeMUbNmzfXjBkz5HJVbKf39OnT2rx5sw4ePKiWLVvqX//1X9WsWbMqnrRijDEaOXKkbNsO9CjATSMuCAqXDtmvFxdjjDIyMjRmzBhlZmbKtm1ZlqW4uDi9/vrr6t27d4UDVVWICoIBB/qoNYwxOnDggFJSUrR3716Fh4frF7/4herWrauDBw/qscce07p168ROMVB5xAW1RlFRkcaNG6fDhw8rOjpaS5cu1a5du7R+/XrFx8fL5/MpNTVV+/fvJzBAJREX1ArGGC1btkxbt25VaGioXn31VfXt21d16tRRt27dtGTJEjVt2lQ5OTlKS0tTSUlJoEcGajTiglohLy9P06dPV2lpqZKSkjRkyJCysxXLsnT33Xfr5ZdfVkhIiLZs2aIPPviA1QtQCcQFQc8Yo3fffVffffedIiMj9cILLyg8PPyy77EsS4MGDVKPHj3k9/s1bdo05efnB2hioOYjLgh6p0+f1sKFC2WMUZ8+fXT33Xdf9ZP2Xq9XkyZNktfr1TfffKP333+f1Qtwk4gLgpoxRh999JG+++47eb1ejRkz5mc/9W5Zlu655x4lJibKtm29/fbbOnPmzC2eGAgOxAVB7ezZs1q0aJFs29Yvf/lLde7c+ZrXBwsNDdVTTz0lj8ejr776Sn/6059YvQA3gbggaBljtGfPHmVkZMjtdmv48OEKDQ295nMsy1K3bt3UuXNnlZaWatGiRbpw4cItmhgIHsQFQcsYo6VLl+rcuXOKi4tTQkJCha5qHB4ermHDhsmyLO3cuVNfffXVLZgWCC7EBUErJydHGzZskCQ98sgjioqKqtDzLMvSfffdpxYtWqioqEirVq1iawy4QcQFQckYo/T0dJ04cUL16tXTI488ckP3YmnUqJH69OkjSVq7dq18Pl8VTQoEJ+KCoHTu3DmtWLFCxhj98pe/VFxc3A0937IsJScny+PxKCsrS7t27WL1AtwA4oKg9OWXXyojI0Mul0uDBg1SSMiNXQDcsix17NhR//AP/6DS0lI+8wLcIOKCoGOM0apVq1RSUqLbb79dPXv2vKnbE0dERKhv376SpE8++UQnTpxweFIgeBEXBJ3Tp0/rD3/4gyTp/vvvv+m7OVqWpfvvv1916tTR8ePH9emnn7J6ASqIuCCoGGO0detWHTlyRF6vV8nJyZX6eW3btlWHDh1k27bWrFnDjbyACiIuCCp+v1/Lli2Tbdvq1KmTOnTocFNbYpeEhYWVbY3t2LFDubm5Dk0KBDfigqBy6NAhbd++XZZlKSUlRR6Pp1I/z7Is/du//Zvq1q2rH374ga0xoIKIC4LKqlWrdObMGTVp0kR9+vSp1KrlktatW6tDhw4yxugPf/gDW2NABRAXBI3Tp09r5cqVkqQ+ffqoefPmjvzcsLAw3X///ZKkXbt26fjx4478XCCYERcEjY0bN+rw4cPyer0aOnSoI6sW6a9bY7/61a8UGRmpvLw8bd++3fGtMWOMiouLuUgmggZxQVAwxuiHH35QkyZN1LVrV911112OxUWS2rRpo/bt21fZ1pgxRi+99JKSk5O1ZcsWznVQ493Yx5aBasqyLKWmpqp///4qLi5WWFiYoz8/LCxMffr00a5du7Rz506dOHHCsW03STp27JhWrlyp3NxctWvXzrGfCwQKKxcEDZfLpZYtWyo+Pt7RVYv0t3eN1alTR3l5edqxY4djqwtjjNatW6fjx4+rfv36GjBggOPzA7cacQEqKC4uTr/4xS9k27ajW2Nnz57V8uXLZYzR//t//09///d/78jPBQKJuAAV5PF4yi7D/+c//1l5eXmV/pnGGH3++efKzMyU2+3WkCFDbvgim0B1RFyACiq/NXbixAnt3LnTka2xVatW6ezZs7rjjjvUvXv3yg8KVAPEBbgBbdu21Z133lm2NVbZuJw+fVrr16+X9NeLbDZs2NCJMYGAIy7ADfB4POrdu7ckafv27ZW6DL8xRtu3b9f//d//KTw8XA8//DAH+QgaxAW4AZZlqXfv3mWX4a/MBypt29bKlStVWlqq9u3bq3379g5PCwQOcQFuUPnL8H/44Yc3/a6x77//Xp9++qkk6eGHH1Z4eLiDUwKBRVyAG+TxePTggw9K+uu7xo4ePXrDP8MYow0bNujkyZNq0KCBYxfZBKoL4gLcoEtbY1FRUTp16pQ2btx4w1tj586d06pVqyRJ9957r1q1alUVowIBQ1yAmxAbG6tu3brJGKOVK1fq/PnzN/T8/fv3a9++fXK5XBo4cKDcbncVTQoEBnEBboLb7VZKSopcLpcyMjL0xRdfVHj1YozRqlWrVFxcrNtuu009e/ZkSwxBh7gAN8GyLCUkJKhly5YqKSnRsmXLKhyXU6dOae3atZL++tmWmJiYqhwVCAjiAtykmJgY9evXT5K0Zs0a5ebmXvc5xhj96U9/0pEjR+T1ejVw4MCqHhMICOIC3CTLsjR48GDVq1dPx44d0wcffHDd1cv58+e1ZMkS2batf/zHf1SHDh3YEkNQIi5AJbRr105JSUkyxmjhwoXKz8//2e81xigjI0O7du2Sy+XSsGHD5PF4buG0wK1DXIBKcLvdGjNmjLxerw4cOKCVK1f+7OqltLRUb7/9tkpKStSmTRv16tWLVQuCFnEBKsGyLHXt2lWJiYmybVszZ8686tnLpUvrf/TRR7IsS48//jgXqURQIy5AJYWGhiotLU1169bVd999p2nTpsnv91/2PefPn9eUKVNUUFCgO+64Q0OGDGHVgqBGXIBKsixLnTt31vDhwyVJ8+fP19q1a8u2x4wx+uCDD7R+/Xq5XC4988wzatq0aSBHBqoccQEc4Ha7lZaWprvuukvFxcUaO3asPvzwQ/3444/asGGDJkyYoPPnz+vee+/VY489xqoFQY/7qQIOady4sebNm6f+/fvryJEjGjp0qFq2bKns7GwVFRXp9ttv18yZM1W3bt1AjwpUOVYugEMsy1LHjh31/vvvq2vXrrpw4YK+/fZblZSUqH379lq2bJnat2/PqgW1AisXBAVjjGzbrhb/x92xY0f98Y9/1Pbt23Xo0CHddttt+pd/+RfFxMTIGHPdD1ratl3p2ycDgUZcUOO5XC4dP35cqampgR7lqv73f/9Xf/7zn2/oOUePHpXLxcYCai7L8CsSajjbtnXy5MmbviNkdeRyuRQdHc2l+FFjERcAgOPYFgMqqPzvYdXhbAeoztjUBSooMzNTLpdLmZmZgR4FqPaICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOIC1ABxhjl5+dLkvLz88XdwYFrIy7ANfh8Ps2aNUtxcXFKSEiQJCUkJCguLk6zZs2Sz+cL7IBANWUZfgUDrmrTpk3q16+fiouLJemy1YplWZKkiIgIrV69WklJSQGZEaiuiAtwFZs2bVKvXr1kjJFt2z/7fS6XS5ZlKT09ncAA5RAX4Ao+n08tWrRQSUnJNcNyicvlktfrVXZ2tqKioqp+QKAG4MwFuMI777yj4uLiCoVFkmzbVnFxsZYuXVrFkwE1BysXoBxjjOLi4pSVlXVD7wizLEuxsbE6dOhQ2XkMUJsRF6CckydPqlGjRpV6fnR0tIMTATUT22JAOYWFhZV6fkFBgUOTADUbcQHKiYyMrNTz69at69AkQM1GXIByoqOj1bp16xs+N7EsS61bt1bDhg2raDKgZiEuQDmWZWns2LE39dzU1FQO84GLONAHrsDnXIDKY+UCXCEqKkqrV6+WZVlyua79Ern0Cf01a9YQFqAc4gJcRVJSktLT0+X1emVZ1k+2uy59zev1asOGDUpMTAzQpED1RFyAn5GUlKTs7GzNnDlTsbGxlz0WGxurmTNnKicnh7AAV8GZC1ABxhht27ZNPXv21NatW9WjRw8O74FrYOUCVIBlWWVnKlFRUYQFuA7iAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAgBwHHEBADiOuAAAHEdcAACOIy4AAMcRFwCA44gLAMBxxAUA4DjiAlyHbds6ffq0vv/+e0lSbm6uioqKAjwVUL1xm2PgZ5w9e1affPKJli5dqj179igvL0+FhYWqX7++WrVqpcTERA0dOlTx8fHcmRK4AnEBriIrK0sTJ05Uenq6mjdvrh49eqhjx46qV6+eTp06pYyMDG3btk0XLlzQ+PHjlZqaqoiIiECPDVQbxAW4wtdff62BAwfq6NGjGjdunEaMGKF69eopMzNTfr9f4eHh6tChg3JzczVjxgwtXrxYKSkpmj59OoEBLiIuQDmnTp3SAw88oG+//VYLFy5U79695Xa7lZWVpS5dusjn86lVq1bavXu3oqKi5Pf7tWjRIk2cOFHPPvusJk+eLJeLo0wgJNADANXJ3LlzlZGRodmzZ6tPnz6XheLChQvy+/3y+/2SJMuyFBoaquHDh+vo0aOaPXu2evfurU6dOgVqfKDa4Fcs4KK8vDwtXrxYXbt21aBBgyq8AgkJCVFqaqoaN26sBQsWiM0AgLgAZfbs2aOjR49q8ODBCg8PV2lp6WV/LjHG/OSxmJgYPfTQQ9qyZYt8Pl/g/hFANcG2GHBRZmamwsLC1KlTJ6WlpWn//v1lj5WUlJR9tuXEiRNKTk5WSMjfXj6jR49Wt27dNGfOHOXk5KhBgwa3fH6gOiEuwEV5eXkKDw9X/fr1tXv3bu3cufOq31dSUqKtW7de9rVevXrpnnvukW3brFwAERegjMfjkW3b8vv9crlcPzlzsW277O9XPmZZls6fPy9JCg0NrfphgWqOuAAXtW7dWkVFRcrOztbUqVOVn59f9lhubq5SU1NVVFSkJk2aaM6cOYqMjCx7PD4+Xtu3b1d4eLiaNGkSiPGBaoW4ABd16dJFYWFh+vjjjzVlypTLVidZWVllZywRERFKSEi47FzF7/drw4YNio+PV7NmzW757EB1w7vFgIvatWunrl27auXKlTp8+HCF31JsjNHu3bu1efNmDRw4UB6Pp4onBao/4gJc5PF4lJaWJp/Pp7S0NJ05c+a6gTHGKDc3VxMnTlRcXJySk5Nv0bRA9UZcgHK6d++uCRMmaOPGjRo1apRycnJkjJHb7VbTpk3VrFkzNW7cWC6XS8YYHTx4UEOHDtWRI0c0Y8YM3oIMXMS1xYArnDt3TlOmTNEbb7yh22+/XaNHj1ZiYqI8Ho/cbrdKS0tVWFiodevWaf78+QoNDdW8efPUs2fPQI8OVBvEBbiK0tLSsoP9vXv3yuv1qlmzZqpTp44KCgp07Ngxud1u9e3bV5MmTVKbNm0CPTJQrRAX4BqKi4uVkZGhHTt26LPPPtP69evVv39/JSQkqHv37mrTpo3cbnegxwSqHeICVNC+fft01113ae/evVz5GLgODvQBAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcx83CgAoyxsgYI8uyZFlWoMcBqrWQQA8AVJZt2zp+/LhKS0ur/L91KS5VzeVyqWnTptxCGTUWKxfUePn5+RowYIDatm0b6FEqxRgjv9+v0NBQHThwQCtWrFBMTEygxwJuCisX1HjGGDVv3lwzZsyQy1UzjxGNMfrjH/+otWvX6uWXX9aLL74o27YDPRZw04gLgsKlc5CaGBdjjDZs2KAnn3xSp06dUnh4+C3Z4gOqUs17JQJBxBijjRs3avjw4Tp58qRiY2M1fPhwzlpQ4xEXIECMMdq0aZMef/xx/fDDD2rVqpWWLVumu+++O9CjAZVGXIAAMMZo8+bNGjZsmPLy8nTHHXdo2bJl6ty5c6BHAxxBXIBbzBijLVu26LHHHtOJEyfUsmVLvffee+rSpQufn0HQIC7ALWSM0SeffKLHHntMx48f1+233653331XXbt2JSwIKsQFuEUuhWXo0KHKzc3VbbfdpnfffVfdunUjLAg6xAW4BcqH5dixY2rRooWWLl2qe++9l7AgKBEXoIpdOmN59NFHy8Ly7rvv6p//+Z8JC4IWcQGq0KW3G1+5FUZYEOyIC1BFbNvW+vXrLzu8f++99wgLagXiAlQB27a1du1aPf7448rLy1OrVq20fPlyzlhQaxAXwGGlpaVasWKFRowYoVOnTqlNmzZasWIFbzdGrUJcAAeVlpZqyZIlGjNmjHw+n+Lj47Vq1Sp17tyZsKBWIS6AQ/x+v+bOnatf//rXKigoUPv27fX++++rQ4cOhAW1DnEBHHD+/HnNmDFDaWlpKioqUufOnbVq1Sq1a9eOsKBW4n4uQCWdO3dOU6dO1WuvvaZz587p3nvv1TvvvKM77riDsKDWIi7ATTLG6OzZs/qP//gPvfnmm7pw4YISEhK0aNEi/d3f/R1hQa1GXICbYIxRSUmJJk+erLfeekulpaXq3bu35s+fryZNmhAW1HrEBbhBxhgVFRXp+eef17x582Tbth566CHNnTtX0dHRhAUQB/rADTHGqLCwUM8995zefvttGWM0cOBAzZ8/n7AA5RAXoIKMMSooKNC4ceO0cOFCSdKQIUP0+9//XlFRUYQFKIe4ABVgjNGZM2eUmpqqJUuWSJKGDRumWbNmqV69eoQFuAJnLsB1GGP0448/auzYsVqxYoUsy9ITTzyhN954Q3Xq1CEswFWwcgGuwRij/Px8jR49WsuXL5dlWRo5cqSmTZtGWIBrIC7AzzDG6PTp0xo5cqTef/99uVwuPfXUU3r99dcVERFBWIBrIC7AVRhjdPLkSY0YMUJr1qyR2+3WM888o9dee42wABXAmQtwBWOMfvjhBz3xxBNKT0+X2+3W+PHj9Zvf/EZerzfQ4wE1AnEByjHG6MSJExo+fLg2btyo0NBQTZgwQZMnT1Z4eHigxwNqDOICXGSMUW5uroYNG6bNmzcrLCxMzz//vCZNmiSPxxPo8YAahbgA+mtYcnJyNHToUG3btk0ej0cvvviiJk6cqLCwsECPB9Q4xAW1njFG33//vR599FHt2LFDHo9Hv/nNbzR+/HjCAtwk4oJazRijI0eOaPDgwfrv//5vhYeH63e/+52eeeYZhYaGBno8oMYiLqi1jDE6fPiwBg8erL/85S+KiIjQq6++qqeeekohIbw0gMrgFYRayRijgwcPatCgQdq3b5/q1KmjqVOn6sknnyQsgAN4FaHWMcbom2++0aBBg/TFF18oMjJS06ZN0/Dhw+V2uwM9HhAUiAtqFWOMvvrqKw0aNEhffvml6tatq5kzZ+rRRx8lLICDiAtqDWOMvvzySw0aNEhfffWV6tevr9mzZyslJYWwAA4jLqgVjDH6n//5H6WkpOibb75RVFSU3nrrLSUnJ8vl4hJ7gNOIC4KeMUZffPGFUlJSdODAATVo0ED/+Z//qYcffpiwAFWEuCCoGWP0+eefKyUlRd9++60aNGiguXPnqn///oQFqELEBUHramF5++231a9fP8ICVDHigqBkjFFmZqZSUlJ08OBBNWzYUPPmzdODDz5IWIBbgLgg6BhjtG/fPqWkpOjQoUNq2LCh5s+fr759+xIW4BbhlYagYozR3r17NXDgQB06dEjR0dFasGABYQFuMV5tCCoZGRlKSUnRd999VxaWBx54gLAAtxivOAQFY4z27NlTFpaYmBgtXLhQ999/P2EBAoAzFwQFY4xWrVqlrKyssrD07t2bsAABQlwQFCzL0osvvqizZ8/qV7/6le677z5Jkm3bAZ7sxtm2LWNMoMcAKsUy/K8YNZzP51NycrJatWql0tLSoLhO2OHDh7V8+XLFxMQEehTgphAX1Hi2bevkyZM1cpXyc1wul6Kjo4MilKidiAsAwHGcuQAVVP73MMuyAjgJUP3xVhqggjIzM+VyuZSZmRnoUYBqj7gAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXAAAjiMuAADHERcAgOOICwDAccQFAOA44gIAcBxxAQA4jrgAABxHXIAKMMYoPz9fkpSfn3/ZLY8B/BRxAa7B5/Np1qxZiouLU0JCgiQpISFBcXFxmjVrlnw+X2AHBKopy/ArGHBVmzZtUr9+/VRcXCxJl61WLMuSJEVERGj16tVKSkoKyIxAdUVcgKvYtGmTevXqJWOMbNv+2e9zuVyyLEvp6ekEBiiHuABX8Pl8atGihUpKSq4ZlktcLpe8Xq+ys7MVFRVV9QMCNQBnLsAV3nnnHRUXF1coLJJk27aKi4u1dOnSKp4MqDlYuQDlGGMUFxenrKysG3pHmGVZio2N1aFDh8rOY4DajLgA5Zw8eVKNGjWq1POjo6MdnAiomdgWA8opLCys1PMLCgocmgSo2YgLUE5kZGSlnl+3bl2HJgFqNuIClBMdHa3WrVvf8LmJZVlq3bq1GjZsWEWTATULcQHKsSxLY8eOvannpqamcpgPXMSBPnAFPucCVB4rF+AKUVFRWr16tSzLkst17ZfIpU/or1mzhrAA5RAX4CqSkpKUnp4ur9cry7J+st116Wter1cbNmxQYmJigCYFqifiAvyMpKQkZWdna+bMmYqNjb3ssdjYWM2cOVM5OTmEBbgKzlyACjDG6PTp0yooKFDdunXVsGFDDu+BayAuAADHsS0GAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDjiAsAwHHEBQDgOOICAHAccQEAOI64AAAcR1wAAI4jLgAAxxEXAIDj/j86fEjGPfEIoAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3c92b0d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_12_unsupervised_learning-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_12_unsupervised_learning-checkpoint.ipynb new file mode 100644 index 00000000..943ae09b --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_12_unsupervised_learning-checkpoint.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 12: Unsupervised learning" + ] + }, + { + "cell_type": "markdown", + "id": "6465ec94", + "metadata": {}, + "source": [ + "In this example, we will use KAN for unsupervised learning. Instead of trying to figure out how a target variable $y$ depends on input variables, we treat all variables on the equal footing (as input variables). Below we contruct a synthetic dataset where we have six variables $x_1, x_2, x_3, x_4, x_5, x_6$. $(x_1, x_2, x_3)$ are dependent such that $x_3={\\rm exp}({\\rm sin}(\\pi x_1)+x_2^2)$; $(x_4,x_5)$ are dependent such that $x_5=x_4^3$. And $x_6$ is independent of all other variables. Can we use KANs to discover these dependent groups?\n", + "\n", + "The idea is that we treat the problem as a classification problem. The dataset that satisfies these interdependent relations are 'positive' samples, while corrupted samples (by random permutation of features across samples) are 'negative' samples. We want to train a KAN to output 1 when it is a positive sample, and output 0 when it is a negative sample. We set the last layer activation to be Gaussian, so positive samples will have zero activation in the second to last layer, while negtive samples will have non-zero activation in the second to last layer. We can then define the relation implicitly as $g=0$ where $g$ is the activation in the second to last layer." + ] + }, + { + "cell_type": "markdown", + "id": "94056ef6", + "metadata": {}, + "source": [ + "Intialize model and create dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a59179d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + } + ], + "source": [ + "from kan import KAN\n", + "import torch\n", + "import copy\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "seed = 1\n", + "\n", + "model = KAN(width=[6,1,1], grid=3, k=3, seed=seed, device=device)\n", + "\n", + "# create dataset\n", + "\n", + "\n", + "def create_dataset(train_num=500, test_num=500):\n", + " \n", + " def generate_contrastive(x):\n", + " # positive samples\n", + " batch = x.shape[0]\n", + " x[:,2] = torch.exp(torch.sin(torch.pi*x[:,0])+x[:,1]**2)\n", + " x[:,3] = x[:,4]**3\n", + "\n", + " # negative samples\n", + " def corrupt(tensor):\n", + " y = copy.deepcopy(tensor)\n", + " for i in range(y.shape[1]):\n", + " y[:,i] = y[:,i][torch.randperm(y.shape[0])]\n", + " return y\n", + "\n", + " x_cor = corrupt(x)\n", + " x = torch.cat([x, x_cor], dim=0)\n", + " y = torch.cat([torch.ones(batch,), torch.zeros(batch,)], dim=0)[:,None]\n", + " return x, y\n", + " \n", + " x = torch.rand(train_num, 6) * 2 - 1\n", + " x_train, y_train = generate_contrastive(x)\n", + " \n", + " x = torch.rand(test_num, 6) * 2 - 1\n", + " x_test, y_test = generate_contrastive(x)\n", + " \n", + " dataset = {}\n", + " dataset['train_input'] = x_train.to(device)\n", + " dataset['test_input'] = x_test.to(device)\n", + " dataset['train_label'] = y_train.to(device)\n", + " dataset['test_label'] = y_test.to(device)\n", + " return dataset\n", + "\n", + "dataset = create_dataset()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "79665292", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model(dataset['train_input'])\n", + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "45760ca2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + } + ], + "source": [ + "# set the (1,0,0) activation to be gausssian\n", + "#model.fix_symbolic(1,0,0,lambda x: torch.exp(-x**2/10),fit_params_bool=False)\n", + "model.fix_symbolic(1,0,0,'gaussian',fit_params_bool=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d951ae17", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model(dataset['train_input'])\n", + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "aa26622b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.80e-01 | test_loss: 1.78e-01 | reg: 3.77e+01 | : 100%|█| 50/50 [00:13<00:00, 3.76it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=50, lamb=0.002, lamb_entropy=10.0, lamb_coef=1.0);" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9d162e40", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAFrCAYAAAAdNsRvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAArHUlEQVR4nO3dfVxUdb4H8M8ZBkYTLEHUVB7SMq11LSwrNYueRIEVNx9o1as2gvm4tpmZu17Kl77cStba7qCphQ9dtdTVxGe75pI3TVeUNC3c1LRMLUAR5GGG+d4/vEyoKKC/mXNm5vN+vXipcDjz/X09Mx9+v3POoImIgIiISCGT3gUQEZHvYbgQEZFyDBciIlKO4UJERMoxXIiISDmGCxERKcdwISIi5RguRESkHMOFiIiUY7gQEZFyDBciIlKO4UJERMoxXIiISDmGCxERKcdwISIi5RguRESkHMOFiIiUY7gQ1SInJwdWqxVhYWEICgpCWFgYrFYrcnJy9C6NyLA0/ppjopo5HA6MGTMG8+bNg9lshsPhcH2t6t+pqamw2Wwwm806VkpkPAwXomsYOXIk5s+fj+s9RTRNQ0pKCt577z0PVkZkfAwXohrk5OSgc+fOdd5+7969iImJcWNFRN6F51yIalCfpS6z2YyMjAw3V0TkXThzIapBWFgYCgoK6rx9aGgo8vPz3VgRkXdhuBDVICgoCHa7vc7bBwYGoqKiwo0VEXkXLosR1SAkJMSt2xP5OoYLUQ2SkpLqdc6lb9++bq6IyLtwWYyoBrxajOjmcOZCVIOYmBikpqZC07TrbqdpGlJTUxksRFdguBBdg81mQ0pKCgBctURW9e+UlBTYbDaP10ZkdFwWI6pFTk4OMjIysGLFChQVFaFx48bo378/Ro8ezRkL0TUwXIjqqOo8DM+vENWOy2JERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciIhIOYYLEREpx3AhIiLlGC5ERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciIhIOYYLEREpx3AhIiLlGC5ERKQcw4WIiJRjuBARkXJmvQsgMrqCggLs27cPW7ZsAQBs2LABFosF7dq1Q2BgoM7VERmTJiKidxFERnTx4kVkZmbCZrPh3LlziIyMRMOGDVFQUID8/Hx06dIFU6dOxf333693qUSGw3AhqsG5c+cwduxYbNu2DePGjcPAgQPhcDhQWVnpChibzYZt27Zh9uzZ6NOnDzRN07tsIsNguBBdweFwYMKECVi/fj2WLFmCrl27oqysDI8//ji++eYbjBgxAunp6bDb7Zg3bx5mzZqFFStW4MEHH9S7dCLD4Al9oivs2LEDy5YtQ3p6Orp16waTyQQRQUlJCS5cuICysjIAQFBQEEaOHImkpCSkpaWhvLxc58qJjIPhQlSNiGDhwoXo3Lkz4uPja13qCgwMxLhx43Do0CF89dVXHqqSyPh4tRhRNSUlJdi9ezesViu2bNmCkydPAgAqKipQUFAAAPj6668xZ84cAICmaYiLi0OHDh2wa9cuLo0R/T+GC1E1xcXFKCwsRFRUFGw2GzZv3nzVNtnZ2cjOzgYABAQEYMOGDYiKisIPP/zg6XKJDIvhQlRNQEAAzGYzysvLERISgiZNmri+dv78eTidTlgsFtxyyy1XbR8UFKRX2USGw3AhqqZx48Zo1aoVcnNzYbPZXCfvS0tLkZiYiCNHjmDAgAGYPn2663uCg4Px6quv4umnn9arbCLD4Ql9omosFgt69eqFrKwsAEBkZCQiIyPRunVr1934wcHBiIiIQGRkJCIiIpCbm4uzZ8+ia9euepZOZCgMF6IrDB06FGVlZUhPT0dFRcU1txMR/PLLL3jttdcwYMAAREVFebBKImPjshjRFaKjo/HXv/4VY8eOhcViwUsvvYSgoCBER0fD4XCgefPmAIBjx45hwoQJCAgIwMsvv8w79Imq4R36RDVwOp1YtmwZpkyZgpYtW2Lw4MG45557cMstt6CwsBBffPEFli1bhnvvvRfvvvsuIiIi9C6ZyFAYLkTXceTIEcybNw+bNm3CL6dPAyUlCLrtNnT47W8xdOhQ9OnTx3XlGBH9iuFCVAfFxcXI/+c/YU9IQMiWLQiLjYXZzFVlomvhs4OoDoKDgxF8++2ApgFhYQCDhei6eLUYEREpx3AhIiLlGC5ERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciIhIOYYLEREpx3AhIiLlGC5ERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciIhIOYYLEREpx3AhIiLlGC5ERKQcw4WIiJTTRET0LoLIK4gATidgMgGapnc1RIZm1rsAIndwy89MIpdCRfG+NQYV+SCGC/kcEcHh5ctR9N13hn7hFhE0btsWHZKTDV0n0Y1guJBPKjxwAO1SUtAwOFjNDr/5Bjh9GnjsMWVLYqXFxcibPx9ITlayPyIjYbiQbzKZ0CgsDLc0bnzz+3I6gbQ04KuvgIMHgRYtbn6fAEwWy6XzN0Q+iEc2UW0qK4GzZ4GSkksfRFQrhgtRbUQAh+PXP4moVgwXotqIXJq9VP1JRLViuBDVpur+FoYLUZ0xXIhqw5kLUb0xXIhqI/LrjZNOp761EHkJhgtRbaqWxQDOXIjqiOFCVBdVsxfOXIjqhOFCVJvqocJwIaoThgtRbXjOhajeGC5EteE5F6J6Y7gQ1aZq5sJzLkR1xnAhqg2XxYjqjeFCVJuqu/MBLosR1RHDhag2nLkQ1RvDhag2PKFPVG8MF6LacOZCVG8MF6LaVD/nwnAhqhOGC1Ftqs9cuCxGVCcMF6La8GoxonpjuBDVpipYrvw7EV0Tw4WoNpy5ENUbw4WoNrxajKjeGC5EteHMhajeGC5EtWG4ENUbw4WoNgwXonpjuBDVpnqgOBz61UHkRRguRLVxODhzIaonhgtRbSorfw0XzlyI6oThQlSb6jMXhgtRnTBciGpTPVDsdv3qIPIiDBei2lRfFmO4ENUJw4WoNpy5ENUbw4WoNtXPuZSX61sLkZdguBDVpvps5eJFvjMyUR2Y9S6AyK1UBMHFi7/+vaRE3X6JfBjDhXzX/v3A2rX1+56aQuPbb3/9+86dwMsv31RZLr/7nZr9EBkQw4V8V14e8PbbavYVFHTpIy/v0ocK7dur2Q+RATFcyDc5nbjQpg0cs2fX/Xs07dpfa9IEaNwYOH78pkurUtqmDXD0qLL9ERkJw4V8Utj99+P7PXuuHxj1cfEi8OOPavZVZc8ehN1/v9p9EhmEJsIzk+R7vOmw1lQFIJGBcOZCPsktL9jVA4uBQHRdvM+FqK727QMCAi79SUTXxXAhIiLlGC5ERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciIhIOYYLEREpx3AhIiLlGC5ERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciIhIOYYLEREpx3AhIiLlGC5ERKQcw4WIiJRjuBARkXIMFyIiUo7hQkREyjFciGqRk5MDq9WK2NhYOEUQGxsLq9WKnJwcvUsjMixNRETvIoiMyOFwYMyYMZg3bx7MZjM6Ohz4F4AHABwwm+FwOJCamgqbzQaz2ax3uUSGwmcE0TWMGTMG8+fPB3ApaKqr+nfV19977z3PFkdkcJy5ENUgJycHnTt3vuxz9wOumcu+K7bfu3cvYmJiPFQdkfHxnAtRDeqz1GU2m5GRkeHmioi8C2cuRDUICwtDQUHBZZ+73swlNDQU+fn5HqqOyPg4cyGqwYULF9y6PZGvY7gQ1SAkJOSqz32DS7OWb+q4PZE/Y7gQ1SApKemqcy6luLQcVnrFtmazGX379vVUaURegedciGpQ09Vi18OrxYgux5kLUQ1iYmKQmpoKTdOuu52maUhNTWWwEF2B4UJ0DTabDSkpKQBw1RJZ1b9TUlJgs9k8XhuR0XFZjKgWOTk5yMjIwIoVK1BUVITGjRujf//+GD16NGcsRNfAcCGqo6rzMDy/QlQ7LosREZFyDBciIlKO4UJERMoxXIiISDmGCxERKcdwISIi5RguRESkHMOFiIiUY7gQEZFyDBciIlKO4UJERMoxXIiISDmGCxERKcdwISIi5RguRESkHMOFiIiUY7gQEZFyDBciIlKO4UJERMqZ9S6AyOgKCgqwb98+bNmyBQCwYcMGWCwWtGvXDoGBgTpXR2RMmoiI3kUQGdHFixeRmZkJm82Gc+fOITIyEg0bNkRBQQHy8/PRpUsXTJ06Fffff7/epRIZDsOFqAbnzp3D2LFjsW3bNowbNw4DBw6Ew+FAZWWlK2BsNhu2bduG2bNno0+fPtA0Te+yiQyD4UJ0BYfDgQkTJmD9+vVYsmQJunbtirKyMjz++OP45ptvMGLECKSnp8Nut2PevHmYNWsWVqxYgQcffFDv0okMgyf0ia6wY8cOLFu2DOnp6ejWrRtMJhNEBCUlJbhw4QLKysoAAEFBQRg5ciSSkpKQlpaG8vJynSsnMg6GC1E1IoKFCxeic+fOiI+Pr3WpKzAwEOPGjcOhQ4fw1VdfeahKIuPj1WJE1ZSUlGD37t2wWq3YsmULTp48CQCoqKhAQUEBAODrr7/GnDlzAACapiEuLg4dOnTArl27uDRG9P8YLkTVFBcXo7CwEFFRUbDZbNi8efNV22RnZyM7OxsAEBAQgA0bNiAqKgo//PCDp8slMiyGC1E1AQEBMJvNKC8vR0hICJo0aeL62vnz5+F0OmGxWHDLLbdctX1QUJBeZRMZDsOFqJrGjRujVatWyM3Nhc1mc528Ly0tRWJiIo4cOYIBAwZg+vTpru8JDg7GK6+8ArPZjJ9++gm33367XuUTGQZP6BNVY7FY0KtXL2RlZQEAIiMjERkZidatW7vuxg8ODkZERAQiIyMRERGB3Nxc5OXlITMzE61bt8bDDz+M6dOnY//+/eCV/uSvGC5EVxg6dCjKysqQnp6OioqKa24nIvjll1/w2muv4YUXXsDp06exePFiREdHIz09HZ07d8Ydd9yBMWPGYNOmTa5ZEJE/YLgQXSE6OhozZ87EBx98gOnTp+P8+fMwmUyIjo5Gu3bt0Lx5cwDAsWPHMHz4cBw8eBCjRo1C06ZNMWjQICxfvhxnzpzB1q1b0bdvX2zZsgXx8fFo1qwZfv/73yMzMxNnzpzReZRE7sU79ImuICJwOp1Yvnw5pkyZgpYtW2Lw4MG45557cMstt6CwsBBffPEFli1bhubNm+Orr75Chw4dkJWVhWbNmtW4v8OHDyMrKwvr1q3Dzp07AQBdunRBQkICEhIS0LFjR759DPkUhgtRNVVPh6oX+iNHjmDevHnYtGkTfvnlFwCX7szv0KEDhg4dij59+uDbb79FfHw8QkJCsHHjRrRp0+a6j/Hzzz9j48aNyMrKwpYtW1BcXIyoqCgkJCQgMTERPXr0gMVice9AidyM4UKEq0PlSsXFxcjPz4fdbkdISAjCwsJgNv96seXRo0fRq1cvXLhwARs2bMB9991Xp8ctLy9Hdna2a1bz/fffIzg4GM888wwSExPRq1cvhIeH3/T4iDyN4UJ+r7ZgqauzZ88iISEBeXl5WLNmDR5//PF613Hw4EGsW7cO69atw5dffgkAeOSRR1zLZ/fccw+Xz8grMFzIr6kKlioXLlxAv379kJ2djSVLlqBfv343vK8zZ85gw4YNyMrKwtatW3Hx4kXccccdruWzRx99lDdukmExXMhvqQ6WKhUVFXj++eexfPlyvPPOOxgzZsxN77OsrAzbt293LZ/98MMPaNy4MXr27InExETExcUhLCxMQfVEajBcyC+5K1iqOJ1OTJo0CbNnz8aUKVMwbdo0ZY8lIsjNzXUtn+3ZswcmkwndunVzLZ/dfffdXD4jXTFcyO+4O1iqS09Px6RJkzB8+HDMnTv3sosAVPnpp59cy2effvopSktLceedd7qWz7p16+Z6dwEiT2G4kF/xZLBU+fDDD2G1WhEXF4dly5a53vTSHUpLS7Ft2zZkZWVh/fr1OHXqFG677bbLls+qvxknkbswXMhv6BEsVTZv3ox+/fqhU6dOWLt2LUJDQ93+mCKCnJwc1/JZTk4OAgIC0L17dyQmJiIhIQF33XWX2+sg/8RwIb+gZ7BU2b17NxITExEeHo6NGzciIiLCo4//448/Yv369cjKysK2bdtQVlaGu+++23WepmvXrm5ZtiP/xHAhn1b98DbCCe68vDzExcXB4XBg48aNuPfee3Wpo6Sk5LLls9OnTyM0NBRxcXFITExEz549ceutt+pSG/kGhgv5LCPMVmpy6tQpxMfH48SJE1i7di26deumaz1OpxP/+te/XMtnubm5MJvNePTRR13LZ23bttW1RvI+DBfySUYNlirnz59H37598eWXX2L58uVITEzUuySXEydOuJbPPvvsM1RUVKBDhw6uoHn44YcREBCgd5lkcAwX8jlGD5YqZWVlGDJkCNasWYO5c+fCarXqXdJViouL8emnnyIrKwsbNmzA2bNn0bRpU/Tq1QuJiYl4+umn0bhxY73LJANiuJBP8ZZgqVJZWYnx48dj7ty5mDZtGqZMmWLY2p1OJ3bv3u1aPjtw4AACAwPx2GOPuWY10dHRepdJBsFwIZ/hbcFSRUQwY8YMpKWlYdSoUXjnnXe8Ytnp+PHjWLduHbKysvDPf/4Tdrsdv/nNb1xXn3Xp0sUrxkHuwXAhn+CtwVLdggULMGrUKPTt2xeLFy9GgwYN9C6pzoqKirB161bX8ll+fj7Cw8MRHx+PxMREPPXUUwgODta7TPIghgt5PV8Ilipr167Fc889h4ceegirV6/2ysuBKysrsWvXLtfy2aFDhxAUFITY2FjX8pmn7/Ehz2O4kFfzpWCpsmPHDvTp0weRkZFYv349WrZsqXdJN+W7775zXX2WnZ0Nh8OBTp06uZbPHnjgAZhMJr3LJMUYLuSVjHZzpGoHDx5E7969YTabsWnTJrRr107vkpQ4f/48Nm/ejKysLGzcuBGFhYVo0aIFevfujcTERDz55JNo1KiR3mWSAgwX8jq+OFupyYkTJ9C7d2/8/PPPyMrKQpcuXfQuSSmHw4EvvvjCtXz27bffokGDBnjiiSeQmJiI+Ph4tGrVSu8y6QYxXMir+EuwVMnPz0efPn2Qm5uLlStXomfPnnqX5DZHjhxxXX22Y8cOVFZWIiYmxrV8FhMT4zf/776A4UJew9+CpcrFixeRnJyMzZs34/3338fgwYP1LsntCgsLXctnmzZtwrlz59CyZUvEx8dj3Lhxur0nG9Udw4W8hoj4XbBUEZHLwtWf+lA17qoemEwmvxq/t2K4kFt402Hlrhcqd/TAXQHrjn3yGPBv/OUNpJw3vagA7nnBFhEsX74c3333naFfuEQEbdu2RXJystI6/X38xHAhcpsDBw4gJSXFkHemnz59GosXL8aQIUOwfPlyJCcnK3+MAwcO4LnnnkNmZiasViuaNWum/DFuVnFxMebPn++W8fs7hgu5lZF+GvT0jMpkMiEsLMxw7xpcUlKC1157DWvXrkVISIjbbmA0mUxYvXo13n33XRw6dAhLly5FkyZNDHVMWCwW3sDpJuwq+QVvW6pzF7vdjv/8z//EJ598gpiYGAwaNMitjzdo0CD06NEDW7duxZgxY1BaWurWxyPjYLiQz2OwXFJZWYl33nkH//Vf/4XIyEhkZmYiPDzcrY8ZHh6ORYsWoVOnTli5ciVmzJiByspKtz4mGQPDhXwag+USp9OJDz74AGlpabj11luRmZmJDh06eOSxW7VqhYULF6JFixaYPXs2VqxYwf8XP8BwIZ91vRcwI637u5vT6cSSJUvwpz/9CUFBQZg/fz569OjhsR5omoaOHTvCZrPBZDJhwoQJyM3NZcD4OIYL+R1/C5YPP/wQ48aNQ0BAAObOnYvExESP90DTNCQkJGDy5MnIz8/HyJEjUVBQ4NEayLMYLuSTrvVTsb8Fy+LFizF27FhomoaMjAz0799ft6ujTCYTXnrpJSQkJOBf//oXJk+eDLvdrkst5H4MF/I5DJZLwbJo0SKMHz8eJpMJc+bMQXJysu6X3TZo0AB///vfcdddd2Hx4sVYuHAhl8d8FMOFfAqDxbjBAlz6f2jdujXmzJmDhg0b4tVXX8XOnTsZMD5I/6ONSBEGy6VgWbp0Kf74xz8iICAA7733HgYOHGiIYKmiaRoee+wxvPbaaygqKkJKSgpOnjzJgPExxjniiG4Cg+VSDz755BPXOZY5c+boeo7lekwmE0aNGoXBgwfjm2++QWpqKs6fP8+A8SHGO+qI6onBcqkHO3bswAsvvAC73Y6//e1vhpuxXMlisWDWrFno0aMHPv30U4wfPx4XL17UuyxSxLhHHlEdMFgu9eDIkSMYPnw4zp07h9dffx3Dhg0zdLBUadKkCTIzM/Gb3/wGS5cuxfjx41FUVMQZjA8w/tFHdA0MlkvOnTuHESNG4Pjx43jhhRdc51u8gaZpiIqKwrJly9ChQwcsWrQIQ4YMwdGjRxkwXo7hQl6JwXKJ3W7Hn//8Z/zv//4vevbsienTpyMwMFDvsupF0zS0b98eq1atwiOPPIL169fjySefxIwZM5Cbm4vCwkKUlpaivLwc5eXlKCsrQ3l5Oex2+2W/oZOMhW+5T16HwXKJiODjjz/GBx98gDZt2sBmsxnyd8fUhaZpuOuuu7B69WrMmDEDixYtQlpaGt588020aNECt912G4KCgiAicDqd0DQNwcHBiIiIwEMPPYQnnngCd9xxB38FsoEwXMirMFguERHk5eVh0qRJMJvNePfddxEVFeXVfdA0DU2bNsWsWbMwbNgwfPzxx8jOzsbJkydx7NgxVFZWQtM0aJoGEUFFRQU+++wzLFq0CGFhYRg4cCAmTZqEVq1aeXUffAXDhbwGg+VXZWVlePHFF3HmzBlMmjQJzzzzjM/0ISAgAJ06dcJvf/tbVFRUoKioCCUlJXA4HNA0DSaTCSKCkpISHD9+HNu2bcM//vEP2Gw2/M///A/mzp2L7t27+0w/vBXDhbwCg+VXIoL58+dj69at6NKlCyZPnuwVV4bVl6ZpsFgsCA8Pv+bvnenYsSMSEhIwYcIETJs2DR9++CH69++PhQsXomfPnn55fBiF7x2R5HMYLL8SERw8eBDTp09HcHAw3n77bYSEhOhdlq6qrjjLyMjA66+/jvPnz2P48OHYvXs3T/briOFChsZguVxpaSlefPFFFBQU4OWXX8YDDzzgt724ksViwcSJE/GXv/wF+fn5sFqt+OmnnxgwOmG4kGExWC4nIsjIyMD27dvRvXt31xtT0q/MZjMmTpyIP/zhDzh8+DAmTpzIt/XXCY9MMiQGy+VEBDk5OZg5cyZuvfVWzJ49G40aNdK7LEOyWCx48803ce+992LVqlVYtWoVZy86YLiQ4TBYrlZcXIwJEyagqKgIU6ZMwX333efX/ahNeHg40tPTYTabMXXqVJw9e1bvkvwOw4UMhcFyNafTiVmzZmHnzp2IjY3FqFGj/LofdaFpGp544gkkJyfj2LFjmD17NmcvHsZwIcNgsFxNRPDZZ59h9uzZCA8Px9tvv42GDRvqXZZXCAgIwF/+8hc0bdoUCxYswL///W+9S/IrDBcyBAbL1UQEp06dwtixY1FeXo6ZM2eiQ4cOft2T+oqOjsaoUaNQWFiIv/3tb3A6nXqX5DcYLqQ7BkvNysrKMGHCBOTl5WHQoEEYNGiQ3/ekvjRNwwsvvIDbb78dH330EY4ePap3SX6D4UK6YrDUrLKyEn/961+xZs0axMTE4I033vC6dzs2iubNm8NqteL8+fOYM2cOz714CMOFdMNgqZnT6URmZibeeustNGvWDPPnz0fTpk31LstraZqGESNGIDQ0FEuXLsWZM2f0LskvMFxIFwyWmjmdTixduhQvvfQSLBYL3nvvPXTq1Mnv+3KzWrdujWeffRZnz57FRx99xNmLBzBcyOMYLFeregv5jIwMjB49GiKCv//97+jdu7df90UVTdMwcuRIWCwWfPDBBygrK9O7JJ/HcCGPYrBcTURw+vRpjB07FhMnTkRgYCDmzp2LQYMG8e1dFOrYsSMeeeQRHD58GF988YXe5fg8HrnkMQyWy4kIiouLsXTpUjz11FN4//33cccdd+Djjz9GcnIyg0WxgIAAjBgxAk6nE++//z4vS3Yz/j4X8ggGy69EBOfOncOmTZuQkZGB3bt3w2w24z/+4z/w+uuvIyIiwi/74m6apqFnz55o0aIFtm7dijNnzvD92dyIPxqR2zFY4Prd70ePHsUbb7yBHj16YOjQodizZw+6d++OlStXYt68eQwWN2vSpAkSEhJQUFCADRs26F2OT2O4kFsxWACHw4GcnByMHj0a3bp1w5///GecPHkS8fHxWLVqFbKyshAXF4fAwEC/6oseNE3D4MGDERAQgP/+7/9GZWWl3iX5LC6Lkcf50wtoXl4eMjIysHr1aly4cAGtWrXC+PHjMWTIEHTs2BFms9mv+mEEMTExuPPOO7Fnzx4cP35c73J8Fmcu5FH+9kL6+eefY8mSJQgPD8fMmTOxY8cOpKenIyYmhjMVnTRs2BBJSUkoKyvDZ599pnc5PoszF/Iof7p5zel0okePHpg0aRKeffZZ3H777QBgqDvEi4uL3XbVlNPpxJkzZ1BSUuKW/d+MuLg4hIaG4qGHHsL69ev1LscnaeJPz3byCG88pFTPIEQEK1euxNGjRw09OxERtGnTBv369VNap7+Pnxgu5CbedFi560XF33vg7+P3dwwXIi9R/anqry+G7IH34Al98hr+/nPQvn37YDabsW/fPr1L0Q174D0YLkREpBzDhYiIlGO4EBGRcgwXIiJSjuFCRETKMVyIiEg5hgsRESnHcCEiIuUYLkREpBzDhYiIlGO4EBGRcgwXIiJSjuFCRETKMVyIiEg5hgsRESnHcCEiIuUYLkREpBzDhYiIlGO4EBGRcgwXIiJSjuFCRETKMVyIiEg5Q4dLTk4OrFYrwsLCEBQUhLCwMFitVuTk5Ohdmsf4ew/8ffzArz2IjY2F0+lEbGwse+BnPfDK54EYkN1ul9TUVAEgZrNZALg+qv6dmpoqdrtd71Ldxt974O/jF2EPRNgDbx6/IcMlNTVVNE27rJFXfmiaJqmpqXqX6jb+3gN/H78IeyDCHnjz+A0XLnv37r1uI6/82Lt3r94lK+fvPfD38YuwByLsgbeP33DnXGw2G8xmc522NZvNyMjIcHNFnufvPfD38QPsAcAeePv4NRERvYuoLiwsDAUFBXXePjQ0FPn5+W6syPP8vQf+Pn6APQDYA28fv+HCJSgoCHa7vc7bBwYGoqKiwo0VeZ6/98Dfxw+wBwB74O3jN9yyWEhIiFu39wb+3gN/Hz/AHgDsgbeP33DhkpSUVK91xr59+7q5Is/z9x74+/gB9gBgD7x+/PpeT3A1b79CQgV/74G/j1+EPRBhD7x9/IYLFxHvvrZbFX/vgb+PX4Q9EGEPvHn8hgwXb74rVRV/74G/j1+EPRBhD7x5/IYMlyp79+4Vq9UqoaGhEhgYKKGhoWK1Wg03/XMnf++Bv49fhD0QYQ+8cfyGuxSZiIi8n+GuFiMiIu/HcCEiIuUYLkREpBzDhYiIlGO4EBGRcgwXIiJSjuFCRETKMVyIiEg5hgsRESnHcCEiIuUYLkREpBzDhYiIlGO4EBGRcgwXIiJSjuFCRETKMVyIiEg5hgsRESnHcCEiIuV0C5eSkhI0b94cmqahTZs2sNvtNW5XVlaG7t27Q9M0WCwWbN++3bOFuhF7wB74+/gB9sBnxy86evvttwWAAJB58+Zd9XWn0yn9+vUTAKJpmixbtkyHKt2LPWAP/H38IuyBL45f13ApKyuTyMhIASBRUVFSXl5+2ddffPFFV8NnzZqlU5XuxR6wB/4+fhH2wBfHr2u4iIgsWLDA1bQ5c+a4Pl89ySdMmKBjhe7HHrAH/j5+EfbA18ave7g4HA5p166dAJCIiAgpLy+XVatWiclkEgDSv39/qays1LtMt2IP2AN/H78Ie+Br49c9XEREli9f7kpmq9UqDRo0EADSo0cPKSsr07s8j2AP2AN/H78Ie+BL4zdEuDidTrnvvvtcTQUg9957rxQWFl73+5YsWSKpqanSuXNnCQoKEgCSmZnpkZpVu5Ee/PDDDzJ79mx5+umnJSIiQgIDA6V58+by+9//Xnbt2uW54hW5kR4UFhbKuHHj5OGHH5bmzZtLUFCQtGzZUmJjY2XlypXidDo9N4CbdKPPgyu98cYbru/fuXOne4p1kxvtQVRU1GXfU/1j5MiRnilegZs9Bv7xj3/IU089JaGhodKgQQOJjo6W5ORkOXHihHsLr4EhwkVExGazuZrZrFmzOjWj6oBq2rSp6+/eGi4i9e/BK6+8IgCkbdu28vzzz8vkyZPl2WeflYCAADGZTPLRRx95qHJ16tuDI0eOSKNGjeTJJ5+UkSNHyquvvipWq1WaNWsmACQlJcVDlatxI8+D6g4dOiQWi0UaNWrkleEicuOvBbfeequkpaVd9ZGVleWBqtW5kfE7nU5JTU11vR6MHj1aXnnlFRkyZIhERkbK559/7oHKL2eIcMnLy5OmTZu6GtqoUSM5c+ZMrd+3detWOX78uIiIzJw506vD5UZ6sGrVKsnOzr7q89nZ2RIYGCihoaFeNZW+kR44HA6x2+1Xfb6oqEjuueceASAHDx50V8lK3ejzoIrD4ZAHH3xQunTpIoMHD/bKcLnRHkRFRUlUVJT7C3SzGx3/O++8IwBkzJgx4nA4rvp6Tc8Rd9M9XM6cOSNt2rQRABIWFuZq6vjx4+u1H28OF1U9qO6ZZ54RALJnzx6FlbqPO3pQdfnmmjVrFFbqHirGP2PGDAkKCpKDBw/K0KFDvS5cbqYHvhAuNzr+ixcvSmhoqLRp00aXELkWXcOluLhYHnjgAQEgwcHBsnfvXklKShIAYrFY5Pvvv6/zvrw1XFT2oLr4+HgBIPv27VNbsBu4owelpaXSqVMn0TRN8vLy3FC1OirGf+DAAQkKCpJp06aJiHhduNxsD6KioqRFixaycOFCmTFjhmRkZMj+/fs9VP3Nu5nxf/LJJwJA/vSnP0lZWZmsWrVKZs6cKXPmzJEjR454cBSX0y1c7Ha79OrVSwCI2WyWDRs2iIhIbm6uaJomAOT555+v8/68MVxU96DK999/LxaLRVq0aFHjFNlIVPWgsLBQ0tLSZOrUqTJy5EiJiIgQAJKWlubmEdwcFeO32+3SuXNn6dSpk1RUVIiId4WLih5c64R+XFyc/Pzzz54Yxg272fFPnTpVAMikSZPk7rvvvmz8JpNJXnrpJU8N5TK6hYvVanU1YP78+Zd9reptDgICAuTbb7+t0/68MVxU90BEpKKiQnr06CEAZPHixapLVk5VD44dO3bZkyowMFDeeustw18tpmL8r7/+upjNZtm7d6/rc94ULqp6sH37dvn555+lqKhIdu3a5XrBfuSRRwx9HNzs+EeOHOna5sEHH5Tdu3fLhQsXJDs7W9q3by8AJCMjwxNDuYwu4ZKWluZq5tSpU6/6+sGDB103Dg0YMKBO+/S2cHFHDyorK10ncr3hKil39MDhcMixY8dk5syZEhQUJH379jXUOnR1Ksa/f/9+CQwMlMmTJ1/2eW8JF3ccA1UqKyule/fuAkDWrVunqmSlVIw/JSVFAEjDhg3lxx9/rPH727Zt65b6r8fj4VL9LQ6GDh16ze2Sk5MFuPQmbXU5b+BN4eKOHjidTnn++ecFgAwePNjwd/K66zio7s0339Ttp7baqBp/p06dpH379lddFegN4eKJY+D9998XAPLqq6/eXLFuoGr8EydOFADy6KOP1vj9d955pwCo9/1SN8uj4bJ+/Xoxm80CQJ566inX+nBNDh8+LAEBAQJAevfuXeu+vSVc3NGDyspKGT58uACQ5557zvDnWdx5HFS3f//+G/qJ191Ujr+m8ww1faxevdqNI6o/Tx0DVSe7//jHP95kxWqpHP/8+fMFgCQmJtb4/VUXCpw6dUpZ/XVhhgf17t37mr+r4Ert27eHw+Fwc0Wep7oHTqcTI0aMQGZmJgYOHIglS5YgICBARalu46nj4NSpUwAAs9mjh3mtVI7farXW+Pns7GwcOXIEv/vd7xAeHo7o6OgbKdVtPHUMfPnllwDg0+OPjY0FABw+fPiqr9ntdvz73/9Go0aNEB4efmPF3iiPRpkbecvMRaXKykoZNmyYAJfe1M6o5xbcad++fXLu3LmrPp+fn+96G40lS5boUJm+vGFZTJWvv/66xiWfzz//XBo0aHBTl/R7i6r72q68IGDatGmupXJPM9aPdPW0YMEC7NixAwBw4MAB1+eqfkNbUlISkpKSdKrO/aZNm4aFCxciODgY7dq1w/Tp06/aJikpCffdd5/ni/OQhQsXYsGCBYiNjUVUVBQaNWqE77//HuvXr0dxcTGeffZZ/OEPf9C7THKjjz/+GG+++SaefPJJREdHw2Kx4ODBg9iyZQtMJhPmzp2LyMhIvct0q4yMDHTt2hUpKSlYs2YN2rdvj3379mHbtm2IiorCW2+95fmiPB5nClX9dHatD6Pf43Czahs//GAm9/nnn8uwYcOkffv20rhxYzGbzdKsWTOJi4uTpUuXGvoSVHfyp5nL9u3bZcCAAXLnnXdKSEiIBAYGSuvWrSU5OVm+/PJLvcvzmBMnTsiwYcOkRYsWEhgYKBERETJmzJh6vYWQSpqIiOcjjYiIfJlJ7wKIiMj3MFyIiEg5hgsRESnHcCEiIuUYLkREpBzDhYiIlGO4EBGRcgwXIiJSjuFCRETKMVyIiEg5hgsRESnHcCEiIuX+DwyZPbWM48nNAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot(in_vars=[r'$x_{}$'.format(i) for i in range(1,7)])" + ] + }, + { + "cell_type": "markdown", + "id": "b239996d", + "metadata": {}, + "source": [ + "This gives the dependence among $(x_4,x_5)$. Another random seed can give dependence among $(x_1,x_2,x_3)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b5975f8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_13_phase_transition-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_13_phase_transition-checkpoint.ipynb new file mode 100644 index 00000000..ae7a4241 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_13_phase_transition-checkpoint.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 13: Phase transition" + ] + }, + { + "cell_type": "markdown", + "id": "6465ec94", + "metadata": {}, + "source": [ + "In this example, we will use KAN to learn phase transitions in data. Phase transition is an important concept in science. We consider a toy example $f(x_1,x_2,x_3)$ is 1 if $g(x_1,x_2,x_3)>0$, and is 0 if $g(x_1,x_2,x_3)<0$. $g(x_1,x_2,x_3)={\\rm sin}(\\pi x_1)+{\\rm cos}(\\pi x_2)+{\\rm tan}(\\frac{\\pi}{2}x_3)$." + ] + }, + { + "cell_type": "markdown", + "id": "94056ef6", + "metadata": {}, + "source": [ + "Intialize model and create dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a59179d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + } + ], + "source": [ + "from kan import KAN, create_dataset\n", + "import torch\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "model = KAN(width=[3,1,1], grid=3, k=3, device=device)\n", + "\n", + "# create dataset\n", + "f = lambda x: (torch.sin(torch.pi*x[:,[0]]) + torch.cos(torch.pi*x[:,[1]]) + torch.tan(torch.pi/2*x[:,[2]]) > 0).float()\n", + "dataset = create_dataset(f, n_var=3, device=device)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3837440b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.5060, device='cuda:0')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.mean(dataset['train_label'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fe38f7c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model(dataset['train_input'])\n", + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8627b850", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + } + ], + "source": [ + "# set the last activation to be tanh\n", + "model.fix_symbolic(1,0,0,'tanh',fit_params_bool=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3957140b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model(dataset['train_input'])\n", + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "be0b0daf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 7.71e-02 | test_loss: 1.17e-01 | reg: 2.43e+02 | : 100%|█| 50/50 [00:09<00:00, 5.32it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.2\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=50);" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2f9b37a8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6d85bda", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_14_knot_supervised-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_14_knot_supervised-checkpoint.ipynb new file mode 100644 index 00000000..11bbfbef --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_14_knot_supervised-checkpoint.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "134e7f9d", + "metadata": {}, + "source": [ + "# Example 14: Knot supervised" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0893a344", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './knot_data.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6j/b6y80djd4nb5hl73rv3sv8y80000gn/T/ipykernel_75986/3212158569.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# Download data: https://colab.research.google.com/github/deepmind/mathematics_conjectures/blob/main/knot_theory.ipynb#scrollTo=l10N2ZbHu6Ob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./knot_data.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 934\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1214\u001b[0m \u001b[0;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1215\u001b[0m \u001b[0;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m self.handles = get_handle( # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m 1217\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 785\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 787\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './knot_data.csv'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "from kan import *\n", + "import copy\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "seed = 42\n", + "torch.manual_seed(seed)\n", + "np.random.seed(seed)\n", + "\n", + "# Download data: https://colab.research.google.com/github/deepmind/mathematics_conjectures/blob/main/knot_theory.ipynb#scrollTo=l10N2ZbHu6Ob\n", + "df = pd.read_csv(\"./knot_data.csv\")\n", + "df.keys()\n", + "\n", + "X = df[df.keys()[1:-1]].to_numpy()\n", + "Y = df[['signature']].to_numpy()\n", + "\n", + "# normalize X\n", + "X_mean = np.mean(X, axis=0)\n", + "X_std = np.std(X, axis=0)\n", + "X = (X - X_mean[np.newaxis,:])/X_std[np.newaxis,:]\n", + "input_normalier = [X_mean, X_std]\n", + "\n", + "# normalize Y\n", + "max_signature = np.max(Y)\n", + "min_signature = np.min(Y)\n", + "Y = ((Y-min_signature)/2).astype(int)\n", + "n_class = int((max_signature-min_signature)/2+1)\n", + "output_normalier = [min_signature, 2]\n", + "\n", + "dataset = {}\n", + "num = X.shape[0]\n", + "n_feature = X.shape[1]\n", + "train_ratio = 0.8\n", + "train_id_ = np.random.choice(num, int(num*train_ratio), replace=False)\n", + "test_id_ = np.array(list(set(range(num))-set(train_id_)))\n", + "\n", + "dtype = torch.get_default_dtype()\n", + "dataset['train_input'] = torch.from_numpy(X[train_id_]).type(dtype).to(device)\n", + "dataset['train_label'] = torch.from_numpy(Y[train_id_][:,0]).type(torch.long).to(device)\n", + "dataset['test_input'] = torch.from_numpy(X[test_id_]).type(dtype).to(device)\n", + "dataset['test_label'] = torch.from_numpy(Y[test_id_][:,0]).type(torch.long).to(device)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e262aeca", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def train_acc():\n", + " return torch.mean((torch.argmax(model(dataset['train_input']), dim=1) == dataset['train_label']).float())\n", + "\n", + "def test_acc():\n", + " return torch.mean((torch.argmax(model(dataset['test_input']), dim=1) == dataset['test_label']).float())\n", + "\n", + "model = KAN(width=[n_feature,1,n_class], grid=5, k=3, seed=seed, device=device)\n", + "model.fit(dataset, lamb=0.005, batch=1024, loss_fn = nn.CrossEntropyLoss(), metrics=[train_acc, test_acc], display_metrics=['train_loss', 'reg', 'train_acc', 'test_acc']);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2254d060", + "metadata": {}, + "outputs": [], + "source": [ + "model.plot(scale=1.0, beta=0.2)\n", + "\n", + "n = 17\n", + "for i in range(n):\n", + " plt.gcf().get_axes()[0].text(1/(2*n)+i/n-0.005,-0.02,df.keys()[1:-1][i], rotation=270, rotation_mode=\"anchor\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54778a24", + "metadata": {}, + "outputs": [], + "source": [ + "scores = model.feature_score\n", + "features = list(df.keys()[1:-1])\n", + "\n", + "y_pos = range(len(features))\n", + "plt.bar(y_pos, scores)\n", + "plt.xticks(y_pos, features, rotation=90);\n", + "plt.ylabel('feature importance')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_15_knot_unsupervised-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_15_knot_unsupervised-checkpoint.ipynb new file mode 100644 index 00000000..95ecd68b --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_15_knot_unsupervised-checkpoint.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "134e7f9d", + "metadata": {}, + "source": [ + "# Example 15: Knot unsupervised" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0893a344", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './knot_data.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6j/b6y80djd4nb5hl73rv3sv8y80000gn/T/ipykernel_76001/3712353914.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Download data: https://colab.research.google.com/github/deepmind/mathematics_conjectures/blob/main/knot_theory.ipynb#scrollTo=l10N2ZbHu6Ob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./knot_data.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 577\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 934\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1214\u001b[0m \u001b[0;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1215\u001b[0m \u001b[0;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m self.handles = get_handle( # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m 1217\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 785\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 787\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './knot_data.csv'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "from kan import *\n", + "import copy\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "seed = 2024\n", + "torch.manual_seed(seed)\n", + "np.random.seed(seed)\n", + "\n", + "dtype = torch.get_default_dtype()\n", + "\n", + "# Download data: https://colab.research.google.com/github/deepmind/mathematics_conjectures/blob/main/knot_theory.ipynb#scrollTo=l10N2ZbHu6Ob\n", + "df = pd.read_csv(\"./knot_data.csv\")\n", + "df.keys()\n", + "\n", + "X = df[df.keys()[1:]].to_numpy()\n", + "mean = np.mean(X, axis=0)\n", + "std = np.std(X, axis=0)\n", + "X = (X - mean[np.newaxis,:])/std[np.newaxis,:]\n", + "\n", + "# normalize X\n", + "X_mean = np.mean(X, axis=0)\n", + "X_std = np.std(X, axis=0)\n", + "X = (X - X_mean[np.newaxis,:])/X_std[np.newaxis,:]\n", + "input_normalier = [X_mean, X_std]\n", + "\n", + "dataset = {}\n", + "num = X.shape[0]\n", + "n_feature = X.shape[1]\n", + "train_ratio = 0.8\n", + "train_id_ = np.random.choice(num, int(num*train_ratio), replace=False)\n", + "test_id_ = np.array(list(set(range(num))-set(train_id_)))\n", + "dataset['train_input'] = torch.from_numpy(X[train_id_]).type(dtype)\n", + "dataset['test_input'] = torch.from_numpy(X[test_id_]).type(dtype)\n", + "\n", + "def construct_contrastive_dataset(tensor):\n", + " y = copy.deepcopy(tensor)\n", + " for i in range(y.shape[1]):\n", + " y[:,i] = y[:,i][torch.randperm(y.shape[0])]\n", + " return y\n", + "\n", + "dataset['contrastive_train_input'] = construct_contrastive_dataset(dataset['train_input'])\n", + "dataset['contrastive_test_input'] = construct_contrastive_dataset(dataset['test_input'])\n", + "\n", + "dataset['train_label'] = torch.cat([torch.ones(dataset['train_input'].shape[0],1), torch.zeros(dataset['contrastive_train_input'].shape[0],1)], dim=0).to(device)\n", + "dataset['train_input'] = torch.cat([dataset['train_input'], dataset['contrastive_train_input']], dim=0).to(device)\n", + "\n", + "dataset['test_label'] = torch.cat([torch.ones(dataset['test_input'].shape[0],1), torch.zeros(dataset['contrastive_test_input'].shape[0],1)], dim=0).to(device)\n", + "dataset['test_input'] = torch.cat([dataset['test_input'], dataset['contrastive_test_input']], dim=0).to(device)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e262aeca", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def train_acc():\n", + " return torch.mean(((model(dataset['train_input']) > 0.5) == dataset['train_label']).float())\n", + "\n", + "def test_acc():\n", + " return torch.mean(((model(dataset['test_input']) > 0.5) == dataset['test_label']).float())\n", + "\n", + "model = KAN(width=[n_feature,1,1], grid=5, k=3, seed=seed, device=device)\n", + "model.fix_symbolic(1,0,0,'gaussian',fit_params_bool=False)\n", + "model.fit(dataset, lamb=0.001, batch=1024, metrics=[train_acc, test_acc], display_metrics=['train_loss', 'reg', 'train_acc', 'test_acc']);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ede24f0", + "metadata": {}, + "outputs": [], + "source": [ + "# seed = 2024\n", + "model.plot(scale=1.0)\n", + "\n", + "n = 18\n", + "for i in range(n):\n", + " plt.gcf().get_axes()[0].text(1/(2*n)+i/n-0.005,-0.02,df.keys()[1:][i], rotation=270, rotation_mode=\"anchor\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3fb6b7a", + "metadata": {}, + "outputs": [], + "source": [ + "# seed = 0\n", + "model.plot(scale=1.0)\n", + "\n", + "n = 18\n", + "for i in range(n):\n", + " plt.gcf().get_axes()[0].text(1/(2*n)+i/n-0.005,-0.02,df.keys()[1:][i], rotation=270, rotation_mode=\"anchor\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_1_function_fitting-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_1_function_fitting-checkpoint.ipynb new file mode 100644 index 00000000..ba369ab8 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_1_function_fitting-checkpoint.ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 1: Function Fitting\n", + "\n", + "In this example, we will cover how to leverage grid refinement to maximimze KANs' ability to fit functions" + ] + }, + { + "cell_type": "markdown", + "id": "94056ef6", + "metadata": {}, + "source": [ + "intialize model and create dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a59179d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + } + ], + "source": [ + "from kan import *\n", + "\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "# initialize KAN with G=3\n", + "model = KAN(width=[2,1,1], grid=3, k=3, seed=1, device=device)\n", + "\n", + "# create dataset\n", + "f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)\n", + "dataset = create_dataset(f, n_var=2, device=device)" + ] + }, + { + "cell_type": "markdown", + "id": "cb1f817e", + "metadata": {}, + "source": [ + "Train KAN (grid=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a87b97b0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 4.16e-02 | test_loss: 4.35e-02 | reg: 9.79e+00 | : 100%|█| 20/20 [00:03<00:00, 6.03it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "markdown", + "id": "52294efd", + "metadata": {}, + "source": [ + "The loss plateaus. we want a more fine-grained KAN!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3f1cfc9d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.2\n" + ] + } + ], + "source": [ + "# initialize a more fine-grained KAN with G=10\n", + "model = model.refine(10)" + ] + }, + { + "cell_type": "markdown", + "id": "f3cc5079", + "metadata": {}, + "source": [ + "Train KAN (grid=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "898b1794", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 6.96e-03 | test_loss: 6.10e-03 | reg: 9.75e+00 | : 100%|█| 20/20 [00:02<00:00, 7.32it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "markdown", + "id": "bcdc0d3d", + "metadata": {}, + "source": [ + "The loss becomes lower. This is good! Now we can even iteratively making grids finer." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a1c25e8a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.46e-02 | test_loss: 1.53e-02 | reg: 8.83e+00 | : 100%|█| 200/200 [00:10<00:00, 19.67\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "saving model version 0.2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 2.84e-04 | test_loss: 3.29e-04 | reg: 8.84e+00 | : 100%|█| 200/200 [00:15<00:00, 13.09\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.3\n", + "saving model version 0.4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 4.21e-05 | test_loss: 4.04e-05 | reg: 8.84e+00 | : 100%|█| 200/200 [00:09<00:00, 21.22\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.5\n", + "saving model version 0.6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.02e-05 | test_loss: 1.24e-05 | reg: 8.84e+00 | : 100%|█| 200/200 [00:10<00:00, 18.76\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.7\n", + "saving model version 0.8\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.64e-04 | test_loss: 1.74e-03 | reg: 8.86e+00 | : 100%|█| 200/200 [00:17<00:00, 11.72" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.9\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "grids = np.array([3,10,20,50,100])\n", + "\n", + "\n", + "train_losses = []\n", + "test_losses = []\n", + "steps = 200\n", + "k = 3\n", + "\n", + "for i in range(grids.shape[0]):\n", + " if i == 0:\n", + " model = KAN(width=[2,1,1], grid=grids[i], k=k, seed=1, device=device)\n", + " if i != 0:\n", + " model = model.refine(grids[i])\n", + " results = model.fit(dataset, opt=\"LBFGS\", steps=steps)\n", + " train_losses += results['train_loss']\n", + " test_losses += results['test_loss']\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "6be8ba55", + "metadata": {}, + "source": [ + "Training dynamics of losses display staircase structures (loss suddenly drops after grid refinement)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "156f68a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGwCAYAAABFFQqPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMTklEQVR4nO3deXxU9b3/8deZSWayJ4SQhAAJQVCJYdGACi6gWBQVt9aiRYRW+6sFq5TrUmuvrVSlt7VcrA222lbt4nWrYhcrYFWggiwRFImKKBAgCSGBZLKQbeb8/hgyGEJIQmZyZnk/H480mTMncz5zsMk739UwTdNEREREJALZrC5ARERExCoKQiIiIhKxFIREREQkYikIiYiISMRSEBIREZGIpSAkIiIiEUtBSERERCJWlNUFBDuPx0NpaSmJiYkYhmF1OSIiItINpmlSW1tLVlYWNlvn7T4KQl0oLS1lyJAhVpchIiIiJ2HPnj0MHjy40+cVhLqQmJgIeG9kUlKSxdWIiIhId7hcLoYMGeL7Pd4ZBaFOFBYWUlhYiNvtBiApKUlBSEREJMR0NazF0F5jJ+ZyuUhOTqampkZBSEREJER09/e3Zo2JiIhIxFIQEhERkYilMUIiIiIWcbvdtLS0WF1GSIqOjsZut/f6dRSERERE+phpmpSXl1NdXW11KSEtJSWFzMzMXq3zpyAkIiLSx9pCUHp6OnFxcVqwt4dM06ShoYGKigoABg4ceNKvpSAkIiLSh9xuty8E9e/f3+pyQlZsbCwAFRUVpKenn3Q3mQZLi4iI9KG2MUFxcXEWVxL62u5hb8ZZKQh1orCwkLy8PMaPH291KSIiEobUHdZ7/riHCkKdmDdvHsXFxWzcuNHqUkRERCRAFIREREQkYikIiYiISJ8bOnQoS5YssboMzRqzTH0VNNdBbD+I0R5mIiIS/CZPnszYsWP9EmA2btxIfHx874vqJbUIWaTk97PgsdHsWP1/VpciIiLiF6Zp0tra2q1zBwwYEBQz5xSELFLVaAJwyFVncSUiImI10zRpaG615MM0zW7VOGfOHFatWsVjjz2GYRgYhsEzzzyDYRgsX76ccePG4XQ6WbNmDZ9//jlXX301GRkZJCQkMH78eN588812r3ds15hhGPzud7/j2muvJS4ujhEjRvC3v/3Nn7f5uNQ1ZhGPLRoAs7XZ4kpERMRqh1vc5D2w3JJrFy+8lDhH13HgscceY/v27eTn57Nw4UIAtm3bBsA999zDo48+yrBhw0hJSWHv3r1cfvnlPPTQQ8TExPDss88yffp0Pv30U7Kzszu9xoMPPsjPf/5zfvGLX/D4448zc+ZMdu/eTWpqqn/e7HGoRcgi5pEg5HErCImISPBLTk7G4XAQFxdHZmYmmZmZvtWcFy5cyFe+8hVOOeUU+vfvz5gxY/jOd77DqFGjGDFiBA899BDDhg3rsoVnzpw53HjjjQwfPpxHHnmE+vp6NmzYEND3pRYhi3hsDkAtQiIiArHRdooXXmrZtXtr3Lhx7R7X19fz4IMP8o9//IPS0lJaW1s5fPgwJSUlJ3yd0aNH+76Oj48nMTHRt59YoCgIWcS0e1uEcJ/8suAiIhIeDMPoVvdUsDp29tfdd9/N8uXLefTRRxk+fDixsbF87Wtfo7n5xH/8R0dHt3tsGAYej8fv9X5Z6N71UGdrC0JqERIRkdDgcDhwu91dnrdmzRrmzJnDtddeC0BdXR27du0KcHUnR2OErNLWIuRRi5CIiISGoUOHsn79enbt2kVlZWWnrTXDhw/nlVdeYcuWLXzwwQd84xvfCHjLzslSEOpEwDddPTJGSF1jIiISKu666y7sdjt5eXkMGDCg0zE///u//0u/fv2YOHEi06dP59JLL+Wss87q42q7xzC7u4BAhHK5XCQnJ1NTU0NSkv9WgF7/h7s4p+Qp1vW/lgnfe8ZvrysiIsGtsbGRnTt3kpubS0xMjNXlhLQT3cvu/v5Wi5BVjnSNGWoREhERsYyCkEUMu7drzNAYIREREcsoCFnEiPIGIZtHs8ZERESsoiBkkaNBSC1CIiIiVlEQsohNQUhERMRyCkIWaWsRMsxWiysRERGJXApCFrEfGSxtV4uQiIiIZRSELGKLPtI1phYhERERyygIWcQe5fR+VouQiIiIZRSELOJwelfAtJsKQiIiEhomT57M/Pnz/fZ6c+bM4ZprrvHb650MBSGLxMTGAhBlah0hERERqygIWSQ2PhmAOLMBbfcmIiLBbs6cOaxatYrHHnsMwzAwDINdu3ZRXFzM5ZdfTkJCAhkZGcyaNYvKykrf97388suMGjWK2NhY+vfvzyWXXEJ9fT0/+clPePbZZ3nttdd8r/fOO+/0+fuK6vMrCgBxSf0BSOQwDc1u4p36pxARiVimCS0N1lw7Og4Mo8vTHnvsMbZv305+fj4LFy4EwO12M2nSJL797W+zePFiDh8+zL333svXv/513nrrLcrKyrjxxhv5+c9/zrXXXkttbS1r1qzBNE3uuusuPv74Y1wuF08//TQAqampAX2rx6PfvhZxJqQAEGc0UV5/mHhnorUFiYiIdVoa4JEsa679w1JwxHd5WnJyMg6Hg7i4ODIzMwF44IEHOOuss3jkkUd85/3hD39gyJAhbN++nbq6OlpbW7nuuuvIyckBYNSoUb5zY2NjaWpq8r2eFdQ1ZhEjJsn3dUPdIQsrEREROTlFRUW8/fbbJCQk+D5OP/10AD7//HPGjBnDlClTGDVqFNdffz1PPfUUhw4F1++8iGgRuvbaa3nnnXeYMmUKL7/8stXleNmjOYyTWJo47DoEZFtdkYiIWCU6ztsyY9W1T5LH42H69On8z//8T4fnBg4ciN1uZ+XKlaxdu5YVK1bw+OOPc//997N+/Xpyc3N7U7XfREQQuuOOO/jWt77Fs88+a3Up7TQY8cSaTTSqRUhEJLIZRre6p6zmcDhwu92+x2eddRZ//etfGTp0KFFRx48UhmFw3nnncd555/HAAw+Qk5PDq6++yoIFCzq8nhUiomvsoosuIjEx+MbgNNq9/9Efdh20uBIREZGuDR06lPXr17Nr1y4qKyuZN28eBw8e5MYbb2TDhg188cUXrFixgm9961u43W7Wr1/PI488wqZNmygpKeGVV17hwIEDjBw50vd6H374IZ9++imVlZW0tPT92nqWB6HVq1czffp0srKyMAyDZcuWdThn6dKl5ObmEhMTQ0FBAWvWrOn7QgOgMdo7hb7JdcDiSkRERLp21113YbfbycvLY8CAATQ3N/Puu+/idru59NJLyc/P58477yQ5ORmbzUZSUhKrV6/m8ssv59RTT+VHP/oRv/zlL5k2bRoA3/72tznttNMYN24cAwYM4N133+3z92R511h9fT1jxozhm9/8Jl/96lc7PP/CCy8wf/58li5dynnnncdvf/tbpk2bRnFxMdnZ3nE1BQUFNDU1dfjeFStWkJXVs1H4TU1N7V7L5XL18B11X2NMBhz+CNNlUb+wiIhID5x66qmsW7euw/FXXnnluOePHDmSN954o9PXGzBgACtWrPBbfSfD8iA0bdo0XzI8nsWLF3PLLbdw6623ArBkyRKWL1/OE088waJFiwDvqHV/WbRoEQ8++KDfXu9EWuIz4RDY68v75HoiIiLSnuVdYyfS3NxMUVERU6dObXd86tSprF27NiDXvO+++6ipqfF97NmzJyDXASBxIACxhxWERERErGB5i9CJVFZW4na7ycjIaHc8IyOD8vLuh4dLL72U999/n/r6egYPHsyrr77K+PHjj3uu0+nE6XT2qu7uiu43GICEZo0REhERsUJQB6E2xjFLf5um2eHYiSxfvtzfJfmFM9UbhFJaK7s4U0RERAIhqLvG0tLSsNvtHVp/KioqOrQS+VthYSF5eXmdthz5Q+KAIQAMMA9iejwBu46IiAQfbbjde/64h0EdhBwOBwUFBaxcubLd8ZUrVzJx4sSAXnvevHkUFxezcePGgF0jJcM7681ptOA6VBGw64iISPCIjo4GoKHBok1Ww0jbPWy7pyfD8q6xuro6duzY4Xu8c+dOtmzZQmpqKtnZ2SxYsIBZs2Yxbtw4JkyYwJNPPklJSQm33XabhVX7hzMmjoMkkkotNft3k9zfuk3nRESkb9jtdlJSUqio8P4BHBcX16PhHuJtCWpoaKCiooKUlBTsdvtJv5blQWjTpk1cdNFFvscLFiwAYPbs2TzzzDPMmDGDqqoqFi5cSFlZGfn5+bz++uu+XWxDXY2tH6meWhqq1SIkIhIp2nZbbwtDcnJSUlJ6vXO95UFo8uTJXfbxzZ07l7lz5/ZRRV6FhYUUFhYGfA+UensyeKBZq0uLiEQMwzAYOHAg6enplmwrEQ6io6N71RLUxvIgFKzmzZvHvHnzcLlcJCcnB+w6h6NToAVa6zRzTEQk0tjtdr/8MpeTF9SDpSNBsyMFALO+ytpCREREIpCCkMXcMakAGIcVhERERPqaglAn+mIdIYDWeO96SEl1OwN6HREREelIQagTfbGOEEDDoAsAGFq3BZrrA3otERERaU9ByGIj8s6kxowjilZaDpZYXY6IiEhEURCy2Ij0BCpJAaBqfwB3uhcREZEOFIQsZrMZ1Nj7A1Bftc/iakRERCKLglAn+mqwNEC9wxuEmg6VBfxaIiIicpSCUCf6arA0QHPMAADcLgUhERGRvqQgFATc8ekAGHXac0ZERKQvKQgFAVuidy0hR6P2GxMREelLCkJBwJmSBUBck/YbExER6UsKQkEgrr83CCW6D1pciYiISGRREOpEX84aS0ofCkCyWQt16h4TERHpKwpCnejLWWOZGel84hkCQM0nbwX8eiIiIuKlIBQEEmOi+SjW2/LUUPSixdWIiIhEDgWhIHFo2FUApO5/Fzwei6sRERGJDApCQSL3jPE0mVE4PYehRpuvioiI9AUFoSBxVm46n5uDAGjYu9XiakRERCKDglCQSI13UB41EIADez6zuBoREZHIoCDUib6cPt+mJd67nlB9pbrGRERE+oKCUCf6cvp8G3eCt0XIcJX22TVFREQimYJQELEle8cIZdR8AO5Wi6sREREJfwpCQcQ9+FwOmw5SW8oxP/671eWIiIiEPQWhIHLaqafxrPtSAA5s0MKKIiIigaYgFESGpyfgGTEVAGfpBjBNiysSEREJbwpCQWb42AtoMe0kt1aCa5/V5YiIiIQ1BaEgM2hAKnvNNO+DQ7utLUZERCTMKQgFmSGpcewnFYDGg3ssrkZERCS8KQh1wooFFQGSYqKpsnlbhGr2a2FFERGRQFIQ6oQVCyq2aYxN936uUhASEREJJAWhIGTEe4NQc91BiysREREJbwpCQSg2MRkA9+EaiysREREJbwpCQSgmPgUAo7ne2kJERETCnIJQEIqKTQQgurXO4kpERETCm4JQEHIeaRFyehqsLURERCTMKQgFodgE7xghBSEREZHAUhAKQnFJKd7P5mFrCxEREQlzCkJBKCExBYA4GjHdrdYWIyIiEsYUhIJQYnKq7+s6l9YSEhERCRQFoSAUFxfPQZIAqCrdaXE1IiIi4UtBKEhV2b2rS1eXfWFxJSIiIuFLQagTVm262qY+JhOAhgq1CImIiASKglAnrNx0FcCdOgKA9B0v4W5ptqQGERGRcKcgFKRGXHU31SQw3PMFH6181upyREREwpKCUJBKGjCI4tSpADTu+8jiakRERMKTglAw65cDQLRrt8WFiIiIhCcFoSDmTMsFIOHwPosrERERCU8KQkEscXAeAMNbP4MWbbchIiLib1FWFyCdyxg2hgozhXSjGh7OhNOugPPuhJQhENsPTBOaXNBUC+4Wq8v9EtPqAtpLHAhxqV2fJyIiEUdBKIglxzv4mXENd5rPEWs0w6f/9H5Iz0THwfytEJ9mdSUiIhJkFISC3Lv9r+f3+y5mtPE5d0S9Sr5tJ/2NWt/zbmwcNmJpIdrCKjsKljahZNOFvaWB1qoviFIQEhGRYygIBbn5l4zgV28ZVDcm8yP3WPYcbCAaN7E04sZOPTGAYXWZQestxwKG2crZVVHD8GyrqxERkWCjIBTkpozMYMrIDN9jV2MLNQ0t1Bxuoa6plRa3B8+R5pe2OGQoF/kYf/H+J+5uCaYxVCIiEiwUhEJMUkw0STHRDLG6kBDxuS0KPOAOqsHkIiISLDR9XsKaBzsAZquCkIiIdKQgJGHNY3iDkMejICQiIh0pCElY8wWh1laLKxERkWCkICRhzW14h8GZGiMkIiLHEfZBaM+ePUyePJm8vDxGjx7NSy+9ZHVJ0odMX9eYWoRERKSjsJ81FhUVxZIlSxg7diwVFRWcddZZXH755cTHx1tdmvSBtq4x3ApCIiLSUdgHoYEDBzJw4EAA0tPTSU1N5eDBgwpCEcJjeFfc9qhrTEREjsPyrrHVq1czffp0srKyMAyDZcuWdThn6dKl5ObmEhMTQ0FBAWvWrDmpa23atAmPx8OQIVqFJ1KYtrYWIQUhERHpyPIgVF9fz5gxY/j1r3993OdfeOEF5s+fz/3338/mzZu54IILmDZtGiUlJb5zCgoKyM/P7/BRWlrqO6eqqoqbb76ZJ598MuDvSYKHb4yQ221xJSIiEows7xqbNm0a06ZN6/T5xYsXc8stt3DrrbcCsGTJEpYvX84TTzzBokWLACgqKjrhNZqamrj22mu57777mDhxYpfnNjU1+R67XK7uvhUJQp4js8bQYGkRETkOy1uETqS5uZmioiKmTp3a7vjUqVNZu3Ztt17DNE3mzJnDxRdfzKxZs7o8f9GiRSQnJ/s+1I0W2kybps+LiEjngjoIVVZW4na7ycjIaHc8IyOD8vLybr3Gu+++ywsvvMCyZcsYO3YsY8eOZevWrZ2ef99991FTU+P72LNnT6/eg1irbYyQqRYhERE5Dsu7xrrDOGY7ddM0OxzrzPnnn4/H4+n2tZxOJ06ns0f1SRA7MmtMXWMiInI8Qd0ilJaWht1u79D6U1FR0aGVyN8KCwvJy8tj/PjxAb2OBJZmjYnIyWps0SSLSBDUQcjhcFBQUMDKlSvbHV+5cmWXg557a968eRQXF7Nx48aAXkcCzKbB0iLSc39ct4vT//sNVmzr3jAMCV2Wd43V1dWxY8cO3+OdO3eyZcsWUlNTyc7OZsGCBcyaNYtx48YxYcIEnnzySUpKSrjtttssrFpChekLQvrLTkS674HXtgHwvf/bzKcPdT6zWUKf5UFo06ZNXHTRRb7HCxYsAGD27Nk888wzzJgxg6qqKhYuXEhZWRn5+fm8/vrr5OTkWFWyhJIjQcjwqGtMRLrPjpvTjD183JptdSkSYJYHocmTJ2Oa5gnPmTt3LnPnzu2jirwKCwspLCzErYX4Qpu6xkTkJPxP9FN8zb6an7fMAKZbXY4EUFCPEbKSxgiFCXWNichJ+Jp9NQB3Rr1icSUSaApCEt7ausZMtQiJSM85DXWrhzsFIQlv9rYxQgpCIiLSkYJQJ7SOUHgwbApCIiLSOQWhTmiMUJiwOwDNGhORXti2zOoKJIAUhCSsRcf3835urrG4EhEJWS/NtroCCSAFIQlr8f28W7HEtlZbW4iIhBT3sb8eu1jmRUKXgpCEtaTUTAAS3K4u16sSEWnTxDGbb9dXWlOIBJyCUCc0WDo89EsfCEAqLmobNU5IRLqnxXC0P+Daa00hEnAKQp3QYOnwEJucDnjXAvls736LqxGRUNFiO6ZFqEZBKFwpCEl4c8RTb0sAYPfHRRYXIyKhwjTs7Q/U7LOmEAk4BSEJe/v7nwOA7Yu3LK5EREKFwTFjCmv2WFOIBJyCkIS96GETAUis/kQDpkWkW3bF5bc/4FKLULhSEJKwlzFsNADZnr3sqmqwuBoRCQVtfzNt8Jzm/UJjhMKWglAnNGssfDgyRgIw1Chn0+caMC0iXTOOfC41+3u/0BihsKUg1AnNGgsjyYNptsUSbbgp+mCz1dWISEjwNgmVmmneh3Xl4NYSHOFIQUjCn2Hg6T8CgJqSj6hv0gasItI9VWYSHls0mB6oLbO6HAkABSGJCM6sPAAm8T4bdh60uBoRCX7eFiEPBo2x3hXq1T0WnhSEJCIYY28C4Cr7OnaUH7K4GhEJFSYG9TFtQUgDpsORgpBEhpyJNNviiDOaaCj92OpqRCToHV1qo9bp3bxZ22yEJwUhiQw2O9Up3u6x2AMfWFyMiIQKEwOX40gQqtaiiuFIQagTmj4ffjxZZwKQWrNNCyuKyAkZX/oZUe7M9X6xa83RBYYkbCgIdULT58NP6vBzATi1dTulNY0WVyMiocAEPk7wbtND5Xb4xXCFoTCjICQRwzHU+8Msz9jN1i9KLa5GRILb0bBTY8ZDdLz3QUOlNxBJ2FAQksiRPJia6AFEGR6qP37H6mpEJIi1bbpqYtDQ3Apfferokx++aFFVEggKQhI5DIOKQV8BIGfvMmtrEZGg1tb7ZWJwuMUDp18BX/mp9+CaR2GPhk2ECwUhiSitwy8FIKvxC4srEZFgZnzp68PNR1ajH3X90YO7/9On9UjgKAhJREke7N2AdaCnHE+rttoQka4dbnF7v0gaCBf8l/frA59aV5D4lYKQRJQBg4bRZEbhMFo5VLbD6nJEJGgdHSzd0Ow+enjgWO/n8q19W44EjIKQRJTo6Gg+tw8FoOpjNW2LyImZGBz+chAacmQq/f6PoL7KmqLErxSEOqEFFcNXaXIBAM0737W4EhEJWl9aK6i++Uvd6IkZkJHv/Xrzn/q4KAkEBaFOaEHF8BU90LvVhr16t8WViEiwMzGob3K3P3j2t72fi5f1eT3ifwpCEnGSMk/xfm7SoooicnzGl8YI1TUdM7HilCnez2UfQsPBPqxKAkFBSCJO+pBTARjgPoDb7e7ibBGJTKbvf5tbPbS4PUefShkCA04H0w2rH7WmPPEbBSGJOJmDc3GbBg6jlapy7SYtIp0zj6woVH9sq1Db4oqb/wyusj6uSvxJQUgijj3aQYXRH4Da/VpYUUS6Vt98TOvx8EsgcxQ01cA/5msj1hCmICQRqSoqA4DGAzstrkREgtPRvcbgOC1CNhtc9zuwO2D7G/CfxQpDIUpBSCJSjWMgAJ6Du6wtRESCknHM4w4DpgHST4cpP/Z+/e+FsOJHAa9L/E9BSCLSwYQRAKTsf8/iSkQkKJntPlHX2MmWPBNvh0se9H69rhD2bAh4aeJfCkISkQ4MuhiArOoicLdYXI2IBJ/23VyuxhP8nDh/Poye4f2e52ZoSn2I6VEQ2rBhQ7vpxuYx/aFNTU28+OKL/qlMJIDScvJoMqOx44aavVaXIyJBxjhmjNChhi7+YLp0EaTkwOGD8PI3FYZCSI+C0IQJE6iqOrq3SnJyMl98cXTWTXV1NTfeeKP/qhMJkFMzk9hjDgDAPKgB0yLSXtuf+W2/JGsamk/8DfH9Ydr/eL/+4h14fqbCUIjoURA6tgXo2MedHRMJNrlp8ZTgnTnmKvvM4mpEJNi0tQjFOqIAqO6qRQjgtGneliGAkrXwv2dA+UeBKlH8xO9jhAzj2LH2oUmbroY3Z5SdmphBALhKFYRE5PjinHagG11jbSbMha//CTCgpQF+PxX+ficc1JplwUqDpTuhTVfDX0tSDgCtVeoaE5FjHOndiHNEA1DdVdfYl+VdBfM/hP7DoaUeip7xBqJ9RQEoVHorqqffUFxcTHl5OeDtBvvkk0+oq6sDoLKy0r/ViQSQOzkHKiGursTqUkQkSCU4vUHoQF1Tz74xJRvmbYBP/uFdX6i6BJ66GKb9HFKHgccNp1wMUY4AVC090eMgNGXKlHbjgK688krA2yVmmmbYdI1JBEjNhc8hqXGf968//bcrIke0/TToF+8NQqXVh3v+IjY75F0NQ86BZ6+Cyk/hX/ccfT57InzlQe95kS71FIhNseTSPQpCO3eqC0HCh3PAMABiPfVw+BDEpVpckYgEi7Y/91PinABU1jXT2OImJvokQktiJsxdB+/8DDb9wfsLv7rEO6D691/xW80h7cYX4LTLLLl0j4JQTk5OoOoQ6XP9U5IpN/uRaRyCgzsVhETEp23WWJzDTpzDTkOzm7KaRnLT4k/uBW12uPh+7wfAgU/hH9+H6j1+qjjERcdYdukeBaGDBw/S0NDA4MGDfce2bdvGo48+Sn19Pddccw3f+MY3/F6kSCBkJcdQYqaTaRzCPLQTY3CB1SWJSNA40iZkGGSlxLKjoo7S6sMnH4SONeA0+Obr/nkt6ZUezRqbN28eixcv9j2uqKjgggsuYOPGjTQ1NTFnzhz+9Kc/+b1IkUDI6R/PHtO7llCd1hISkeMwDBiUEgvAvkMnMU5Igl6PgtB7773HVVdd5Xv8xz/+kdTUVLZs2cJrr73GI488QmFhod+LFAkER5QNV6x3LaH6/TssrkZEgonhmxTkbREC2HcyA6Yl6PUoCJWXl5Obm+t7/NZbb3HttdcSFeXtYbvqqqv47DP9ZS2hozV5KACGFjsTkU4MSvGOXzmpmWMS9HoUhJKSkqiurvY93rBhA+eee67vsWEYNDX1cK0FEQs1p+UDkFqzDVp7sGCaiIS5ji1CpTUKQuGoR0Ho7LPP5le/+hUej4eXX36Z2tpaLr74Yt/z27dvZ8iQIX4vUiRQnANHUmkmEe1pgg+es7ocEQk2xpeCUHWjxcVIIPQoCP30pz/ltddeIzY2lhkzZnDPPffQr18/3/PPP/88kyZN8nuRIoEyODWe37Ve7n3wz7tgy3O+pfVFJHIZHP05MOhLY4S0sXj46dH0+bFjx/Lxxx+zdu1aMjMzOeecc9o9f8MNN5CXl+fXAkUCaeyQFL7rvpKJ9m1cyFZY9l1Y+2uY+RIkD7K6PBGxmmGQkRSDYUBzq4eq+mbSEpxWVyV+1ONNVwcMGMDVV1/dIQQBXHHFFe0GU4sEu8zkGHLTEpnbfCfv9v8api0aKrbBsts0ZkhEAO8M0/REb/jRgOnw06MWoT/+8Y/dOu/mm28+qWJErLBg6qnc/lw9M/ddx/SMC1lSexf2nathw29h4vesLk9ELOCbPn9kD8KslFj2u5rYd+gwowenWFeY+F2PgtCcOXNISEggKiqq035SwzAUhCSkXDk6i2i7jbte+oC/708j2T6Th6KfxtzyHIaCkIjgHSe0uaRaawmFoR51jY0cORKHw8HNN9/MqlWrOHToUIePgwcPBqrWk1JbW8v48eMZO3Yso0aN4qmnnrK6JAlCl56RyVv/NZlbzs/lDffZ3oMVH3s3YxWRCOT9Y984sg/9IM0cC1s9CkLbtm3jn//8J4cPH+bCCy9k3LhxPPHEE7hcrkDV12txcXGsWrWKLVu2sH79ehYtWkRVVZXVZUkQGpDo5L+vzGPulRPY5cnAwMQs3WJ1WSJiJW8O+tIUerUIhZseD5Y+55xz+O1vf0tZWRl33HEHL774IgMHDmTmzJlBuZii3W4nLi4OgMbGRtxut6Y/ygl945xsPjOyAaj4fLPF1YiINY4uqAhoUcUw1uMg1CY2Npabb76ZBx98kLPPPpvnn3+ehoaGHr/O6tWrmT59OllZWRiGwbJlyzqcs3TpUnJzc4mJiaGgoIA1a9b06BrV1dWMGTOGwYMHc88995CWltbjOiVyxETbOZQwAoDmvVusLUZELOb9NZmlbTbC1kkFoX379vHII48wYsQIbrjhBsaPH8+2bdvaLa7YXfX19YwZM4Zf//rXx33+hRdeYP78+dx///1s3ryZCy64gGnTplFSUuI7p6CggPz8/A4fpaWlAKSkpPDBBx+wc+dOnnvuOfbv338yb1siyMG08QCkla4Cj9viakSkrxnHPG4bI1RZ10xji34mhJMezRp78cUXefrpp1m1ahWXXnopv/zlL7niiiuw2+0nXcC0adOYNm1ap88vXryYW265hVtvvRWAJUuWsHz5cp544gkWLVoEQFFRUbeulZGRwejRo1m9ejXXX3/9cc9pampq18UXzOOfJHDM7Am4dsWS1FoNFcWQOcrqkkTEAkdmz5McG02cw05Ds5uymkZy0+KtLUz8pkdB6IYbbiA7O5vvf//7ZGRksGvXLgoLCzucd8cdd/iluObmZoqKivjBD37Q7vjUqVNZu3Ztt15j//79xMbGkpSUhMvlYvXq1Xz3u9/t9PxFixbx4IMP9qpuCX1n5qaz7Z1cJtiL8ezbjE1BSCSyHDOW1Diy59iOijpKqw8rCIWRHgWh7OxsDMPguec635zSMAy/BaHKykrcbjcZGRntjmdkZFBeXt6t19i7dy+33HILpmlimia33347o0eP7vT8++67jwULFvgeu1wubSQbgQpy+vEX+zAmUMyBz98no0BrY4lEEt9eY8bRTrK2ILTvkMYJhZMeBaFdu3Z1ec6+fftOtpZOGUb73lrTNDsc60xBQQFbtmzp9rWcTidOp/aRiXTRdhsx6cOhAmrLvyCj628RkTD35c1XJXyc9KyxY5WXl3PHHXcwfPhwf70kaWlp2O32Dq0/FRUVHVqJ/K2wsJC8vDzGjx8f0OtI8Eob7J055qjba3ElItL3jiyo+KU/ugdp5lhY6lEQqq6uZubMmQwYMICsrCx+9atf4fF4eOCBBxg2bBjr1q3jD3/4g9+KczgcFBQUsHLlynbHV65cycSJE/12neOZN28excXFbNy4MaDXkeCVPuRUAPo1l1lciYgEA60lFJ561DX2wx/+kNWrVzN79mzeeOMNvv/97/PGG2/Q2NjIv/71LyZNmtTjAurq6tixY4fv8c6dO9myZQupqalkZ2ezYMECZs2axbhx45gwYQJPPvkkJSUl3HbbbT2+lkhP5JxyOh7TINFowFVZSlJaltUliUgfaWsHMr/UXtAWhPZqjFBY6VEQ+uc//8nTTz/NJZdcwty5cxk+fDinnnoqS5YsOekCNm3axEUXXeR73DZQefbs2TzzzDPMmDGDqqoqFi5cSFlZGfn5+bz++uvk5OSc9DVFuiM5KYk9tkyGmGXs2/6+gpBIRDraNTYiPQGA3VUN1DS0kBwXbVVR4kc9CkKlpaXk5eUBMGzYMGJiYnzr+5ysyZMnd7nlxdy5c5k7d26vrtNThYWFFBYW4nZr4axIdiAmlyGHy3CVbIWJV1pdjoj0FbNtjNDRQ/0TnOSmxbOzsp739xziotPSLSpO/KlHY4Q8Hg/R0UcTsN1uJz4+PNdS0BghAWhJ9C6d0HpIA6ZFItGxf6YPP9IqpO6x8NGjFiHTNJkzZ45venljYyO33XZbhzD0yiuv+K9CEQvZUwZBBdjrNWBaJJK0rSN07EIt6Yne338HXI19XJEESo+C0OzZs9s9vummm/xajEiwiU0d5P3cWGFxJSLSt9q6xtp3nKQneqfQV9Q2dfgOCU09CkJPP/10oOoIOhojJADJGdkAJLVU9mghTxEJT+lJ3hYhBaHw4bcFFcONxggJwICB3iDUn2qqG1osrkZE+srxttgAyEz2tgjtrqrv65IkQBSERE7AmeSdFZJkHGZPVY3F1YiI1cYOTgHg8wP1VNapVSgcKAiJnEhMMp4jwyVLS/2/j56IBLljWoT6xTvIG5gEwJvF+62oSPxMQUjkRGx2Gu3eH3pbP9tlbS0iEhSuHDMQgBUKQmFBQagT2nRVfOL7A/Dpzl24PSde/FNEwoNv+vxx5kecPTQVgOJSV1+WJAGiINQJDZaWNjFJaQBENx5k+bZyi6sRkb7VMQmdfqRrrNzVyKH65r4uSPxMQUikC7Y07y70Z9p2sPSdHV2cLSJhwbf1U8cglOCMIjs1DoCPy9QqFOoUhES6MvxiAM6zfcRH+1zsOdhgcUEiEmi+6fPHCUIAIwcmAlCsIBTyFIREupJ1JgAjbKXYcbNlT7W19YhI3+lkDdWRR7rHPi6r7cNiJBAUhES6kjIUomJx0MJdUS/y0d5qqysSkT7TWYtQWxBSi1CoUxDqhGaNiY/NBjkTAfhu1N+J37va4oJEpK90tqlO21pCOyrqaHF7+q4g8TsFoU5o1pi0c+1vcNu9S+vnHXrT4mJEJNDaxgiZnewvOLhfLInOKJrdHj4/UNeXpYmfKQiJdEdCOnsnPgxAUpMWUROJFJ1ttGwYBqcfGTCt7rHQpiAk0k0J6TkApHoqtbCiSNg7sqDiCc7QgOnwoCAk0k3J6d6d6DM4RJU2WxQJa76usRNEIQ2YDg8KQiLdFNVvMACJxmEOHFD3mEgk6KRnDFAQChcKQiLd5Yhnvy0DgIaSzRYXIyKBZPh6vztPQqdlJGIzoLKumYraxj6pS/xPQagTmj4vx7Mn9jQAoko3WVyJiPSNzoNQrMPO0LR4QOOEQpmCUCc0fV6Opyz1bAAy9vzL4kpEJLCONAmdaLQ0R9cT+lArzocsBSGRHsiccCMAWY07aKiusLgaEQm0LnIQ43L6AbBh18HAFyMBoSAk0gPjRp7CHmMgAHFLRsAzV8Kudy2uSkT8zbfp6olGSwPnDU8D4L0vqjhQq9mkoUhBSKQHDMOgdkDB0QO71sAzl8PL34Ld66wrTEQC5MRBaERGIqMHJ9PiNvn3x5pNGoqirC5AJNScMusxnn0yi/KDLu6Nft578KO/ej9GfR0SM8GMwL2HYpLh7P8HsSlWVyLiB91fNHXSqQP4cG8N63ce5IazswNYkwSCgpBIDzkT0/j6937GN5/ZwKgvLuGnia9w1YBybKXvw9YXrS7PWs5EOPe7Vlch0mu+dqAuusYAzsntz+PsYP0XVZim2em2HBKcFIRETkKsw87SmQVM/d865tfeROzVZ3Gp5z9QtuXID06jWz9Aw8bnb0H5VmjUwnISec7KSSHablBa08jOynqGDUiwuiTpAQUhkZOUGu/g2jMH8dSanfztgzIunXk9jL7e6rKs0VzvDUKeVqsrEfGTI3uNGV0PpY1zRDHhlDRWbz/Ay0V7ueey0wNdnPiRBkt3QgsqSndcPXYQAG9sK+dP7+2m1R2BY4MAbEf+pjLd1tYhYpFvHBkb9OKmPbRE6s+BEKUWoU7MmzePefPm4XK5SE5OtrocCVJnZCUxNS+DFcX7+e9lH7F4xaekJTgjqlcM4DuHy/gqcKiukX5WFyPiB92dPt9mysh0UuKiqaxrZlupi7FDUgJXnPiVgpBILxiGQeHMs/jLe7t5/K0dVNU3c6ihxeqy+tyBKDdEwa4DNQpCEpGi7TbGDknhnU8PsKXkkIJQCFEQEumlaLuNOeflcsPZ2XxaXkt9c+SNk6n+29+hBkyPusYkPBjmkTFCPfietiD0wd6awBQlAaEgJOInMdF2xkToX4GrYhxQA4YGS0u46cZg6TZt///fon3HQooGS4tIr5k2+5EvNEhUIteZQ1KwGbCzsp4dFdqNPlQoCIlIrxltQUhdYxImDN/0+e53jqXEOZgyMgOAP63bHZC6xP8UhESk10zD28tumOoak8g2e8JQAF4u2kttY+RNnAhFCkIi0mtHW4TUNSbhoafT59ucN7w/pwyIp77Zzc/f+DQAlYm/KQiJSK+1jRFSi5BEOsMw+O8r8wD403u72aoZZEFPQUhEes0wtLK0hJuejxFqM/m0dK4akwXAY//+DNPs/k720vcUhESk92zeHyWGZo2JADD3olOIshm8+fF+nt+4x+py5AQUhESk92zRgNYRkvBhHOernjg9M4m7Lj0NgPte2coDr30UuXsRBjkFoU5o01WR7jPUIiRhyuzFxoH/74JhXHeWd2PmP67bzbmL3mL9F1X+Kk38REGoE/PmzaO4uJiNGzdaXYpI8LO3TZ/XGCEJFz3fYuNYNpvB4q+P5clZBcRE26isa2Lec5s1rT7IKAiJSK8ZRtusMQUhCQ8nO33+eKaekcnqey5iSGoslXVN3P3Sh+omCyIKQiLSa4avRUg/3CW89D4GeaUnxvDItaOw2wze2FbOj5Z9hMej2WTBQEFIRHrPppWlJbz0drD08VwwYgBLZ56FYcDzG/dQ+PYOv722nDztPi8ivWbzLaioFiEJE22NNX7oGvuyS8/I5OFrRvHDV7fy+Fs7OPeU/mQmxfj1GqEoLcFJrMNuybUVhESk14wjLUI2jRGSsNE2WNq/QQjgxrOH8ObH+3nrkwqu/806v79+KPr97HG+DWv7moKQiPSaYddgaZHuMgyDh67J56bfr6esutHqcoKCzeb/wNldCkIi0mttm67aUNeYhIe2WWNGgH5BZ6XE8tZ/TQ7Ia0vPaLC0iPRa26wxdY2JSKhREBKRXmsbI2SoRUjChHF0tLSldUjgKQiJSK9psLSEm0BMn5fgpCAkIr1mizoyRkhBSMJEgGbPSxBSEBKRXmvbYkODpSVc+HOLDQluCkIi0mt2ezSgFiERCT0KQiLSe3a1CEl40WDpyKEgJCK9Zm+bPq8gJOFGOSjsRUwQamhoICcnh7vuusvqUkTCTts6QnZ1jUmY0KyxyBExQejhhx/mnHPOsboMkbDU1iJkR7vPS3gxNFg67EVEEPrss8/45JNPuPzyy60uRSQsmdHxAMTQbHElIv5idn2KhAXLg9Dq1auZPn06WVlZGIbBsmXLOpyzdOlScnNziYmJoaCggDVr1vToGnfddReLFi3yU8UicixbTBwA0bSCu8XiakR6z7fXmFqEwp7lQai+vp4xY8bw61//+rjPv/DCC8yfP5/777+fzZs3c8EFFzBt2jRKSkp85xQUFJCfn9/ho7S0lNdee41TTz2VU089ta/ekkjEMY60CAHQXG9dISIiPWT57vPTpk1j2rRpnT6/ePFibrnlFm699VYAlixZwvLly3niiSd8rTxFRUWdfv97773H888/z0svvURdXR0tLS0kJSXxwAMPHPf8pqYmmpqafI9dLtfJvC2RiOJ0xtJi2ok23NDSALEpVpck0iu+diC1CIU9y1uETqS5uZmioiKmTp3a7vjUqVNZu3Ztt15j0aJF7Nmzh127dvHoo4/y7W9/u9MQ1HZ+cnKy72PIkCG9eg8ikSDOaecwTgA8TWoRkvBhaNZY2AvqIFRZWYnb7SYjI6Pd8YyMDMrLywNyzfvuu4+amhrfx549ewJyHZFwEu+IouFIEGpqqLW4GpHe04KKkcPyrrHuOHawmmmaJzWAbc6cOV2e43Q6cTqdPX5tkUgWE22j3HSCAY0NtcRaXZBIL2nT1cgR1C1CaWlp2O32Dq0/FRUVHVqJ/K2wsJC8vDzGjx8f0OuIhAPDMGg0YgBoaqizuBqR3tOmq5EjqIOQw+GgoKCAlStXtju+cuVKJk6cGNBrz5s3j+LiYjZu3BjQ64iEiyabNwg1N6prTEKfusYih+VdY3V1dezYscP3eOfOnWzZsoXU1FSys7NZsGABs2bNYty4cUyYMIEnn3ySkpISbrvtNgurFpFjtdhiwA2tjWoREpHQYXkQ2rRpExdddJHv8YIFCwCYPXs2zzzzDDNmzKCqqoqFCxdSVlZGfn4+r7/+Ojk5OVaVLCLH0WhPADe4G2qsLkWk19ragbSgYvizPAhNnjwZ0zzxUuZz585l7ty5fVSRV2FhIYWFhbjd2kRSpDsao5KgGcyGg1aXIiLSbUE9RshKGiMk0jNNUUneLw4fsrYQEb/QYOlIoSAkIn7hdqYAYCgIiUgIURASEf+ITQXA1lRtbR0ifqBNVyOHglAntI6QSM/Y4r1BKLpZg6VFJHQoCHVCY4REeiYqMQ2AuOYqiysR6T3jOF9JeFIQEhG/sKcOBaBfawW4W60tRqSXtLJ05FAQEhG/iEsdRJMZjR0PuPZaXY5I7/hykIJQuFMQEhG/yEyJY6/p7R4zD+2ythiRXvINllbXWNhTEOqEBkuL9MzgfrHsMdMBqN//hcXViIh0j4JQJzRYWqRnYqLtVEZnAlBfvqOLs0VCg7rGwp+CkIj4zeH4wQA0V+6ythARkW5SEBIR/+k/AoCEyg+giz0ERYLZ0Vlj+jUZ7vQvLCJ+E3XKhTSbdvo17YUqdY+JSPBTEBIRvxlzyhDWe0YC0LrtNbUKScg6usWGxYVIwCkIdUKzxkR6buTARN5znANA1Ns/hcV58Jfr4R8LoLXJ4upEus+Xf5SEwl6U1QUEq3nz5jFv3jxcLhfJyclWlyMSEgzDwFYwm23vvckZtt1QW+r9APC0QNaZMHoGOOKtLVRE5AgFIRHxq29NGsll7z9Kc10VY22fM92+juvs/4H3/wjv/xHP2qXYJt0FwyZDYqbV5Yocl2+wtBZUDHsKQiLiV/3iHSxfMJl/bi3jjY+G8cBnp+Ey47jQ9iHDbOXYDn4Gr36H3bZsVl34POfnZTMsPZHKuibqGltxRtsYmBxr9dsQAdQzFgkUhETE71LiHMw8J4eZ5+RQ3XAm//PGaaytbWT8579iuFnCWNvn5HhKuPmdiWx5axhn235CZaMNz5Fhi2fnpnJubioDU2JxRmko45fZbQaTTh1ASpzD6lLCnFqEIoWCkIgEVEqcg0XXjQagseVZDtQ2Ub71HyS9fRt23Iy1fcEGbqbe6eQLcyB1ZhzvlpxBaUkqr3tO4YCZYu0bCDIebFw46hQKZ55ldSmRQU1CYU9BSET6TEy0nSGpcTDp6zBxOqx9HPPtRzAwiTeaGGXsAmCCvdjaQoPci3tnAE9aXUZY06yxyKEg1InCwkIKCwtxu91WlyISnqJjYdI9GBNu984o218MjdXwxSo4tBNq9sL+j6yuMiiNbnrf6hIihmJQ+FMQ6oSmz4v0EUec93POBO/n06Ydfc6jP0S+7LONKxjxrxuIN+usLiXsHV1QUVEo3CkIiUjwstmtriCoRCUOACBBQSjgNH0+cmg6hohIiHAkpAKQaNZr+5I+YigIhT0FIRGREOFM9AahKMODp9FlcTXhTYOlI4eCkIhIiIiPT6TJjAagsfagxdVEBuWg8KcgJCISImKibdTg3aetsbbK4mrCm81Q12OkUBASEQkRhmFQj3f7keb6GourEQkPCkIiIiGkwfAuN9DSoDFCfUJ9Y2FPQUhEJIQ02rwtQi2HFYT6hHJQ2FMQ6kRhYSF5eXmMHz/e6lJERHyabd4xQm4FocBptzSBklC4UxDqxLx58yguLmbjxo1WlyIi4tNk93aNeRprLa4kjH0pCBn6NRn29C8sIhJCWqO8QchsUhAS8QcFIRGRENISleD9olnbbATOl1qEbOoaC3cKQiIiIcQd7R0jhFqERPxCQUhEJISY0d4WIZtahALGND2+r7XXWPhTEBIRCSEehzcI2VvrLa5EJDwoCImIhBKnNwhFKQgFjOn58hgh/ZoMd/oXFhEJIYYzEYBoBaGA0SpCkUVBSEQkhNhjjgQhd4PFlYQzRaFIoiAkIhJC2oKQU0EoYEzP0cHSaPp82FMQEhEJIfbYZABiPApCIv6gICQiEkIcsd4WIQfN4G6xuJrwZKItNiKJ/oU7oU1XRSQYOeKSjj7QoooivaYg1AltuioiwSg2Lo560+l90HDQ2mLC1Jenz2usdPhTEBIRCSFxDjulZpr3Qc0ea4sRCQMKQiIiISQ22s4+BaGAMvnSFhuaNRb2FIREREJInONoEPIc2m1xNWHKNLs+R8KGgpCISAiJc0Sxw8wCwFP6ocXVhCez3RAhtQiFOwUhEZEQEhNt433zVABse9+DZq0n5H9fTkIKQuFOQUhEJIQYhkFZzAj2mmnYmlzw4fNWlxTWDAWhsKcgJCISYvolxvOae6L3QZm6x/yt3fR5dY2FPQUhEZEQMyDRyWeewd4HlZ9ZW0yYUwwKfwpCIiIhZkCik8+PDJimcru1xYQhTZ+PLApCIiIhJj3RyRfmQO+D+go4fMjagkRCmIKQiEiIOWNQMvXEUmnr7z1QucPagsKMebRBSJuuRoAoqwsQEZGeGT+0HwCftA7kfFsV7H4XhpzEBtFNte1/6wsARpPL6hKkDykIiYiEmIHJsQzuF0tR7QjOt30Eb/4YTr8S0oZ3/0Vemweb/xy4IkNY3Je+1hih8Kc2PxGREDQupx/vuMcePfDp611/k2nCvxfCs9Nhy/8FrLZw8R/3GWCLtroMCTC1CImIhKBhAxJYZo5ga9IFjHKtgbd+CttehYwz4JIHYV8R7FgJn/4Lvvo7yD4Xqj6HNb88+iKDz4Y5/7DuTQSp2sYWCh56k2ai+EQLKoa9iAhCUVFR5OfnAzBu3Dh+97vfWVyRiEjvDEmNBeDJmFt43PUfcDdD6fvej81/an/yCzdBv1zwtHofDxgJk+6BoedDlLOPKw8BUTaaUUtQpIiIIJSSksKWLVusLkNExG+yU70jWd53JcPF98O212D/1uOfXH/A+9FmxFcg/7o+qDI0tVtXWg1CYS8igpCISLgZciQIldYcpnnif+G48G5vd9iaxdBQBeNugfyvwr/ugYqPobYUhn8FUobA2JkWVy8SPCwPQqtXr+YXv/gFRUVFlJWV8eqrr3LNNde0O2fp0qX84he/oKysjDPOOIMlS5ZwwQUXdPsaLpeLgoICYmNjefjhh5k0aZKf34WISN8akOAkJtpGY4uHfdWHyU2Lh0EFcMNf2p94xaMnfJ0P91bz8D8/prFV0+jbuD1fWllam2yEPcuDUH19PWPGjOGb3/wmX/3qVzs8/8ILLzB//nyWLl3Keeedx29/+1umTZtGcXEx2dnZABQUFNDU1NThe1esWEFWVha7du0iKyuLjz76iCuuuIKtW7eSlJR03HqampravZbLpfUkRCT4GIZBdmoc2/fXUXKwwRuETsJTa3ayfudBP1cXHtISnERp+nzYszwITZs2jWnTpnX6/OLFi7nlllu49dZbAViyZAnLly/niSeeYNGiRQAUFRWd8BpZWd49efLz88nLy2P79u2MGzfuuOcuWrSIBx988GTeiohIn8pNi2f7/jq2l9cy6dQBPfref35Yxv9tKGHLnmoA7r3sdEakJwSgytCVPygZm4JQ2LM8CJ1Ic3MzRUVF/OAHP2h3fOrUqaxdu7Zbr3Ho0CHi4uJwOp3s3buX4uJihg0b1un59913HwsWLPA9drlcDBky5OTegIhIAI0ZksLybft5v6Tne40t+tfH7D10GIB4h52bzs0mMUYzpSTyBHUQqqysxO12k5GR0e54RkYG5eXl3XqNjz/+mO985zvYbDYMw+Cxxx4jNTW10/OdTidOp6aTikjwOyfX+7PszY/3s+dgg28A9YmYpkl9s9sXgn7+tdGcOSRFIUgiVlAHoTbGMfMXTdPscKwzEydOZOvWTqaUnkBhYSGFhYW43e4ef6+ISF84K7sfowcn8+HeGjbtPthlEGp1e/jab9b5usPSEpx8fZxavCWyBfUWG2lpadjt9g6tPxUVFR1aifxt3rx5FBcXs3HjxoBeR0TkZBmGwejByQBs31/X5fk7DtT5QhDApWcE9ueoSCgI6iDkcDgoKChg5cqV7Y6vXLmSiRMnWlSViEjwGJGeCMCn5bUnPG/TroO8sHEPAGMGJ7P5v7/Cw9eOCnh9IsHO8q6xuro6duzY4Xu8c+dOtmzZQmpqKtnZ2SxYsIBZs2Yxbtw4JkyYwJNPPklJSQm33XabhVWLiASHthahTbsO8pf1uzl7aCojMrzh6B8flvKTvxXzjbOH8Ku3jv6cPTUjkX7xDkvqFQk2lgehTZs2cdFFF/ket83Ymj17Ns888wwzZsygqqqKhQsXUlZWRn5+Pq+//jo5OTkBrUtjhEQkFOQPSibOYcfV2Mr9r35EaryD39xUwIpt5fzuPzsB2oWg4ekJfH28xgWJtDFM0zS7Pi1yuVwukpOTqamp6XQRRhERK72+tYzXt5axsng/TSdYIXrJjLFcc+agPqxMxDrd/f0d1GOERESka5ePGsivv3EWYwan+I4lOKP4zoXDGDYgngtPHcD1BYO5LD/TuiJFgpTlXWMiIuIfM8/N5ovKehpb3Py/C4dxx5QR3Hf5SKvLEglqCkKd0BghEQk1V48dxNVj1fUl0hMaI9QFjRESEREJPRojJCIiItIFBSERERGJWApCIiIiErEUhDpRWFhIXl4e48ePt7oUERERCRANlu6CBkuLiIiEHg2WFhEREemCgpCIiIhELAUhERERiVgKQiIiIhKxFIQ6oVljIiIi4U+zxrqgWWMiIiKhR7PGRERERLqgICQiIiIRK8rqAoJdW8+hy+WyuBIRERHprrbf212NAFIQ6kJtbS0AQ4YMsbgSERER6ana2lqSk5M7fV6Dpbvg8XgoLS0lMTERwzD89roul4shQ4awZ88eDcIOMN3rvqH73Dd0n/uG7nPfCdS9Nk2T2tpasrKysNk6HwmkFqEu2Gw2Bg8eHLDXT0pK0v/J+ojudd/Qfe4bus99Q/e57wTiXp+oJaiNBkuLiIhIxFIQEhERkYilIGQRp9PJj3/8Y5xOp9WlhD3d676h+9w3dJ/7hu5z37H6XmuwtIiIiEQstQiJiIhIxFIQEhERkYilICQiIiIRS0FIREREIpaCkEWWLl1Kbm4uMTExFBQUsGbNGqtLChmLFi1i/PjxJCYmkp6ezjXXXMOnn37a7hzTNPnJT35CVlYWsbGxTJ48mW3btrU7p6mpie9973ukpaURHx/PVVddxd69e/vyrYSURYsWYRgG8+fP9x3Tffafffv2cdNNN9G/f3/i4uIYO3YsRUVFvud1r3uvtbWVH/3oR+Tm5hIbG8uwYcNYuHAhHo/Hd47u88lZvXo106dPJysrC8MwWLZsWbvn/XVfDx06xKxZs0hOTiY5OZlZs2ZRXV3du+JN6XPPP/+8GR0dbT711FNmcXGxeeedd5rx8fHm7t27rS4tJFx66aXm008/bX700Ufmli1bzCuuuMLMzs426+rqfOf87Gc/MxMTE82//vWv5tatW80ZM2aYAwcONF0ul++c2267zRw0aJC5cuVK8/333zcvuugic8yYMWZra6sVbyuobdiwwRw6dKg5evRo88477/Qd1332j4MHD5o5OTnmnDlzzPXr15s7d+4033zzTXPHjh2+c3Sve++hhx4y+/fvb/7jH/8wd+7cab700ktmQkKCuWTJEt85us8n5/XXXzfvv/9+869//asJmK+++mq75/11Xy+77DIzPz/fXLt2rbl27VozPz/fvPLKK3tVu4KQBc4++2zztttua3fs9NNPN3/wgx9YVFFoq6ioMAFz1apVpmmapsfjMTMzM82f/exnvnMaGxvN5ORk8ze/+Y1pmqZZXV1tRkdHm88//7zvnH379pk2m8184403+vYNBLna2lpzxIgR5sqVK81Jkyb5gpDus//ce++95vnnn9/p87rX/nHFFVeY3/rWt9odu+6668ybbrrJNE3dZ385Ngj5674WFxebgPnee+/5zlm3bp0JmJ988slJ16uusT7W3NxMUVERU6dObXd86tSprF271qKqQltNTQ0AqampAOzcuZPy8vJ299jpdDJp0iTfPS4qKqKlpaXdOVlZWeTn5+vf4Rjz5s3jiiuu4JJLLml3XPfZf/72t78xbtw4rr/+etLT0znzzDN56qmnfM/rXvvH+eefz7///W+2b98OwAcffMB//vMfLr/8ckD3OVD8dV/XrVtHcnIy55xzju+cc889l+Tk5F7de2262scqKytxu91kZGS0O56RkUF5eblFVYUu0zRZsGAB559/Pvn5+QC++3i8e7x7927fOQ6Hg379+nU4R/8ORz3//PO8//77bNy4scNzus/+88UXX/DEE0+wYMECfvjDH7JhwwbuuOMOnE4nN998s+61n9x7773U1NRw+umnY7fbcbvdPPzww9x4442A/psOFH/d1/LyctLT0zu8fnp6eq/uvYKQRQzDaPfYNM0Ox6Rrt99+Ox9++CH/+c9/Ojx3MvdY/w5H7dmzhzvvvJMVK1YQExPT6Xm6z73n8XgYN24cjzzyCABnnnkm27Zt44knnuDmm2/2nad73TsvvPACf/7zn3nuuec444wz2LJlC/PnzycrK4vZs2f7ztN9Dgx/3Nfjnd/be6+usT6WlpaG3W7vkF4rKio6pGU5se9973v87W9/4+2332bw4MG+45mZmQAnvMeZmZk0Nzdz6NChTs+JdEVFRVRUVFBQUEBUVBRRUVGsWrWKX/3qV0RFRfnuk+5z7w0cOJC8vLx2x0aOHElJSQmg/6b95e677+YHP/gBN9xwA6NGjWLWrFl8//vfZ9GiRYDuc6D4675mZmayf//+Dq9/4MCBXt17BaE+5nA4KCgoYOXKle2Or1y5kokTJ1pUVWgxTZPbb7+dV155hbfeeovc3Nx2z+fm5pKZmdnuHjc3N7Nq1SrfPS4oKCA6OrrdOWVlZXz00Uf6dzhiypQpbN26lS1btvg+xo0bx8yZM9myZQvDhg3TffaT8847r8MSENu3bycnJwfQf9P+0tDQgM3W/tee3W73TZ/XfQ4Mf93XCRMmUFNTw4YNG3znrF+/npqamt7d+5MeZi0nrW36/O9//3uzuLjYnD9/vhkfH2/u2rXL6tJCwne/+10zOTnZfOedd8yysjLfR0NDg++cn/3sZ2ZycrL5yiuvmFu3bjVvvPHG407VHDx4sPnmm2+a77//vnnxxRdH/BTYrnx51php6j77y4YNG8yoqCjz4YcfNj/77DPzL3/5ixkXF2f++c9/9p2je917s2fPNgcNGuSbPv/KK6+YaWlp5j333OM7R/f55NTW1pqbN282N2/ebALm4sWLzc2bN/uWhfHXfb3sssvM0aNHm+vWrTPXrVtnjho1StPnQ1VhYaGZk5NjOhwO86yzzvJN/ZauAcf9ePrpp33neDwe88c//rGZmZlpOp1O88ILLzS3bt3a7nUOHz5s3n777WZqaqoZGxtrXnnllWZJSUkfv5vQcmwQ0n32n7///e9mfn6+6XQ6zdNPP9188skn2z2ve917LpfLvPPOO83s7GwzJibGHDZsmHn//febTU1NvnN0n0/O22+/fdyfy7NnzzZN03/3taqqypw5c6aZmJhoJiYmmjNnzjQPHTrUq9oN0zTNk29PEhEREQldGiMkIiIiEUtBSERERCKWgpCIiIhELAUhERERiVgKQiIiIhKxFIREREQkYikIiYiISMRSEBIREZGIpSAkIiIiEUtBSETC3pw5c7jmmmusLkNEgpCCkIiIiEQsBSERCRsvv/wyo0aNIjY2lv79+3PJJZdw99138+yzz/Laa69hGAaGYfDOO+8AsG/fPmbMmEG/fv3o378/V199Nbt27fK9XltL0oMPPkh6ejpJSUl85zvfobm52Zo3KCJ+F2V1ASIi/lBWVsaNN97Iz3/+c6699lpqa2tZs2YNN998MyUlJbhcLp5++mkAUlNTaWho4KKLLuKCCy5g9erVREVF8dBDD3HZZZfx4Ycf4nA4APj3v/9NTEwMb7/9Nrt27eKb3/wmaWlpPPzww1a+XRHxEwUhEQkLZWVltLa2ct1115GTkwPAqFGjAIiNjaWpqYnMzEzf+X/+85+x2Wz87ne/wzAMAJ5++mlSUlJ45513mDp1KgAOh4M//OEPxMXFccYZZ7Bw4ULuvvtufvrTn2KzqVFdJNTp/8UiEhbGjBnDlClTGDVqFNdffz1PPfUUhw4d6vT8oqIiduzYQWJiIgkJCSQkJJCamkpjYyOff/55u9eNi4vzPZ4wYQJ1dXXs2bMnoO9HRPqGWoREJCzY7XZWrlzJ2rVrWbFiBY8//jj3338/69evP+75Ho+HgoIC/vKXv3R4bsCAAV1er60VSURCm4KQiIQNwzA477zzOO+883jggQfIycnh1VdfxeFw4Ha725171lln8cILL/gGQXfmgw8+4PDhw8TGxgLw3nvvkZCQwODBgwP6XkSkb6hrTETCwvr163nkkUfYtGkTJSUlvPLKKxw4cICRI0cydOhQPvzwQz799FMqKytpaWlh5syZpKWlcfXVV7NmzRp27tzJqlWruPPOO9m7d6/vdZubm7nlllsoLi7mX//6Fz/+8Y+5/fbbNT5IJEyoRUhEwkJSUhKrV69myZIluFwucnJy+OUvf8m0adMYN24c77zzDuPGjaOuro63336byZMns3r1au69916uu+46amtrGTRoEFOmTGnXQjRlyhRGjBjBhRdeSFNTEzfccAM/+clPrHujIuJXhmmaptVFiIgEozlz5lBdXc2yZcusLkVEAkRtuyIiIhKxFIREREQkYqlrTERERCKWWoREREQkYikIiYiISMRSEBIREZGIpSAkIiIiEUtBSERERCKWgpCIiIhELAUhERERiVgKQiIiIhKx/j+coN0myA62aAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(train_losses)\n", + "plt.plot(test_losses)\n", + "plt.legend(['train', 'test'])\n", + "plt.ylabel('RMSE')\n", + "plt.xlabel('step')\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "markdown", + "id": "6ed8d26b", + "metadata": {}, + "source": [ + "Neural scaling laws (For some reason, this got worse than pykan 0.0. We're still investigating the reason, probably due to the updates of curve2coef)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8301085c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'RMSE')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_params = 3 * grids\n", + "train_vs_G = train_losses[(steps-1)::steps]\n", + "test_vs_G = test_losses[(steps-1)::steps]\n", + "plt.plot(n_params, train_vs_G, marker=\"o\")\n", + "plt.plot(n_params, test_vs_G, marker=\"o\")\n", + "plt.plot(n_params, 100*n_params**(-4.), ls=\"--\", color=\"black\")\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.legend(['train', 'test', r'$N^{-4}$'])\n", + "plt.xlabel('number of params')\n", + "plt.ylabel('RMSE')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c521e5e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_3_deep_formula-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_3_deep_formula-checkpoint.ipynb new file mode 100644 index 00000000..93346074 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_3_deep_formula-checkpoint.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "134e7f9d", + "metadata": {}, + "source": [ + "# Example 3: Deep Formulas\n", + "\n", + "The orignal Kolmogorov-Arnold theorem says that it suffices to have 2-Layer function composition (inner and outer functions), but the functions might be non-smooth or even fractal. We generalize KA representation to arbitrary depths. An example a 2-Layer KAN (with smooth activations) is unable to do is: $f(x_1,x_2,x_3,x_4)={\\rm exp}({\\rm sin}(x_1^2+x_2^2)+{\\rm sin}(x_3^2+x_4^2))$, which requires at least 3-Layer KANs." + ] + }, + { + "cell_type": "markdown", + "id": "7854503c", + "metadata": {}, + "source": [ + "### Three-layer KAN" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2075ef56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.76e-02 | test_loss: 1.79e-02 | reg: 1.05e+01 | : 100%|█| 20/20 [00:05<00:00, 3.60it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from kan import *\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", + "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1, device=device)\n", + "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n", + "dataset = create_dataset(f, n_var=4, train_num=3000, device=device)\n", + "\n", + "# train the model\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=0.002, lamb_entropy=2.);" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b8c880c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.2\n" + ] + } + ], + "source": [ + "model = model.prune(edge_th=1e-2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "585b699c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ee39c97b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 9.21e-03 | test_loss: 9.23e-03 | reg: 1.04e+01 | : 100%|█| 50/50 [00:10<00:00, 4.89it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.33e-03 | test_loss: 3.25e-03 | reg: 1.05e+01 | : 100%|█| 50/50 [00:10<00:00, 4.72it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.13e-03 | test_loss: 1.07e-03 | reg: 1.04e+01 | : 100%|█| 50/50 [00:09<00:00, 5.33it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.93e-04 | test_loss: 3.75e-04 | reg: 1.04e+01 | : 100%|█| 50/50 [00:05<00:00, 9.74it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.60e-05 | test_loss: 3.78e-05 | reg: 1.04e+01 | : 100%|█| 50/50 [00:04<00:00, 10.01it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "grids = [3,5,10,20,50]\n", + "#grids = [5]\n", + "\n", + "train_rmse = []\n", + "test_rmse = []\n", + "\n", + "for i in range(len(grids)):\n", + " #model = KAN(width=[4,2,1,1], grid=grids[i], k=3, seed=0, device=device).initialize_from_another_model(model, dataset['train_input'])\n", + " model = model.refine(grid=grids[i])\n", + " results = model.fit(dataset, opt=\"LBFGS\", steps=50, stop_grid_update_step=20);\n", + " train_rmse.append(results['train_loss'][-1].item())\n", + " test_rmse.append(results['test_loss'][-1].item())" + ] + }, + { + "cell_type": "markdown", + "id": "8c345302-c8bc-4585-8022-c5d90eb64341", + "metadata": {}, + "source": [ + "Author's note: The scaling isn't optimal. Possibly because of updates on curve2coef, to be investigated. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "94f3930a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.009214929305016994, 0.0033308672718703747, 0.00112761405762285, 0.0003925061319023371, 3.601737262215465e-05]\n", + "[0.009230277501046658, 0.0032473765313625336, 0.0010660917032510042, 0.0003754299250431359, 3.784598084166646e-05]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n_params = np.array(grids) * (4*2+2*1+1*1)\n", + "plt.plot(n_params, train_rmse, marker=\"o\")\n", + "plt.plot(n_params, test_rmse, marker=\"o\")\n", + "plt.plot(n_params, 10000*n_params**(-4.), color=\"black\", ls=\"--\")\n", + "plt.legend(['train', 'test', r'$N^{-4}$'], loc=\"lower left\")\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "print(train_rmse)\n", + "print(test_rmse)" + ] + }, + { + "cell_type": "markdown", + "id": "f53644fe", + "metadata": {}, + "source": [ + "### Two-layer KAN\n", + "\n", + "Now we show that a 2 two-layer KAN performs much worse for this task" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ae7b654b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 5.98e-02 | test_loss: 6.11e-02 | reg: 1.25e+01 | : 100%|█| 20/20 [00:08<00:00, 2.35it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kan import KAN, create_dataset\n", + "import torch\n", + "\n", + "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", + "model = KAN(width=[4,9,1], grid=3, k=3, seed=0)\n", + "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n", + "dataset = create_dataset(f, n_var=4, train_num=3000)\n", + "\n", + "# train the model\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=0.002, lamb_entropy=2.);\n", + "model.plot(beta=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "869828f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.98e-02 | test_loss: 2.21e-02 | reg: 1.70e+01 | : 100%|█| 50/50 [00:15<00:00, 3.23it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.15e-02 | test_loss: 1.40e-02 | reg: 1.71e+01 | : 100%|█| 50/50 [00:13<00:00, 3.75it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 6.69e-03 | test_loss: 9.05e-03 | reg: 1.72e+01 | : 100%|█| 50/50 [00:13<00:00, 3.69it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 4.38e-03 | test_loss: 8.05e-03 | reg: 1.73e+01 | : 100%|█| 50/50 [00:15<00:00, 3.17it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 2.02e-03 | test_loss: 9.89e-03 | reg: 1.73e+01 | : 100%|█| 50/50 [00:17<00:00, 2.88it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "grids = [3,5,10,20,50]\n", + "\n", + "train_rmse = []\n", + "test_rmse = []\n", + "\n", + "for i in range(len(grids)):\n", + " #model = KAN(width=[4,9,1], grid=grids[i], k=3, seed=0).initialize_from_another_model(model, dataset['train_input'])\n", + " model = model.refine(grid=grids[i])\n", + " results = model.fit(dataset, opt=\"LBFGS\", steps=50, stop_grid_update_step=30);\n", + " train_rmse.append(results['train_loss'][-1].item())\n", + " test_rmse.append(results['test_loss'][-1].item())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4f0a99fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.01983197219669819, 0.01147659495472908, 0.006687900051474571, 0.004380852449685335, 0.002016218611970544]\n", + "[0.022097894921898842, 0.013952379114925861, 0.009049860760569572, 0.008054238744080067, 0.00989140197634697]\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n_params = np.array(grids) * (4*9+9*1)\n", + "plt.plot(n_params, train_rmse, marker=\"o\")\n", + "plt.plot(n_params, test_rmse, marker=\"o\")\n", + "plt.plot(n_params, 300*n_params**(-2.), color=\"black\", ls=\"--\")\n", + "plt.legend(['train', 'test', r'$N^{-4}$'], loc=\"lower left\")\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "print(train_rmse)\n", + "print(test_rmse)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5776b6e1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_4_classfication-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_4_classfication-checkpoint.ipynb new file mode 100644 index 00000000..2887838d --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_4_classfication-checkpoint.ipynb @@ -0,0 +1,504 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 4: Classification" + ] + }, + { + "cell_type": "markdown", + "id": "31bcb9ac", + "metadata": {}, + "source": [ + "## Regression formulation\n", + "\n", + "Let's first treat the problem as a regression problem (output dimension = 1, MSE loss). " + ] + }, + { + "cell_type": "markdown", + "id": "908489de", + "metadata": {}, + "source": [ + "create the two moon dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "763d1fb4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kan import *\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_moons\n", + "import numpy as np\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "dataset = {}\n", + "train_input, train_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", + "test_input, test_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", + "\n", + "dtype = torch.get_default_dtype()\n", + "dataset['train_input'] = torch.from_numpy(train_input).type(dtype).to(device)\n", + "dataset['test_input'] = torch.from_numpy(test_input).type(dtype).to(device)\n", + "dataset['train_label'] = torch.from_numpy(train_label[:,None]).type(dtype).to(device)\n", + "dataset['test_label'] = torch.from_numpy(test_label[:,None]).type(dtype).to(device)\n", + "\n", + "X = dataset['train_input']\n", + "y = dataset['train_label']\n", + "plt.scatter(X[:,0].cpu().detach().numpy(), X[:,1].cpu().detach().numpy(), c=y[:,0].cpu().detach().numpy())" + ] + }, + { + "cell_type": "markdown", + "id": "06649143", + "metadata": {}, + "source": [ + "Train KAN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0a59179d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.55e-01 | test_loss: 1.56e-01 | reg: 3.94e+00 | : 100%|█| 20/20 [00:01<00:00, 15.55it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "(1.0, 0.9980000257492065)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = KAN(width=[2,1], grid=3, k=3, device=device)\n", + "\n", + "def train_acc():\n", + " return torch.mean((torch.round(model(dataset['train_input'])[:,0]) == dataset['train_label'][:,0]).type(dtype))\n", + "\n", + "def test_acc():\n", + " return torch.mean((torch.round(model(dataset['test_input'])[:,0]) == dataset['test_label'][:,0]).type(dtype))\n", + "\n", + "results = model.fit(dataset, opt=\"LBFGS\", steps=20, metrics=(train_acc, test_acc));\n", + "results['train_acc'][-1], results['test_acc'][-1]" + ] + }, + { + "cell_type": "markdown", + "id": "2d92afc4", + "metadata": {}, + "source": [ + "Automatic symbolic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ec64a6b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fixing (0,0,0) with sin, r2=0.9654733538627625, c=2\n", + "fixing (0,1,0) with x, r2=0.975755512714386, c=1\n", + "saving model version 0.2\n" + ] + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - 0.853 x_{2} - 0.3885 \\sin{\\left(3.1242 x_{1} - 1.5464 \\right)} + 0.7063$" + ], + "text/plain": [ + "-0.853*x_2 - 0.3885*sin(3.1242*x_1 - 1.5464) + 0.7063" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lib = ['x','x^2','x^3','x^4','exp','log','sqrt','tanh','sin','tan','abs']\n", + "model.auto_symbolic(lib=lib)\n", + "formula = model.symbolic_formula()[0][0]\n", + "ex_round(formula, 4)" + ] + }, + { + "cell_type": "markdown", + "id": "cee6c7c8", + "metadata": {}, + "source": [ + "How accurate is this formula?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dd5226ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train acc of the formula: tensor(0.9980, device='cuda:0')\n", + "test acc of the formula: tensor(0.9970, device='cuda:0')\n" + ] + } + ], + "source": [ + "# how accurate is this formula?\n", + "def acc(formula, X, y):\n", + " batch = X.shape[0]\n", + " correct = 0\n", + " for i in range(batch):\n", + " correct += np.round(np.array(formula.subs('x_1', X[i,0]).subs('x_2', X[i,1])).astype(np.float64)) == y[i,0]\n", + " return correct/batch\n", + "\n", + "print('train acc of the formula:', acc(formula, dataset['train_input'], dataset['train_label']))\n", + "print('test acc of the formula:', acc(formula, dataset['test_input'], dataset['test_label']))" + ] + }, + { + "cell_type": "markdown", + "id": "8a77c90a", + "metadata": {}, + "source": [ + "## Classification formulation\n", + "\n", + "Let's then treat the problem as a classification problem (output dimension = 2, CrossEntropy loss). " + ] + }, + { + "cell_type": "markdown", + "id": "b03f2dd0", + "metadata": {}, + "source": [ + "Create the two moon datatset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "71c1d738", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGdCAYAAAAfTAk2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOyddXgUVxeH35nduAd3d3d3t0KhSFugWCkFPgpUgBYpUKxQihUKFIoULRR3d3d3J0gS4ro79/tjk8CS1QgQmPd50rIz1zbZnTlz7jm/IwkhBCoqKioqKioq7xHy216AioqKioqKikpKoxo4KioqKioqKu8dqoGjoqKioqKi8t6hGjgqKioqKioq7x2qgaOioqKioqLy3qEaOCoqKioqKirvHaqBo6KioqKiovLeoRo4KioqKioqKu8d2re9gLeBoig8fvwYDw8PJEl628tRUVFRUVFRsQEhBKGhoWTNmhVZtuyj+SANnMePH5MjR463vQwVFRUVFRWVJPDgwQOyZ89usU2qGjj79+9n4sSJnDp1Cj8/P9asWUOrVq3Mtv/vv/+YNWsWZ8+eJTo6mmLFivHzzz/TqFGjhDYLFiyga9euifpGRkbi7Oxs07o8PDwAwy/I09PTvjeloqKioqKi8lYICQkhR44cCfdxS6SqgRMeHk6pUqXo2rUrbdq0sdp+//79NGjQgLFjx+Lt7c3ff/9NixYtOHbsGGXKlElo5+npybVr14z62mrcAAnbUp6enqqBo6KioqKiksawJbwkVQ2cJk2a0KRJE5vbT5kyxej12LFjWbduHRs2bDAycCRJInPmzCm1TBUVFRUVFZX3jHc6i0pRFEJDQ/H19TU6HhYWRq5cuciePTvNmzfnzJkzFseJjo4mJCTE6EdFRUVFRUXl/eWdNnB+++03wsPDadeuXcKxwoULs2DBAtavX8+yZctwdnamWrVq3Lhxw+w448aNw8vLK+FHDTBWUVFRUVF5v5GEEOKNTCRJVoOMX2XZsmX06NGDdevWUb9+fbPtFEWhbNmy1KxZk2nTpplsEx0dTXR0dMLr+CCl4OBgNQZHRUVFRUUljRASEoKXl5dN9+93Mk18xYoVdO/enX///deicQMgyzIVKlSw6MFxcnLCyckppZepoqKioqKi8o7yzm1RLVu2jC5durB06VKaNWtmtb0QgrNnz5IlS5Y3sDoVFRUVFRWVtECqenDCwsK4efNmwus7d+5w9uxZfH19yZkzJ0OGDOHRo0csWrQIMBg3nTt3ZurUqVSuXJknT54A4OLigpeXFwAjR46kcuXKFChQgJCQEKZNm8bZs2f5448/UvOtqKioqKioqKQhUtWDc/LkScqUKZOQ4j1w4EDKlCnD8OHDAfDz8+P+/fsJ7WfPno1Op6NPnz5kyZIl4eebb75JaBMUFETPnj0pUqQIDRs25NGjR+zfv5+KFSum5ltRUVFRUVFRSUO8sSDjdwl7gpRUVN4W9y4/4Pqp2zg4aildtzjeGbze9pJUVFRU3ippPshYReVD5sndZ/zaZQYX9l9JOKbRamjcvS69f++Co7PjW1ydioqKStpANXBUVCxw7/ID1s3YyoltZ1H0CqVqF6Nl3yYUKp8vVeZ78TSIb6oNJeh5sNFxvU7P5rk7CXgUyKh1g2ySKVdRUVH5kFENHBUVM+xdcYhxHachSaDXKQDsXnqAHYv20WdqN1r9z/YyJLby39TNBD0LRtEric4JRXB04ynO779MqVrFUnxue/C785TNc3Zy/dQtHJwcKF23OKVqFSNT7gx4+lovgqeioqKS2qgGjoqKCfxuP2V8p2mJDI14Q+ePb+ZTsEI+ilYumKLzbvt7t0njJh6NVmbHwn1v1cDZPHcnU7+eA5KUsNZjm04nnK/UrCxdRnUgf5k8ifoKIbh0+Bo7Fu4l8GkQ6bL40rBLbYpUKqB6pVRUVFIU1cBRUTHBhlnbsBR+r9HKrJ2+OcUNnODnluuk6XUKgU9emDwXGxPLvpVH2Pb3Hp4/DCBD9nQ07laXmm0r4+DokCLrO7fvEr/3mg0C4v6TiONbznB61wUm7hxBsaqFEo7HRMcy9rMpHFpzHI1WRq9T0GhlNs3ZQe32VRm06H9oHdRLkoqKSsqgXk1UVExwdu8li54UvU7h7J5LKT6vdyYvAv2CzJ7XaGXSZ0uX6Hh4cDiDGv7CtRM3kWUJRRH43XrC2T0XWTtjM+O3DcPN09Xi3BGhkVw9dgO9XqFA2Twms7ZW/bYBWZYt/m6EItDH6JjU7Q/mX5ma4JmZ8/0iDq87Abz0hMX/f9/KI2TIno6eEztbXKOKioqKrbxzSsYqKu8CtmyXpMaOSpPu9ZA15r+Wep1Co651Eh2f2vsvbpy+DYCiCKP/Xz95m+l9/jI7ZmxMLLO/W0S7LF8yqOFofmwyhg7ZvmLCF9MJCwpPaCeESAi2toaiCB5e9+PSoasAhASEsmnOToRi2usjhGDdH1sJD4mwOraKioqKLagGjoqKCcrWL2nR0NBoZco1KJXi87b+phkZsqdDo008tyRJ1GpbhaJVjLfF/B8Hsm/lYbOGh6JX2LP8kMmtLUVRGN1uMqunbCQ64mVBWr1Oz+6lB/m29gii4o4LIWwybl7lwbXHAJzdcxFdjM5i25ioWM7vu2zX+CoqKirmUA0cFRUTNP+qgcHAMeOlUfQiVbKoPNN5MOXQL5RrVNpobicXRz4Z2JzB//RL5F26dOiaVcND0StcOnQt0fFT289xZP1Jk54VRa9w+8I9ts7fDRgK2+YvkwdJtt115Rq3LRYbbdm4iSc2OtbmsVVUVFQsocbgqKiYIFOuDAxbOZDRbX9DUV56LjRaGUUvGDDnKwqWSx0tnPRZfRmzYQhP7j7j5pk7ODhqKV6jiPkYGhvFyE2Jlm/9ezeyxnJMzaY5O2jV12DMfdyvKb9+McOm+ZxcHCnfyODlKlAur019TGVeqaioqCQF1cBRee+JCI1k15IDXD1+A61WQ/lGpanyUXmrGTtVP6rA/KtT2TBzGye2nUUogpK1itGyTyNyFc2R6uvOnDsjmXNntNquSJWCSJJk0oCJR5KlRFtbAE/vPrfs/RHw/EFAwsv6HWtyds9Fti/Ya3XOdt+3TDDKchbORslaRbl46CqKLvF8Gq1M6bolyJovs/m1qKioqNiBWotKrUX1XnNy+zlGfTKJyPAoNBoZkNDr9GTKnYHx24aRvUCWt73EFGFU20kcWnvCpLEia2Sqt67EsBUDE50b9tF4jm85Y9HIyZo/MwuvT094LYRg74rDrJm2mesnbyZkQmm0MkIRCOCTAc3pMaEjsvxyF9zvzlP6VxtK0PMQo/lkjYxvFh+mHvqFjDnSJ+Xtq6iofCDYc/9WDRzVwHlvuXflIb3KfI9ep08UYxJ/U51/ZQoubs5vaYUpR0hgKN/XHcntC/eQMHhWJFlCKIJ8pXIzcfcIPHzcE/Xb9+8Rfmk/2ey4kizRdfSnfDrkY7NtXjwLZs/Sgzx/GIBPJi/qfFqdDNkTp7IDBD55werfN7H1792E+IfilcGTJt3r0WZAM7WYqIqKilVUA8cKqoHzYfB7zz/ZtmBPgofBFP1mfUmLrxome67w4HD2LD+M360nuPu4U6tdFaPtFkVROLf3Ek/v+eOV3oNyDUqmeNHM6Mhodv1zgM3zdhHwKJB02Xxp2r0e9TrWwMnFyWQfXayOATWHcf3k7UReHI3WYAT+eWZiqpRfEEKo6sUqKip2oRo4VlANnA+D1um7EhoYZrGNm5crS+7NsiqCZ4kt83Yxo998YqNiDUHIcUHJDb+oTf/ZPTmz6yJTv57Ds/v+CX3cvd3oNuZTWnzdKMnzphRhQeH81mMmB9ccNxInLl6jCD8s6MOts3fZ9vcent33xzeLDw2/qE2NNpVU1WEVFZU3jmrgWEE1cD4Mmrt9TnRkjNV2DbvU5vv5fUye0+v1HN1wiq3zd/P03nN8M3vToHPthPIHh9Ye5+fWE032lWSJ8g1LcXL7ORCms5hSq2hnUnhy9xlnd19E0SsUqVKQrPkyMeyjCZzZdSEh0ypeJblwpfyM3zoUNy+3t71sFRWVDwjVwLGCauB8GPStNITrp26ZVc+NR6PVsPzR7EQxIDFRMQxvOYFTO84nusEXKJuX8duH0qvM90ZZRiaRMFe2CRd3Z1b4zX0n44Cm9p7L5jk7EhSRX0XWyNRoU4mhyxMHLquoqKikFvbcv1WhP5X3lpZ9G1s1bsCg2nvt+M1Ex+f+8A9ndl0ASIhPib/Z3zp3l36Vf7Ru3IBZ4wYgMiyKI+tPWh0iJiqGJ3efERIQan2+FCAkMJSt83ebNG7A8PvY/+9Rnj3wN3leRUVF5W2jGjgq7y31Pq9BoQr5bWv8WrBreHA4m/7aafEG/+jmk+QuEVmWCHoabPZ8sH8IM/43j9bpu9Epbx/aZOjGt3VGcHbPxWTPbYkrR29YLa0ghFBLK6ioqLyzqAaOSppHURQuHrrK4XUnuHnmTkKsiyzL/Lj0G6v9tY5ailQuYHTs6vGbxEalftkARRGky+Zr8lzQ82D+V/lHNvy53ahO1MUDV/ih/ij2/Xsk1dZli+fLnnYqKioqbxo1DUIlTbN3xSHm/LDYaKsoT4mc/G9GD0rUKELWfJmp2rICRzaYrrckyzKNutTG2dWJhzf8cHJxJH02X2Jj3kxNJEmS+GvwP1zYf5mWfRuTo1C2hHMLh6/g6b3ESsOKIkCC37rPpGLTMqkSv1OwfF6rJRzAoKKsoqKi8i6ienBU0iw7Fu9jzKdTEsXB3L30gB/qj+TioasAfDvva/KWzAWQUCgyvlJ44cr50To68Emm7nQt1I/PcvaiV5nvObrx9Bt5D0IIntx5xoY/t/NliW85tPY4AJHhUWxfuNe8gSEM8Tv7VhwG4OH1x2z9ew/bFuzB787TZK/LN7MPtdpVNVtRXdbKlG9U6r1RglZRUXn/ULOo1CyqNElMVAzts/YkLCjc5HlJlihQNi9/HB9vaB8dy74Vh9m2YA/+jwPJnCsDdT+rwerfN3L30gMjQyJeAfitIEGmnBnwyeTJ1eO3LDbVOmho0qMej2895dT2c0ZjVGlRge/mf51kgT5drI6rx28yqdtMHt3wQ5LianpKICGRNX9mJu8biW9mH5vHFEJwbu8l7py/z7MH/nhn8iJzrgxUaFIGVw+XJK1TRUXlw0JNE7eCauCkfQ6sPsqotr9ZbTfv8hRyFs5m8tyin1eyZMxqq9swlpA1Eoretq/QoIV9WTdzG9dP3rI+p4XU8pdzy7h7uxIWHJGogKWskclTIifTjozF0cnBpvWBIZ5p5cT1rJq8geDnIQA4ODng5uWKotfjm8WHJt3q0ahbHbvEEa8ev8G4z6fy+FZi75KTqyOfDm7NZz+1VpWNVVRULGLP/VuNwVFJk/g/CrTJ0+L/MADPdO5smrOTrfN3E/gkCEWv4OjkQEx0bLKMm6z5M+Pq6cLN03estvVM50Gt9lWZ2X+BbXPaYDMpeoWQANNKzYpe4dbZu6z7YystejUk0O8F7t5ueKYz7dERQnB653l+6/Enz19L/Y6NjiXEP4TiNYowfttQHBxtN5gA7l1+wHd1RxITFW3yfHREDAuGLyc2OpYuozvYNbaKioqKOVQDRyVN4pPJy6ZtpMjwKHoUH0iwf4iR0WAtBTqehK0ZE3Qc+gn1O9XkwoErTOo20xD7YqZt229bGAyDFHJQSLKEq4cL4SERFo2hOd8tYu73ixMyy0rXLc4XP7ejePUiCW2EEMzs/zdrp28xO46iGFLCdyzaT9Me9exa65Ixq9HFxCKs2HXLJ6yl5f+a4JPxzRbd1MXqeHTDDySJbPkzqyUoVFTeE9QgY5U0SaXm5XBxN589JEkSuYplZ/Z3iwzieEnYiNVoZVzjtmHkuOBkjdbwlfn8pzbU71QTSZIoWbMoM09OoEglQ6p5fGBufNvG3evS7oeWAJStXyLheHKQZcngCbLhfb26C31+32W+rfMzRzeeSji2c/F+i8ZNPJIssXH2drvWGRMdy/5VRy0WPI1HURT2rTxs1/jJQa/Ts+SX1XTI1pMexQfSo9gAPs3Ri2Xj1qDX6d/YOlRUVFIH9VFFJU3i4uZMtzGf8cc38xOdiw/jqNOhOguGLU/yHHqdwtCVAwnxD2X/v0cID4kgV5HsNOtZnzwlchm1dfd24/cDozm++Qy7lx4g6HkIWfNmonH3egmGD8DH/Zqxb2Xy9Wv0OoXIsCi7+yl6BUmCX7vMYPmjOTg6ObBq8gabtvuEIvAzEUNjicc3n6CPtc1YkDUyQc/Mix6mJIqiMLr9ZA6vPWFkAAY9C+bvoUu5de4uPy79BllWnwFVVNIqqoGjkmZp9b8myBqZ+T8tJTw4IuF4uqy+9Jv5JVeP3UCj1STpaVzWyBStUpBy9UsiSRJ1P61utY9Go6FKi/JUaVHebJtiVQvR748eTOv7F5L0drK1hIDQwDCOrDtBxWZluX3+ns193bwtBxYLITiy4SSLRqzgzsUHdsU46XV6MmRPZ3P75HBo7QkOrTlu8pwQsG/lYep3rEnl5uXeyHpUVFRSHtXAUUnTfNS7EY271eHktnME+4eQKVcGStUphkajMVlfyhJaBw1CCPQ6hVK1izFs5UCjrJ6HN/zYOGsbZ/deQtbIlGtQiha9GpAxZwazY+pidRzfcoZn9/zxTOdO5RblafF1I4pULsiAmsOICjcdeJvaaLQa7l99RCU7buCyRqZBp1pmzwshmN73LzbMsm8bKx4HJwdqtauapL72smn2dqtChkvG/qcaOCoqaRjVwFFJ8zg6O1K1ZYVEx0vVKcaSMaut9tdoZQpXLECxqoVwdHGkykflKVgun1GbHYv3ManbTCSJhHiSW2fvsmryBkas+i7RjTAyPIpl49awbsYWIkIiE7aAnFwc6Ti8LdVaVXhrxg0YtqocHLU4uzqRr3Rubp27azGeR5IlPHzd+ah3I5Pnn957zoJhy9n5z/4kr6nHuM9x93azq8+zB/48uuGHi7szBcrlRaPR2NTvwdXHVr1LV49dx+/2U7LkzWTXmlRUVN4NVANH5b2ldJ3i5CqanQfXHlnUqlH0gt5TuyYyauK5de4uE7v+kWg7SdErCEVhVNtJLLg2jYw5M6DX61k88l9WTlpvVMsqvm90ZAzzhiwh8ElQ8t/gKxgcTRK2yloJIfh7+HIuHLxC9oJZuHX2rsX22fJn4ec13+OTydvoeEx0LFO/nsOOhftsnvt1fLN403X0pzTuVtfmPn63n/Jr1z+4eOBKwjHPdB50H/cZTXvUt9rfzccVHlhpJGDdjC30mtzF5nWpqKi8O6hCf6rQ3ztLTFQMe1ccZsfifQQ9CyZznow07VGfSs3KWgz+9H8UwMY/d3B4/Qkiw6J48SSI6MiYxA0lQ7bVd/N60/CL2mbH+63HLHYs2ms+E0gCVw8XFL2CRqsxigcyh4OTFr1OsepFkCTbjZbUQNbK9J7SlY++boQkSURFRHN653nCgyPIUSgr/03ZxN6Vh+2OJer3x5eky+aDp687RaoUtNnzAvDk7jN6FBtg+m8KdB7ZjuY9G3Bk/UkiQiPJXjArFRqXRqN9OceKX9fx1+B/rM6VIUc6lt770+a1qaiopC6qkrEVVAPn3SAmKoY9yw+xY9E+XjwNInPeTDTtXo/KLcoR9iKc7+uN5M6F+wnbO/ExExWblWXEqu9MKvSe33+ZH5uOJfYVET+NVkavU3D3cSU22hBw7JvFm6ofVaDF1w3Jlj8LoS/C2Dp/D/tXHSEyLIq8JXPx0dcNKV69CJ/m/Ar/h4Fv9HcTj5ObE7roWJvSrCVZokzd4lw5eiNJGVbmSJ/dl0l7fmb/yqMsG/df8seWYO2LhXYpIb9K38pDrMZXyRoZRVGQZcNnxjeLDz8s6EO5BqUACAkMpV3mL60GoHtl8GTV03lJWqeKikrKY8/9O1VzIPfv30+LFi3ImjUrkiSxdu1aq3327dtHuXLlcHZ2Jm/evPz5Z+Knp9WrV1O0aFGcnJwoWrQoa9asSYXVq6QmQc+D6VNxMJO6zeT8/svcv/KIk1vP8nPriQxtPp6xn0/l3pWHwMvtnXiD5cSWMyw0kf4dFhTO0BbjiYmKMfKMxBsHYS8iiImKIToymmf3/NFoNWTOk5G7lx7QrUh/5g5azNVjN7h36QH7/z3MgJrD+XPgglTJdLK1JEF0eLRNxg0Yfk9CwAq/uXQf+1lylmeE/8NAuhXpz/yflqaI4eTh6063wt/Q1OUzmrl9Tt/KQ9j5z370euvZbuEh4TYFj8drBMV/Dl48DeKnZuO4cuwGAJ6+HlT7uKLFMWSNTP7Suc2ej42J5ebZO1w/dYuoiLcXT6WiomKaVDVwwsPDKVWqFDNmzLCp/Z07d2jatCk1atTgzJkz/Pjjj/Tr14/Vq18Gih45coT27dvTqVMnzp07R6dOnWjXrh3Hjh1LrbehkgpM6Dyd+1ceAYkNmJPbz3J6x/lE9ZXiEYpg/axtRIZFGh3fvnAvUWFRFg0SoQgQhpTklZPWMef7xfzUbCwhAaFG/eKNitVTNpEhR/oUEeczWocQyCk8JsDdS/dxcnHEzdstxVSTAbN/i6QQGhBG4JMgYqNjiYmM4drxm0zoPJ3R7SZb9Kgc2XCS3hV/TNKcBuNPsHD4S8O4fZz4ojkUvcJHfRonOq7X61kyZjUdsn3F12V/oE+FwbTL0oM53y8iOlI1dFRU3hXe2BaVJEmsWbOGVq1amW0zaNAg1q9fz5UrLwMHe/Xqxblz5zhyxCCO1r59e0JCQtiy5aXyauPGjfHx8WHZsmU2rUXdonq7PLj2iG5F+id7nN/2jqRkzaIJr39u/SuH150wW1rBFNYE7iRJIlOu9Dy5+zw5S01E/LZZarD49h+c2HKGaX3/SpKC89tCkqDnxM58MrBFonP/Td3ErAELUmSeVc/m4ZXe8L3/Z/QqFo5YYVCGjvscxH8mmvasT/9ZPY28bUIIxneaxu5lBxP9bmVZokTNoozb+pPd9bpUVFRs453ZorKXI0eO0LBhQ6NjjRo14uTJk8TGxlpsc/iweYn36OhoQkJCjH5U3h5n91xKEe/C64ZJUkx1oQgk2fxihBA8ufucrnFFIFPKk6NPRpFPawhFULxGkTRl3IDh7/ff1E0oivHvxu/2U/4cuDDF5gl9EZ7w747DPuHn/76ncOWCCcfyFM/Jd/N7JzJuAM7svsjupYmNGzDU6zq39xK7lhxMsbWqqKgknXcqTfzJkydkymSsOZEpUyZ0Oh3+/v5kyZLFbJsnT56YHXfcuHGMHDkyVdaskgRSwGno4ORAvtfiI0rUKMKR9Sex585uybh5lWZfNaB0vRKsnbaZc/suIRTBi6f2lxXIlCcjUWGRBD8PtbuvLXhn9CJjzvRotBpK1S7KxYNXU81TZAlZI1lMzTfH8wcBBPuH4pPRC71eT3hwBNP7/pVimWRaBw2+mb2NjlVrVZFqrSoSEx2LUBScXJzM9t88d4dF71t8va7GXeukyHpVVFSSzjtl4EDi4Mv4C9urx021sRS0OWTIEAYOHJjwOiQkhBw5cqTEclWSQPHqha3bIBIJGTCJTskSjbrWSSQK17BLbRaOWEF0ZIzNgcHW2kmSRJa8GfFM54FXek+KvvKk38KzE1E2Bt2my+pD3+ndKV69EG0zfWlTH3uRZIlW/2tCSGAYm2bv4PnDwJSwJW2cHKO/af7Sebh+6naShgp4HMjC4SvYuXif2VTwpKDRytRqXxVXDxeT501l5b3Ow+t+Fg3GpNTrUlFRSR3eKQMnc+bMiTwxz549Q6vVki5dOottXvfqvIqTkxNOTuafylTeLHlK5KJEjSJcPnLN5M1C1shUb12JQ2uPg4mYU6EIilQuyLMH/ty5cJ8XT4PxyeRF2folGLl2EMNajEcXq7OpDpKzmxNOrk6EBoaa9DgIIWgzoIWRAR30PJhLh67ZbNwA/G9Gd07tuMC4z6fa3McSr8YOxcePVGpalsotyvFl8QGEBIYlnI/X0nF0diB9dl8igiMJep7C27Sv/eqSYtxIkkT2gpn5vu5IIsMik+R5ejWWxui4Rsbd242uoz+1e8xX8UrvYTVuy91KvS4VFZU3wztl4FSpUoUNGzYYHdu+fTvly5fHwcEhoc2OHTsYMGCAUZuqVd9MDRuVlOHHpd8woOZwnt59jsCQ2RR/4yhcqQBe6T0sGigTuyTOzPNK70HPiZ2Zd/l3NszazuF1x4kIjSLYPwShV4xufJJk2CnrO707uYvl4IcGo4gKj06YM15zp0HnWjTv1QCAyLBI/vjmb3Yu3m9XAU8PH3eWjlvDzdN37Co+aYnyDUvz4NojIkIiyVE4Gx993ZAabSvTo+gAQl+EG92A472gsdE6Gn5Rh89/asOTu88ICQjlxdNghjYflyJrSi5CCGKj9USERib592RuK6tC49L0mdqNTLnM1w2zhbqf1eD0zgtmz8uyRH0L9bpUVFTeHKmaRRUWFsbNmwbNijJlyjB58mTq1KmDr68vOXPmZMiQITx69IhFixYBhjTx4sWL89VXX/Hll19y5MgRevXqxbJly2jTpg0Ahw8fpmbNmowZM4aWLVuybt06hg4dysGDB6lUqZJN61KzqN4NwkMi2DZ/D1sX7CboWQiZc2ekWc/6VGtVgU9z9EpyraYfFvSlQeeXN5kH1x4xc8ACTm47m+BpyF4oK93GfEaN1obPjP+jADbM2s7elYeJCo8iT4lcfNS7EVValEeSJHSxOr6r+zNXjt6w++Zbq20V9q06kqJBv47ODqz0m4ub18ttuuNbzvBTs7EW+3ln9GL5w9lotIbCohGhkfSpMJhHN/3Mrs87kye+mX24c+F+qlY/b/plfTbP3ZkqY0/YMZyy9Uoke5yYqBh6lf2BxzcTb1XJGhmv9B7MOf8b3hm8kj2XiopKYt4ZJeO9e/dSp07iYLsvvviCBQsW0KVLF+7evcvevXsTzu3bt48BAwZw6dIlsmbNyqBBg+jVq5dR/1WrVjF06FBu375Nvnz5GDNmDK1bt7Z5XaqB825y8dBV/p20nmObTiUrMNYrgyfLH85G62DsoHz+MICnd5/h5u1G7mI5bBbbA9i99ADjOk6zey1Ne9Tj/IHLPLrul7LxMBL0n9WTZj0bJBya/9NSVk5ajz7WsnfpjxPjOfjfMTbO3kFoYJjVqtpaBw31Pq/BnhWHiUnBmJjX6TSiLYtH/pu0zq/FAL2KrJWp0rw8P//3fZLX9iqBT14wqu1kLh26iqwxZNUpeoVcRbMzYvV35CiULUXmUVFRScw7Y+C8q6gGzrvHjsX7mNjlD2SNlCJZP79sHEKlpmVTYGUGvq8/kvN7L5mM7zCFNaMhuUiSRIfBreg25qVi8d9Dl7Hi13VWt88y58nIs/v+tq/PgvGQkpSqU4xzey7Z1afjsE/Y+c9+ntx5ZrFdxhzpWXJvVnKWl4jrp25xZtdFFL1CsWqFKFGjiF1Gs4qKiv3Yc/9+p2JwVNIukWGR7F56kMtHryPLMmXrl6R664o2CZ75Pw7kt+4zEUKg16XMnfTxTT9WTlzHvSsPcXZ1onrrSpSuU9zuG1BYUDib/9rJxQNXbDZugFQ1bsAQa3L3onE57FJ1irN07H8W+2m0GqvGQOLJ7F1d0nhw9RFaRy26GJ1N7YtULsgXI9tzZMNJq211Oj3n9l6ieI3CdhX2tETBcvnMVqBXUVF5+6geHNWDk2zO7b3EiI9/JTwkAlkjI2EodZA+ezrGb/2JXEUtp+T/M3oVi0f9m6JGgayRE+QDJElCr9NTpFIBRm8YnKBia40H1x7Rt9KPRIRYrw7+thj277fUbFMZMBg9X5b8lofXHr0V7Zvk4ujiSLMv67Nm2marbWWNzJJ7s0if1Zd/flnFop9X2hQf5JvFhx7jP6eBGgisopImSbNKxippj0c3/fix2VgiQiMNxQ11SsLNNdDvBd/XG0m4FQPh5pnbKe7xUPQKQhEoeiVhy+baqVsMbznBJtE4RVHoX31oqhs3kiyh0WpsFhx8nTEdfufcPsO2jiRJjF43CN8sPoZtpbgh5SSOnVzM6c2YIyYyhkC/FxQqb9krIskSH/VuRPqsvgal6TvPbA5+DvR7wa9fzGDLvF12rU1FRSXtoRo4Ksli7fQt6GJ1Jm8wil7hxbNgdizaZ3EMBycHqzdhSZbInDuDTYaAuTaKTuHyket8nvtrxneexuUj18yOsWvpQUICwqzOlRQk6eUay9YvybxLv9NlVAey5s+Mu48b2QpmsXksRa8wss0kYqIMwb9Z8mbir4u/03dad4pWLkj2gllwck19DajXf+ee6Tz4YlR7u8fZv+oohSsVIF1WH8ztJsoamRI1iiCEYOv83Wz7e4/d88z+Ti2MqaLyvqMaOCrJYv+qo1YrTR/8z3Kl94pNy1qNb5EkiXylclvd7gLr6sTPHwSwd/khvqk21GzWzpZUSlcG+Kh3Y76e3IW/Lk5m/NahZCuQhc9+bM3C69NZE7CAxl3r2hUrFBoYxr5/jyS8dvVwoUHnWmQrkIXHt54SaYcgYVLoNLwt9TvWJG+pXBSrVoivJ3dh4Y3pFCiX1+6xhBBs/msn47b8RO7iOQ0HX/tVCEVhdLvJjOs4NclZV+HBERzdeDpJfVVUVNIGapCxSrKI9xyYRUBUhOUn5Vptq/D30GUEPH5hdqtK0SscWnciqctMRPw22qKRK8lXJjdVP6pgdD4sKNxUt2Tj7ObE11O6WAx0Pb75tF21l7QOGq4dv5kQV6KL1TGkyRiuHrNfs8cUGgeNYZvv9erZGoms+TLTYcjHCWUOAp+84OaZu1w7cZPClQpYVf01RWy0joc3njD77CS+rTOCCweuGJ2PV5zes+xQkt+TLEsEPApMcn8VFZV3H9XAUUkWeUvm4uLBq2ZvpBqtTP7SeSyO4ejsyK87hvNDg1E8fxCQGss0i6yRWTV5g5GBc37/ZZ7d90+V+WKiYggPjsDT18N8m+hYu8YUGIycePavOsrlw+a33+xBliU8fT148TQo8bwCek3ugqOTA8H+Icz43zyDRy/us+Dk6kS2All4eO2x3fPGRsXw9N5zg3GTCmkQiiLwyqAmGKiovM+oW1QqyaJln8YWvQR6nUKLrxtaHcfRxZEs+czXE0stFL1iMNAUw3s4t/cSP9QfRXhw6gQXK3rBhM4zzGrVnNh6hqd37Uvj1sfqqdCkTMLrrfN3p1hgcfoc6Qj2N123SpIkZvb/m/CQCAbWGm5k3ABER0Tz8NpjtI72P0flLZWb0zvOp2qK+s0zSSsGqqKikjZQDRyVZFGjTWXqd6oJYBQUGn+D/WJke/KVym1xjGD/EPpXH8rFg1eTvR5ZI+Ph627XDV4ogqjwKKIjo5ne968EYye1OL75NDP7/53o+O5lB/mx2Vi7CmFqtDK5iuWgzCtlCJ4/CLBLs8ccbt6u+D8MtLht+PjmE+Z8v5gH1x6bbaeL0VGsWqFEsTSWGN1+MucPXDEbaJwSbPhzB5Fhkak3gYqKyltF3aJSSRaSJPH9330oXq0wq6ds5MFVw3ZEgXL5aP9DS2rEabS8SrB/CLv+OYDf7ae4+7gR/DzEYvyNPSh6hUGL/8f147dYP2sbQc+CrazfkMXV0uuLZM9tDxtmbaf9oFZkzJEegMjwKKZ8Ndtmj0V8bEvGnBkYs3EIsvzyWSV9Nl8e3/SzaOTIGpnCFQuYzSRzcNLSY0JHpn41x+o6Dv53zGKcjUYrkyVPJoYuH8CdC/cJfRHOnwMXEOwfavZvfv/yQ+5ffmhx7uQSHRHNxYNXqdC4jPXGKioqaQ7VwFFJNrIs06xnA5p+WZ/IsChkjYyzmdTktdO3MPu7hej1ChqNjCKE1SwsW5EkieqtK1GxcRkqNSnL58Pa8PyBPz1KfEt0RLTJm7AQ9se8pMxiYd/KI7T9tgUAB1YdtSnbqXyjUsTG6HD1cKFGm8rUalsFR2dHozaNutbh7J6LFufuNvYzStUqytndF1kzfTOBfkEJpwtVyE+/mT1YMWGt1fUIIaxqBel1Ck/vPSd9tnSkz5YOgNJ1ijH7+8XsWXYwVQt4WiM22jbVZBUVlbSHauCopBiSJFkUd9u97CB/fDM/4bVOsVwzyR6c3Zxo2acxXUZ3SEixlmWZTLky8sv6wfzUbCyxMboEj4FRds9buL9qNDKhgaEJrx/d8DNkK1kplNnz107kKZHLYpta7aqwdvpmbpy+k8hDIskSLu7OzBuyJOH95yySnZZ9GpO3ZG6y5M1IrqI52L/qCPtXHbX6PiQkPNN7GBlIryNrZHwzexsdC34ewqE1x203blKpHpZPFu+UH1RFReWdQI3BUXkjCCFY9PNKu+IwrNFx2CeM3fITE3YMZ6XfXHqM75iogjhAqdrFmHd5Cm2/bUH2glnImDM9bp6uKboWe9Hp9GTJ+zKo2s3LFWHDFp2bl6vVNg6ODkzYPoyan1ROJMCnddAQFW7szXpw9RF/D13Oi6dBCTpDa6ZvSaiUbYl6HWvQtEd9i20VvUKDzsalERYMX0GsPZ4zYdD30TpqyZQrQ4r97cZ9NjXVJAFUVFTeLqoHR+WNcP/KQx7d8Eux8ep0qMYXI21Xys2UKwM9xnekx/iOADRyaJ8qHgF3b1f6TOvO0Y0nObD6aIJmy+s4OTtSq11VwFAWwjezt+XAYMlQ3DFjzgw2rcPNy42flg3gq0mduXjwKkLAvpWHOLrxVCKvTrzmzox+8ylcqQB6nZ5rx29ajYnyyuBJ/z97EhkWxdb5uwl8GpRou1HWyBSrVojyjUsnHAt9EcaRDSft3prS6/SsejYPN09XxnWaxp6lB+3SCzLFk7vP2Dx3J+2+b5mscVRUVN49VA+OyhshpdV09yw/xMbZOxId938UwINrj4gMNz/fi2fBKV77SpIgc+4MLLn/J1nzZeLxracmjZv4rKA+U7txbu8lBjf+hRbuHZnwxQzLEwiDgN+xzfap76bPlo7a7atRsUlpjm48bbEIZ0xkDD1LfsvXZX+w7l2RoHClAji5OOGdwYvfD4ymSMUCxk1kiVrtqjBm4xAjYcNg/9Akxd1ER8VwIG7brMOgVmgdk17DKx6hGMo9qKiovH+oHhyVVEOv03Ns02nO7L5ATERMklRtLTHvxyU07FIbRycHjm48xaKfV3LjtEHbxNHZgUZd6vDFqPY8uPaYNVM3cW7vJZAk0mfztWn8zHky8uSObZo05RuV5oeFfbl15i4/1B9p1huTPkd6vprYmb0rDlktYfE6dy8+YGjzcXQb8xmfDvnYrr7+jwLNau8klQqNSif8O3PujEw5+At3Ltzj6vGbaLQaytQrQYbs6RL1887gmaTPgkar4flDgxBknuI5Gb1+MKPbTSY8OAJZKyc5WP3RzSf80GAktdtXp+5n1c0GyKuoqKQtVANHJVW4f/URPzUby5M7z9DEqeymdLZM2ItwTm47S4h/KL/1mGX0NB8TFcumuTvZt+oIIf6haLRygvcixD/U3JBGBPgFIkmSxW0QSZJo0KkW3y/ogxCCgV+NQB9XyTxRW1nCycWRe5cfcGjNcTvfLQlep/k/LaVsg5IWq277Pw5k45/bObD6KNERMeQsms3u+cwha2TcvFwT9I9excXDBY1WgxKnLWQKd283qraswJH1J+3ypOlj9Xhn9Ep4Xa5BKZY/msPeFYe5ceoWlw5f4/a5u9i7a6XoFc7uvsSZXRdZNu4/ftvzs81bgSoqKu8ukkjuJnYaJCQkBC8vL4KDg/H0VOXaQ1+EsXPxfu5deoCzmxNVW1WkRI0idhV8fJWwoHC6FfnGos5JSvH1lC78Neift5ruO/fCZHIXy8GVYzfoV+VHq+1dPVyICE26wJxGK1O/Uy2+m9fb5Pkrx24wuNFoosKjE37/CQZecrKRJEPWlLuPGxO2D6NA2ZfFNENfhDH5y1kcXHPcaPyy9Uvww8L/kS6Lj9FQ9y4/oG+lIcRExdr1GVlwbRrZCpiutv5rlxnsXnIAfTI+cxqtTK6iOfjzzEQkScLvzlM2/rmD8/svI8sS5RuWpsmX9Uif1TYvoIqKSspiz/1b9eB84OxedpDfus8kNlqXkAmzesomilUtxKh1g/BMZ75mkjm2L9hL0LOQZAeA2sLD635vx7iJMxQ++7E1uYsZMo9s3c5KjnEDBl2Zq8dvmjwXFRHN0ObjiAqLNlJkToi9SeKfpEC5vHhn9KJikzI06FQTNy+3hHOxMbEMbjiam2fvJhr/3N5LDKw1nD9OjOfO+ftcOnwNjUamTL0S/L5/NJN7/smNU7aVTJAk2PfvET77sbXJ85lyZUh23Lhep3D7/D3O77tMgN8Lfv1iOkK89J5dPX6T5b+uZeSaHyjfsFQyZ1NRUUlNVAPnA+bc3kuM7zgtwRB5NUbjyrEbDG85gd8PjLbLk3Pj9G2Wjl2d+saNBOmy+HBh/+XUnccMWfNl5tPBH9Ooa52EYx6+7m9sfkdnB5PH9644TEiAhS04yaAPpOgVZFmyuaTD4MX9yFnY9DbX/n+Pct2MkaLXGco5dC/Sn8AnQQYjWhgyx0rUKMIvGwbz4mkwD6/78e9v67l+8pbZrUwhYNuCPWYNnIZdavPPL6tsej+W0Gg17Fp6gG1/70nkXVL0CrFRsYxoNYGFN6YnCBfGRMUQ8PgFzm5O+GTyTvYaVFRUko+aRfUBs2TMarNZKIpe4dLha4Zqzjay85/99Kk4mGAbY1yShYDoyBjuXnyQ+nO9wrB/B7Lo5gwWXJtG4251jYy/UrWLWvV4ufu44eppXgzRFmRZolrLiibPnd9/ybJ+zSveCFuNm1zFcpCjUFaz57cv3GO19lfgkyDDnHolwbN06cg1vq83khyFslKrbRWEIqzGaQVbqNOVJU8mPv+xjcX+NiHBteM3zdbBEkKgi9WzcfYOwoLCmTVgAZ9k7E7n/H1pl+VL/ld5iN3ZbioqKimPauB8oESGRXJm1wWL8Q8arcbmTB+/20+Z2PUPmwKJZY1MtoKm4yhsxcnVMdUqflsiW/4sZMmbyaRXy8HRga6/fGqxf7cxn/FR78ZJTm+WZAknNyea9Khr+rwkpXiBym6/fGrRixf4JChJxT0VncL9K4/Yu+IwAJnzZEDWWr4kZciROCvrVb4Y1Z6vf+9i1sNlC/pYPc8fBlhMqVf0Csc2naZ/9aGsnbHFSAbh2slbDG0+ji3zdiV5DSoqKslHNXA+UKIjY2xqFxUebbVNeHA4f/Sbb/O2lKJXqPpRBZvamiM6IuaN1zBydncmV9HsFts0/6oBvad0xcnFUB9KE3fDdnJx5OvJXWjRqyGdRrSlXAND/IYpz4dneg8aflEbZzenOINFMhhEErh6ujB+61B8M/sk6gdQsmZRizdme/HK4EnVlpb/VplyZbBJ9dgUkiyxa8kBABp3q2c11VuAxWrvkiRx88ydZNUXc3R2QOugsdrO/1GgySrq8Z/Lqb3nWt4uVFFRSVXUGJwPFA9fdzzTeVi8ACt6JSGA1hRCCP4ZvYpl49cQG2XjDUWCup9W5+zuCymui5PaFK1c0GQpiNf5uF9TGnWtw6E1xwl4HIhvFh+qt66UUKfL0cmBXzYMZv+qo2yas51HN57g4eNO3c9rUO/zGqTP5oskSfSa/AU7Fu7jwsErSBKUql2c+p1qGspMmKF2+6rM/eEfQl+EmlVRthVZI9OiV0Or7Rp0rsWxTUnbkhGKINjfsO1UrkFJ8pXOza2zd822v3fxAdP7/EXmPJnIUTgrlZqWRaN9aYz43XnKjkX7krSWeGKiYilWLSchAZfMGouyRiYsKNyiB1Qfq2f5+DX0nNg5WetRUVFJGmqa+AecJr5g2HKWjV9j9iLt4OTA8kez8fQ1HVeyYNhyloxZbfuEEvT+vSv1O9ektW/XpCw5RZE1MpKEzR6Pn5b3p3a7aqm8quRz7cRNBjUcTWRoZJK2jsDwu3H3dmPuhd/MeovAEJg+rOV4Tmw5m6R5NFqZ6q0rMXT5QAD6VhrCtROmM8ReX5+iV/DJ7M3gRf+jbP2SAAxvNYEj608maS32YKtx7ujiyOrn81XxQBWVFMKe+7e6RfUB035wK/KVym02HiR/mdwJWy2vE/Q8mOUT1to3oYA6n1Z7K9W7TeHi4UztDtWMPAAmkcAznQfVWpkO7L128hbzf1rKrAEL2DJvl8UyEW+CQhXyM//KFDqNaEfekrnImj8zzm623WDjfxeZ82Tkt70jLRo3ALuXHkyycQMG47Lplw0SXj+7/9ymfvFGedCzYH5qNpZrJ27y/GEARzekrnGj0cpIksT/ZnS3qX1MZAx7lh1M1TWpqKiYRjVwPmBc3JzpPv4zswbHlWM3GPbRBJPnDqw6areInyRJ7Ft5BHdvN0Ml7bdYzVuSIGfh7Axe1I+VT+YydsuPpMvik8jYkzUyGo2GQYv+h4OjceBqWFA4PzQcRd+Kg1k5cR3rZ25lcs8/aZ/lSw6ttV+pOCXxzexDx2GfMPvsJBZen06bAc2txslUbl6OT4d8zLitQ/n76lSL25PxrJ+1LekB0xLU+bQ6ZeoWTzjmlcE+j6pQBIoiWDTqX7b9vYfU+lDJWhnvjF7U7lCNGcfH0fyrhji5mjb+X0WSJE5sO5sqa1JRUbGMauB84Kz8dZ35G5SAM7su8O9v6xOdCvYPtTuwVCA4tfM8ulgdrfs3S8pyUwwhoMXXhvgST18PKjQqw6wzE2nRqyFO8dsJEpRvVIrJ+0dRsUmZ1/oLRnz8K+f2XAIMnghdrB4ERIZHMartb1w6fO2NvidLfNS7EW5erib/ZhqtTKbcGfhxWX++GNme8g1LIcu2/W0fXHuUpDgqz3QefDGqA4MW9TXK0GrYubbdBpOiVzi++TT3Lj9INaNZ0SmM2/ITgxf1I3exHMwbsoToCOuB+kIIrh2/Sb+qP/FTs7HsWLSPmCjbAvxVVFSSh2rgfMCEvgjj9E7LqeIAcwf9g//jQKNjGXOmt794o4Aj604woMYw6n5WnVptqwIkOQMnOWTNn5m6n1Y3OuaT0Yv/zejBfwF/s+zBn6x9sZAxG3+kaOWCifpfOnSV8/sum/7dxd3vl461Iz4plfHN7MNve35OSLPWaDUJGV45i2Tntz0jcXFztntcV3f7NH1qfFKZmScnsPzRbD7/qY1RlXGAJj3qkTFHeqvp4okQhjR9Ww2zpHDnwn2iIqL5vv4oVkxcZ3O/5w8DuHL0Oie3neXXLjP4qvR3ib5PKioqKY9q4HzARITYVjJAKIItc401PWq0qWRzXMfr3Dh9h2m95/LNn1/y/YI+FKlUAFdPF9y8zGcHpTRdf+lgNvbG0cmB9NnSWcxWOrD6mMXYHUWvcGLLGaIirKfZvynylMjFwhvT+WXDYNp9/xEdBn3MpN0/M/vsJDLlSlpxyTqfVrfJQHX1dOGLke0ZunwABcrmTbTdF4+7txuT94+iSMUCdq3DwdmBep1qpnjF9FdxdHFk8c8ruXr0hl1xZPEerviAb7/bT/n541/fSCkTFZUPGdXA+YDxyeRlNoj4da4cu2702sXdha9/T1omlKJX2LfyCK19uzKp6x+4erkyZuMQ1r5YiIu7/V6E17EkdCfJEt4Zvaj+caVkzWGoJ2X5BiUERL9DBg6ARqOhUrNydBvzGV1Gd6BU7WJJLqoK0LJvY1zcnU0aObJGwjuTF6PWD2Kl31w6DvvEJg9LxhzpmXLwF8Zstl64FAAJ6n9ek7L1SlCqTrFU8Qg6OGkpXr0wG+fssKjDYwt6ncK1E7e4fOS69cYqKipJRjVwPmAcnR1p+EVtm9qa8lY07VGPIUu+IVNu46d/e1RkhYCTW88yoOZw+lcfSoGyeezfnngFZ3dn6n5eA40JoTaNVkbrqGXoigE26dlYIkehrFZTsD183HD3cbPYJq2TMUd6Ju4agW9mbwA0DpqEz0quojmYcXQcVZqXx8nFfm9fhUalyZo/s9W4Gg8fdzr/3BZJkgxFMBvFiShq5ITPgbO7M3lK5LRaUsIUkiTRsm8TQgPDbPZ6WkOjlTm943yKjKWiomIaVejvA6fTz+3YvnCvZWVjCco3Km3yVN1Pq1O7fVVunLpNWFA4mfNk5Pb5+4z6ZJLNa4h31b8alCtJUiIXfrz2iTlkjUyzL+vT67cv6PfHl6yevJENf27jxdNgtA4aarWvSvsfWpGneE6b12aOBl/UZv7QZegV01siskam2VcNE8WYvI8UKJuXf+7M5MiGk1w5ch1Zq6Fs/RKUrlM8Wd4hSZJo/0Mrfu/5p9k2rp4uzDr9K+mzpePOhXusmryRiweuotFq8ErvQf6yeaj7WQ1qtKmMo5MDE76Yzq4lB+wKjM5dPAdtv21hc2kQBycHYq0qKUs2bacJIYgIjcTByQFHp6SXn1BR+RB5Ix6cmTNnkidPHpydnSlXrhwHDhww27ZLly4v5elf+SlWrFhCmwULFphsExX1dvVH0iI+Gb34afkAs+dljYSHtzv1O9U030aWKVQhP+UalCJb/iwUqVwgyanDhvHi+kqGJ914j0CWvJmo3b6qySd6WWNI4233/UcAuHq40GlEW1Y8nsumiCVsilzK4EX9UsS4gbiA5OkGLRRTqeW5imanw+BWKTKXvfjdecrFQ1d5fOuJXf2EEOj1SYth0Wg1VP+4El/+2onuYz+jTN0SyTJu4ilbv4TZ1HHfLN7MOfcbmXJm4Njm0/QuP4hdS/YTERqJXqfnxbNgjm8+w/5VR9DEbVsJRdi9rnuXH9Kvyk84uzuTMWd6q+3Hbf0Jd2/Lnju9Tk+RyubjjKIjo1kyZjUdsn9FK+8vaO72OT81H8vFQ1ftWruKyodMqntwVqxYQf/+/Zk5cybVqlVj9uzZNGnShMuXL5MzZ+KbzdSpUxk/fnzCa51OR6lSpWjbtq1RO09PT65dM07DdXZOfvzGh0iVFuUZOLeX4UlZeqnQKkkSrp6ujNs21GLA7eukz+pLhUalOb7lTJLWE7/107xXQ2KjYtE6aqnYtAyVmpUFIEehbKz6fQORoXEGrQRl65VgwJyvEgnTSZKEo7PpOKPnDwNYP3Mb+1YeJio8itzFc/JR70ZUbVnBpliRZj0bkC6rL/+MXpWgvuvq6UKT7vXoNKKtXb+zlODKsRvM/m4hlw69/F4UrpifnhM7U6JGEbP9Lh66ysqJ6zi++TR6nULu4jn4uF8zGnWt/VY9UBGhkXxX52dCX4QlOifJEnqdgrObE+EhEfzSfjJ6nWLk9Yv/HB9ee4L+1YfyzayeFKlckF1LzT9gmULRK/g/CmB6n7+o06EaK341n0GVJV8mStUqxke9G7Fs/BqTniJZI5MhezrKNy5tcozoyGh+qD+KK8duJPQXiuDktnOc2HqWn5YNoFbbKna9BxWVD5FUL9VQqVIlypYty6xZsxKOFSlShFatWjFu3Dir/deuXUvr1q25c+cOuXLlAgwenP79+xMUFJSkNamlGkzjd/spG//czpVjN3Bw0lKpaTkadqlt9WnUFHuWH2TsZ1OTvBZZI9Okez36/9nT5PmoiGguHrxKTFQMeUrkJEueTHaNf+3ETX5oMIqo8OiEba/4LbB6n9fgh4V97Uo5fvEsmOiIaHyz+LyVrYSLh67yfb2RKDq9UWyQJEvIssTYLUMpW69Eon47Fu9jYpc/kDRSQqHL+O3Bmm2r8OPSb96akbPuj63M6DfPbCy3JEt0GdUBd283pv/vL5sym/pM7cr8n5YRFRGdJP2enEWzc//yQ7PnnVwd+ffpPDQamZ+aj+Ps7otGZR1kjYyLhzOTdv9M/tJ5TI6xeNS/LB71r+n1SYbYuRWP5iTpe6miktZ5Z0o1xMTEcOrUKRo2NC7Y17BhQw4fPmzTGPPmzaN+/foJxk08YWFh5MqVi+zZs9O8eXPOnDHvLYiOjiYkJMTo50Pk2slbrJy4jhW/ruPS4WuJYlyy5M3El792YvK+UUzYPpzW/ZuZvIjGRMXg/yiAyDDzAZcvngYna61CUYiNMR/H4OzqRPmGpaj6UQW7jZuY6FiGthhPVHiUUUxP/L93LTnA+pnb7BrTJ6MXmXNnfCvGjRCCqV/PSWTcwEul3ym9Zif6e/s/DuS37jMRQhhV8Y5vt//fI2xfsDfV12+O3UsPWIwvFopg15IDXD1xI2ELyhoz+y/gq0md0Tq81AECbN62smTcgKHK/cH/juHo7Mi4LT8xcG4v8pfJg6uHC+mz+dL22xbMPT/ZrHGj1+tZP3ObeeNLQGxULDsX77dpvSoqHzKpukXl7++PXq8nUybjG1CmTJl48sR6fICfnx9btmxh6dKlRscLFy7MggULKFGiBCEhIUydOpVq1apx7tw5ChRIvK89btw4Ro4cmbw3kwro9XpePAlCo9XgndErRWIWTOH/KICRn/zG1WM3ElJoFb1CvtK5GbH6O5sNhOcPA/hn1L/s+Gc/sVGxyLJE1ZYV6Di8LflK5TZqe2zTqWStWQg4sv4kjR3b4+btRoNOtWgzoDkZsqdDURTO7r7IlaOG91O2QUkKlc9n89gHVx8l6JkFA0yC1b9vpGWfxqn2N0lJbp65w92LD8yeF4rA79ZTLh68arRVteWvXQgLGc+SLLFm2maadK+Xksu1mbCgcKz5l0MCQnFw0GKrhLGskbhx+g5zzv3G2ulbOPDfMWKiYvDN7M39K4+SvWaNVsb/oUHET+ugpUn3ejToXItDa09wbPMpnt73Z/fSgzTqWhvvDF6J+ocGhln+bGLwAt25cC/Za1VRed95I1lUr98khLAt0G/BggV4e3vTqlUro+OVK1emcuXKCa+rVatG2bJlmT59OtOmTUs0zpAhQxg4cGDC65CQEHLksF5nJ7WIjYnl30kbWDt9c4KnI3fxHHQY9DH1Pq+RonNFhkXybe0RPL1nKGL4qsfi7sX7DKw1gjnnJuHh425xnKf3nvO/ykMIDghNeNpXFMHh9Sc5tuUMv24fRvHqRRBCcGjtcU7vvJDstYcGGmIvQvxDWTNtM9sX7uXbeb2Z+8NiHt3wQ6OVEQLm/7SUYlULMXzVt1aLQ4IhW0vjoEEfayagVsCTO88Ieh6CT8bEN6F3jSd3ntnUzu/2UyMD59bZOxY1XYQiuHvxPoqipIhC8LWTt9gwcyuXj17HwdGBKi3K07xXA9JnS2eyfY7C2Xh4w8/Iu/Q6Qc+CiY6MsVngT69TOLb5NF+Mak/f6d3pGxcoHvjkBZ/m7GVxLpvG1yt4Z3zpNve7/ZRBDUfjd/spskZGCMG+lYdZMHw5gxf9j1rtqhr1Nxcv9jqONupXqah8yKTqFlX69OnRaDSJvDXPnj1L5NV5HSEE8+fPp1OnTjg6Wv4yy7JMhQoVuHHjhsnzTk5OeHp6Gv28LXSxOoa3nMCCYcuNtnHuXXrI+E7TWPTzyhSdb+fi/Ty+/RS9iQu3Xqfg/zCAgbWG8+9vGwgJCDU7zox+8wj2D010A1D0CvoYHeM7Tyc2Vsf4TtMY2cb2FHFbUfQK4cERjGozMSE7SK9TEgy2q8dv8F3dkTbV+bH1Zp0UzZS3gYevZeM0Hs90HkavHZyslzbQaDUp4sVaPmEtfSsOZuc/+3lw9TG3z99j2fg1dCn0Def2XTLZp1nPBjYZHLuWHMA7o6fNAn/+DwPokK0no9v9xvOHAYChlEWjL+yvg/U6Do5aarQxPHzFxsTyQ4NRCRXSFb2CUARCEehidYz5bApXjhlfs1w9XChZqyiyxvw69Do9VVtWSNY6VVQ+BFLVwHF0dKRcuXLs2LHD6PiOHTuoWrWqmV4G9u3bx82bN+nevbvVeYQQnD17lixZsiRrvW+C7Qv2cnL7uUTxEPGvF4/6lzsX76fYfDv/2W/VeX/34gPmDlpMh+xfsWz8f7x4zUXu/yiAYxtPm9WgURTB07vP+bnVr+xedjCFVm5iHr2CEneDeB29TuHB1UfsX3XU6jil6hQz773B4HHMWSRbIoPgXaVEjSJ4W/E0uXu7Uba+cZBxxaZlLXpwNFqZys3LJdvAObn9HPOGLAEwMrQVvUJMVAzDPhpvMlOqfMNSNns0Fb2Cb1br3rtX2x9ce5z/VR5CgN8LAPpO756QqRcfn2OvwdNpeLuEuLUDq4/x5M4zkw8XCIMB/e+kxIVsP/uxtVkRSY1WpmD5fJSuU9zkeRUVlZekug7OwIED+euvv5g/fz5XrlxhwIAB3L9/n169egGG7aPOnTsn6jdv3jwqVapE8eKJv8gjR45k27Zt3L59m7Nnz9K9e3fOnj2bMOa7zLqZW5EsmBwarczmOTtTbL6QwDCrcQxg2I6IjY5l/o/LaJ/tS8Z+NoWg5wZD5+F1P5vq5hzfcsauGj0pjSxL7LYhBbhKi/JkypXB7BO/EIJ237dME/E3YPCydB/7mcU2X4xsn2j7o1bbKmTIkc7s70FRBG2/+yjZ61v123rzv2tFEBUWbTKYWZIkvl/Qh3INSlmdIyQgjOCnwdT4pLLVtvEoOoUXz4L5Z9S/gGF7aNTaQfy+fxSNutShassKFtPrX+fL8R2NtI+Objxp0auk1ykcWX8i0XerXINSfPtXbzQOGkMWnOalFlTekrn5ZeOQNPPZVFF5m6S6gdO+fXumTJnCqFGjKF26NPv372fz5s0JWVF+fn7cv2/ssQgODmb16tVmvTdBQUH07NmTIkWK0LBhQx49esT+/fupWLFiar+dZPPg2mOLxoJep3D3svmAUXvJXiCL3bV5hF6wb9UR+lcfRlhQeJKLatqLk2vy4goURRASmNgT8DoarYaxW37CK72H0Y0i/qm9zYDmNpeweFdo3K0u/5vRI+FvpdHKIIGTiyNfTepMy76NE/VxdHbk1x3DSRfn+ZA1MpJk8FpoHDQMXvQ/ilYplOy1nTNXdT0OgeDcXtPbVBqNhoLl89pUviM2RseBVUdp/31LNFrZps+9olPYvnBfwtbmlaPXWTtjC/tWHeHk9nM8i4tds4VW3zQ1+jzFRMVarVul1+lNXg8ad63Dsgez6TbmM+p9XoMm3esybutQZhwflybiwlRU3gXeSJBx79696d27t8lzCxYsSHTMy8uLiAjzsui///47v//+e0ot743i7OpEbJT59GdJlnD1cEny+OEhEexcvJ9jm08TGx2Lu5ebxZuLORSdgt/tp6yZupnPhrYmXVYfAh6/SPK6bCE6wnr8jCU0WpnsBW3bpsxZOBvzr0xl+8K97Ft5mMiwKPKWzEXzXg0pXq1wstbxtviodyMadK7JobUn8H8UiG9mb6p9XNGi4GD2gllZcH06B1Yd5djmU8RG68hfJg9Nute1KWDbJqx5/wQWjf58pfPYHPwryRKnd51n3uUpbJi1nR2L9hISYNnojYmKIdg/lKMbTzGtz1wkXpYJeXLXNgMnY670iSQC8pXMzZH1J81+/+K3Qs3FQflk9KLDoFY2za+iopIYtRbVG6Z2+2psnrvD9L48Bpd9zU+SplJ669xdBjUYTXBACBKG+4r0StkDe7ePFL3Cxjnb6TSiLR2HtWXq13OStK6U4lXBNFPodQrNvmxg83ju3m60/qYZrb9plhLLe2OI2GuIyH9Bdx9kTySX5uBYA0nS4OLuQv2O5stqmMLRyYF6n9dI8Qy+eIpXL8L5/ea9OJIsWdwKqtqyPF4ZPAkJCLUqzicUwY3Td9A6aOn12xc4uTiy/Ne1Vg2kXqW/S/D+iSTss370daNEx5r0qMs/v6wyv1YErf7XNOH15aPXWT9zK5cPX8fBSUvVjyrQ4uuGZMyZwewYKioq5lGrib9hWvdvhtZBazJDR6OVyZo/s11xBPFEhkcxuOFoQ7CmePnQnHBDEODibn8pi0C/IBRFoVnP+nQZ3SEhJuBNxgDEbzUUrVyQYtUKmc1uatytDsWrp03viy0IIVBCxiMCWkDEEojZC1GbEC96IgI7IJTkiSumFm0GNLdo3Dg6O9C4W12z/R0cHRi6fABaBw22fuwiQg0ilHU+rW6T98eWrU1LNOmRWCsofbZ0DJj9FUiY3C6r0qI8Tbob3veSMav5pupP7F1+CL/bT7l/5RErJ62na5H+nN6VfMkFFZUPEdXAecNkL5CFcVuH4h6nO6N10KBxMAQQ5iicjYk7hydJDXfPskMEPQ8xeyPRaGVK1S7Gj8v62zWuRqtBlg0Gzec/tWHpvVl0Hd2B3MVyJDul1ha0DhpqtavKmE0/Mnn/KCZsH8YnA1vg6vlyG88nszc9f+3EgDm93u/gy4glEDE/7oXe+P+xFxFBA031eutUbl6OTsMNteReVQ+WNTJaRy0///eD1Yy10nWKM+PYeApXLmh1Pq2jNqEoZu5iOajzafVU/ay6ebma1ZFq3K0uk3b9TAkThvedC/fZu+IwJ7aeYcGw5UDiLLPY6FhGtJpASKB5CQcVFRXTpHotqneRd6EWVUx0LAdWHeXq8RtoHbRUaFKGMnWLJ/kGPfKTSRxae9yiC1/rqGVL1DJWTd7A7O8WIcuS2XTUV5l5cgIFyuY1Onb91C36VBicpLXay6KbM8iS11g3KToymgfXHqPRashZOFtClsn7ihB6xPPaoDy12E5KtwnJwXyV6rfJhQNXWDdjC5eP3kjYgvmod6NEf1tLREVE0z7rlwYPjYmPrqyVadCpFt/NexnzFxMdy4y+f7H17z1W433sRdbItP22BT3GdzQ6HhsTy6E1x7l48CqR4VEcXHOciJAIk2vOXjArj289sRgrV6NNZfr/2TPNSBeoqKQW9ty/VQMnDRbbfHrvORtmbTPo6SiCkjWLcvfifc6ayUSJR5YltsauQJIkTm4/x8pf13Jm90Wr8xUom5c/ToxPZHyNaP0rh9eeMN0pCTE/5pi8byQlahRNmcHSKCL2OiKguZVWMpLHd0huPd7Imt4WB/47xuh2vyGBkYEua2XSZfFhxrFxJgOknz8MYM20zSa1Z5KCLEtkyJGeP06Mxyv9y+vIjdO3Gdp8HIFPgtA4aFBeq3KeVLQOGjoM/pjOP7ez+iDkd+cpW+ft5uENP9w8XanZtgpl65dIEUVqFZW3iT33bzXIOI1xbNMpfm4zySB6F/fEd/fyA4ResWhUyLJEgXL5Ei6M5RuWQpKwycC5cfo2rdN1JSY6Bu8MXjTuWpeP+jQyzGVuzhQ0mwfWGkH5RqXoNekLMuRMn6wss7SL+cy7l0ggkpeJlhao0boSE3eOYNHIlZzfdxkAR2cHGnSuTeef25rN/sqQPR11P62eYgaOogi+GNneyLgJ8HvB9/VHEhkaBWBRUNJedLF6/hm9Co1WQ8dhn5htt3zCWub9uARZNpSGkGWJLfN2UaRyAX7ZOARPX9ULpPJhoHpw0pAH59n953xRsB/6WJ1N4n2vM3jx/6j3uSHD5r+pm5g1YEGS1iFJ4J3JmxdPgpLU/1Uy5cqA/6MAs1llpihRowif/tiaCo1KJ3v+tIJQwhHPqgBRFttJPnORnGq9mUW9AwQ9DyYiJBLfLD44u1rXa9LF6uiQ/SuCn4cke25JAp/MPsw9P4lNc3axee5Onj3wt1+WQcIoNd0ajs4OrPSbi5uXW6Jzu5ceYFzHxPX4wLCdVqJGESbt/tm+9amovEPYc/9W/ZVpiI2zdxjq2Vjw0oBxIOerLB69isPrT3D1+I0kGzdgyNAKehqU5P4JSDD4n/8lpMHaGn506fA1fmwyhs1zU07x+V1Hkt3AtTVgLtZIBjkrOKZOqve7incGL7Lmy2yTcQOGCt9tvzWvzizJUoLwoTWEgEC/F/Qs9R1/D1vGk7vPkqQ5ZRjL9ieWmKhYjm48bXKMJWNWm92+UvQK5/Ze4tcuM2yq2aaiktZRDZw0xKkd5y1eQBVF4Jneg3xlcps8//iGHyM+/pXZ3y0yawTZSkr4/UrUKErxakWYe+E3vp3X2+aikfG/g2l95ibUEfoQkNy/A21BEn9tNSC5IPlMR5LUr7Q16n5WjQLlDEHz8bZAfJZV/tJ5mHPuN1qY0LUxR6BfkFV9HosIaNWvCWBbgVdJlgg1kdb+9N5z7l95ZNVY2rF4Hz81H4cuVpe09aqopBHUq2EawpanvNDAMK6fuG2mv+H/lw5fs2tLKLXIkMMXACcXJxp3rUO6rL529ReKYNvfe1Jjae8kkuyO5LsUyX0gyNkw7G24g2sHpHTrkBxKWB3jQ2fz3J10zNOHW2fvxnk6DAaFi5szA2Z/xbQjY3B0cWTXkv02j5kSu/ze6b2YengMlZqXsz6fIsiSN2Oi47HRtsRpAQLO7r7IriXW67apqKRlVAMnDVGqVjGr9XWsPkkKG9q8Ic7vvWz0OkPO9HbXzbqXgnW70gKS7Ibk3hM54x6kTFeRM51G9hyBpM35tpf2znNy+zl+/2p2QoC+ECLBOImOjGbdH1uJjoxhZv+/iQiJfKNrW/X7RjbN2cHV4zdtkjw4t9/4u/PsgT9Xj9/Ewdk2DS1Jltjw5/YkrVVFJa2gZlGlIVp83ZD/pm5KkbGslT14E7yuHtu4a12Ob0ocW2AOSZZwcnkzhUDfRd5rUcNUYNm4/8x+7vU6hdvn79Gr9Hc2159KScJehLFz8X6bY3j+nbieh9ce8/3fffi9558c/O+4XZ4koQge33yS1OWqqKQJVA9OGiJrvswMWvQ/ZI2cSBHWbt4BJ47ja0+bVT8qT+k6xW2KQwDDTanax+9+BXmVt094aATn9122bNRLthfXTA3sDVA+sv4kfSsO5tDaE0naJnP3TpyFpfJ+IIRARB9CefENin8rlMBuiMi1iA9ARuJVVAMnjVH30+r8efpXGnWpQ8Yc6UmfzZdi1QrZNYaTqxPfze+NRisbGUdv2iNQvmEpo9carYbRGwbT9Mv6aB0tOxc1Wpl8pXJRvlEpi+1UVAD+tCVr8B0w+u3l8a2nScrckjUGxWeV9w8hdIjggYgXXSF6O+guQ8xhRPAPiICPEfqAt73EN4aqg5OGdHDMce3kLfpWtL1sQo/xHWn/Q0v87jxl46ztnN17CUmCsvVLEvoinI1vaG9+xrFxFKqQ3+S5kIBQLh2+xsqJ67h48Gqi894ZvZh1+lfS2xmYrPLhcfPsHb4u+8PbXsY7g6yR8fB1Z+6Fyfhk9Hrby1FJYUTYDETYdExb7BpwKI+cbvGbXlaKoSoZf2AULJeXHIWz8fDaY4uuaidXRzqPaEfb7ww6IFnyZOLLXzsZtdHF6jiw+miKCKHF82rcQ/y/e0/pata4AfBM58GTO89MGjcAQc+C2Tpvt0VFVxUVgK3zdiNrJBT9B/csh4OTlthoHbJGRpIM27pZ82dm5JofVOPmPUSIGET4Qsy7I/UQewwRexXJIXEB2PcN1cB5D5Akib7TujGkyRizWVIf92tK11864OJuucyB1kFL19EdmNJrjsX5bHX8NexSG6EITu04jxCC0rWL0apfU4paqQodGxPL4lH/Wmyz4te1tBnQzOp7UvmweXr/+Qdp3EiyROGKBfhqUmdO77yAolcoVq0QpWoXUwPU31d010AEW2kkIcLngedwJPn9LtuhGjjvCWXrl2Tclp+Y0W8+D64+SjieOU9GvprUmeofV7J5rGY9GxAVHs28H5egi9Eja2UUnYIkS9RqW4Wo8GjO7LlIVJjlsgEAn/74MdnzZ7X7/Vw4cNWkmNmrRIVHc3L7eWq0tv29qXx4eKbzsM2Dk4IFYt8E1t6TUARNutejUIX8Fr2lKu8RwpZ4LAFR6xBRWxBu3ZDcv0GSrEsTpEVUA+c9omz9ksy79DvXT93G/2EAPpm8KFypQJIqCLcZ0JyGXWqz/98jPH8YgE8mb2q1q4J3BoNb++fWv3LIXCXxV3B0crR7boCIkIgUbafy4VLvsxpsX7DXekNhiO0Kemb+CThjzvT4PwpMckkGk0hxXlE7ZBscnLSM2TiEeT8u5cbpO4nWI2tkCpTNQ632VVNunSrvPtr8gDPWatYZiIHwPxFKOJLXsFRe2NtBzaJ6z5AkiULl81GtVUWKVimUJOMmntjoWLSOWtJl8SF/mTxGVZMz5U6spGoKW0TLTJGjkG1eH1vbqXy4lKlXgtJ1i9u0LfO6cSNJkqGMgwRt+jdj1ulfKVMvhRWjhe3fE62jhu5jP2PVs/mUqVeSCduHUbt9VaNsSFkjU6dDNSbsGI6jk23CfyrvB4aade2x69Ye+Q9C934KpqpZVO9BFlVKExMdy+Qes9i99KBRrE2uotkZsuQb8pXKzbFNpxjaYrzFcbIVzMLfV6Ymeb+/X5UfuXbylsmnZVkjkb1gVv66+LsaT6BilciwSCZ1n8X+f4/Y1F6SJRycHKjWqiLZC2ShfqeaZM2XOeH8tgV7mPLVbHSx+tRaslkm7f6ZUrWLGR0LfPKCy0euA1C0SkF8M9tWMFTl/UOISERgN4g9hW37rhok9/8hufd+A6tLPmo1cRW7EUIQHhJBRFgkX5X+ll1LDiQKJL5/5REDaw3n8a0nlG9cmqz5M1sUGWz3XctkGR/9Z3+Fk6tjojlkjYzWwYFv5/VWjRsVm3Bxd2HYioEsvv1HQqFNSwhFoIvR4enrTuef2xkZNzdO32Zq77nodG/euAFDUc3X8c3sQ/WPK1H940qqcfOBI0kuSL4LkTx/AW0BW3ogFP9UX9fbQDVw3iOEEJzedYGFI1aw6OeVnNt7yWq2k16vZ+30LXxRoC+tvL+gpWdnHl7zMzt+ZFgUyyesRaPRMGbTj/hk8jI8JMTZGfEKy63+14Qm3esm6/3kLZmLGcfGU61VxQQjR5IkKjYtw7QjY6xmYqmovI6Hrzv3Ltnmjlf0CtsW7EFRjD2I84YsQR+rf2sByV7p3+/MF5XkI0mOSK7tkHxXYj3UVkGSM1tpkzZRt6jeky2qh9cfM7zVrzy4+ihuP1+g1ynkLp6DUWsHkSVvpkR99Ho9v7T/nYNrjhkO2PhJcHDSsjF8CbIsEx4SwY5F+9i38jARIZHkKZGT5r0aUrxaymoshAWFE/QsGM/0Hnj6qhd4laRx9fgN/lf5R7v6rA9djIubM2DYCmqftWdqLM0mPHzdWf5ojhpbowKA0N1HRK6E2OsguyA5NQDnhkjSy+QOJeg7iNoEmPM4ykgZ9iJp0oaRowr9fWCEBIYysPaIBHE+/Suu8wdXHzGw9gj+uvAbbl7GtWd2/XOAg/8ds3u+2Ggd0RHRuLi74ObpSqu+TWjVt0ny3oQV3L3d1No5KsnGWgmQ13Fxd8bJ5eXNIuhZyglgvoqskREIhJVU9m5jPlONGxUARPgCROg4DBsxekBGRG2BsFzguxBJY0jAkNz7IaL3ggjHpJHj9nWaMW7sRd2ieg/Y8tdugp4FmwzG1esUAh4Fsn3hvkTn1s7YYsgQsRMnVyecXD/cKt5vChF7DRE+HxE2FxFjX7VoFdPkKZ7TsK1qA7JGpnG3ukaZiAlbsilM+0GtyFcyN4DJ76SLhzN9p3cnd7HsTOo2k+/rj2Ts51M4vuVMoi00lfcfEbULEToWg9s93miJ+xzoHyICeyDiNHEkbU6kdCvAoZzxIJI3ksePSO793tSy3ziqB+c9YPO8nRY1NIQQ7Fqyn4/7NTU6fvfSA7u0N+Kp3b6qTennQgguHLjC5cPXkGSZ0nWLU6h8Prvne18RSjhEbUDEXgTJAcmxJjjVBBGMCBoAMUd4+QyigCYf+ExH0qqibUlFo9XQ/odW/PntQovtZI2MV3oP2v3Q0ui4TyZvyjcszemd581q4Ugayaon5nUuH7nGsH8H8uDqYw6sOkp4SASSJJG7WHZyFslOhSZlmNprDjP+Nw+NVkavU5A1MnuWHaJU7WKMXj9IVfT+gBDhszFcG0x9BvWgvwkxB8DJUFBV0uZDSvcPQncXdLdAcgPHskZbWe8jagxOGo7BiYmOZXS73zi64ZTVtq6eLkiSRHRkDDmLZKNl78bMGbSY8CD7hPK0jlqW3v/Tah2bhzf8GNlmIncvPkgIEFb0CkUqF2T4qm8/+CKZIvoAIqhfnNs4/jlDB5o8gAz6uyR2J2tA8kRKvwFJY5sOkUpihBD80W8+6/7YmmAsvE7pOsUZ+FcvsuRJHLt28+wdvqk2FF2MzqSRk69ULu5cuI9ix8ODJEt4pffkj+PjyJgzQ6Lz835cyooJa0168WSNTO32VRnyzzc2z6eSdhFKGOJZWSuttODaHtlzxBtZ05vEnvu3auCkYQNnzGdT2Lv8kG2NX5FDkCQJgSB9Vl9ePA0yeYE3hayVGbflJ8rWK2nyvC5Wx/5VR9kwaxuXj1w3rV+jlcmSJxN/npmI8we6zSViryECWgM6Ekd2m3sqe+W821fIHgNSbX0fCjdO32bLX7t4fPspbp4u5CySjdzFcpKvTB6yF8hise+1Ezf5veef3Dp3L+GYh687nUe04/KRa+z790iS1I5lrUzT7vX4fGgb0mdLB0BkeBTtMvcgKjzabD9JklhybxYZsqeze06VtIVQAhHPKltppQWX1shev7yRNb1J1CDjDwD/RwG2GzdgdB+Nt2n9Hwciy7JRtW9zlK1fkt5Tu5KrSHaT5yPDo/ip6VguHLiCJIE5s1nRKTy64ceeZQdp0r2e7et/jxDhf2MwYkz9kqzdFBWIXAMmDBwhYkCEIYSEFLUBEbUelGDQ5kNyaQ9OtVXdoFcoUDYvBWZa18QxRaEK+Zl1eiK3zt7l0c0nuHm5Uqp2URwcHdBoZfassOO7+QqKTmHLvF0cWnucaUfGkjl3Rq4cvWHRuIE4iYid52nUpU6S5lVJQ0jeIGcC5amFRjokh+JvakXvLKqBk0bZu9I2RVaLCHDzckUXoyPSSuFMRVFY+stqnt57zqNbTwkNCMHD152GX9Shdf9mLByxgkuHrxmGtVbTUJLYteTAB2vgEL0V8ymbNiCMM3mE7g4ibGZcKqjOcOzVBvqHiOjd4NQUvH97bwvrvWkkSSJ/mTzkL5PH6Hi9jjWZ88Niq0aJOfQ6hWD/UKb3/YsxG39EH6uzrd9bUFVWefNIkgyunRFhkzD9kCSB5ALOLd700t45VAPnHSQqIprD607g/zAArwyeVP+4YqIU74DHgSkyV2hgGFMP/0L/6sMsenHO7r6Y6FjQsxBWTd7Atr93ExYUbrNLXghBSEBoktec5hFJu/EZkEDz0osmYi8jAj8HEYV5oynuePRmRLAWvMYjSepXPylER8Xw78T1HNt8Gr1eT8nqRWkzsLnR1pCrhwvFqhXm1PZzSZ5H0Ssc33KGZ/efk690bmSNbPX7VbCCGsCfFhBKEEQsRUT8C0oAaDIYPKyunyLJtml8CdcOEL0fYl+X+dAAEpL3VENdqg8c9Sr3jrF1/m5mDVxAREhkwkVtWu+5dP65Pe2+/yhhiyEl07SnfDU7SdlUYLgQhwaG2RVQqdHKZLMS4/Beo80HuhskVQpXcukAGAxFEfQ9iEisb23FEbUeob8PPvORZPckzf+hsn/VEcZ+NtVIZ+rGydusnrKRrr98ymc/tgYMwf/n9iR+ILAbAef3X6F+x5rUaFOZA6uPmjRyNFqZguXykb90HhODqLxLCP0TRMCnoPjxMq37ASJsMkSuAt9lSBrzcVQi9rLBWxu9A5Pxe051kNz7ITmkrNBqWkXVwXmH2LP8EL/1mEVESCRAwsUsJiqWvwb/w6rJGwFY/ftGloxelWLz3rmQvEqy9hg3YHDBN+vZIFlzpmUk145J7CmDQylwbWt4GXsO9Dew2biJJ/Y8InhIEtfwYXLp8DVGt59sZNy8yt9Dl7FkzGpObD3D1WM3UqwI56wBf/PiaRD/m9GdbPkzJ9LIMaSzezJkiZpBlRYQwYNAeULi76xiMHRChpnvG30MEdAOondi9uEo5rhhe0oFeEMGzsyZM8mTJw/Ozs6UK1eOAwcOmG27d+9eJElK9HP16lWjdqtXr6Zo0aI4OTlRtGhR1qxZk9pvI1VRFIW/Bv9jsc3ikSs5s+eiVQ2Pd53a7atSroHpTKwPApdPwLEmiRXj4r6O7j8guX8L0qtFEx1AWwQcKkDsVYP3JvLfJC5AgejtCN3DJPb/8Jjzw2KrDrcFw5bzY9OxfFt7RIoFc4cEhvHvpPV4pfdk+rFxdPvlUzLnzojWUUu6LD50GNSKP89MNFmKReXdQuhux2lbWdhKjt6F0CeuBSiEDhE8EEOMnbkHGgVEOCJ0Qsos+D0g1beoVqxYQf/+/Zk5cybVqlVj9uzZNGnShMuXL5MzZ06z/a5du2aUApYhw0ttiCNHjtC+fXtGjx7Nxx9/zJo1a2jXrh0HDx6kUqVKqfp+Uourx2/y7L7liq6RYVEsHL7crHbH20aSJKtqu7JGpkC5fB90No8kacFnJkQsQoQvjHuiAxxKIrn1RHKub3jt1hURuQZCfgVCQXcNdFcREXNBzgFKcjxvAmIOg7Zdct/Oe0/oizAuxwXQ20qKqW8IWD9zGz0mdMTN05UOgz+mw+CPU2ZslTdL7AUbGgmIvQia17bwo/eBkriKfGL0EL0boQQiyR+21hi8AQ/O5MmT6d69Oz169KBIkSJMmTKFHDlyMGvWLIv9MmbMSObMmRN+NJqXmR9TpkyhQYMGDBkyhMKFCzNkyBDq1avHlClTUvndpB6hNgbd3r344N00bmSJfKVzJfzbHIpeYe4Pi9n81643tbR3EklyQHLrbihyl/EoUsbTyOlWvjRuAPT3IeQXIDzugI6Ep79kGTcJE6TAGO8/8VvGb4voyBim9JqjlupI89iavahF6G6jhP6GEjQIJXQiIuaoHf0V0D9O4hrfL1LVwImJieHUqVM0bNjQ6HjDhg05fPiwxb5lypQhS5Ys1KtXjz179hidO3LkSKIxGzVqZHbM6OhoQkJCjH7eNTLltk2ZVuv47qX4yrJE3U+rM+3IWH5Y0JfCFfNbrdezYPhys/EMHxKSJCPJviYDfkXYX1h2SScTh1KpM+57hk8mL7QObzcfY8tfuzix9exbXYNKMnGshPVbrqOhzpR/Ywj/C6LWQ/h8iFiIXQ8kkpdBEDB8MUroZET4IoSSMpm3aYlUNXD8/f3R6/VkymS8P5wpUyaePHlisk+WLFmYM2cOq1ev5r///qNQoULUq1eP/fv3J7R58uSJXWOOGzcOLy+vhJ8cOXIk852lPLmL5aBgubzIZrwfkiSRPpsvVZpXQKN9t2LDS9Uuxjd/9sTB0YEGnWvRZkALq/EKL54EcfnI9TezwDSIECJO1yY1jECNYTvMoajJeUXMOcPTY8hYRMR/CPF2PRhvG0dnRxp2qf1W1yBrZNbN3PpW16CSPCRNBnBuhfnbrgTawhC1Mu61/pUf+xChkxDPqiNCf4HwvxChYwyvw2Z8UJ7AN/JY8nq8hRDCbAxGoUKFKFSoUMLrKlWq8ODBAyZNmkTNmjWTNOaQIUMYOHBgwuuQkJB30sj53x89GFh7BMTqjdJB47d8+v/Zk4w507N90V7TA8S//Vc+v/F9LaaBx5VxsKRAbAp3bzd+Wt6fsvVLGhXfDA8Kt9DrJeHB9tXB+rBQgOTo5WAozqm/Hfci/g8rg+yL5PV7ouZCCUK86AOxJ4jX0xDoIHSMQSDQqXby1pOG6TKqPYfWHif4ecp7f21RElf0CrfO3E3xuVVSBiF0EHMClBegyQoOpUzejyTP4QjlMSRsOelf/t+xhmGMlCB6yysvdAn/F2HTkCRXcOuWMvO846SqKyB9+vRoNJpEnpVnz54l8sBYonLlyty4cSPhdebMme0a08nJCU9PT6Ofd5HCFQvw+/7RFKtayOh4vlK5Gb9tGJWalSNPiVwMWtgXWSMnFLEEwxOeg6OWX9YP5re9I/n2r6/pM60bQhFWL54OTg50/rkt3nEFNDUOGjRaw1aYk6uTwQB65bsqa2R8Mnkx/ehYyjcsnaiyuK0aN1nzZ7ap3YeIJGlAzprE3hpwrI2UbhWSx2DQ5AacQc4Cbr0NxTq1xga+EAriRU+IPR13RE/ChVGEIV70NlQ9/0DxyeTN7LOTqNSsbIoGyLt5ubLk3ixc3J2ttnVyfb8rP6dVROQ6xPNaiBdfIIL7IwLbIfwbIaITh0xIsiuSzwIknzng1AgcyoFzEySfv8H1cyD1vaUGL45l5fr3hVT14Dg6OlKuXDl27NjBxx+/jPzfsWMHLVu2tHmcM2fOkCXLy5tmlSpV2LFjBwMGvKzHs337dqpWrZoyC3+LFCqfj8n7RuF35yn+DwPxzuhJjkLZjNrU/awGmfNmYu30Ldw+fxdHZ0fKNyxF814NyZgjPQAlaxZl/yrbyjn0nd6dpt3r8emQ1hxed4JrJ26hddBQoXFp8pfNy9b5u9k0ZwdP7z7Hw9edBp1r0bJPY3wyeZscr0TNImTJm5End5+bNK5kjUyhivnJWTibid4q8Uiun1uQYzfbC5CQ3PsYlEzduiK5dbXeLeYIxJ41c1IAAhH2J5LPDDvW8n6RLosPv2wYQuiLMB5e90PWSFw5eoM/+s1P8phtv/sIFzdnytQrweF15p/eJVmi5idVkjyPSuogIlYjQkxoSunvIV50B58FSE7Gmb2SJBvqwr3uEY3aZts33f0bCJtOkmPzRBhEHwHn979uWapvUQ0cOJBOnTpRvnx5qlSpwpw5c7h//z69evUCDNtHjx49YtGiRYAhQyp37twUK1aMmJgY/vnnH1avXs3q1asTxvzmm2+oWbMmEyZMoGXLlqxbt46dO3dy8ODB1H47b4wseTKRJU9ij9TD64+Z/d0ijm06nbCXmrdkLopVLZRg3Dy66cedC/e5e8m2TBsPH0OAq9ZBS81PqiS6kLbq24RWfZvYvHZZlvl2Xm8GNxyNIgmj7TZZI+Po7MA3M7+0ebwPFrdOhjgc3WXT57WFDanjSBicsTqQfZC8JiI52hdALKK289Jlboo4jQ6h++DLPHj4uFOkUgEA9q2wnCzxKpIcr+tlELts2bcxnr4etM/6JTFRsRb7CkVQum6xZK1bJWURIhoROtbcWUAgQsciOa2zbUBtAZuaSU51EGGzgWR4YUTYy3/GXoHonQgRiaTNb/AovSdigal+pWrfvj0BAQGMGjUKPz8/ihcvzubNm8mVy5BS7Ofnx/379xPax8TE8N133/Ho0SNcXFwoVqwYmzZtomnTpgltqlatyvLlyxk6dCjDhg0jX758rFixIs1q4NjKw+uP6Vt5CJGhUUaBYncu3mfoR+Pp/XsXjmw4xZldtugtGHBw0lK6TuIL573LD9g6fw9P7z/H09eDep/XoHj1wja750vVKsbk/aOZ/9PShDpWkiRRsWkZuo/9nNzF3r0YqHcOJcRQqyY+SMoIGcnjO9DkMSibigjQ5genukiSg/1ziXATc7yO3lBH6wM3cOJ5dv95grq4LQhFUPmjcuQqkp2GXWpz88xdxn42xeb+gxv9wlcTO/PJQLWI4jtB9F4QluQ9FNBdQcReR3IoaHU4SZsX4VAhbpvY1IOGBrRFkRyKIpxqQfR2klruBU1OhBJmEA+M3otRzF3IL+D1q7FkRRpFEh9SSHUcISEheHl5ERwc/M7G45hiWMvxHN98xnTRvTi7Q5KsByy+SrYCWQgPDkevUyhWtRAt+zbh5NYzrJ6yCY1WRtELZI2EXqdQoXFphq/6Dmc762AF+L0g+HkIvlm88c7gZVffDxkl6DsLmVQSSN7guwgiV0DULiAWHEojuXZCcrJvO0OE/YGw5vaW0yNlOPTeiTTePHuHDbO2c/3kLRydHajWqiKNu9XFM53lwodLx/7HwhErbC4yK2tkPhnYgi8ndERRFD726UJEqP0xF0OWfEPdT6vb3U8lZRHhiw1ZSlaMDMlnPpKTbX8vobuLCGgPIgTj770GJDekdMuRtPlRIrdCcL8krFoGTV5ItxGCehjEPhN95+O2uX2XIDmWS8IcqYs992/VwEkjBk7gkxd0yPZVslL8ZI2MUBQk2XRlYlsqFrt4OPPZkNY079UQd2+1Wm1yEbHXERFL4iTcJXCqjuT6Oci+iGdVeZkBYQ7Z0C/hYhi3zeTWG9mjv+3r0D9BPK+NeQNHRnL/H5J7H5vHTAssH7+GeT8uNVIHl2QJdy9Xxm8fRsFy5it0/9FvPhtnb7er7lTFpmUYs/FHlk9Yw7whS+1eryRB9kJZmXdpyntnaKY1RNRWRJB1I0NKtwHJoZDVdgnj6h8btqAi/8OQSekIzk0M2VkxxwzFdUUU6O9inwdHA2iRfBcDEiKwrYW2MjhWR/b9y47x3wz23L/fLUEVFbM8veefbP0CRa+gdXIwa8TY8iQaGRrF/KHL+LrcD/g//vCEo1ISEbEKEdACIlcaLlb6OxCxFOHfDBGxAuvGDRgMkldvsHH/Dp+JiNpjqoNJJE1mJI+f4l69flmQDXWwXG0IVk5DHNt0ink/GoyMV9XBhSIID4nkxyZjiYown6qfLquP3YVmT247x6G1x9mxaF+S1iwEPLj6GL/bT5PUXyUFcaoNUmKBzpdIoC1o+LEDSZMV2WskUqYzSBmPg+9yiN4P4X8atq90VwzXCqvGjWT8b8eqSOlWIDmWRkRtxbIysgIxBxBKmIU27z6qgZNG8PC19EWynVgrwYy2IBTB8wf+TOz6Rwqs6MNExF5BhPyE4SL1uoGih7DEOjX2oUFELLCrh+TWCcn7D4Mxk3DQHdy6I/n+gyS7JnNN7xYrJ603klp4FUWvEOwfwp5l5hMX6nWsafdDh6JXGNXuNx5ce2RXv9eJtmB4qbwZJMkZyeMHc2cBCcljiN2eNoPY5imI/NcQ/P/iy7gtKzsfcJ1qImU4YvAgZTiE7DvvpbinCMeq3DzC4C1Kw6jRgmmEbPkzk69ULm5fuG9XjE1qodcpnN5xnofXH5O9YFL1Wj5cRPhiDM8XFrY3JM+4IMak/L31EHPK7l6ScwMk5wYI/XNDQLEmI5L0/umv6PV6Luy/bFHYUpZlzuy+QJPu9Uyez5A9He1/aMXy8WvsmtsQMm4qcNw2HJwdbC7tYg96vZ7TOy/w4OojXNydqdyiPD4Z1Zg5S0iuHQAtInQiiBcvT2iyInmORHKqZtd4IvYyIug70N9M5so0oC2IpEkHmnSJ163NjbCmkCy5g+ydzHW8XVQDJ40gSRJdx3zG0BbjTCfVgPnjqciVYzdUAycpxBzGsgS7ApJb3JNbUkm6g1bSZEjGvGkAYV21W2BdJLPbmE+JiYrhvymbbJ5ar1MM6eIyCDulTGSNTMNOtXD1SNk03nP7LjGh83SePwhIUFbWaGVafN2IryZ1fuu1uN5lJNdPwKWlQZ1YCYxTMi5n0LuxA6G7hwj83BBfk2z0SC5tzJ92aQWhkzC/DS6DS/ukZWS+Q6hbVGmISk3L8tPS/rh5GbYKNFoNkiSh0co06V4XFzfraqgpTbzisYq92GCJyh7g8RPWXcmm0IBDEUTMOYRI/rbk+4ZGq6FAubwJpUxMIqBoFcvBoZIk8fXkLpSsVdTyWK8PLYRV4+b18STZUI+u08/tEo5Fhkdx5+J9Ht30s7pdpigKT+4+w+/2U6NCt9dP3WJwo18IeGSIqYs36vQ6hXUztjKtz7sXaPquIUkOSE41kFxaIjlWsNu4ARBhf8YZNylQf861C5I2r9nTkuyL5DncwgAaQ2BzGkfNokojWVSvEhMVw6G1J/C7/RQPHzeqt66ETyZvBtYazoUDV1JmEhu8QbJGZubJCYQEhOLg5EDB8vlwdErbFv+bQgkaAlFrMX8x04BrJ4ObODyZ6sFyOiS3noaLniQhhGLQzZGcP2jRvt1LDzCu4zST5yRJwsnVkWUPZtuULRj45AV9Kw3h+YMAm+bOmj8zHYd+wqTuMwHjAH83bzfKNyzJ3YsPuHf5YaK+Hr7uNOvZgLCgMHYs3Ed0ZAwA2Qpk5vOfPqFB51pG7RVFYf0f2/j3t/U8u+8PGCqkt/6mGW2/+4ifW0/k+BYz8hNxLLwxnaz51NIqEFcIN3oXIuIfiL0Ckgs4N0Jy7ZioBIrtY+oQT0sDMclbnORl+K679bAa+yNEJOJ5fVCemzirMYiGpt+IJPsmb00pjJomboW0buCY45NM3Qh+bkl4yja+ntKFoKfBrJq8kdho00//siyTPocv/g8DEy6MHr7utPu+Je2+/yhRfSoVY0TsZUTAx5i3ImVItxYCPzNSHU0WLh1AdoeIFXGxPQ7g3BTJvReS1nw69PuKEII/+s1n3R9bjSQSZI2MxkHDL+sHU7Z+SZvHCwkM5bMcvRIMDnNIkkSvyV/Q+ptmPHvgz+a5O7l+8haxMToeXHlEgN8LgwaVDXXkjAcGBHQZ3YHPf2qT8B6n9JrN5rm7TK6jYtOyHNtkOVZL1sh0Gt6WjsM+sX0t7ylCKIjgoRC1CmPlbw3giOT7F5JjBfvHVUIRz5KoOeM+CMmhMEiOcUU+bYuZExErECHDLLSQkdy/QXL/OmnrSiVUA8cK76uB09K7MxEhSY96d3ZzYtKenylUPj9g8BSN+PhXTm47l3ADiP+/o7MDsbE6hD7xx6dln8b0nd49yev4UBAR/yJChmIcbKwBBJLXJNBkMuzJpygyxlo38RfmRXaXd3gfEEJwfMsZ1s3YwvVTt3F0dqD6x5Vo2bcx2fLbVjT2VZZPWMu8IUsstilbvwS/bByCg+NLb2dIQChflvyWoGfBNgsHmkWCRTdnkCVPJs7uucj39UYmazitg4bmXzWkz7QPowK1JUTEyrjvrClkgxhfhgN2ZxwKoTcYOCLCvgVJ3kgZjyVJE0kJ6AixJ7DoqtfkQc6wze6xUxN77t8frn/6HSDoeTB7lh3C/2EA3pm8qfNpNdJnTbo7MHPujNw+fy9Jfau2rMDgf/oZxfE4OjsyZtOPnNx2jq1/7+bZfX/SZ/VFo9Vw4L+jZp8u1/2xlWY965OnRK4kreVDQXJtCw4lTAr9Sdr8iOijdoymwWC4WHteef3mqQeiEcEDIP3OJMUOpGUkSaJS07JUalo2RcZr/0NLgp+HsGryhoRg3XicXJ3oOLQNbQY2NzJuADbP3cmLp0EpkiEpyzLb5u+hy+gObJyzw0jEMFFbjWTQ8rEwrV6vkCn3ex50biMifAHm9+8Vg2c0aiO4tjNx/rWxRIyhHIvsjiQ5I1zaQsQ/2BWDI4IMqdxSEiQcxAusXi+SleTw9lENnLeAEIIVE9ayYPgKFEVBE+cVmTtoMW2//Yju4z5L0hZP6wHNmNR1ZpLWdPPMHRydE8fPyLJMxSZlqNikTMKxTzJ1t3gh1mhlts7fw9e/d0nSWj4kJIfCSF6jTZ90KIjhK2qD4J+mKOhtr0FmjAL6hwYjy860VhVjJEniq0mdafF1Q7Yv3Iv/w0DcfVyp2LQchSvmN5v9tGvJgZSTfxCCR7eeAHDv0gOzxg2Aohc4Ojug1+nNG0GyRL2ONVNmbWkYoYTbkL6tQcSeRsK8gSP0TxBhMyFyDQalYg3CuTG4fApRW+Lqz9kTaJzUelR5QXfbwlwyaHInbex3BNXAecMIIZg1YAFrpm1OOKZTXn7AVk5ch7OrE51GtDXqc+vcXUL8Q8mYM32itGwhBJcOX0OWZbLkzYTfnad2f+af3ffn2KbTVP3I8v6xXqcn+Lllq16vV3h631Tgmoo9SLIvwrmZ4YnQ2gVPf4Hk6QTIhsrkqoGTImTNl5kvRrZn99KD/DtpHat/N6SR5ymRk6otKyIUBV2Mjnylc1O9TWVCX4Sn2NySLOPmaXiid/Nytfqx8M3sQ2RYJKEvwk1uj3X95TNVDwewPZvR/MOp0D1ABLYDJYiX32k9RG2FqN3g/buhtlz0XmxSKtYWRJITB8ELEQWxlwAFtIWQ5MRbOZJre0S0pe0nBeSMiLBZ4FASHKukOQ+vauC8QcJDIhjWYrzVTKcVE9fRZmBznt57ztzvF3Nm9wWjejdFqhSkz9RuFCqfjxNbzzC97zwj6XYHJwezwcHm0DhouHTwqlUDR9bIuLg7ExlmXqtBo5HxslKoUMU2JM+fELrLoLuJ9QtecjwAAqQ3LzPwPjPn+8UJW1Xx3LlwnzsX7oNk+J7odQoe/eaTIWc6Xjx5YXfpB1PodXpqt68KQJ0O1bl0+JrZtrIs0/CL2tTvVJMZ38zn+ObTCR+j9NnT0XlEW7NChx8akuyK0JYE3UXM12zTIzlWNTuGCPn5NePmZT+IhtDxSOm3gfIEEXsZgr+Pi8sxNZ8AXFGijyJJLgb9HdkHEfYHRCx8JTnBEeHyMZLHICT5FUV8x2rg3AKiNph/09HbENHbDevT5AbvGTZVRn9XUIOM32CQ8bCPxnNs82mbXNEtvm7EhlmWg7sy5EjH84cBZu9rzb6sT7FqhcmUOwPf1h5hcSyNVkObAc35ckJHq2ub1ucvNs3dgWLB9f3b3pGUrFnU6lgq1hFKGCJsBkQswGK17+SSfg+yNlvqjf8BcW7fJb6r87NNbSVZQpZlI22apCJrZIpVK8Rve0YiSRIRoZF0LzaAF09eJNqCkjUybl6u/HVxMr6ZfQB4/jCARzf8cHF3Jn/ZPGg0qs7Vq4ioLYigb8yc1YDsi5Rhj8lMJqF/hHheF6vVx32XJGRiiZiziBdd44wcGzw6UgYQz0yck0FbFCndUqRXHmSE0EP4XENZFyXwZVuEifk0IHkgpd+ApMlkZS2ph1ps8x3k3pWHHN14yuZ9dmvGDWDQ3LAw3P7VR6ndoSrFqxcmQ47Ect2votfpKV23uE1ra/f9R7i4O5us4yPLEuUblaZEjSImeqokDTnuKSt1q0dLyuNUHf9DYuGIlTa3NVwTBOmy+dglFmiKIpULMHrdoISsGlcPFybvHUm2AoaMMI1Wg8bBYLRkyJ6O3/b8nGDcPLv/nEC/F2QrkIVCFfKrxo0JJOcm4NYn7tWrvx8JJE8kn/nm07R1t7DFyyqCh6AEtEUJGYfAEbTlbOpnkOc2ZdwAKKC7BBGrjI5KksYgE5HhAFK6DeD+LeaTFfQgQgz6P2kEdYvqDXFs4ykjrQ1rSJJ1KXlrhAaGcWLrWaq0KG9Vaj1z7gyUa2Cb5kfm3Bn5ff9oxnT4nXuXH8aJxwkkSaLuZzX45s+eSUpb/NARQkDMQUNWVewlw5aRc2NDTSqTYlwpjO4+JEHDQ8UYRVG4bGFbyBR6nULw81A+HfIxG2ZuS3JMTuOudXHzMo7JyJI3E3MvTObs7ouc2X0RoSgUr16ECk1Ko9FouHz0On8N+sdo67x0neL0mNCRQuU/PH0ka8ge3yCc6yLClxmMBskVybkhuLRGki3EKkk2ltfQ3zf8xF6EiL9TZtFxiMjlSG6JvfSS5AAOhRBhk0ksJfEqCkSuBY9vU3RdqYVq4LwhYqJibbrpG9zVksXMB3sIePyCM7svGsXomCJznowmM7dePAvm/N5L6HV6CpbPlxDgnKd4TuZemMylw9cMGVhODpRvXJqMOdKnyLo/NIQQiJDREPkPRgJi4X9heJp6A4XGZDVuKiW4cOBKkrabdDE6Og1vy+dDP+Hhtcf83utPrh61r+jio5tPmNbnL6IioshdNAcNu9TGO4MXsixTtn7JBOHCB9cecfHAVZ4/DOC37jMTxf6c33+ZATWHMWn3zxStnHZiLt4UkkMJJO8S9nVyKA2Sj3FRToukQMkGIwTorXhplSCsboOL5IvJvilUA+cNkbdULpsuelqtBjdvN4KeBafIvOmy+LB3+aFEmhyvc3bPJWJjYhP0OaIjo5nZ/2+2/b3XaN1l6pXg+7/7kCF7OiRJoni1whSvVjhF1vpBE7U2zrgB4wtbSl/kzCC5gmP1NzPXe87Tu0nztmXIkS7B05q3ZC4KlsvHteO37EofXz5+DRqtJqFQ6N9Dl/HNn1/RuGsdwGC4zP52IddP3bY4jqJXQAimfDWb2WcnqR7ZFECSHMC9NyJ0zFtchJVsOE0eiD2P+euOBJqklaN4G6gxOG+ISk3Lki6r5T32DDnSMfXwGCo1LYOsSf4FxcPXnfKNSxMaFGbTRfLA6mOAwcX+88cT2TJvdyKj7Py+S/SvPpSQgLRjxacFRPh8kh9j82p/DYbnFwlwBOeWlnu69bZbfVXFNB6+7tYbvYYkQdZ8mZj69RwW/byS9TO3ERkaZTLOzfwYhr+/XqdH0SkIRaCL1fNbj5mc3H6OM7sv8EP9kdw4c8em8RRFcOfCfW7a2F7FBlw7g1vPtzS5jORqocI4ceKjFh+qBJLrZym6qtRE9eC8ITRaDUNXDGRQg9HodTqjLShJkshWIDPTjozFw8edVv9ryvaF+5I9Z8+JnXF0csDT17ZMsesnblL30+qc3nmBk9vPmWyj1yn4Pwxg3R9b6TS8rck2KvYhRJRBgya5yOmQPEeDJhsiaguIMCRNLnBpiSR7IcKLIUInAbG83AbTGGrNuH2Z/PlVACjXoCTu3m6EBdkeRyMEnNt7mXN7Lyd5XnMJsRISEzpPJzoiGr1esXun0+/2UwqUNV+ZWsV2JEkC54aI8DlveGZDhhfWjBOHsuDSDiJNBcnL4FAGXCwbSe8SqgfnDVK8WmFmnhxPvc9ronU02JbeGb3oOOwTZhwfj4eP4ckvf5k89J/9FUCSsipkjcwPC/tSq21lZg1YwM7Fe632kWQJKS4GZ/vCvRafHBVFsGXebrvXpWIOW/7G8W00iY9pS4L3bKQM+5Gc6yE5FEb2GIDsOQzJrXNC4KPk1gUp42Ekz1Hg1gOcW4NzC4T+OURtMkjHqyQbR2dHvhjV/o3MJckS2QtlRaM1n/EkhCDoWbBBuyoJYVy2VFNXsQORilIPRtcSiYRbvLYwku8yq5XBJUlC8hyF5DEI5FfiKSVXcO2C5GshS+wdRPXgvGFyFc3B93/34dt5X6OL0eHg5GByf7tpj3oUrpifdTO2cGrXeSKCIwkNtK2qdIYc6ajRpjLf1hrOzbN3bcrcEopISBMPeBxotU/Q85SJEfrQESIWgyJpCUNGhtkAPwFuX4PuBkTvA/SgLYLk9gU4f2SoRxO5GkV3FXBGcq4PDmUTfbYk2RPhUALCpsVlZhlujCJyOYSmB5+5SA7FUu8NfyC07NMYfayev4cuIzoyJiGDUpIlMmT3pXyj0uxedpCosOhkzSMUwcNrj1NNQcArvQcla6l6VimKNj/ggMGTmhLE//GFYVz3fobvf8wREHpwLI3kYFuGLGBQK3brDq5fxKW260GbxyAmmMZQhf7eoNBffCq1vX38HwWi1+mZ/d0iDv53zGJ7jVamQpMylKhehL+GLLEp9kaSJbLmy8T8K1ORZZnxnaexd/khi5lcmXJl4J87Sat7pQIiag8ifC7EnjQckDOD8sRMaxlkb6QM+5AkJ0P/Vz5LImonIvi7ODGw+GcWncHA8Zlp9NQm9AEI/0ZxmRCmPhvuSBm2I2nUbLiUICI0kvk/LWP9zK0GmZK4y621oP93hW9m9aT5Vw3e9jLeO5TgkRBpueq8VaR04FgFojfy0siRAAWcmiB5T0xT3hZbUYX+3iEeXHvE5C9n0cKjIw017eiYtzcrfl1HVITlJzchBFv/3kO3It/wWc5edMrbh/P7Lhtqy1hAr1No0asRG2Ztt/kCKhRB0PMQNv65AyEEDb+oY9G4kWWJpl/Wt2lslcSIsLmIoK8g9vTLg8qrafyy8b8ldySfeUiSE0IJRAmdhnheG+VJSZRnNRBBfQweHMBQmDOuOGfsOcSLnsaxGZErLBg3AGGI8L+S+xZV4gj0e8HGP7cbZABe+Tu8q8aNrJFBAkdnB3r99oVq3KQSksd3oE2mGKrQxxk38FJ5OO66Hb0NEZK8bC3DtWYqyrMaKE+KoTyrhQj7A6GkHe+96sFJRQ/OpcPXGNRgFLrY14KKZYkCZfMyafcIXNxNu/3mDVnC8glrE8mfSLKEq4cz4cGRxh3i2jXuVocBc3rR2KF9koQCe/7aiU++bcHwVhM4tilxWQmNViZjzgz8ceJlzJCK7YjY64iA5pYbOVQwyKZLLkjOjcC1raHwpu4hIrBD3NaS7X9cyWcBkpOhPo7yvDnor1vp4Yac+YzN46uYZ2b/v1k3c6vFsibvCg061SJr/syky+pDzU8qJxIMVElZhIhGhM2F8GmpNIPWoFCssaxibwqh90MEdIh78Hr1syuDJiuS73IkTcYUW6k9qB6cdwBdrI5Rn0wiNjo2kTdEKIKbZ+6YlXO/efaOwbiBRPcxoQjCgyOp+1l1suR9WQ8kS55M/G9GD3pP7crupQfRWFEuNsffw5YRFhTOsJXf0rJPYxycXhlHgvKNyzDl4GjVuEkiInIZxoHCryODEmAQ3dM/QEQsR4QvQOifGrahFH/sixTVIqJeKfshbHn6CkfRPUIoQWrgcTI5se3sO2/cuLg70++PHvywsC8dh31Ck+71VOPmDSBJTkgujVNxBh3E7E9STxE0CJRnJI4JVEDvhwgZluzVvQnUIONU4siGUwQ+CTJ7XtErbP5rJ11/6YCTi5PRuU2zd6DRyha3ifauOEyWvJloP6glDTrVImeR7BzbdJpPc/QiPCgiyTVtdDF69v97hGY9G9Bnajc6jWjL1vm7uX/lEZlyZ6BO+2oJtWtUkkDsJSzrTCigvw36OJecCIbwOYb6L8K2IHNj9CBeSVeWfV/bDjODfyMEMYAW4dwMyb03kjZPEub/sHmXjZs6HapRuUV5qnxUHhc3tZL8W0FOj+XSCMlE2B/ELnS3IfaohRZ6iN6L0D1E0mZP+treAKqBk0rcPH0bjYMGfaz5m1lkaBRP7jwjV1FjZcirx29aLdWg6BUe3fBj5cT17Fy8n95TuzH2098Tsp+Suscva2UCHhukxB/e8GN0u9+4fe6ewWASsGjESio2LcPgxf1UL05SkJyxrezCq+cVYyPFLgQoIS9fOlYF3RXzzROI99zoIGojInoH+C5RM6zspGStojy998zs91nWSPhm9sH/UaDJ84kw8dGRNRKK3r7ve8ac6Rn8Tz+T5VlU3hyS7I1wqgvRe0gV1XJtIfv7xF60oZEwZH2+4waO+ulOJbSOWpuMDAcnB6PXl49c49ZZ25VDhWLQuIivJ2Mp7sYWr44+Vs+1Ezc5t/ciA2sO4+7FBwnzxIdrndx2jsENR6OL1dm8ThUDklNSg7OTESoXewEh4i6erl2xP6dYDyIaEfSdWTE5FdO07NvYIK5nBkUR/LJxCCsez2H4qm/ROmgxl2jp4eNGkYoFgHjdKkPDQhXy45PJigT/azx74E+H7F8x78elBD55QUhgKCEBoerf1wxC/xwRuRYRsRJhkwFgO5LHQFIrz9+wzWzv39RWv4eD9SZvGTXIOJWCjK+fukWfCoPNN5Aga95MLLg+3aCTIUs4ODnwZYmB3L/yMNmVxBNNJ0kUq1aImKhYbp65Y1HnRtJICBueCIf/+y012lROyWW+Fwgl3JCpJPsmStMUSijieX0QIbyxOlOAlG49koOhZpgSPCxOqdT+D5nkuwzJsVwKr+79ZsOsbUzr+xcazcttZ41WRq9X6PfHl7To1TCh7emd5xn5ySQiQiORZdmQffXKg1KBsnmp3roSjs4G/ayStYpSoGxeVk5cx1+D/7H7uiFJBkMp/nqQvWAWPhnYgqZf1lfrTxEXCBwyCiL/w+j7qi2O5D0JSZt8hWehBCOeVSLVtqlcuyB5DLH57yn0AYjnNUjIxjSJo0E0VH5zMivxqEHG7wAFy+WjVJ1i5hWBBRSrWpjuxfrTwr0jzVw/p1eZ77l3OeWNGzBsPeUqmoN+f/RA1sjIFrw5thg3skZm55KkBbC9r4jYyygvvkY8K4t4XhPxtBxK8HCE/mXMiyR7IPkuMsTCAG/uK/hSVEzyHAZO8cGNmrg12LYOEfGvGnhsJy2+bsT0I2Op1a4qPpm98cnsTe321ZhxdJyRcQNQtn5Jlj+aw4DZvchdLAdCEUYenZtn7/D30GUE+r2gzYDmCSUU2gxoTrWPKwGY9QCZQghh9LDz8IYfU3rN4fevZn/w3hwhBCLoG4hcTaKHEd0VRMCnCL057So75gmbSaoZNwARCyBqvc3NJU06cGmN+WuCBK4d3opxYy+qBycV08RDAkIZ0vgXrp+6naBkKmtlFJ1C7uI5uHvxAZJEgkEjSVKqXVQkWeLLCZ1o+20LLh68wuSes3lw9VGyxixWtRBTDv6SQitM24joY4gX3TFcCF+9GBpqwEjpViFpsrxsL6IhagsicivEpHbZC2ekjEeQZOPMGBF7ERG53pCSDrZfBLXFkXwXpIkLXFrl/P7LfFt7hMU2k3b/TKnaL2Oi9Ho9B1YdZf3Mbdw4fYeo8Cij64u9jNn0IxWblEla5/cAEXMSEWipdpMGXDshe/6YrHmU540NiQWpieSO5DMfybH0/9k76zgrqjaOf8/Mze0g7RZsQUTAfhFBQTEQu1FsxG5M7Ba7E1tUDCwMwG4QO1Ca7bgx87x/nLtxd2/MvbsLC8z381lx554559zdvTPPPPF7HA0XCSFlp8SqsJr61oEF/sGooltTigiKvUyrICs/eDZDqfZL983k/u0aOB2sZGxZFp+/8Q0fPPsJtZV1rLlRT0rXLObesx5r97VSqaOaXpNn5t1LUVcdqxcRPnphFlcedHNWa5keg90O3pHzHjst43Nnz/qZNx54l39/nU9BaT67Hbwjg0b2w5NlafuKRsRCFu+WpKwSwAT/YIziO1qfay9DFg2gTTk2KTEg5xCMgtQ3S5EwsmhHkHIHc5rgH4JRfFu77HB1ZtmCMt594iMWz1tKUbdCdj90R3qs142rDr6Fj1+clTQ52fQYDNx3ey597qykcy/5dylvPPAen7/1DXNmpdM+iscwtSL6VVNShNlXcXQo93lShpJVHkb3r5K/7mSdhf1Byto0hzMUquhOVMCZeKOIDeGZSN1L+tpm9EAFDwDf9knDXWItQaqugfo3aPy5GV1QuWMh54h2CXtmcv9eLneUSZMmccMNNzB//nw233xzbr31VnbaaaeEY1988UXuvvtuvvnmG0KhEJtvvjkTJkxgzz33bBzzyCOPcMwxx7Q6t66ujkCgc5U7mqbJDsP7ssPwpryFM3e+pNGj015stctmLP2vjPm/L4ybt8ErdMqtxzQaNw3H19y4Z6KpHGFFbYYet3tG59i2zR2nPMBrzcrgDVPxyUufseE263Hd25dQ2GUl9AqEP0rRZgF0WeU07OgClPUrWAvBLAXfQC3g59s59qTUAUaOpxcqL/lNsAGlfJB3IlJ1nYNJLQi9iVjz47xSLs4REZ686gUev+I5ENHXA1t4+JKn2feUofz85W8pKymtqM0nL33Kgd2OZfNBvdjv9L3YZrctGl+PhCN8/9FPLPhrEf6czOX6bcvm92//yuq9rTLYS0mbJyfViFgolUrbKg1GF7CWh4EjSPlpSNEkjED6a7dSBvgHofyDnM1ulyHLDgJrPnE/N3sJUnUV2Iu0gvNypMMNnMmTJzNu3DgmTZrEoEGDuPfeexk2bBizZ89mnXXWaTX+ww8/ZI899uCaa66hqKiIhx9+mBEjRvDpp5+y7bZN7tKCggLmzp0bd25nM26S8fu3f7WrcRPI9XPJs+OJhKJcOOxq/vzxn8bXRIQtdurFrge3/iPttk6XtKXsiVAKdjtkR7baObMmfC/c/Bqv3TsNoPHi3VDe+sf3f3PVwbdwwzupPQ2dkuhvpNeysGHpCKS50J4qRnz9dFO8DvHgBHRpt+FQtC3nWB2uqrnfwWCB8BcQHNGmHa4O2LbN1+9+z+yZP2OYBn332IqfPvuVRy+b3GxM02fwlbvedGTo27ZQsaSKT1//khmvfM6Rlx3E0ON2Z9HfS7j6kFtY/M9STI+RdYgqGoliWRam2Yab98qM2YOm8EwSVHHbjBtABYcj1be0aQ7n2FA+Fjv3VIz809t1Zql5AKz/SHodrLkPCR6I8qzXruumosNDVP3796dPnz7cfffdjcd69+7NyJEjmThxoqM5Nt98c0aPHs2ll14KaA/OuHHjKC8vz2pPK6rZZgOjuh9H+eLK1IOcSKXE2HH//lQtq2b2zLlEQq0z3w3TYL3N1+a2GVcTyPGz8K/FPHPtS0x7bDqhuuQJo0opPD4TESEa1h/yvKJc9j9jbw69eP+MLnzRSJRD1h5L+aLUSrr3fnMjG2y1ruN5OwNS+xRSeTkdF2ZqwKSp34zDP5C8izDyjnK8gkT/0LF369e0Y1XhjajgPo7nXh3588d/mLD/Dfz7y/xGY8O2bEyPiRVNfuM0TANEsFdwz6qdD9yBC58et1oaORL5EVm6X4oRBuSegJE/PsUc3yN1r4FUoMy1Ibh/K6+n2MuQxUPS9IhLRXZCgar4IZR/xyzWa42IIIv6xapDk2FC7vEY+ek9yqnoNFVU4XCYL7/8kiFD4isFhgwZwowZMxzNYds2VVVVlJSUxB2vrq5m3XXXZa211mL48OF8/XXy3jmhUIjKysq4rxXJTgfsgOlJ8aNXcNSE0Y7n+/jFT/n2gx8TGjcQczd/9xdvPfw+f82Zx0l9zmXqg++mNG4M00CZikufO5vnFjzIzdOv4LZPrmLyf/dxxGWjMr7g/TV7XlrjxjAUX7z1TUbzdgr8u9NROhbxmBAcDfmXofIvwpEDtvYRx7NL9C9k6SiwnOgwKfD2cTz36sjS+WWM3+VS5v+uq+isqN3ouU1l3ID+zHr83g4R4sskDeLD52cx7bHVs1pSeTeHwIFJXjXB6InKPTrhqyJ12GVjkaUHQO1jUPcKUn0Hsng3pPruuLHKKIlVVjb0jMrgd+7fOzsxP0yk5tEszkuC1KYxbgAErLYVtmRKhxo4S5YswbIsunfvHne8e/fuLFjgrLzupptuoqamhoMOOqjxWK9evXjkkUeYMmUKTz/9NIFAgEGDBvHLL78knGPixIkUFhY2fq299toJxy0v9jtjL21AJCjVNkyDku5FDDl613Zfd8qkt7jh6LuoqaxNKyHfde1SLnpqHDsM70teUS5b7tSbzQZsii+QeTwf0l/QAVAqrYJzZ0SZPSB4IB1v5IR1N/D61yDnYFLrVMSwlzieXapuiCkmp/tdmeDfrdPLtK9optz1JjUVtVmHo/c/Yy9yi3IAkstNZIHH66HvHluxw4i++IKpP8/KULxy5xvttvbKhiq8EnJPBdU8zKvAvyuqdDLKKEl4nlRcDKEPYt81VFbagI1U34LUvhC/jnczVNcPUIU36BJt79YON+gHO8sWLpEvsjgv2T4CpBf+U2AUtd+aDlguIhwtM6dFxFE29dNPP82ECROYPHky3bo1dS7dYYcdOPzww9l6663ZaaedePbZZ9lkk024447WVSoAF1xwARUVFY1f//zzT8Jx7cW8n//jrjMe4pjeZ3D0Jqdx85i7+e3bPxtfX3vTNblyyvkEcvz6d24ajR6dkp7FXP/uZXRdq5S1Nl0jo6et9Pv6l7mf/+rogrv4n6VcdfAtvHr3W2nHOmHtTdfAn+NPOca2bDbdfqN2WW95owouhUBDl3AT7V0xaH+jx4bIl0i5QzevcqY2akd+hdA0HIkPetZHFV7jbP3VmHef/KhNuXbbD+vDk3/dzfj7x7L7oTtS1C0zteJkmF6Ta9+6hCtfOZ8t0+TRiS388f3qm2yslImRf7oWtSt+BFV8H6rrdIziu5N205boP/ohJGnYSCE1d+oqpbi1fKjgvhiF10Dxk6Ruyhuj/kWws72ftd/tXykTAiNIvWcLFVi+Ie0OTTLu0qULpmm28tYsWrSolVenJZMnT+a4447jueeeY/Dg1PL2hmHQr1+/pB4cv9+P35/65tpW/pn7L6/dM41Zr3/Jf78t0NVLsfj5gj8X8cZD73HGpBMYfqIu0eu7x9Y8Pe9e3n3iI+Z8+jOmadJ3yNbsuP/2eH36pjRq/AhuOfHedttjJv1qGi7Mt5/yAGtu3JM+g7dq09rBvCDDjtudKZPeSnjRN0yDnht0Z9vdt0hwdudHKR+q6CYkehJS8zCE3otVYXQQIYeGp3+XtEMkNAvKxuAo/u/bGVV0O8rIcbb+akx1RXb9wwzDoOeG3dlix14opRh23P8Ydtz/ePKqF3h0wuSs+8zpuRXrb9mU4xbM9aeUlwDw+Dq/JH9Ho1QQ/AOdDQ6l07WKhWqiv4J3k4QjDMOHnXc6dFjysQm+9sm/aUDlnYCE3og1+Gx5jTf0tcipZ6qd6FAPjs/no2/fvkybNi3u+LRp0xg4MPkfy9NPP83RRx/NU089xd577512HRHhm2++oWfPFVOy+vp90zhuszN56Y6p/PfrAt0EutkFw4raIHDbyfcx9/Om5M3cghz2OXlPznv0NM5+6GR2O3hQo3EDMOz4/zHs+P8BLVzUWToFsvEGGabBsze8kt2CLTj26kPYZLsN9D6a7cUwDXILglz2wtkrvzy8iH56s5dH2acDAgenfFmsRUjZiYCTrsMGyr+ja9w4ZM2Neqbv/9biZcM0MH0m5z5ySqvPwrDjd8cX8DnqKZcM2xZGnjq08fsBI7ZLadyYHoMd99s+6/VWZkQspP497IoLsMvPQKrvdKZcLHU4urVKXcqXjbyTIPdkZ5vNGBuV21pqpS0ozwaokifAXDN2pMGDrSAwPCYOuHyv7x0eoho/fjwPPPAADz30EHPmzOHMM8/k77//ZuzYsYAOHx155JGN459++mmOPPJIbrrpJnbYYQcWLFjAggULqKhoSlC9/PLLeeutt/j999/55ptvOO644/jmm28a51ye/DhjLreedF+rnjGJME2Dl+6Y6nhupRRn3nsiV712AdsN3YYua5aw1iY9GbhPv6z2apgGhpnZH5ht2Xz1zndYVtv7JgXzgtz0/uWcfNuxrNN7LfxBHyU9ijhw/Aju++4m1t+itWzAyobOYwnTodLrjlEoa27qIXXP4sy4iREY1qYdrU6MGDsk7TVhrY17NoanlaHYYXhf7ph5DZsNaJ04WtKjmCunnIfP703ZaiUVa27Sk51GDWj8fpeDBtB1rdKEOT4NN6MDzhze6rVVHbEWI0v3RcrHQt3LUP+WNnAW74rUPp36ZM8mpA/1esCTvlrUyB+H6jodlXcGBPYD1daqXxNQqIIrHasaZ4LybonqMk2H8/LPQxVchur6PkbRjdoLtpxZLkrGkyZN4vrrr2f+/PlsscUW3HLLLey8884AHH300fz555988MEHAOy6665Mnz691RxHHXUUjzzyCABnnnkmL774IgsWLKCwsJBtt92WCRMmMGDAgFbnJaI9y8SvGHUTM175zHFybOkaxTwz7742rRmuD3PQGmOoKa/N+Nwdhvfl09e/yrglxNT6p+K8Sy6t0U3qBuK41NNcE/yDof5tsOc3O74eWH+2w45MVN54VN6YpCPspaMg8q2z6XKObrMs/epExZIKTut/IfP/WJR8UKzaf5vdtuDsh06i+7qJ8zqas3R+GVPvf4cv3vqGmspa/pnzLyI4+kwrBQeMH8GJNzQ9VM77ZT7nD7mShX8txjBj4Sql8Po8XPDkGewY63G1uiBiI0v3h+hckhkqqvh+VJLwr0gUWbxrLME/ibJ5YDiq8HqIzgbrb224+LZHpcmZsxduGysEcIoXvP1AqvVefH1ROYfENQkVsSDyDdiV4FkH5dkwg/mXP26rhjS0p4Gzb+GR1FaldjU2p7hHIc/+90Cb1gSY8crnXLb/9c5lExQUdS3kiT/u4pYT7uXdJz/KaL0z7z2RvcakzoVa3ZHIHGTpvmlGmeDfQ7uHvVujlKGTDSPf6TJLc20w19T6G9HfaWvHcVX8IMqfWDUcwF6yP0R/SD9RzrGo/HPaLGq2ulCxpJIzd76UeT//5yhnxjANevXfmJunX56xBMMXb3/LVaNvpqaitlEhPBWmx2Tyf/c1ign+8tXvPHnV88yY8gViC6bXZItBvRh//1jW2LBHRntZFZDQJ0hZqvCNAd5tMUqTe3Ik/Dmy7BgS9qYze0L+pVB9U8yIiqGKUflnonKSh5XtxYO1QeQEb19U8SSUUZx8n7UvItU3x9rMNJy3DapgAsqbmZDr8qLT6OCsDth2ZqGIYF77uOkG7tuPG9+bQM8NUidrNyJQvqiCSWc+wjkPn8JmAxIntyXjjlMfoGLJitUP6oxI5Cek+i7sqpuQcHItpiZslG87lG9bLYWOlkRXvm1Q/p1RnvV1wnLxo+BtUO42cVRREYcBxprgSyOz7utL2suAtz9GwfmrvXHz76/zeeD8J5iw//Vcd9QdzHz1i6Sh20njHubfX+Y7Tgi2LZvZM+byxVsOvWnN2G7I1jzz732c9eDJ7HTADmnHW5bFjFc+B+Crd7/n9IEXMfPVLxv3akUsvv9oDhftfQ2VS6sy3s/KjoTeJ3X9TaySMUV5tvL1Q5U+D/4hNH52VR7kHAkFV0H5qRBtURQjZUjlpbpIIdm8wQNwettWwZGpjZuaJ5DK8+ONG4DId7pTeiRNeHslYOXsbtiJ2GzAJnzz/o+Oy0EX/bWYmooacgsdyue3IBqJMuOVz/nqne+xLZtDL9wfZcDr973DT5/+mtZNPfW+dwgE/fTeYRNmz/rZsQfIsmymPTadA8e70vwAYlch5Wc267ar0Lo0Hhr0LhJjQCB94rwyu6BKn0IiP0LoE0SiUPdMmp5XDZigfLGkvqaLodhVEHof7ArwrA2+HVE5hyC1aQS/Il8g4W9RvuVbAdGZeHriSzx08VMYhoFt2ximwTuPf8iG26zHxDcvprhZCXf54gqmPzsj4xJxwzR4/+mP6b9X5gKKgRw/Q4/ZjZ7rd+ODyalFVA3DoLayjkg4wjWH3IoVtVoZYrZl899vC3nowqcYd++JGe9npUaSC6DGjwsBeUlfVt5eqOLbEAnrsJIqQCkTe9mR6GtF4r8PqboZggeijPzWL+YcolupSBrtGxVsJluRYA27OkXfORsII1U3oEraHm1YkbgGThvZf9xwvnrne8fjoxGLeT/PZ9N+meu9/P3Tv1ww9CoW/b0E06OfCt548F3yinO54uXzmPnqFzx/86tpnxpfvO31jNc2TIN5c//L+LxVERFByk5uJpTV/Cm+QXwvSSuF3BNRZmnr40lQ3s3BuznYFUjNrQ7OMCAwEpV3AsqzfuN+qbkLqb4XnVAc25vRFfKvBFUCsizFnBay7Ajo+tZq2Vzzvac/5qGLngKa5BMahDL/+OFvJux3Pbd+fFVjUu7v3/2dlWClbdl88OwMopEo+5+xd8JE43T03LB72i4etmXzx/d/8+6TH6X0ytqWzduPT+eEG48kJ3/5J4iuKJS3F1KXJjRsdHEsWqeUD5QWVBRrAYRnpTkjDPVvQs6o1i9JXXrjBlCF16XuQVf/hl4nKRaEP0KsxSiza9r1OituiKqN9N+rD4dcoPuVOC3f9AUyT9atqazlnN0nsORffSOyolajOnBtRS0XDLuaL976pk0aGSkRCK5GF7mURL6AyKckz48xgJaNXwOovHG6GiILxHKiSGxCYBRG0cRG4wZAqm9Hqm+nqVoq9jdiL4GKk9IYNw2EkJrHM9z1yo+I8NTVLyQtb7WjNrNn/sycT5vCDV5f9s+NVsTioxc+5YwdL85KZDOvKJetd9k8rfLx2499wB2nPoiRqmUMEKmPsCBVkvSqSGAf9Oc32fXcQOUcnl3ItmU4KCFm8nF1r5D+tm2CL3nend7HAtKHvQXshWnGdG5cD047cOzVh7L1rpvz9MSX+PaDH1OO7bJWKets5lziftE/S1j452I+f/Nrli0sT/hkZttCJBRJ2+upLVhRi51HOatSW9WR+qnoj06yVgk2EILih1HWv2Dkg28nlJHcnZ1yvbpXoOJ8ByMtlD8+B0PsZVCTTCxScC6qJFD7ELbyo/JORKmWBtyqydL/lvHX7Hkpx5gek1mvfsFmO+i8tk37bUhuYQ41FZlXOUIzkc1TH2DzQb0cNZ/9ccZcnrjyeb54+xtHYWexhUh9xFHlVbp2DqsaysiDopuQ8tNiR5o/yCjw9oXc47Kb3OjiYJCVdJzYi0jfXNMCKQdSeHCMUhwVMBjOvc2dEdeD00703WNrbnxvAgP22S7l09OhF+znqErizx//4bwhV3DYuicxfpdLeXriS2ndzvW1oax71jR6nxLc7wzToM/gLem1krZRaHdsJ11/bZR3a1TOQajAsOyNm/BXSMW5pL8YGTrkFNgj/nD922nOzcTjZ0PN3ciyoxHJQDtnJSZZA9vmKBU/zhfwae2YNmqaKRRTJr1FJBxhxiuf8/Idb/D+M59QV1MfN27GlM8Zv8ulfPXOdxn9OtMZN0rBWpuuwZobrX6VVCowGFU6Wcs4NHg6jDVQ+eeiSh5GqSyV8SNzQSXIrYnDC4GhCV9RRlecaGxJzTOIlcJbFBhKag+OAd7tVvqQtOvBaWfOe+w0Lht5Pd9+8COmx9RVFrHriOk1+eq97/n5y9/5/bu/CNWFWGPDHuxxxC4M3LdfY17NX7P/4fSBFxKqdZjsFsMwDAzTyKr/jdiiQ1Ai1FXX4/Ga2CLYUZs+g7fk4snjV36V4XZCedZD0t1JVBGotiv+Ss0D6OeQVEaK0gmMxQ/oeH/z863Fbd5DPDZEvobaxyH3+Haeu/PRZa0S8opyqS5Prj0SjVhs3HeDuGOHXrQ/C/5cxNuPfJD12iLCzFe/4KMXZlG5tEq3fxEhkBfg6MtHs/+4vQnVhbn+qDsR2yYrwY+Gj3SCc0XgiEsOXG0/98q7Far4jljPqGirz1am2FU3Qs19pPMrqLzTUEaSvmPBfZy1b6i9D6l9APIvROUe0XoNowTJPRFq7kq0A0Ch8h32u+vEuAZOO5NbkMMN717G1+99zy0n3BsXv7YiFh+/8Gnc+L9+nMfMKV+w+aBNuWbqReTkB7ln/KOEasMZGSqGabDhNutx4PgRXHHgjUQjmeunFHbJ577vbmL65Bn8NXse/hwfO+7Xn422XT/9yasTwQOh+s4UAwzIOTSugikbRCTWkThdwmMPVJeXWnU2FrFjfXEc3PnMdZzrayBIzROo1cDA8fq8DD9xD569cUrCz6MyFHlFuey4f7wYnmmanP3gyXh8Hqbe907W6y+b39Tyo8HjUl9dzz1nPYpSivySvKxDYaAfioq6FbJsfhmmx9SGuwAiHH/t4ex+aJpcjtUA/Tlum3EjoY9jxg0k9cCoXJ2jl3NU8r2YayLe7SHyWboVAQupuhLM7qjAkNZz5Z0OyodU3w008woaPVGF16B8fdOs0flxDZwOQCnF/N8WZpScN3vmz9xy4r2ceMMRfDHt28wiB+gQ1T4nD2XAiO146KfbmHzdy3zw7AzHaseGafC/w3YimBtg6LG7Z7b4aoYye0D+ObEyy5YlKyZ4NkBlG6OPQ1+k0mLPh+hv4Is3cAhNh2jqnDBQuly86A6k7PhmlWHp1vwPkUha5dVVgcMuOZBvP5zNT5/+EpfEb3oMDI/Jpc+dhc/f+ueglOLoKw7mjfvfzVg53AmPXPYMex3/P0yviZXFAw1oHa8jLh3FBlutwweTZ1BTWcsaG/Rgz2N2pcuaK3f+RWdCah9Hh4RSFCYE9kPlHp1+MofVWxqFVE9KbOAoBXknaW2e8IdaydhcB1E5EPkcifwAvj5aMHAl9eK5Bk4H8dLtU1EKx25jsYXpz85g19GDMjJuGtzWux+6IzsdoJ8ie67fnXH3nEjZwgpmvvpF2soqwzTILcxhxEl7Ol94NUflHgdGd6T6LrB+ix0NQM6BuloqkYZFpmsoA/FsDNGf041Equ9FlcT3KJO6yaS+qMbOzj9XN9AseRLqX0IqrgDSGcZeVpfLRyDHzw3vXMqUu95iyqS3WPDnInxBH7seNJBRZ+/DepuvnfTc4m6FDD5iZ955/MN2N3LqqupZ8m9Z1pWTylDk5Af532E7EswLZlWW7uKQ8Nek/hzaEP0u7TQSmgGhTDyCAtHZiLVAP5glQBm5EBiGWAuR8tMh8jXS2CjT0r21iu5COeid1dlYPa5Qy5loJJq28iIRYgv//pyZ1kzPDbtzwLjh7DSqP3ed/hB/fP8Xwbwgg/br36hWmo7u63bl8pfPpbRnctVLl9ao4HAt2mf9C9SDuWa7N5RTOUchlRelGSUQ/hC75lFd3eQbiPKsDdG/SesBUoUob8ONLYKEPiG9cQOY62b8VCcSAmshqADKTN9zqTPhD/oZdfY+jDp7Hy30ZzgPP556x3HM+/k/5sz6BWXoXk8N/7YJBWtu1COrnDtlKHx+L1e8cl67qau7pEB5HDy4pvaGil2NlJ9KVo18pT71y1Knta6sf2JHmq0R/Q1Zdhh0eTWlMnJnxDVwOgBlqKwvYAVd8tlgq3X544e/k57v8Zo89NNt5BXlkleUy8MXP81B3eMbKn72hpO2ATopevdDd8zogu3ShFIKPM7L/jMmeABUTwL73/Rjq66OXUMV4h8S6zycRvWtmYiXVFwC9Q5FIK1fkchsR/1qxK5Cqu/UnctjjQLFszkq7xRUYOXrb5bpZyUnP8hNH1zO9Gdn8saD77Lo7yWUrlGCYcD3H/2U/UZEK6kPGLEdn079Kqmh09KTnFMQZK8xg9n3lKH0WC+xofnbt3/yx/d/4w/66DN4y6yV111i+HeHuhdIFaJS/l1Tz1H/miORvwSL6/5XqaibkqLBr6U1s2qfhbyVS9XaNXA6ANM06Tt4K7569/uMn642G7AppT2LuWjviUnHjDp7H3qur3tQvXzXG7qEPEs22W5D17jpxChlIDmHQvWNOI9dCoSmgblWmnMUKqhFKiU6D+pfzmANE6mdjCq8PPVO7Gpk2aGxvjvNnwrnIOUnQ8EEVM6hDtdcefH6vAw+fGcGH75z47FD1xnbpjmLuhWy3Z7bsNUum3Hl6Fv4/I2vdSWm0jl5hqEYcdKe1FbVUVNeQ88NejD0uN1Zt3dyg/yvOfO48Zi7+OmzXxuP+QJe9jt9L4656pDGSk+XzFA5RyJ1L5D4gcPQrRUSKRc3Q8JfJTk/HRZIBFKUtkvdlDRz20jdyyjXwHEBGHXOvnzxdmaN87bZfQvW6bUm6/Rak4snn8mtY++jalm1Lv22bTxeDwedvQ9HXTG68ZxHL52c9R79QR9d13YTCTsLEv0H6t9EpBxlrguBvVBGHirnAKT6ViCSwWy2rooyuoG9lNZPjqbWzQnGLqqhaWR28bTiOyEnQWrub23cNOwPkMordXf1lVgOPhUikjSUV1+TOmyQjtPvOh7TYxLMC3LN6xcy94vf+PDZGdRU1rHWJj3Z48hdGjuGO2HBn4s4c8eLqamsizsero/w7A2vULGkkrMeOLlNe15dUd5NoOhWpHw8+rPY8HlQoHJQxfe3qoJsjkS+j7VXyCasaUH9q7qPVdIFytPPLeVZrL1icQ2cDqLP/7bkjLtP4I5T7kckvahWbmEO5z16auP3Ox84gAH7bMesV79k/u8LKSjNZ8tdejNv7nxmvPwZNZV1LP1vGdVlyfU5UmGYBoOP2JmfPv2Fmopa1tpkjZTJki4dh0gEqZwAdc+jjQwDwYLKq5DcE8Cah47PZ2LggK7o2hykKlYd1eCps3X4SsLI4j0Q37axcFY6hdTmKFCpwxYiNtQ+nWZOgboXVzrXdyoi4Qiv3/cOUya9yby5/+EL+NjxgP4cdPa+carE622xDj/OmJtVDk0g10//veObcm663YZssNU6sV5YFp4MW0Y8fc2L1FbVJdyPCLz50PvsP24462+xTsb7dQEV2BO6vgd1zyHhLwET5R8Ewf2T696ATv5ddjSpe0elwkDC36BSGTjmuhD9nZRVXubKl2SspCPqFzs5lZWVFBYWUlFRQUGB8yecbFjw5yJevPV1pkx6M2kDvmB+gAd/uIWuayeW5w7Vhbj37Md448H3iIbTK6umwzAN8kvysKJWnIG0ab+NOPO+E9lw6/XavIaLc+yKS6FuMsmfoDIxPFrg6YvR5WkkMhvCs5DIT1Df4I5uuJilr7RKhCq4GpXCrS52BbKoX9LXG9cO7otReG3G63dGwqEIl4yYyNfvfq9/mw0inx4DpRRXvHIe/YZuC8AHkz/h6kNuzXqtC58ax24HDwLAsiyeufZlXrjlNaqW6TwNX8DLnsfszpjrDkubSGxFLfYpOIJwfXIj2vQYHDBuOGOuby0c5+IMkTqof1f3mjK6QeB/SQsTRGwIz9TdxaPOGzq3xgPBkRiF1yTfV2g6UjYm6esAqvB6VHBkG/bRPmRy/3aTLzqYHut14+Rbj+Ger2+k5wY6b8YwjMa8l3U3X5t7v7mxlXFTXV7Dy3e+wS0n3Muxvcfx2r3T2sW4CeT66d1/YyoWV7by/vzy1e+M2/Fi/vzxnyRnu7Q3Yi1IY9xAcmEwB2Gd6PdI9A+dDBzYO5ZE3FJfJ1PjxgRjDQgOTz1MpWpY2DgorSdoZeKFm1/j6/d+0Em9zX6lVtTGitpcOfqWxlYLO48awM6jBmSlMWJ6Tf79ZT6gvcM3HXc3j1z6TKNxAzq09Pp90zjnf5cTqkvdWqOuuj6lcaPXQffDc8kKqX0KWTQQqRiPVF2v/100EKl9qvVYawmy9ACk7Jg2GjcAUZR/59RDfDuDfxiJP68G+Abq68dKhmvgLCfW23xtHvn5dq5+/UIOGD+cvU4YzEXPjOP+725qTBhu4L2nP2b0GmOYdMbDvPnweyz6e0m7dAlXSrHvqcPiEgibY1s24foID13U+gPn0kHUv53liWbMgEhHGFl6CGJXQd1zaGMmlaeo+b8J1gQw10aVPNbqyVMkpJ9QYyjlB/9upO55E0Ul6buzsmHbNi/f+UbSz6qIUFddx/tPfQzoB50LnzqDsTcdRbd1nTRhbLZW1CavSBuGP3z8E9Mem564Ea9l8/MXv/HGg++lnC+YF8CfpqmmUrhSElkitc/pMLQ0PFTGHlqkBqmcgNQ+2zRWbGTZ8RCd0w4rxx5G/P9LOUophSq6KaZu3CxcpnIh9zhU8b0rpaina+AsR6rLa/j09S+ZctebvHbP21x98K2c0u98Zkxp0qv59oMfufbw2wnHOv1mE59Phojw/E1TsKLJn9hty2bWq19SsaSy3dZ1SYFUkdoASIYF9gJSdgxuXGMZ1L0Ui/un+nuyAS/4tm92zAfefhDcD3IOQxXfh+ryJsqzjq6QqnkEe9Eu2At6Iwu3RBZujb14KFL7LCI2Kvek2DyJngxNPbd3O4fvuXNTsaQqrrVCIkyPyc9f/t70vWmy3xl7seN+/VOc1RoRYZ3eawIw9YF3MIzkXiABXrs3tSFtekz2OHIXDE/yW4IVtdnjqF0z2qdLLMeu+qbUY6puRkR70KT+NbBmk11YWsX/a5SiSh5yZJwo5dHSDd0+QZW+jCp9EdVtJkb+Odk3F13BuEnGy4mqsmrGDbqYf39dEGe0/PrNH1w28nrG3XMCe5+wB09e/YLW0LE6JjUqWR5Qc0SEsoUVGVVguGSJuR6QZehReQBfs6fC5EjN/boBqIM5jZLHELtMd003umiV45bzWUt1+bf1R+s5rN+Ryosh/CWq8FpU0V1IxdkxDQ8PjSEyX39U0e0rrQx8S7wOk3q9/vhxX7/7PS/e6lB/qAEFbzz0LpZl88Ezn2Cn8vAKLPwzfdPVQy7cn49emEVVWU3CB6vhY4ekLDF3SUL4M7CXpR4jyyD8KeLbHionZLmQAqMnmGuCCmqNqcAIrVScySzKBw70rVYGXANnOfHU1S+2Mm6ARnf2nac9SN8hW/P1u22Lt7aXQmphV9e4WS4EBkNlQcyTk8nvzQTfThD5ztlp9kJgoYM5dwTQiqUJVEsl+jdS96wWLbOXpp6u/iUI7K6rR/yfQN0bSPRnrWQcGIzybuFg4ysPeUW59Np+I37+4rekBocVsei/d3wTwymT3kKZGT7UCHz43Cw+fG4mtoPz8ovz0o7ptnYXbptxNTcdfzfff9gUHgnkBRg1fgSHX3qg8/25NGGn9uo1jStHap/JUswPQMDsilH6ZJbnr3q4Bs5yIBqJMvWBd1KGmyzL1nH0LDEMxZob9+SfuZm1emg1j2nQZ4+tKO6WvGzRpf1Qyg+FE5Hy09BuZaduaRuVewxS/zrUPtFOu9FzJkNqHmrWYNTJPg2k5nFUYE+dr5Ozf9qU45WdQy7Yn8v2uz7ha6bHYJ3ea9Fn8JaNx3767Bc+nfpVVh5b27JRKUJTDRimwR5H7uJozjU36snNH1zBP3P/5c8f/sEX9LHVLpsRzHWS7+WSCDGcaY2JKoCqC9uwkgnmBm04f9XDzcFZDlQuraK2hXhWSwzTYMm/y8gvSf+k1Qqlzz/z/rF4vNkrjRqGwvQYHHtVCr0El3ZHBfZAFT8C3m2bHfVoD43KIz5/xQQMVMGVKF9fVM4RZJfD0xwTUKiCCShf4nwYqZ+GVF2Ldhc5N8ISJUqK2Ej9+9hlJ2MvGY697Cik7kXdq2olZ+C+/Rh701EopTBMfXlt+HeNDXtw9esXNlZQzp71M+N3ubRN1ZHpvLXKUOSX5LHvqZklcq+96ZrsdMAO9N+rj2vctAGJ/g0V56cZpbTGTOQ7oC3ijxYq56A2nL/q4XpwlgOB3EB6kViBvMIcRowdwjPXvoxtO08wW2vjnpz1wElssWNvgvnBuFLRTOi2XlfOe/Q0Nu7jPgUsb5R/B5R/B8RaqMNVRneUkY/Y5VD3IhL6ACQM3m1QOYc0dvZVnvWRoklQfhIZJyV6tgIEfNvG5tww6VCpvoes9HhaVHqJhHXH4tB7NOnvGEh4JtQ8BCWPpVR0XRk44MzhDNy3H1Pvf4c/Z/9DIDfAjvv1Z9DIfni8TZfcO055ACuSuf4Q6IeRlHk3MfKL87h5+hWU9HCrn5Y3IhZSdrzWvEmKFvZUBZcjFRe0bcHgaPD2ST9uNcI1cJYDOflBtttj65S9qayoxc6jBrBO7zX57I2v+f27vxxVUBmmos/grdhix95ULKmkvib7p+BDL9ifLQb1yvp8l7ajzO5Ak2yAMoog91hU7rFJzzECu2F3eU8bOdG5ODVCVPHtKHONVsdFbN1cT3lRRrE2srLS4jAhsGf83NW3Qej92HcNN/fYfqO/IeVnoUoezmKtzkXPDbpz3MTDkr7+x/d/8evXCRK0HWCYBl3WKmHR30vS5l9tuXNv1um1ZlbruLSR8EcpGlg24IPiB1D+/ki6nLbGUwbFcu+q9PdGN1Tu8ZBz5CqTsN9euCGq5cThl2rF10R/f4ZpsN2eW7Npv40I5gW5efrljD53X/KL02e/25bw+/d/A/D8za8RjWTv7nZSYeXS+ZDIHFg2KkPdDA8i8e5wkTBSfS+yeGdk8Y7Iov7YS0YidW9muTMzFkJrmL8Oap8k+V3ZgvAnSDSxTtOqxAIHVU0AHp+Hkh5Fjd8bpkGfwVtyy/Qr2HS7jdKeP2fWLxl5g13aDwl9QvrwcQgqr8EuO8OZ4KXRVUs1dJuBKn0N1eUNVNfpqNyjUartt3OxK5CaJ7Arr0Gq70Kiv6c/qRPjGjjLic0Hbsplz59NMF+Lo3m8ZmNsfvth23LJs2c1jg3mBTn26kN5buGDFJTmp5xXKUUwV2sUvPHgu22qoNq0X/IQhUvnxA7NQpYeoj0u+ojDMy1YMgy74kJt2EgEKTsZqb453qUenQNVl4JqXSqeEpWrL8Se9ZuORWaD1KY7EUKzMltrJSTd57qBcx4+hbu/voF+w7bRTXctmy/e+pax257j6GFm2fwyli0ob+NuXTJFInOh7h0cqYRbcyD0FoiDaquCa1DKi1J+lHcTlGdDlGqfDu9S+wyyaBBSdSXUPoFU34ksGYpdfg4i2fbBWrG4IarlyMB9+zH5v/v58LmZ/PVjLDa///asv6XOpxARbNvGNPUfrOkx2eWggbx+/zTsJN4VEWHH/fpj2zYVi7MT5zNMg437bODm3qxESGQ2UnEhRGdnO4P+p+4FBIXybqVd6q28K7HvpZbUiWRKt47wrAeBoajgSJTRMmE+g+TkVZxe/Tei69qlLP4neVgipyDI1rtuzvidL2X+HwvjQtZVZTVUldU4agBvetrnBujiDIl8hyw9nMwShpv/zSf5pZqbgL0IO/JrLBLg06ri7RCWkvq3kMpLmx1pZjzXv4rgQRVNbPM6yxvXwFnOBHL8DGmhBvrrN3/w3I1T+OiFWURCUdbYqAf7njyU4ScNYf8z9uLNh99DbEnqnZk962fW2LgHHp8n44oMwzQo7JLPhU+dke1bclnOSPRXLbLXLlVHAnXPx1SO02HSWpQwVtVVfA/Kv1PqlcTJ36Ygnk1W+XJy0zQ5/trDmXjYbUnHHDVhNFPve4f5vy1MHmZKYdwopVin95oUuZpWyw0RQcrPJ/vO3ymwfobKi/Q6DcfMdSD3RAgemLWhIyJI1W0kt5ZtqH8RsU5FmStXPpcbolrBfDr1K07d/gKmPzuDSEjfAP77bQH3nPUoF+51Nd3W7co1r19IMC95qeZbD7/PObtf7si4GT52CD036I7X76F0jWIOPm8k935zI2ts2KPd3pNLxyJVt8WMm+wqcBLMCNbvpFcMTPD35dkSVfqCA+NGHCu0qnbpwdP52f2QHTnrgZPIKdBh64aQtT/o44Trj2C/M/bS3tt0OTRJ7msiwuhzR7qJp8uTyHdg/UrbvJACvqFQMBGM7qTM47H+QSovQqpvyX4566/YnlN9/lUb+uatOFwPzgqkrrqOqw++Bduy9Q2gAQFB+G76bJ67cQqHXXQA5zx8CpcfcGPWaylDMWDEdpx+1/HuBW8lRuxqCE2j04Rxot8gNY+Bd1MI7IkykxjKkS8Tt3VIgES+W+U9OA0MPXZ3djtkEDNe+YIl85ZS1L2QQSO3Jyc/iIiw9L/0eRk+v5dwKIJCISKYHgMrajP63H0ZfERTF2nLspgz6xeqllXTY72ujaFxl3bEaqek3PCbYPhj+XBp9EUAau5BAiNQ3o0zX8tBqxcwHI7rXLgGzgrk/ac/oa6mPunfr9jCK3e9ycHnj+TdJz5svHBlijIUe58wmJNuOcY1blYwEp2nY9r2Mm0MBEagzG7OJ7CX0WmMmwbqn0fqFVRNRIIHoQouad3cz3FllMHqdlnyB/3sdvCgVseVUvhz/IRqk4ciTY/BTgcOYKudejP9uRnUVNaxwZbrsPeJQ9h0u6aigfee+oj7z3+SJfOacn427rM+p901ht79s7gpuiREIj+332T1UzMYbCJ1z6K8F2W+jrkmTZpUyYiCuX6K1zsnq9eVpJPxy1e/Y3rMlGJfZQvKqVxSxfw/FmVdxr3lTr05Y9IJ2W7TpR0QsZCqiVD7OA3iXoINVTdA3mmQe7Iz49MoISvBvQ5H9FfdZARBFV4Z/7LjKiwb5d85/bDVgGmPT09p3ICWdtjz6F3Zdvct2WvMYCqWVPL2o9N55a43CAT9DNi3H4v/XsItJ97b6tzfvvmTs3a9lFs+vJJN+6UvOXdJjERmI/Xv6IaZtZPbceZIBmMtiGanq6SMIiQwDOrfILGRo0AVQGCPrOZfkbgGzgrE6/c6apTo9Xsp7l6YdSNNp12OXToOqb4dah9r+I7mBopU34ZSBZB7RMJzm6OMPMS/B4QclqBiAEFQ3pgwWHvl7SRDoO5ZJG9sfEKifyfAS9qLtureShxwdSQcinD3mY+kHbflTr3YbOCmALzzxIfcdPzdWFELZSgUilfveTtpvyrbFoja3Hv2Y9w8/Yr23P5KiViLIfwpEAXv1vESB4nG29VI+TgIf4j2gGTSxsQpDsrkQK9v5OsQdt1kpPZ5LR1hdkMFD9JJyCm6iqv8c5HwZ7EGus2vETovTBVep7uMr2QslyTjSZMmsf766xMIBOjbty8fffRRyvHTp0+nb9++BAIBNthgA+65555WY1544QU222wz/H4/m222GS+99FJHbb/D2GF4X6xo8huOYSh699+YvKJc9jhil+w0bhRstcvmbdilS1sRuxJqHkw9pvpORJw9san8M0D5SZ58GEtIV3mQcyiqy6uoLq9AYBjL55lGQX28OKAyiiHnCJJmxILWzil9eKW8kLY3X7z5jaOWK99/9BP7Fh7Jqf3P57oj7yAajiK2YEftxmtLquuGbdl8/9Ec5v+RrtP8qovYtdjl5yGLd0IqxiMV5yJL9tQ90qwFic8RQcpPhfAnsSMW7W/cNBhNTrDAtyOydH+k6nqdCyQVEP0VqboGWbw7dtlJ2BUXI6FP4nM+AWX2QJW+AIGRQLPPn297VMnjqMDu7fSeli8dbuBMnjyZcePGcdFFF/H111+z0047MWzYMP7++++E4//44w/22msvdtppJ77++msuvPBCTj/9dF544YXGMTNnzmT06NEcccQRfPvttxxxxBEcdNBBfPrppx39dtqVbXbfgg23XhfTk/jXYNvCwRfsB8BOB+7Ahtus11hp4RSv38uw4//X5r26tIHQR6QtG5UyiHztaDrl2QhV8hR4WrTVUHmovLOg6weQdy74dwa7EiJfgVGKUXQzqttMKH6YlIZGmzG0Uddy3/lnQ7ChGaCK34NvV+jyMcrjhkoAlv63zPGvyIpYzP38tzatt2Tesjadv7IiYiHlJ0L9K7QyUMKfIcsOQeyK1idGvoHwDDrOI2qAf0SsAW+6a76prwV1b4D1D43hYmj6fymD0Lta96rsGGTZwa3elzK7YxRNRHX7DNXlHVS3WRglj6F8/dr93S0vlLQ05dqZ/v3706dPH+6+++7GY71792bkyJFMnNhaOOi8885jypQpzJnTVCo6duxYvv32W2bOnAnA6NGjqays5I033mgcM3ToUIqLi3n66afT7qmyspLCwkIqKiooKFixGhFL/lvG+UOu5K/Z87RSqW1jGAYiwtibjmL/M/ZuHFu5rIrrj7qTT1//ytHchsfgqlcvoN+e23TQ7l2cILXPIpUXpx2niu5FBXbLbO7IbIj+rmXe/TtA6BOk/Ey0QdVgRFhg9ECVPNzYUNMuPzPmZemYC7QquAaVc2D8XkMzkJp7INygVBwA37ZQcAWGx63oac7HL33apqrJTHlozq2svenKpXHSHkj9+9rASYqByjsDlXdS3FG78ppYPl0HhnxVLogCoyBWTdVcpkHn8YEF3u0g/3zdrsWxx8cEX3+MkkfaedMdTyb37w71V4fDYb788kvOPz++XfyQIUOYMWNGwnNmzpzJkCFD4o7tueeePPjgg0QiEbxeLzNnzuTMM89sNebWW29NOGcoFCIUakrWq6zMTvG3I+iyRgn3fnMjM1/9go9f+pT66nrW3Wxthh3/P3qsF19dU1CSz1WvXsC8n//j6/d+4N9f5vPq3W9hRa1WCcjd1+3KxDcvWi0vWp2ONLF8J+PEWgz1ryH2EpTRFQLDUWYXUHlI5DuofwvsGqCK+Ke3GPZiZNmR0GUaysjR3Yujf0L0xyzfVCqCsXBYs/3XvRjrltzcLVGvcx4qzkFKHkOp5FpPKxtVZdV88MwnLPpnKcXdCtll9EBKezrv6L39sG3JK8qlurxjS3OVodhom/VX2+uE1L1I6goiG6l7vpWBk13JtILiR6D8DJByB5uLrWHX6f35dgUJ6zwZpcC7GQQPwfBtjdRNQRwbN+j5wjOQyGyUd7OM38nKQocaOEuWLMGyLLp37x53vHv37ixYkDi2uWDBgoTjo9EoS5YsoWfPnknHJJtz4sSJXH755W14Jx2L6THZcb/+7Lhff0fj19pkDdbaRHeB3ufkPXnx1td5f/In1FfXs9ama7DvyUMZcvSueLxucnGnwLsdmOuB9TeJ4/QmePugPOu1ekVEdAfumoY8NAPBgqrrkMBIqH8N/WSX7knS0kZO3UuQc0jMve7P8H1sC8GRYC2CmkeBuhbr6oRIVTghLqFR7GVIxSXEu84bsLU4Ws3D0PImspLywi2v8eCFTxINW1rawbK595zHGHXWCI695lAMI32Y2RfwMeb6I7jlhNb5h+2FUgqlFGOuP7zD1uj02ItI+9lJ0OVbedbPwKCIJeoWXIHyD8Bu6ALumNj+wh80O2ZC9CewKxHvrWSXbWLqsJVr4LSNluWvIpKyJDbR+JbHM5nzggsuYPz48Y3fV1ZWsvbaazvbfCdnjQ17cOodx3HqHcet6K24JEEpBYXXI8uOoLUxYoLKQRUmMcBr7oeaSc0ONBhIFtS/gPMqixhVlyM1d8cu7Bnm4US+1fuNfJl4Tc/GqLwzUYH4nC+pnUxCFeRGbKT2Ccgdu9LrNL3x4Lvcc9ajjd9HYxIQgjD5+lfwBXzsceQuVC6rptvapRR3L0JE+Pq9H/huuvambbXL5my7+xbsdfz/MEyDB857nIolmd4U4zFMA1/QS311kye7y1oljLvnRLbdfcs2zb1SY/QEviOlkWO01qmSwD5QlSaEaHQHz4bg3QIVHI3yxO45RlewEz+MOye239C7SOV1qLwTyVw+QiESWqVFNTvUwOnSpQumabbyrCxatKiVB6aBHj16JBzv8XgoLS1NOSbZnH6/H78/w6fVFUQ0EmX6szOZ+sA7LPhjEUXdChly1K4MOWoXgnnBdl/vq3e+44VbX+O7D+eg0InPB4wbzta7upVX7YnybQOlzyLVd+inJgTwQGAYKu90VIIcFJE6bYykJIsUusZu4Zmea0PkixbHDMAPRbej/DvHGSgS+gSpvgciDpL/7cUg1aCcddnujFiWxSOXptZBeeLK53j8iucAbfhuvdsWLPxrEfN/W9jYFPPJq15g7V5rcsUr5zH0mN0YfPhOfP3u9yz4YzGTxj3sqIt4A4ZpILZw7iOnMmi/7fn8zW+oWlpFj/W7sfVumzc29l1dUTkHIKFUgnoGKueguCMiIag4j7TGhFShCq9BmWvEH/dsBOG2GjiNi0DdM5B/OgSGxzy6To2cKMrTu5320TnpUAPH5/PRt29fpk2bxn777dd4fNq0aey7774JzxkwYACvvvpq3LG3336b7bbbDq/X2zhm2rRpcXk4b7/9NgMHDuyAd7H8qK8NceFeV/P9h3MwDIVtC4v+WcLPX/7GS7dP5ebpl1PSw3kcPx1PXfMiD1/8tE5ujnUq/mzqV8yc8gVjbzqKA84c3m5ruYDy9kYVT9IVRnY5GCUJOm43IzRzJZBHt4Ew1NyHCuzSeFRqX0AqL8S5l0jBSl4ePmfWLyybn7q1QlxHFhG+ee/7xu+bS0b8+8t8ztr1Mh744Wbyi/PoN3RbABb+tYjJ17/iaD9KQZ/BW3Hw+SPZOiYVsdP+zsLgqw2+QTq3JTyd1ga/qZtZBg+OOypVN0F4Zvq5JYTUPA7546D+DaTudZBKaE+1YwAiEJ6FKpiAWP/GPKzpvDkGqMKVUrwvEzq8THz8+PE88MADPPTQQ8yZM4czzzyTv//+m7FjxwI6fHTkkUc2jh87dix//fUX48ePZ86cOTz00EM8+OCDnH322Y1jzjjjDN5++22uu+46fvrpJ6677jreeecdxo0b19Fvp0O5/9zH+fGTuUBMhAsa0xYW/LGQiYfd3m5rzZ45l4cv1hVnDcYN0JisfM9Zj/Lr19kpY7qkRhkFKM86qY0biAnzrQxYEPlcJy0DYi1FKhtybpw8TZrg2xmlVg4vazJqKmrbbS7bsilbWM6bD74Xd/yYqw9hrxMGJz1PKYXpMbj69Qt4reZJJr5xUaNx49IapQxU8Z0QPJw4/RcU+P+HKn268XMq1gLs6nti1VNO/q4tqJ+CLBmBVJwL4Y9iUhAd8NAiEZSRpzVriu4A/y5gbkqjJlYcJuBBFd26ymtOdbiBM3r0aG699VauuOIKttlmGz788EOmTp3Kuutql/z8+fPjNHHWX399pk6dygcffMA222zDlVdeye23384BBxzQOGbgwIE888wzPPzww2y11VY88sgjTJ48mf79V96nk5qKGt546L04Y6M5VtTmm/d/4K/Z/7TLei/f8UZS/R3QPW6mTHoz6esuHYtYSxCpW9HbyIwGqfi6F3DuJleAjcpb+VuJrLlxz3adT2zh3afiRVFN0+TMe07kgR9vZotBvRp1sRpCgyU9i7jy1QvYflgffIFV++bVXijlwyi8BNVthpZqKJqE6jodo/hOlFGCiIVdcSWyeBeovoWMSsPtpTFtGujQ9ipebcQq5UEF9kQVTAAlQD2tbvPe7VGlz6P8AzpuP52EDtfB6Yx0Jh2cBr5+73vOHZxGLl3B6XeNYcTYIanHOeCw9U5i0d9LUo5Zp9eaPDj71jav5eIcsRYilVdD6G06X7+p1KiSp1C+7bDLz4b610l/I1CAD1U4ERVcNcKhZ+58CbNn/pz0QSVTeqzXjcd/vyvp69XlNcx67Uuqy2tYY8Me9B2y1WqfV9Pe2JXXQu3DZJ6zlmEBQFaY4OuHKroH7IVavdwoRJYMjxlWLT+DCvCiury80opqdhodHBfnOKoeEYfjHGB6018EPW4Pq+WKWEuRpQfFkoDT3SC1m1m71avp2Atpuk7DgNEFvNvo/1c5OMq9yT8XFTwQZRS2cX+dh9MnjeGMQRcRqg232cgxTIO1e62RckxeUS6DD3ebk7ZEJKwT1/Frvahs57GWxnrIZfP5EjrWyDF0811Viizanka1dHM9sP5MsScLqXkEVXhVB+2r87BcelG5pGfjvhvgC3jTjttql/bRLBgwfDuMFCEqwzTov3efdlnLxRlSc68zXQ4U+HdDlb6A6vYu5J1Bh36U/btB4ICUQ1Te6SilDWIV2IPUZeEm+HfDyD1ulTJuANbfYh3umDWRASO2a3M3DNuyGT52CJZlsXjeUpbOL2vVQ6i6vIYfPp7DnE9/IRzKpPt09ohYSOhjpHYyUv8WIvXLZV0niF2LXXUjsmggsng3ZPFA7CUjkfq3spvQcVPbBKgC2udz2fAw0wyjFHIO10rHoTeJawWT1LhpHAD1qSrHVh3cR/ROQm5BDnuNGcwrd72ZsDmeYRpss/sWrNOrfRRH9zllT6bc/RaibFoGKZVSeHwehp+4amfYdyZELKh7jtQXUwXBQ1D5Z+jmlY2HgxmqmGaG8u8GwQMQoxBqH4kdjcnEY6LyxqFymlWa+AaBZzOIziWxi1xQuSt/zk0y1u29FhNePIerD72V6ZM/afX5coJSsOMBO/DXj/O47aT7G6uz1u61BqPPHcmgkf2479wneOfx6URC2pjML87lgDNHcPAFIzssTCX17yOVl+pwSONm8yBvHOQcsUJ1jETqtFp39AfiPKDROUj5aZB/ISr36Awnrabpbz3TDan056kC8O4I4QQGhyqB/IvA+gmkDuXZGAIjGpOe7fILQFp2/3a6t85jlHYkrgenE3H8tYc16s/EJQ8qncB4/uOnt9taa27UkwkvnI3X70UZzQQUDYUv6OXKV86j2zpd2209lzRIlYOScENf6Ix4qQAJt9SmaU8USEhXm+QeC4ER+hhRQMDXH1okK+rKlAegUWPDE/tqyLm5GeXr24F7XvH89u2ffPBMeuMmmB9gl1EDKO7e5Mkq7l7I0VcdQiQU4eGLn44rPZ839z9uPHYSx/Q6g7cefr/RuAGoKqvhkcue4abj7m7l6WkPJPQxUn5SMx2lhheqkaqrmhm/K4iaR1sbN0BDiEiqJiLW/MzmNNcl+35TDc0skxl9hvbCeDeg9a1YQfAAVHA4Rv45GAWXonIOaarosquhfkqWe1PO28es5LgenE6EP+jn2jcv5uOXPmPq/dOY//siirsXsufRu7H7YTsRyGnfMtr+e/fl8d/v4o0H3+PbD35EKdh29y0ZetzuFHVdtUIHnR6Vg/44phFxM4oSndz++2lEwLsZYi1Alo4CewlxF9XwLGTpaCh+MK4qQ5ldoPQF/XroHZB6lGdT3erBLtOJm5GvAA/KvzMER6HM0g58H8uXaY9+oNs0RJPn4Xh9Hp759z5y8oJYUYv5v2uvSM8NuvPeUx/z8KtftjqnwW4pX5Skn57AtMems9eYwWwxqFfiMVkgIkjVtSRutxEbU3UrBA+Ka9OxvBARpPZJUueuKaT2OVR+Bg+K/l10OMheRva5NC3Pi+Xl+PqDCkL1TYnPqb0fzBLITaBSby8Asg1JCipn9WjP4Ro4nQzTY7LLqAHsMmr5lPCV9CjmsIsO4LCLUudYuHQsSvmQwFCof4PkT2UWKrhP63N92yOhaR2xK6266t0WKR/X2riJ7QkUUnEWdP2wMQ8HYt5H/4A4w0cLAF5EY5dzQCJf6V5bxfejfP064H0sf5YtLE8Yam5OJBxtNE1Nj9nYXw5gyqQ3UYZKO0ciTI/Bmw++164GDtFfIZpOoK5Oq3Qn+BvteMLxYbNkWL9nNKtSXiiciJSNpUHSoM2YG2iBPbsWqm9LOVSqJ0HO4SjlR6K/QuQHwIt4NnC4WMskZwW+gRA8MMvNr1y4Bo6LSydB5Z2M1L9DYoE8A/x7oLwJRNuCI6H6VpDaBOe1BR+q8BaQMgi9lWJu0cZP6EMI7J50Ngl/G1M3TtBwU+qRsjFIybOo8CexrundITgcZZS00/tZfpT0KNah3xQGSiDXjz+JV/bvn/7NyrgBrZn132/t1QogRoKGk60xHI7rCLykr/ZToDL3Lin/rlDyOFJ1S4JWJVlg/R5rnuugd5RUIXWv6Sa5kc+aveABVaw/m6kwNwTrV/3/RhdUzhGQe5w23FYD3BwcF5dOgvJshCp5DMwGwTgD/QRmQGAkqihxcz9lFKCK7wXlp/0+0gaUPIPybgLRv3BUth79JeUIqX00xf5sbaAtHYFUXQc1jyBVVyOLdkSq7+2QnJKOZM+jd00ZnjJMg6HH7J60s3ggN5ECrTMM06CoWzvre5k9HAyyHY5rf5QywD8EbeQkw0IFhmY3v68fRulTqK4fgmdb2vY5a/hbdvgwUjUx1n6hOVGQ8hQnGRAYgdF1KqrbLFTXj/RX3kmrvHpxc1wDx8WlE6F826C6vIsqfhiVfx6qYAKq6wcYRdembGWgfP1QXd6C3JNAZaL70TJ/R3+vCi7B8MW8RSrHwTwWYpch0SZVchHBtuZjV92MvWhArBFguqTIBu9VJPb/UaT6JqTmsdYjI7ORmkeQmoeRyA8O9rj8WH/LdRl63O4kKioyTIP8kjwOOjdxPz6A3UYPbCw0yBTbsvnfYe2rjaM868V0jlLsSeWDP7kHr6PRHbUViXPSTPBsqSv82oLRDaLfslxFOKWaxJ+bBkPJi37PHhoNvMAwVOE1ACijBGV2R6nVTwDSVTLuJErGLi7tidRNQaruAjtVPzEDfDtCeAaNyc3mhqi801DBvZrmEhtZskczyfk0eHqBZ0Oo/whIkgybDaVvYng30GrP5eOaNRUEsMG7NarodpTZvi0TssWyLB69dDIv3jaVUG2o8fiWO/fm7AdPZo0Nk3s7Fvy5iBO2OotQXWvBQMM0Gj1aLcNYhmmwcZ8NuO2Tqxq7k7cXEv4WWXYo+mbb+gavCq9FBfdv1zUzRUIfIuVnxvq4eWgQtsPbF1V8V5vDnSJRZGH7aJGlp8GDm+qhwITAXihfPyT6l66yCgxFeTZcTntc/mRy/3YNHNfAcVlFsWseg6oUaqXebTFKJyN2uTZeVC6Y6yfUMpG6l3XDwBVKLnT9EJbtD9Y8Wl/4TTB7okqnpG9kuhyprarj2w9+JFwfYf0t13GsZTXn01+4dN/rKF9UoZXHRXccX6f3mpxy+7HcM/5R/vj+bwxDIaI9Zv337sN5j51GfnHHvH8Jf41UToDonKaDxhqo/HNQwb07ZM1MEamDujeQ6E+gfCj//8C7Tbtp9NhL9tZJ1x2qHm6gVcrDpPUW+QZhlDzcgXvpXLgGThpcA8dlVUckqpsD2otTjlNdpqE862qPQPgzJPSu1r3xbgqBfeIMBal5EKm6EX1hN0hb0t4RBA6E+udTDFCo/ItQuUcuty11JJFwhI9f/Iw5s37G9Jj02WMr+u6xFYahvTizZ/7MT5/+gukx2W7PreMqsToSifwE1n9gFGvPmVp9sh2k9lmk8uIOXiUAJZOh/IQ01WEmBPfDiIWjVgdcAycNroHjsqojkdnI0pFpRmljgMDeSPmJEPmOpsJKCwigim5ABZqau4q1BKl7DsI/QLi9StMdVJM0bjk/lpOQ4rJlborR9dX22JiLSytsOwrL9ospdXccquhepG4KhF5PPa7kyVVGXsEJmdy/Vx+z28VlNULsUPpBgNj1SNnxMX0N0F6ZmEoxdUj56Uj4Gz028gtSeYXW7mg34yYPAiMBh8KSUk/a0IA1F6m+Z6WrvFqZEGs+Ev4cify8SvycRcL6/YQ+Rqw0JfZVl3W4cQMeJPRhTJ4hBd7t9JdLQlwdHBeXVZJqB2MElA3RH1OOkZp7QZ2ELD0CRzkBjvGhuk1HGfnYta9D5ZkOznGm3irVN+uqs9xj2rZFlzgk+itSeTWEP2k6aG4A+WfFmqyuXIgI1D6IVN8L0tRaQfz/QxVcimpR9i6R72I949pCEKhLtzOI/IQTXZ8V2f+rs+N6cFxcVkXq3yR9CwcTor+lGSMQehcpPwcIkX1fnpYoyDkUZeQDYOTsDUb7Vj9J9R2dqtP1yo5Ef0WWHgThWfEvWH8g5acgdS937Pp2LWJXt6vHSKomIlXXNzNuQP/Nv48sHY1Y8cKFUvscbW+Nks64AbAg+iWpvZXSPsKDqzCugePisipizSd9lYc4kN9vmO8P2sdzE7s5+PdE5Z8d/1LJ4+jKkXZCqiH0cfvNt5ojldeB1NHayI2VrFderiuY2nvdutexl+yHLNoGWdQHWTIMqZ2MSNv+HiX6a4oGoRbYi5Ca++MPhz6mY6unMsX13qTCNXBcXFZFjFJSq7qiK2DazSOTithFWJVAYD9UydOoottaKaoannWg67taTK29kHbU4VmNEWsRhD8k5d+L1EB9+/ZEs6tuQSrOjC9Lt/5AKi/RX23w5kjdS6RTPqbu2UZDSqJ/gv1v1uu1P6buK+WSFNfAcXFZBVHBfUkrEBbcH8z1lsNuRPfEKb5PKzL7+ibNGzDM7uDfibTGmVPMtdtnntUdRx5BE6z2MwAk/C3U3B37rrm3JraPuucg9F72Czh5T1Id81qB1L1I57plWqjco1b0Jjo1nem35eLi0l74BoJvAIk/4iYYRaico7LuzZMx1h+w7DAkMjvtUF3ymso4M8CzGZCmX5MqditM2gujyMEg2+E4Z0jt06Q2dA2k4hLshdtjL+yHXXYqEv7c+QKOVI39oIL6f635zufuUEy0xMPFq1V5eDa4Bo6LyyqIUgaq6G4IDKfVx9yzmQ4Tmd0gMASMNVuPaXd0fympui790MBe2jhJ1ZjTvyuQJoF4NemYvDxQnnVjRmWqnA8TAnu236LRH0lt6NogS3TTSanQyfDLDkNqHnQ2v3cb0uaVBfZpEjE0SljxOS9eCB6IKn15lRGz7EhcA8fFZRVFGTkYRTeiun6AKrgWVXAFqvQljC4v6OaJgFI+VMnDYCyPLtA2hGem1RlRKoAquV+3joi7RMWe5nOOBbFJG8ayF7VryGR1pykpPMlNPvfYNvd6il/QSZPX5mhjSKqu0+GtdISmpx/j3bppO8F9WD45a8kwIPcYjMIrUd7eK3AfKw+ugePisoqjzB6onP11zo2n9YVRedZDdX0LVXg9kLxjebthpZKej+3JuxWqy5uovNPAsymY64B/CKrkCYyC80FZOHuaXpE3pFUL5d8RVXQ7qAZRxgYD0wu5J6LyxrfveoEhZOcxMZHax1OOEJGYlEIqjLgybOXdHMxNnW3BNwjyLgIjmxywRO9Z96ZSOYdmMd/qiyv05+KyCiNSDzWPIrVPgr0A8CD+PVB5J+gLdgyl/BAciUT/gJp7ya4k3GHLBYdP+crsCnmnoPJOaf2adyskXS8sVQTm8unNtLqgAnuCfzcIva8bnqoCCAxGGcXtv1jwQKi+L1YJl4mhakHaXBwbreuUCgGpRGqfQyJfAUr/PVk/kzY5OTxDV5V1eRnKz4xVoDnBQOeWtSi3VwFU0b0o9+85I1wDx8VlFUWkDll2FES+pemCHIXQ20hoGhRPQvl3jTtH5RyJ1D0P9jIyuql4twfPxlA3meRNOA3wboXytENlk/9/YHQFeymJjSoDcg5DuXk47Y5SvvbNtUm2jlEIJY8hZcfpcCMe9N+xg79LFX9rE7scIj8CSv8NGnmIuWaaEKZA6D0k9B5N3iqnnwmByLeoumdRJQ9okcS6V8BaAOGPwS4j/u9WAYLKvxCC+0HdS0j4M33M1x+CI1GG2zcxU9xmm26zTZdVFLvqVqi5h8QGQEzmvevHKCM+10GifyMV42PNNx1g9ER1eQNl5CDVdyPVtyReDwNV8li7VX5I+FttwFFHqydqcwMofQXDWA4hN5cORSQM9W8j4ZmAgLUIwh+R3ItiQs4hGAWXauXjymug/mWaDO8A5BysPYnVt6SYpx1QeVqx278bePuglELsKqT6Dl3mLjV6nKc3Ku9k7SFzSYnbTTwNroHjsqojEkUWDWghQd8aVXANKufAhK/Z4W9h2ZGklZYvmYLh6xVbt6G3z50gtU1jjDUg51AdOgh/Cgj4tkPlHoPy75zBO2tC7Fpk6SiwfiXhTSr3FIz8M7Ka26XzItF5yJKh6L5kLX/vCjBRXV4Dc01k6aGxaqyWRr4B3h3Argbr+wTztCcmYIF3a1TRPSizVL8PCWmPjvK37nklYah/R3uPJKyTioMH6MrH1Ry3m7iLy+qOvSStcQMepLlCbAsM39aovLEpzlfg37PRuAFQSqFyj0d1nYEqugNVcCWq+BHIOQyqb9TueeqBEIRnIWXHI9V3J1sgNbVPgPUbSW9ONXch0d+zm9ul06I8a6GK70K39Wh+CzMAE1V0C8qzAdS9DNHvSezBtCEyA6ik41svxMJakR+QsmMR0d8r5Ud51m1t3ET/QZbshVSMg/rXIfQWUn0bsniXmNigi1PcHBwXl1UR5bCnk/JrKXq7DJS3VZxfco6DuqlgzW19rmczVOHExNMaOY15GhL5HqpviL3SPIchVtZbfQv4tkf5+jrbc8Peap8kdVKzidQ+hyo4L6N5s0GkDupe10JzSqG8/SC4N0qlESN0yQrl3wW6vqNbKYRmoD2C/VE5oxsTcaV2Mg25LUmx/lwOu21cTLecCH0Igd0SjhCJIGXHNMsNavi8xPp9VVwA5tquwJ9DXAPHxWUVRBkliGcLiM4muREQBbsaWbxzLIkTxLMlKu/EWIkuUHV5rGqk1QpandheBEZeyr1IzZM0uukTYiK1T2Rk4IhYYKdTlrXA+svxnNki4W+RshNAymhIRpW6F6Hqeii5H+XdqsP3sDqizO6Qd5qWEkiE9R+dqzEmgInUv4lqZuBIdB5EvgYUItVg/Z3ifAOpvh9V4ho4TnANHBeXVRSVdxJS3rrEWmNqIbW6Z+IPR39Eyk+F/HO1WnDdc0nOF5AQUj0JVXRj6o1EviZ19YkF4a9TTiFiQ/hjpPZ5sP8D1QXwovMwkmGCyk+9tzYi1kL9xN2Yb9TsfUoFsuxo6PKWLnl3Wb4YJWAtW44LpjLiG7BBapDIXKT+dah/Sz8oOMaC8IeIWCjVTv3aVmFcA8fFZRVFBfaA/IuRqmuaHwUs3bE7oQck1jm56gaI/kPqi7YF9VMRuVrr6CTdiINwWaycW6J/6NBT6EO9F28/XfFSc3essWLDfpxo7lio4F4JXxFrAdS9jNgLUEapluT3rJt+ny3nqX0mZtwkyfOQWl06n3dqxnO7tA2VcwBSdT0d78UxoPhpCH8AtY/rBp2piMxGlo5ow3o2+jPgGjjpcA0cF5dVGJV7JAT2QGqf0wm5KhcV2BOpuDrdmRD5kvRKslGwq8BMYeD4d4NoGnE030Ck/i2k/EzitE6sf6H+hWb7aDC20hk3Jng2B99OcUdFBKm+vVmXagNBoPoOJHgIquDSzJ6M699KsxcbqX8L5Ro4y5/gQVD7ZKxJZksjvSE5ORtBy+aY4N8dw78t+LdFjII0/dZEeyCzRoG5rtYickmLa+C4uKziKLMnKv/0uGNin5DmLBvsetI//fp0EnEExLMBKvo72Iu1CJ9/kL4QB/eN6fGkwFoUM26sFmvGJ1mmppkQnLcfqvj2pkaJDdQ+BjV3NTvQ7AZX9wxi5KHyz3GwVgNpGn4CSJoy+wwQazFS+4SuEJJKnXCac4guIXZvenEoIx9KnkLKz4bIZ81fAf8uuv1H7WMk/ttSOrzp7Q/Rr2PCl4mMIUHljmn6zjcQCJJaWqFtRpXKOaJN569OdKiBU1ZWxumnn86UKVMA2GeffbjjjjsoKipKOD4SiXDxxRczdepUfv/9dwoLCxk8eDDXXnsta6zRJFG96667Mn16fKO00aNH88wzz7Sc0sXFpQW62aUDg8HsAvY/KQYowIbyExtni5tVFUPBhSgJp18t/IGeK9twQs7hoAr0Td6/K8q7WashImGk+q4EJzeOgJoHscNfgMrRlTrB/bSibjI8myXxEDQQ8yS1AxL9FVl6WKz8P3aTjM5FKi+Duleg+KFWoo2rO8rsgSp9Aon8ApGvAENXW3nW0RVL1r8QeodWoU/PpqiSR3SyfvTvWGVTQ8hWYl8eVOF1KN82gM7HYtnRQDjJZvJjwn7ZGDixajD/bpBzSBbnr550qNDfsGHDmDdvHvfddx8AJ5xwAuuttx6vvvpqwvEVFRUceOCBjBkzhq233pqysjLGjRtHNBrliy+amp7tuuuubLLJJlxxxRWNx4LBIIWFKS5EzXCF/lxWZ6T+faT8xPQDA6PA8Gk3f1vw7RrTv0nTO6oNqIKrUTmjUo6R8GfIssMzmRVUPqrkoaSVUBKaiZQdlXqW4sdR/v4ZrJtgHRFkybBYVVgiY8qAnCMxCi5s0zqrGzp5/aNYCPdvMEpRwZGIf1eUvVQbumYPRCIQegepfx8IozybxYT3Shvnsiuvg9pHaJ8Gr4bupSYVej5zfR1uDo5GqdU78JLJ/bvDflJz5szhzTffZNasWfTvrz/c999/PwMGDGDu3LlsumnrrqyFhYVMmzYt7tgdd9zB9ttvz99//80666zTeDwnJ4cePXq0nMLFxSUdTkMZ9hIIzSWtlkg6Ip/R9lyHNHi3iftWRCD8GVL/KtjlYK6lQxIZISDVyLJjoev7OuTREt8OkHN07MbW/OcU8wTkHNdm4waA8GdgpRIttKFuMpI3zvXiZIBSBvh30d46QOwypOo2qLgEiYUfRZWAkQ9GiVbdDh4ERqmukrOrUQ0yCXUvkdq4acgjc/JZslGFl4F/KGCt9kZNtnTYT23mzJkUFhY2GjcAO+ywA4WFhcyYMSOhgZOIiooKlFKtwlpPPvkkTzzxBN27d2fYsGFcdtll5OcnLgkNhUKEQk2dYysrKzN/Qy4uqwrebdEdi9Pkj4Q/pMkd3waat2xISiYX/xaY66O8GzctZ9cg5SdDeCY6pGCjDY5snqxtkCqd85LbOvdBKQX5F4B3C6TmQS3kBuDphco9DgLDs1gzAZFvSFuGLHU6kdzYsn3WXM0QuxxZOjoWimpe7r9Ml5tbfyGRb6H6Lt1jSsr1y94+kHtMTAcp5QoOd2KAtw/499B/X26qbNZ02E9uwYIFdOvWum9Gt27dWLBggaM56uvrOf/88zn00EPjXFGHHXYY66+/Pj169OCHH37gggsu4Ntvv23l/Wlg4sSJXH755dm9EReXVQxl5CD+nSH0dpqR7eFqd0objKjcY+Jnqrgg1u8Kmt5DW96LIKGPUAkMHIgZOcF9UMF9tKIxoFSwDeslWqQh9yMdq3fpsEg91E3RQov2EjDXQAUPgsCeaTvLS/Wk1sZNK2z9FTNuAK3zVP6Vsw16ekH0pxQDTAjshyq42PXatAMZ/wQnTJiQ1lj4/PPPgdgHvwUikvB4SyKRCAcffDC2bTNp0qS418aMacpa32KLLdh4443Zbrvt+Oqrr+jTp0+ruS644ALGjx/f+H1lZSVrr7122j24uKyy+Hd3YOCsDARQzbwkEv0bQm/R/tonzvKH2t2wacA3CLg+9RijBDwbpx6zCiP2MmTZERD9hcZwoTUPCc+C2n5Q8kDS349IJCZqmY0hnMHfmn8oKudQpPqeZuXifh3qDO6L8g9EGSVZ7MElERkbOKeeeioHH3xwyjHrrbce3333HQsXLmz12uLFi+nevXvK8yORCAcddBB//PEH7733XtpEoj59+uD1evnll18SGjh+vx+/P4VOh4vLaobybtLpROyzIvd4ULnYoRm6kijyAx0i7NZOlVDZory9Ee/2MW2ixDdhlXNsWi/FqoTYZbqHmlGKMgqR8vOgsblqw99ALPcr8iVSeS2qMMnDub0sVuHUkSiw/kXln6zzeKw/QMJa18bNm+oQMjZwunTpQpcuXdKOGzBgABUVFXz22Wdsv/32AHz66adUVFQwcODApOc1GDe//PIL77//PqWlpUnHNvDjjz8SiUTo2bOn8zfi4rI649k85i7/hcQ3TBOdt5KqFcKKwgBEJ/eamyKLB4K9tGOX9A/q2PkdoIpuQ8qOhuhcmpScY3k5gf21sbcaIJHZSNWtEJ6ONmQMrT8T/jjFWTbUvYDkj09c9q9yaXMyfVoUKD2/UgZ4NuzAtVwgvtd8u9K7d2+GDh3KmDFjmDVrFrNmzWLMmDEMHz48LsG4V69evPTSSwBEo1EOPPBAvvjiC5588kksy2LBggUsWLCAcFhrC/z2229cccUVfPHFF/z5559MnTqVUaNGse222zJo0Iq/CLm4rAwopVCF14EK0Dpvw9QX/NyxK2JraVDg2QLV9QOUZxOoPK3jjRtAmWt1+Brp91CKKn0BVXgz+HYG79YQGI4qeRJVOLG1qGEnxbZD2KGPseveQaK/ZnSuhL/RicDhj4jz0oRnODg7DJHvE76ijLxYGDB9+kT22Cjvtm2aQawFSP2bWvXbWtJO+1p16dAspieffJLTTz+dIUN0Z+J99tmHO++8M27M3LlzqaioAGDevHmNooDbbLNN3Lj333+fXXfdFZ/Px7vvvsttt91GdXU1a6+9NnvvvTeXXXYZprl6J9i5uGSC8vaG0hd0cmX9VHSeiRcCI1B5J4O5NiiQ6jtperpd0YEtAXspYtdC5SXLYT0F5oZgrqlXD32C1D4G4W90/yzfADAKY52rPToPJjobIrNBecC3Kyr3GJRva0eridg6DGXN13P5+seFnbQy9HBUsJ2qs5YjEv4Gqbw0LslWAPFshSq8Uv89JjtXwkj927HfeSjBCKcyBCn+fnMOS+MFaguGrrzK8vcmdjlScWmL/DITCeyj24sYue2201WJDhX666y4Qn8uLvGI1INdAUZRq8aZYv2L1L6gBf/SlsIuD3KBWpaXsaUKb0YFh2NX3Qg19+Gsa3QDuvpJFV6PCu6TcqSEpiOVE3T/rQaMElT+uajg/lntvSOR6F+xxqjvo9tj9EXlHA7eLQAjrphE9xk7neS/s4D2TnlbJ0mL9R+y7KiYyGFb8KK6fYIyilqvYVciS/fXYn8dQgBV8iDK1y/jM0XqkKUHQfRXEvbU8m6DKnl8tcm/6hRCfy4uLisPSgXADCR+zVwTgiORmnuX866SkUUyqFEK/sEQ+Qmi3zo/z78nKjgcqX8vZtxAZpU2eqxUnAe+7VFmYnFSCX2MlJ1IKwPAXoZUnA9ipVVqXp5I/XtI+am0bIwq9a/ERngR/x6ovDFgrq37QaU0SENI9U2oYt2zTKK/QeR7RAyovhPseW3csQHB/RMaNwDUPgVWW9dIhtJtTzy9sju97sVY3lUibN2Cov5tCO6d9Q5XVVwDx8XFJSViLUaWHUznTDh2QOFtGMFhjd9K9G8k9B5UXZP2VBXQ4XWpeZimxN5sEKR2Mir/jNaviCCV15AqBChV12mtHZVdNah21NuZdUpPNpe1MOaNadkYtfn/RyD0FhJ6G4IHkDisFDcrhN7DjsyGymtaNMfMhoaQaux35t0GlX9B8tXrnqXj1LZFhzBrH4O8UzI/u/Z5UidAG0jdCyjXwGnFypGV5uLissKQ2kd1Oa4T/HugSp6FgjvTjwXABHNjOuxSZHRFBfaIO6Q866ByDtPNQFPiA//O+n8jX9O2G6ANke8SvxSdA9avpPRwSCWEpid/Pdlp4c+xy05EFm6GLOyNvWQ4UvuszvXJEqmdjM7XShcitPRXXcMN2gFlx8dK4bNEFUL+JeDZRCfKqyCY64Jvx9SK2h2esGsjtU9neepCUv+sbbCcieeubrgGjouLS2rqXsBRWMazCRTegPJtgwo47L/k6QXezemY6hUDVXRbQkVYpbyolE/TCnKPalZS3NZLpdJJyYmwHd5c7cUZrSi1L+jmoqEPafz9RX9BKi9GKs7K3sgJf0pmSb0tPT0psJfRFtVplXeaFvOL/gJSr7VtrL+g5k5kyf+Q8Oetdxj9m+yCGQrd8sQh9iKySnk1upH682GAmVpbbnXFNXBcXFxSY5c7Gxf9GZYMRmoeApUPhoNmuDmHgZHOk5IlwYNRvu10CKj+fexlx2Av7IO9sB92+TmItw8q7zT0ZdBA3+RMQEHwEFRek/o5/oG0rQ2CNDZ0bIXR1dkURuvWN0lXsxYglRcTlyMT2wcA9a/HmkNmgQMl+uzwkb2XzEDljUM8WyGVF8Xmaf6+bZAQUjYGsZc1HpXa55ElQ8gqrwsB6qH0hViJeZrbqcp3pOLf6rTggWlG2KjgARnPuzrgGjguLi6pcXoDBrCXIFXXIpVXQEGaMm5VjAruG6su6oC+V97NtHFTdT1SfiKEZ4FUg1RA/Wuw7EDdqLPrdG3M5IxG5Z2C6jINo3BCXL6Kyj22DXs0dTgsMCLxy55e2vuV6ildFUIyAykBOoyUyltg6HL3LFC+HeiYW0e2P18PdJmupQ3qHiX5z9HWYara5wGQ8FfNjKHsK/KUuT4q93hSG2cmZFsJF9wfPBuR2MDWVVQE9sxu7lUc18BxcXFJicoZTcaXirqndMVQ/mWJzzW6QPBgpGwsUnktqPSK5RkT/VmXMNc+GDvQ/AZqATZScQ5gQ+5xqNwxEDxQ6/+0QPm2R+Vfir55OvXkxG60qgBV8rAWk0s0SilU/kWx8Ylvzir/Aq2B45TIbFLfcG2Izs0uZBI8CPDSvmFFL9l7yKJNxmj9h6TzAkn9+0jNY0jZsbRZasCzcUwkcKDWREr4OdHCmapFU1inKCMHVfKE7h8X9zM3tGZV8UOrTYl4prg6OK4OjotLSrRGyAGxMtoM9F+CB2IUXonYNUjtwxD+Wid9ereC6klAHR2rZWOAd1uIfEPyfRvg3R6s35pyXMx1Y8bOqFYhBYn+itQ+FXsvPh02iv6mz8fUTRONrmAvAnyowC4Q2CepcRM3d+iTmA5OM70Xoysq/7y0GjotsctOjzVTTXWz92P0iFf2FbsK6l5GIt+C8qB8O0JgSCvjSkIfImUn05hInJSGLugdVaGkUd2+Qhl52Au2JH3Flh8I0x5/e6pgIipHh4fErtEeofo3mo0QMDdCFd2K8m7S5vXE+i8mMqm07pDpPGy5qpDJ/ds1cFwDx8UlLWItQSovh9A0HN+sfDtglMSHQcRagiwZrBNA087T1t5ACp1Xk2l5e2zdnKMxCi50dIZO2FVZ5VjEzyPaILP+A6ME8W6HIoyIhbL/1UaVuX7atgx2zWNQdVWKEQb4h2AU3960duhjrW0jdTR5IiwweqBKHkJ5Norfa3QeUvc0hD4AaxnIUhr7hGHoc1VxB4tDGuDdDqP0CQDshduDlHfges0IHooquCyBETxPKyJLGLybaUOkw/KWVj9coT8XF5d2RZldUMV3INYCiMxBKs4GqUpxhpG4DLvuWYfGDbT9CTvbkEds3dpHkMDejtostFcfKKUU+LZF7A2Rmgeg/HREKprvCsy1IPckCB6Y/MYZ/TnNSjY0Ew6U6G9I2Vi0MdgiMdlerJWEu7wV54lSnrVQ+edA/jl6Dus/pPY5XcGkAuAbDJVnZfL2s0BQeSc1fasCHeAUbKFc7dkElX8e+HZM/PM3u+qcGYmAZwPXuFmBuAaOi4uLY5TZA8weSPhALVyWNDxhJ+yXJKH36ehwRRNR8GyhdWaySmA1kdqnHfeRai/ELkOWHhwLVSX4WVnzdCjE+i+JcGAI6qakXUdFfwP/TvqcmkdJXs5t6fBd/auQc0jy+cw14vZj102lY8QhGwwGD6rgSlTzTu+e9SCcTjfGKSYUXAHhz7RR7tkIlXMQyuyZcLSIDTX3IjUPat2i2BwS2AtVcBHKKGmHPblkgptk7OLikjEq92gtpJbQS2Jqw8K/W+uXJNrBO2tOAPLPJnuDynLgCWl/pPKGWE+kNPuuuQuJ/tn6uL0UqE+zigdp3nep/i3SGYFSdYsOvzgl0d7ahQDknY3q9hEqJ74ySZdLt9W4UYCBKrwRI2cURtENGMV3YOSfkdS4AZDKS5DqW5oZNwAW1E9Flh6i85tcliuugePi4pIxyuyJKnlSh0sAbejELie+ATpnI4HAHmamiZbZX6JUwYUY/oGogomxeZobY8krluLGOEgObk/EroL6V3DmcTKRuudaH1Z5pH9vAqp5/kK6xFxAypFlo3SYMt3Quteh9uH0c2ZFHSo4IrFHJDBMG9dtwdtfN/7MoPWBRL6HRL8LACztjat9om37cskY18BxcXHJDs/6EDgQVBENZdd4tkblHp+4Y7OEwUrWNDARBvgcKiI3x1xLdwDPORgAlbM/qsvbkHsseLfTBlj+uZBzOKnzdAQV2Cvz9ZvPEP4Wu/xc7CV7Yy89EKm+H0nV9sL6E+dhHRuirbtfK6PAgfCcFf/ePL3TjG9YshypviflEKl7Dak4U+sNdRhJyumVD1XyCBkpDLckMgupuhVJ1lojAVL7Aqn/ltrQqsEla9wcHBcXl4wRCSNlx8dk+5uFBKI/IGXHQME1jeWzjedUXR/Lh3GKDzybQfgL0t70vdtB7okosxQ8m7VK+lWedXRCbHOs+UjdS7EeRS3DQQpUMeLbA8Kfg4QRY01U+AOk7mXdm8tcR2sEBYYm9FbZVbdCzSSaJ6lK5HuouR9KHkF5N0vwRjK5MRtg5Cd8ReWdhiybSeJKNEN3SW9WtqxyDkcqnPSAsqDuRaTgooTaKyIRJGX1Vgs8m0PR7bBkP6Ay7XBA95YyusUS3r9FG8J9m1VsGeAfBKF3ne+jJeGPkaWfQPH98Tk+ybD+Ja3XzV6Y/X5cssI1cFxcXDKn9vHWxg3QeCOvvBj8O6NMrYIsdhXUPpNgfCrqY2EOH2kNnMhXEHofVTjB8ezK7Aklj+rqoVZ9ngRkGSwZhDQzfuJ2by9EKj7VvbqK743TipG6qTHjBlq1SpAqbRx2fb91d3DPhmCuGbthpsNCBRKHUZRvWyi+Gyk/N1Y27aFRsTcwHFV4dfwJgWG63Lv+FQfr1usKOpUgRBT+JNZPKh0K/HujCs5FmT2w/QMh9KaD84DgIUj5aTHJgobfiAEEaWy5oNZxNldSLEBpIciuHyYOtzbHKKZVtVVLlCtJsrxxQ1QuLi4ZISJIzeOkNlYk1kU6RuRLtLhaptix89LdHGyom4xd+xqSQWdo5d1Sd59OuX6a18IzkOpb416RmgdJfnm1dIPNOEG42H6Ugco9OcWaDZjg7RNTz02M8u+K6vYxqvBWVN5JqPxzdBuKohtbGVZKGajC68C3k4O1PbE8nwRYThuCCoReQxbvjL3sSPC0Vo9OiOqm/65C7xL/92cT109K/nG4jzR7tJfEmpWm2VZwX9IKHmbbqsEla1wDx8XFJUNCYP+XdpREf2n2TVtKw22chS8sqByPLN5JN9N0WrVScy9tuxQK1D6F2LX6O6mH6Pek600k4VmJXwoeiMo7nYZqnoT4BqGK70urv6OUDxXcS3fZzj0O5Unu2VDK0PouKTEhsFfythFGlzTnJyD8GdQ84mys8sZUo9MlYbeXGI4J0V/TD/MN0CrWSVs15GfdqsEle1wDx8XFJUM8pL90KC261oB3cwfnpCKTcy2ofw1ZdqTWhEmBWEsh+iNt1uaRWqT+7YZvHJ6TeJxSCpV3KpRMBs+WNGUS+MDbD4qfwCh5QCcTtzPKuwkERpI4idcA5YsX1muJf1BigceUNHT+TtdrywTJput3W7BA5aQdpZSBKrpbh/oaf3axfz0bokpjvdlcliuugePi4pIRSnnAvyupq0YslH9w0zlmd/APSXNOe2Jpw6UuXU6Jg/Jop1Sei73saJBQ+u7gWGCuk7TkWqJ/Q/nJEP0BaNAOCutco4qzdE+iDBGxEbsakdTeD1V4NeQcQasUTXM9VMmTKM+Gyc9VPpTD9hbxNIQiU2HFKrOWb3chcVjJp4xcjKJbUF3fQxVcgcq/GFUyGVX6aqs2Fy7LB7cXlduLysUlYyT8NbLsEPTNpuUlxNQS9aVTmro8A2IvQ5YeCtYfLc5peM5K40XxbAnR2ThXJVbg3RKj9PmkI0QiyKKB7VjSbIJ3CwiMhiqHN3rPZqi801GB3RsP2UsPSdEk1Iz1+XKmMyP2MqT6fqibDFINeHUX6rwTUZ71U55H6CPdm8qzMXj7OG47YFdMgLqnHI3t9ORdhJF31IrehUuMTO7frgfHxcUlY5RvW1TRLeiwgkJ7ZmJP/J4NUcUPxhk3AMooQZU+rzVozPW1699cG5V3BuSdlmI1U3tEiu4Gcw3Si9g1IGDNT/0+lBdyDs5gznRYunTZ7ALBhrYGabxW0Z+Q8rFI7bOA7liuk7KTGXIWhD/RXp40iLUIWbI/1D4SM24AIlD/CrJ0PyTyQ9NYCSP1byE1jyF1rwEBVHBfVM7BKF/ihpEiIaTuFeyqG5DqO5HITwCNGkSrBGm9gC6dFbdM3MXFJStUYBh0GwB1LyPRnwC/9kL4dkqa/KqMPMg9DpV7XPwLYiPRf6D+RZrKbWMaLkYPVNE9KE83pPRlpPYJqL6PuKqZpJtMnw+ick9Cap9sZgC0FRPqp+qqpMBuSPWjEP0p1mS0ltYeL+25ksrLITAEIrOdLROdAymShvWcV8X0V1oaSxZIHVJ+BnR5B+qnIJVXxtoMxH7uKgh54yHnyMTGTWg6Un5W7BwPgkD17YhvZ8g9EV355lDbpjNjzUHsCpRRuKJ34pIhroHj4uKSNcoogtyj2+z/UMqAwokQHIHUTtaVMqoAFRwBgX1RRm5sYB6EZwF1ziaWckSiKXVMlJGDiMN3YHQDe1GaQZbWiYl8gdQ8BpEZOMsbiSCLh2nROkfopFwRSWyAWIsh9DbJQ38C1j8xwcaP4o+DNoCqrkahIPfI+DPD3yJlJ9FkODXrMRb+ODbfqtJF29KaT4EhK3ojLhniGjguLi6dAqUU+AelVo4Nfwrhmc4ntRdpzZTAnqnHGb70hVRmL1SXV5DFu6Upkze1R2rZESRWEk6BLI0J2KXDh9Q9i1SMB6lBVKEuL889BmV200Oiv+GoOizOuEmwpepbIecgVLOqOKm5m8T5VzRbsxOkdwb2jYkPNtNGUvnaAM0Aqb4bqX0e5dsGgqMaBSxdOjduDo6Li0u7I9YiJPonIok9LWJXIdG/ETuzsJDUv0pmly0DqZ+afph/D9J6HIxcXcKdc2iaPVgQbvDaOE2Ibo4TwyCsDbeGsmmpgNqHkCX7ItG/9DHVhn5McdupjhO7EwlB6H2ye29JUEXtN5eeEFQOqmACquuHqJInUEWTUKWvQuGNmU8X/RHCHyDVtyOLd0Xq32rn/bp0BK4Hx8XFpd2Q0IdI9R2xHkEAASS4Hyr/DJRRgkTmItW3Qeg99JO+QXUedgAAJ/FJREFUiQSGofLOQHnWTb+AXUFmmjU2OBH8Cx4Idc+kHhP5TjfKzDkc6qfqvJq4vcS8Nb6dY16R5e3B0O0lpHwc4t8Z6t8iYw9SMpq3X5BEeURtwQDfdhB6p53mMwGFKrq1KbTp277xVaF1Dy3n6HYXUj4OSl9CeXu1YS6Xjsb14Li4uLQLUvcSUjYGIt83O1oPdc8iS0dhhz5Elh4Ye/pvMAwsqH8DWbq/rh5qPl90HlJ9F3blBKR6EmL9Cy17N6XF1F3P06DESf+kCIRm6vBG7ong3x3d/6hhqTVQBVeCuQ7LT++nJaK9DTX3gPU77WaImGs2/b8qANXOCbferZupN2eTu9Nwjgf8Q1Glz6H8u7YaJdZSqH0g+33qWfR/naovu6wwXA+Oi4tLmxG7Cqm4lMR5GZZuHlk+Ht00s6UHxtJKwBWXokqf0oJ0Vdfp0mYMQOmGl9W3gXd7MsMCcxNdDp2gy3jTG3DWJ0tqHoCK8c3eQ0D3GMo5FuXZCKUMpOqGDPfYEbSXh0WB0RV8A5uOKBPJGQ01D9BmBWgAbJRvO5SvLwQPQMrPhsgXOH8PRiz5uwKog/D7SF0pGKVx6sES/QdZdjDYS9thz1bMC+nSmXE9OC4uLm2n/lVSK9HasXLiZDdES1cdRX/XXbhrH6YphyVKYyfsyKeZ763qEu0hWrIHUjcVkTqk9insJftjL9oZe+lBSPQPZ3NFZ7d4D/VQ9xJUT6LBi6D8exBXVdQKA1Rp5u9juRN7PwUTWmsa5Z4I5ga03VNlgqeXbhxKrMO7b3syuzXZscq2WL6X1ELtk8jSkXFaQVJxbizU1h5GGaTtcO+ywnENHBcXlzajDYS2h2UkMgepub/tG0oU6rD+QSrGIYv3QCon6FCOvQAi30H1TbEO2ckuiQ1zJbo5CoSmIou2xS47BZFwzNOU7OchYKwEBo65Iar4flRgcKuXlJGPKn0acg4FMg0bNs4CRhdU0Z1xZe7KPwBnCcwNP19FQq+gXYFUXACARH5OI56YKSZ4t2qnuVw6CtfAcXFxaTsqj3YJi0T/0q0B2oK5DvrSlmQ/jTo2Da/Hbo5Si9aWaWmYpJirOVKrwxZlh4N/IHi3jb3giX0pwAsFV8TyYzozeTqJ1r9z0hHKKMQouAQc9mqKJ4jKOwvV5dXWHc69/bRXJ+3tqaFKLNnvxoLI50j0N2im2Nw+WKicI9MPc1mhuAaOi4tLm1GBPUn9dOwgeVQVgdnT2YLBA5t1eW4wSIKQexpY/6TZSzJsIAI5hzSVLavcDOeIrVt9K+Sfhyp5SlddBfdD5Z+H6vYxyr87qUNYnYFqVHhG2lFiV6XV0YnHAHyo0idQeSdoocgWKKVQxfcA6bp4O+wsHpkLKovKKaNB66a5wRu7ZQaPiCWZu3RmOtTAKSsr44gjjqCwsJDCwkKOOOIIysvLU55z9NFH6z/wZl877LBD3JhQKMRpp51Gly5dyM3NZZ999mHevHkd+E5cXFxSoby9wD+YxJeUmGETGJF6jrxTUN5Nna2XcxSq6wxU4Y2o/LNQhdejun0S8zi0xZNkoXw7YnT/DNV9NvhHkF3Ohgm1T6J822EUXIhReDUq91iUUQxGPlnVd+ScgPYwJbts56Crutrpst5MHE/sGqTmcewl+2Ev2gl7ySjsigm6FUQmP29zXVTJoyjvlqnHGT1BtVOujPKDbwfSh1ADUPISqvgR3Si268eokifBvyv65+4B7zaoottRBRc7bjzqsuLo0CqqQw89lHnz5vHmm28CcMIJJ3DEEUfw6quvpjxv6NChPPxwU6dcn88X9/q4ceN49dVXeeaZZygtLeWss85i+PDhfPnll5jmiirPdHFZvVGFNyIV58SUeLUWCURBBVEF10BgKGIUQ+1jsdcMGowHlXdKY88j8Wyu+ywlNCwM8GzRZAgF94l7VYz0vaccvBP9X+VBQg5EAhNixSqBEsyuAkhgL6h/nYw8TZFvUF1eR8rG6lYWrahH790PhGhzMm3MmybWYmTZYWDFBAQR3d8q+m3yc1uhwLc7qniSQ8OgPhYybCsB8PVHGXlIYCTUv0Tin4uC3MMxfJvHH/b1Q/n6tcM+XFYEHWbgzJkzhzfffJNZs2bRv7+O0d5///0MGDCAuXPnsummyZ/U/H4/PXr0SPhaRUUFDz74II8//jiDB+vktyeeeIK1116bd955hz33TCPJ7uLi0iEoIwdVfBcS+QVCbyF2DcqzAQT2ahRcUwUXIblHQ92riL1Ul/EGRjS1FwBU4TXIskNjuTgtb0Z+VOE1yTdhL27ju/CAb+umb6U++6lEkvaJUnknI82ViJ0Q+Qyx/khi3ECDcCLebXQ/q9DbYNeA/W8Wm/cjtS8j4c8h9Hks7NcWz5ig8k/NwOvR0KW+LWsqyD1CN3gFVOGliL0oFlJraOga+9c/FJV3ZhvWcumMdFiIaubMmRQWFjYaNwA77LADhYWFzJiROrb7wQcf0K1bNzbZZBPGjBnDokVNze2+/PJLIpEIQ4Y0NT5bY4012GKLLZLOGwqFqKysjPtycXHpGJR3Y1TeqRgF56FyRjWpyTZg9ATfAJRvoNZXMbq2OL835F+caGagLmXrBam8kvRCccleN3SujFHS7FAbPEL2v8iSvZG611rvwLOBDn+Ql9mcldeTOtRiQWQWKvdojC6vobq+nUUeEUAIQlOg5m6IfkGbq49yT0J5N08/LoZSJni2SD/QszUERsa+MdG3tNjPJzAyzmhRKogqfgBV/IgOl/oG6d93yVNa9TibPB2XTk2HeXAWLFhAt27dWh3v1q0bCxYsSHresGHDGDVqFOuuuy5//PEHl1xyCbvvvjtffvklfr+fBQsW4PP5KC6Ov/B079496bwTJ07k8ssvb9sbcnFxaTNS/zZSdS1YzXLmPJtAwWWNoQCxK6DqSlo/vce+r5mEeLdoVb4skV9iOjVpMHrGmmU2hMhi/3q3ReVfFD82eDDU3Ob8DbbE+k03xLQXoHKPj3tJeTdDih+AsoMzmO9X0j+XCtiVYBSglBcJjoLax2nX3lFOMXqg8s/RXeEzpeBiWDY69Zj8szH8/ZHI0Ujdy7pCzuiGCo5EeTdrNVw3dB2I8g9sPZfLKkfGHpwJEya0SgJu+fXFFzr2nMgdmcxl28Do0aPZe++92WKLLRgxYgRvvPEGP//8M6+//nrKfaWa94ILLqCioqLx659//sngHbu4uLQHUvc6Un6qVjVuTvRXZNlROhwCUPdiLDyVLDxhIDVNOXoiFlL/nta2cYLRHVV4HXi3A3M9naNReDOq5DGU0VS5I9F/wOgOZOMBaZxF/7fqBsRq3YHc8PeBvPMzmC+R5ktLTMTIQ+pewa68Uqs0G6Ust/YRxlpQ/Ch0/RSj24fZGTeA4dsW8q8gqcct/wIMv44QKO9mOpm76FaMggsTGjcuqx8Ze3BOPfVUDj449RPHeuutx3fffcfChQtbvbZ48WK6d+/ueL2ePXuy7rrr8ssvvwDQo0cPwuEwZWVlcV6cRYsWMXBgYqvc7/fj92crRuXi4tJWRCJI5RUN37V4Vd+wpfIqVJdXkHDi5Ny48ZGvEBGQMmTZMbGkZIdEf4DAXhjB/RLv1a7Q7QLC053PmRaF1DwG3k21weHpBd6tUEph5B2LHXonaVJyi905GGPBop0R6mm6xEd1/yippmM9OSb4tsXwD2iX2Yzcg7EDQ6Dqegh/hm5muh0q77TW+jkuLi3I2MDp0qULXbp0STtuwIABVFRU8Nlnn7H99rp/zKeffkpFRUVSQyQRS5cu5Z9//qFnT53R37dvX7xeL9OmTeOggw4CYP78+fzwww9cf/31mb4dFxeX5UHoY5CyFANsiM5BInNx2mxRRKDspFhX70yIgF0GZutCBttaCEtGgJQnOM/Q2jsiONZgaZoZah9Gmhsont5QeKPOWSq6GVl6iMOEYCfJtw3J0c30dqQaVDHIkoRntA8WKieDkJsDDLMEiq5t1zldVg86LMm4d+/eDB06lDFjxjBr1ixmzZrFmDFjGD58eFwFVa9evXjppZcAqK6u5uyzz2bmzJn8+eeffPDBB4wYMYIuXbqw3376aauwsJDjjjuOs846i3fffZevv/6aww8/nC233LKxqsrFxaWTYSfPu4sfNx/lS9dQ09Tlu9FvIPI1mVfaGKDyWx0VuxyWjkxi3IDup1UDeWNRBZfrBNeMwj4t9hn9GVl2CGL9q6vJiu6gSZ03xd69fbPs5m3FjBtf2pFZY6yL1E5BQp8g0l49n1xcsqNDhf6efPJJttxyS4YMGcKQIUPYaqutePzxx+PGzJ07l4qKCgBM0+T7779n3333ZZNNNuGoo45ik002YebMmeTnN12QbrnlFkaOHMlBBx3EoEGDyMnJ4dVXX3U1cFxcOivNK5NSjiuF4H6xyp9klycLlXssEvogxZhUa6zVurILdA+stJ2mBepfReUcggruTdu0ZiyQGqTmfsSugfKTSd2wFL1ezpEgFVmuaYDHmZhiVth/Qf3zSNkxyLLRiJ3Ka9dxiAgS/hy76gbsyquR2heRtrYAcVnpUCLSDg1kVi4qKyspLCykoqKCgoKCFb0dF5dVHpF6ZNHAWA5IIhSY66C6vK3F/sKfI2XHgzQXrNOaJSrvTFTeSdiV10LtI2RsZKhCjO6ft9ifjSzaPtbxPN35Rai8U5GaB5x7plIShPzzoeqy9ENzT0TljUcWboUW88sC3wDw7whVN2R3vmNMbUyZvcH6DvChAoORwH6oyNdI/etgl4NnPVTwIJRvm3ZZVaylSPlYiHxLvOBkvi4H9+8UP96u1b93owil0nnQXFY0mdy/XQPHNXBcXJYLUvtUkkqnmHJw0d2oQFN/H7EWIrXPQOhdbeh4t0blHIaKCfFJ3RSk4uwsdhLA6PFd/N7sKmRR3wzmaKsIXQt8O0D409RzqiKM7p8BYJefF1PlzRQTcg7DKLgYicxGqu/WDUKJ6Ioy79ZQ/w6Z5xilokmxuim/Smj6GcZeDx6CKrgMpbIPLIhYyNL9IfozrZOpFWCiSp/XJfqRX5DqO7QgIjbgg8BwVP5pKHPNrPfg0rG4Bk4aXAPHxWXFILVPIVU3gVQ1HTS66BtbILkKuVj/grUIjNLG6hmRELJoxwzDNQo8m2J0mRI/v0RiXpEVoBWjCrRxEf0uzbh8jO5fAiDRX5Ele2W3XOnrKO/Gjd+LvQypewMlFWCuAf4hSOQ7CM8ATKi5K6t1siJvPEbe2KxPl/r3kfITU4wwITAMlXs0svQIdEjQin9dFaBKn0V51s16Hy4dRyb37w7tReXi4uLSHJVzKAQPgNCHOt/F7Am+QSiV+FIkke+Ryusg8lnTMVUARhcd/sgdozt3O+7OLaicw1rvS3mRwFCof5Pla+SYkDMa7AqI/ph6bQlhl50KeWeipIqmnlNO0N4SlXd6o3EjYiPVt0LNA4CFYKJDORNQ+Zeg8scj1gJkeRo41bdh5xyGYbROAneChN6iqQ1DIiyofxOJ/Ejifl0WSCVSeRmq5JGs9uDSeXANHBcXl+WKUn4I7JF2nIS/1U0eWxovUglWJVh/QugN8O4A+CHyIanDRgb4ttdJzIn2lTsWqX+b5WfgmFp1N+dYsOcjdc+mGR/W4ZTQ27F3mUE3a8/GqNyxqODwxkNSfQfU3NNsUOznLLVI5QVg5ID/f+gO5csrQdeC6juh4ILsTrdrSZ+TFQXrj9R7CM9Aon+7WjsrOR1aReXi4uKSLVJ5Gfqmm+yGFTse+RR8m6C6/wRdpkPJ85B3FphrNw1VhZA7VvciUonLpJV3U8g7rT3fQgoU+HdBlU5GmaUo7xaQO7bpNUekyS7IGYPq+iGq60xU6avxxo1dATX3p569fByy9GCdl7O8VJAh1mU9SzwbkPbn57TE3vo9+324dApcD46Li0unQyI/OesrpUdD7ZOQewqGpyfQE3xbIbknxHpeWWCukdSwaY7y9WnP1OFmmKCKIO9cMHNQ3q1QZs/4tfPOBM+GOkeprdVZeWejcsckb4sTepf0JekSC5sJOhwmtK0s3iH2EkQsCL2P1D0P1n+6vUbO/uAfnLIppsoZhdTcnWJypSvInBhRWTUpTYzYNTG1bQWe3nEtQVw6DtfAcXFx6Xw0b8bpBKnVLRiaiQQqpcCzdoqTEuDto7V40urhJCJFZZXRHbCg6jy9Xe82kHtCXMNQpRQE90VCX0D9c7TFmFDe3il7/mFXEl/dlIyG9xMGcxOwfmnTvhyhcpGy45qSnLGAn5HwdPBuA8UPoYzEXdiVuSbkn49UTaT1+zO0Nyr/fKh/G4ik2ENxzHPVNkTqkaqbofYZGtWlVRAJHoLKH+/I6HbJHjdE5eLi0vkwslDqFaeJxslRyoPKOyPDswwwNwTPNs2O5YNvV8i7ELzbgz0f7Ga9+SLfIeUnIzUPJthE43+yJ4FScwNil2ntl4wMFdGeFBIbFkk2EfvXTHAsGabu9h6eFfu+IR+qIRz5HVJ5cepVc49BFd2h+301HoyFKEsewTC7Q84RKfei8k5us/EhEkXKToDax2hqnYFuJFv7MFJ2kvZUuXQYrgfHxcWl8+HtA0Y3sBc5PMED3t7tsrTKORikWj95Y6Fv0DYg2oMQ/aWZYKEfcg5E5Z2DMnJ0bovUg1GiK7Nqn0eaVYA1EWswWnU9+HdHedZvWt/XD6mbnP0bMLqDd8vGbyX6D1L7JISmgbUEfbPNJhBXlX5IHDGtG3Md3eJC+cD3Pwh/AtZvCfZggArEcl9S5F3Vv4lYC3R7iySowJ6owJ6ItRQIgdE1LrSl8s9GpBrqnqVJDDD2O849SatFt5X6N5sZai0RCH+kfyeBoW1fyyUhroHj4uLS6VDKhPzxSMX5DkabENgXZRS33/q5x+ty9rrXEXsByiiFwN4osxsi9RCZA1jg6RUXLlFGIdDkfZLaJ0gtCmggtZNRBc3eZ2AoVE3MMkymc3mU0l4TCX2ClI1FJ2u3h7cgVQl2IgSkEqPbzKYjdjlSPh7CH6ODCErPafaE4FFQfU2aOW3dWTy4T9rVlVma+LjyoAqvQnKPQ+qmgL1MG0zBka1yo7JFap8hdRjQQGqfRbkGTofhGjguLi6dEhXcHySEVF2r3fqtR+h/PJuiCi5q//WNYsg9vFUgQ6kA+LZ1Nkn0F1J7Syyon4r4B4BvJ5QyUMqHFN4IZWNwru8TI3iYTsYlZkiUNfS3ao/U6Ryyag9hL0XEblQoVkYRquQhJPIzhD8ECYN3C/DtqBOLnU2a+T4SoDzro/IzDUk6xPqH1Pu0wfq7Y9Z2AVwDx8XFpROjcg6BwD4QmoZE/4Dob7q6ytaquyo4GnIOQKngit4qAGLrsIfUPgf2YhwZKPYCpGwMeDbRhk39u1D7INl4XFRus9BK3QtkH45KQM4oqH008/NUQcL2C8q7CXg3iTsm3i1xlPzs3SbzfSxvjJJYNVyyn7/SCe0uHYZr4Li4uHRqlJGrQwcreiNpEGspsuwQsP4iK6Mi+gssTR92SYwB3q3icnkk/EWWc8Xmw6YxJBUcDXnn6waWkW9x/v5MCO7veFVldkMCw1IoSpvgG4DyrOd4zhWFCu6HVKWSOhBUEtFJl/bBraJycXFZbRGxdVNPazFtbcsnlRfFwhLZzpPteQZgoPJb5itlaRIaa4JvEHi30s0nS55CFVyBYZioort10rAjYn2dco/NaHlVcBl4NkLvv+E9xP7fXAtVeF1G860wgvuDuRaJRRJN3X8skK1B6+IE14Pj4uKy2iFiQe1jSM0juoQbwFwXco+H4EGpNWQSzWf9C6H3adcO407xbIQqmIDy9Yk7rHw7IKF3M58vOAIjf3zCl5RZipS+Acv2h+hPafbVC1V0U8pqp4RrGEVQMhnqXtTtK6wFuiFrzigIjkqqgdPZUEYelPy/vXsPirru9wD+/i7sBQRWlHAxTbELVl5CEMG8lbVixx7LjieV2UPnMSon9djlNFjnGS8zjdrFnj+sfGrM05QzOolUHo0TFqCN4KUwLyRZeaFJvCULeUFwP+ePxX3cFvaCLAu/fb9mfqP72+/vx+f73a/Mx99+L+sgdc8BTd/CbSd1/Qionm9xwb8g427i3E2cKKyIOCD2F1pZzbZltlOUDTrz3wK756WtEPuCjgoxMIlV0Ok8/68qjnrImQnORRD9HpSroG4q9TmTSBx/QOz/1bIisq7laHau/hv1r1BRf4G6bqp6uJOmKuDaV4aGDCj9YO8XUJu4mzgRUVsai9tYqr/l/3qXPoJEZUMZRvp/T+XnXk26mwDHWXTck54I15Rwj5B0cUD8PyDnn2xjFlqrATrXovFB6WKg4t+FNP8EXN4GkUaoyNsB0wNcnbcVSn8XoL8r1GGEHY7BIaKwIhc+8FEiomUNkwDo0+F7Q8rIlhV0O0oEYJzg9es0ZRgJlfAlAN9Ji9NV4HKJ3xGoyNugYp6BLvY/oaIeYnJDXQoTHCIKG+L4HWja56PUVaC5OqD7qojegOkRtP0rVQdEPeoccGvIwg1vxQAAcED1mO1HbH0A4zgvsV1Pd90qzUTdGxMcIgobcuEj+PX1kAp8IKuK+xvg+lorwv1Pw0iouP+GUgao+PegYl5o2YCzReRdcO7Y7e0p0LWkSAcgAsq8DMqQ7l9sPf4d/o3DcQDdYAo2kT84BoeIwselAr+KKdNDAd9a6aKB+P9xrsZ7qcA5+yciCSrqsZavkpzJi1IGIOYp54wtsQPQQ+liIM0/QRreahm46wCgAMNYIOZZqKvHII2lgDRC6e92ziYKYHaSMmQAMc9D/ljprZRz/y/DvQHXnagr4iwqzqIiChuO2iFwbl3gjR4qsQJK1/aO3MEkjjrnQGRdLyhdL/f3pBloLAOafwSUETBOhIoc4Pe9HZc+B+z58FxhuWUtnfj3oYxMcKjr4iwqIqLW6BIBx6/eyxjHhyy5AVrWgdH19DgvV/ZA6ha0bAERAUCAhuUQYzaUeblfa6roov4C0d/j3Cm9sQiur60Mo5ybdBru6bB6EIUaExwiChsq+t8gf/wd3sajqOjcTovHX9J0GPL7f+CfT16u28ag8UtI3UUg/n3XjCoRAZoPAs0/ASoaMNzrWiBPRd4CFf93iKMecJwGVHybu24TdWdMcIgofETnAJc2tWyp8Oe9jhRgfBAwZIQiMq/kj9VwxttaYuZw7srd9D1guAfSVAWx5/9ppWETpMdfoWLmX7erdxyg41f0pF2cRUVEYUPpYqF6rweME+H+688ERP/VuXx+gNs0BJvIFaDx/+B9d/EIyOX/hTT/Avl9lnPjTjeXgQvvQOpfDWKkRF0Ln+AQUVhRul5Q8asgV2uBph8AFQnoU7vuHkdyGd6TmxaOesgfqwBpbLv8pY8hPXKhIv3dMJOo++ITHCIKSyrCAmW6D8o4tusmN4BzTR7la9CzABFJwOUv4D0Z0gGXP+vA4Ii6LiY4RERdmFI6IHoGvC8CKIDxfvh+0qMgV892XHBEXRgTHCKiLk71yAMi+sEzyXGOF1IxC6D0KfA96sDh3LqBKAwwwSEi6uKUridU7w2AaSoA/T/fiOgLFbcMKmYOlDIBpn+Bzyc9UVODHC1R18BBxkRE3YDS9YLquRzieBm4ehyACYi81TXtGwBUzDxIYwkgF9Dq11U9noSKuLnTYiYKJT7BISLqRpQuDko/FEp/u1tyA7Qs4td7PaAf/qeLYqBiXoSKebETIyUKLT7BISLSEBV5G1Tv9ZDmn4DmnwHVw7mbuTKGOjSiThXUJzjnz5+HzWaD2WyG2WyGzWZDXV2d12uUUq0er7/+uqvMhAkTPN6fMWNGMKtCRNStqMjboEyToIxjmNxQWArqE5xZs2bh119/RVFREQDgqaeegs1mw+bNm9u85uTJk26vv/jiC8yePRuPPfaY2/m8vDwsXbrU9ToqKqoDIyciIqLuLGgJzg8//ICioiJUVFRg1KhRAID3338fWVlZqK6uRkpKSqvXWSwWt9efffYZ7rvvPgwaNMjtfHR0tEdZIiIiIiCIX1GVl5fDbDa7khsAyMzMhNlsxs6dO/26x6lTp7BlyxbMnj3b471169YhISEBd999N1588UU0NDS0eZ/GxkbU19e7HURERKRdQXuCU1tbi8TERI/ziYmJqK2t9eseH374IWJjYzFt2jS38zk5OUhOTobFYsHBgwexcOFCfP/99yguLm71PsuWLcOSJUsCrwQRERF1SwE/wVm8eHGbA4GvHXv37gWAVnflFRG/d+v94IMPkJOTA5PJ5HY+Ly8PDzzwAIYMGYIZM2Zg48aN2LZtG7777rtW77Nw4ULY7XbXUVNTE2CtiYiIqDsJ+AnO3Llzfc5YGjhwIPbv349Tp055vHfmzBn06eN7qfAdO3aguroaGzZs8Fl2xIgR0Ov1OHLkCEaMGOHxvtFohNHIWQREREThIuAEJyEhAQkJCT7LZWVlwW63Y/fu3cjIyAAA7Nq1C3a7HaNHj/Z5/Zo1a5CWlobhw4f7LHvo0CE0NTUhKSnJdwWIiIhI84I2yPjOO+9EdnY28vLyUFFRgYqKCuTl5WHKlCluM6gGDx6MwsJCt2vr6+vxySef4Mknn/S4788//4ylS5di7969OHbsGLZu3Yrp06cjNTUV9957b7CqQ0RERN1IUBf6W7duHYYOHQqr1Qqr1Yphw4bho48+citTXV0Nu93udm79+vUQEcycOdPjngaDAV999RUmTZqElJQUzJ8/H1arFdu2bUNEhLdN5oiIiChcKBGRUAfR2ex2O3r27ImamhrExcWFOhwiIiLyQ319Pfr374+6ujqYzWavZcNyL6pra+b0798/xJEQERFRoBoaGnwmOGH5BMfhcOC3335DbGys31PWu5prWWw4P4UK9zYI9/oDbAOAbRDu9QfCqw1EBA0NDejbty90Ou+jbMLyCY5Op0O/fv1CHUaHiIuL03yH9iXc2yDc6w+wDQC2QbjXHwifNvD15OaaoA4yJiIiIgoFJjhERESkOUxwuimj0YhFixaF9QrN4d4G4V5/gG0AsA3Cvf4A26AtYTnImIiIiLSNT3CIiIhIc5jgEBERkeYwwSEiIiLNYYJDREREmsMEp5t49dVXMXr0aERHR6Nnz55+XSMiWLx4Mfr27YuoqChMmDABhw4dCm6gQXT+/HnYbDaYzWaYzWbYbDbU1dV5veaJJ56AUsrtyMzM7JyAO8A777yD5ORkmEwmpKWlYceOHV7Ll5WVIS0tDSaTCYMGDcLq1as7KdLgCaQNSktLPT5vpRQOHz7ciRF3nO3bt+Phhx9G3759oZTCp59+6vMarfWBQNtAa31g2bJlGDlyJGJjY5GYmIhHHnkE1dXVPq/TWj9oDyY43cSVK1cwffp0zJkzx+9rXnvtNaxcuRKrVq3Cnj17YLFY8OCDD7r24upuZs2ahX379qGoqAhFRUXYt28fbDabz+uys7Nx8uRJ17F169ZOiPbGbdiwAQsWLMArr7yCyspKjB07FpMnT8aJEydaLX/06FE89NBDGDt2LCorK/Hyyy9j/vz5KCgo6OTIO06gbXBNdXW122d+++23d1LEHevChQsYPnw4Vq1a5Vd5LfaBQNvgGq30gbKyMjz77LOoqKhAcXExmpubYbVaceHChTav0WI/aBehbmXt2rViNpt9lnM4HGKxWGT58uWuc5cvXxaz2SyrV68OYoTBUVVVJQCkoqLCda68vFwAyOHDh9u8Ljc3V6ZOndoJEXa8jIwMeeaZZ9zODR48WPLz81st/9JLL8ngwYPdzj399NOSmZkZtBiDLdA2KCkpEQBy/vz5ToiucwGQwsJCr2W02Aeu508baLkPiIicPn1aAEhZWVmbZbTeD/zFJzgadfToUdTW1sJqtbrOGY1GjB8/Hjt37gxhZO1TXl4Os9mMUaNGuc5lZmbCbDb7rE9paSkSExNxxx13IC8vD6dPnw52uDfsypUr+Pbbb90+PwCwWq1t1re8vNyj/KRJk7B37140NTUFLdZgaU8bXJOamoqkpCRMnDgRJSUlwQyzS9FaH7gRWu0DdrsdANCrV682y7AfODHB0aja2loAQJ8+fdzO9+nTx/Ved1JbW4vExESP84mJiV7rM3nyZKxbtw5ff/013nzzTezZswf3338/GhsbgxnuDTt79iyuXr0a0OdXW1vbavnm5macPXs2aLEGS3vaICkpCe+99x4KCgqwadMmpKSkYOLEidi+fXtnhBxyWusD7aHlPiAieP755zFmzBgMGTKkzXLsB05huZt4V7F48WIsWbLEa5k9e/YgPT293T9DKeX2WkQ8zoWSv20AeNYF8F2fxx9/3PX3IUOGID09HQMGDMCWLVswbdq0dkbdeQL9/For39r57iSQNkhJSUFKSorrdVZWFmpqavDGG29g3LhxQY2zq9BiHwiElvvA3LlzsX//fnzzzTc+y4Z7PwCY4ITU3LlzMWPGDK9lBg4c2K57WywWAM5MPikpyXX+9OnTHpl9KPnbBvv378epU6c83jtz5kxA9UlKSsKAAQNw5MiRgGPtTAkJCYiIiPB4UuHt87NYLK2Wj4yMRO/evYMWa7C0pw1ak5mZiY8//rijw+uStNYHOooW+sC8efPw+eefY/v27ejXr5/XsuwHTkxwQighIQEJCQlBuXdycjIsFguKi4uRmpoKwDmmoaysDCtWrAjKz2wPf9sgKysLdrsdu3fvRkZGBgBg165dsNvtGD16tN8/79y5c6ipqXFL+roig8GAtLQ0FBcX49FHH3WdLy4uxtSpU1u9JisrC5s3b3Y79+WXXyI9PR16vT6o8QZDe9qgNZWVlV3+8+4oWusDHaU79wERwbx581BYWIjS0lIkJyf7vIb9oEXIhjdTQI4fPy6VlZWyZMkSiYmJkcrKSqmsrJSGhgZXmZSUFNm0aZPr9fLly8VsNsumTZvkwIEDMnPmTElKSpL6+vpQVOGGZWdny7Bhw6S8vFzKy8tl6NChMmXKFLcy17dBQ0ODvPDCC7Jz5045evSolJSUSFZWltx8883dog3Wr18ver1e1qxZI1VVVbJgwQLp0aOHHDt2TERE8vPzxWazucr/8ssvEh0dLc8995xUVVXJmjVrRK/Xy8aNG0NVhRsWaBu89dZbUlhYKD/++KMcPHhQ8vPzBYAUFBSEqgo3pKGhwfVvHYCsXLlSKisr5fjx4yISHn0g0DbQWh+YM2eOmM1mKS0tlZMnT7qOixcvusqEQz9oDyY43URubq4A8DhKSkpcZQDI2rVrXa8dDocsWrRILBaLGI1GGTdunBw4cKDzg+8g586dk5ycHImNjZXY2FjJycnxmAp6fRtcvHhRrFar3HTTTaLX6+WWW26R3NxcOXHiROcH305vv/22DBgwQAwGg4wYMcJtamhubq6MHz/erXxpaamkpqaKwWCQgQMHyrvvvtvJEXe8QNpgxYoVcuutt4rJZJL4+HgZM2aMbNmyJQRRd4xrU57/fOTm5opIePSBQNtAa32gtbr/+Xd9OPSD9lAiLSOPiIiIiDSC08SJiIhIc5jgEBERkeYwwSEiIiLNYYJDREREmsMEh4iIiDSHCQ4RERFpDhMcIiIi0hwmOERERKQ5THCIiIhIc5jgEBERkeYwwSEiIiLNYYJDREREmvP/TQUJBVltci8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kan import KAN\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_moons\n", + "import torch\n", + "import numpy as np\n", + "\n", + "dataset = {}\n", + "train_input, train_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", + "test_input, test_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", + "\n", + "dataset['train_input'] = torch.from_numpy(train_input).type(dtype).to(device)\n", + "dataset['test_input'] = torch.from_numpy(test_input).type(dtype).to(device)\n", + "dataset['train_label'] = torch.from_numpy(train_label).type(torch.long).to(device)\n", + "dataset['test_label'] = torch.from_numpy(test_label).type(torch.long).to(device)\n", + "\n", + "X = dataset['train_input']\n", + "y = dataset['train_label']\n", + "plt.scatter(X[:,0].cpu().detach().numpy(), X[:,1].cpu().detach().numpy(), c=y[:].cpu().detach().numpy())" + ] + }, + { + "cell_type": "markdown", + "id": "494fe1d3", + "metadata": {}, + "source": [ + "### Train KAN" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "13ec74e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 0.00e+00 | test_loss: 2.37e-01 | reg: 4.10e+02 | : 100%|█| 20/20 [00:01<00:00, 18.81it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model = KAN(width=[2,2], grid=3, k=3, seed=2024, device=device)\n", + "\n", + "def train_acc():\n", + " return torch.mean((torch.argmax(model(dataset['train_input']), dim=1) == dataset['train_label']).type(dtype))\n", + "\n", + "def test_acc():\n", + " return torch.mean((torch.argmax(model(dataset['test_input']), dim=1) == dataset['test_label']).type(dtype))\n", + "\n", + "results = model.fit(dataset, opt=\"LBFGS\", steps=20, metrics=(train_acc, test_acc), loss_fn=torch.nn.CrossEntropyLoss());" + ] + }, + { + "cell_type": "markdown", + "id": "5e36b0f3", + "metadata": {}, + "source": [ + "Automatic symbolic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "91b4c228", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fixing (0,0,0) with x, r2=0.48220324516296387, c=1\n", + "fixing (0,0,1) with x, r2=0.3202315866947174, c=1\n", + "fixing (0,1,0) with x, r2=0.9358773231506348, c=1\n", + "fixing (0,1,1) with x, r2=0.9290410876274109, c=1\n", + "saving model version 0.2\n" + ] + } + ], + "source": [ + "lib = ['x','x^2','x^3','x^4','exp','log','sqrt','tanh','sin','abs']\n", + "model.auto_symbolic(lib=lib)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "83606957", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - 15.0316 x_{1} + 177.9349 x_{2} - 63.0716$" + ], + "text/plain": [ + "-15.0316*x_1 + 177.9349*x_2 - 63.0716" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "formula1, formula2 = model.symbolic_formula()[0]\n", + "ex_round(formula1, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9fa988e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 60.4718 x_{1} - 156.0295 x_{2} + 16.9$" + ], + "text/plain": [ + "60.4718*x_1 - 156.0295*x_2 + 16.9" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ex_round(formula2, 4)" + ] + }, + { + "cell_type": "markdown", + "id": "0cfce819", + "metadata": {}, + "source": [ + "How accurate is this formula?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ecd368f8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train acc of the formula: tensor(0.8870, device='cuda:0')\n", + "test acc of the formula: tensor(0.8810, device='cuda:0')\n" + ] + } + ], + "source": [ + "# how accurate is this formula?\n", + "def acc(formula1, formula2, X, y):\n", + " batch = X.shape[0]\n", + " correct = 0\n", + " for i in range(batch):\n", + " logit1 = np.array(formula1.subs('x_1', X[i,0]).subs('x_2', X[i,1])).astype(np.float64)\n", + " logit2 = np.array(formula2.subs('x_1', X[i,0]).subs('x_2', X[i,1])).astype(np.float64)\n", + " correct += (logit2 > logit1) == y[i]\n", + " return correct/batch\n", + "\n", + "print('train acc of the formula:', acc(formula1, formula2, dataset['train_input'], dataset['train_label']))\n", + "print('test acc of the formula:', acc(formula1, formula2, dataset['test_input'], dataset['test_label']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82b2337e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_5_special_functions-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_5_special_functions-checkpoint.ipynb new file mode 100644 index 00000000..9894cbf4 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_5_special_functions-checkpoint.ipynb @@ -0,0 +1,357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "134e7f9d", + "metadata": {}, + "source": [ + "# Example 5: Special functions" + ] + }, + { + "cell_type": "markdown", + "id": "2571d531", + "metadata": {}, + "source": [ + "Let's construct a dataset which contains special functions $f(x,y)={\\rm exp}(J_0(20x)+y^2)$, where $J_0(x)$ is the Bessel function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2075ef56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 5.15e-01 | test_loss: 5.86e-01 | reg: 5.84e+00 | : 100%|█| 20/20 [00:03<00:00, 5.89it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + } + ], + "source": [ + "from kan import *\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", + "model = KAN(width=[2,1,1], grid=3, k=3, seed=2, device=device)\n", + "f = lambda x: torch.exp(torch.special.bessel_j0(20*x[:,[0]]) + x[:,[1]]**2)\n", + "dataset = create_dataset(f, n_var=2, device=device)\n", + "\n", + "# train the model\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "markdown", + "id": "2f30c3ab", + "metadata": {}, + "source": [ + "Plot trained KAN, the bessel function shows up in the bettom left" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3f95fcdd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "187d19f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.54e-02 | test_loss: 4.73e-02 | reg: 7.50e+00 | : 100%|█| 20/20 [00:02<00:00, 6.93it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model = model.refine(20)\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d50bcef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "733a2a41", + "metadata": {}, + "source": [ + "suggest_symbolic does not return anything that matches with it, since Bessel function isn't included in the default SYMBOLIC_LIB. We want to add Bessel to it." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "031db28f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " function fitting r2 r2 loss complexity complexity loss total loss\n", + "0 0 0.000000 0.000014 0 0 0.000003\n", + "1 x 0.001602 -0.002298 1 1 0.799540\n", + "2 sin 0.161428 -0.253977 2 2 1.549205\n", + "3 cos 0.161428 -0.253977 2 2 1.549205\n", + "4 1/x^2 0.099456 -0.151116 2 2 1.569777\n" + ] + }, + { + "data": { + "text/plain": [ + "('0',\n", + " ((x)>,\n", + " (x)>,\n", + " 0,\n", + " (x, y_th)>),\n", + " 0.0,\n", + " 0)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.suggest_symbolic(0,0,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4b8549a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['x', 'x^2', 'x^3', 'x^4', 'x^5', '1/x', '1/x^2', '1/x^3', '1/x^4', '1/x^5', 'sqrt', 'x^0.5', 'x^1.5', '1/sqrt(x)', '1/x^0.5', 'exp', 'log', 'abs', 'sin', 'cos', 'tan', 'tanh', 'sgn', 'arcsin', 'arccos', 'arctan', 'arctanh', '0', 'gaussian'])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SYMBOLIC_LIB.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "5db9e7cf", + "metadata": {}, + "source": [ + "add bessel function J0 to the symbolic library. we should include a name and a pytorch implementation. c is the complexity assigned to J0." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cbde1924", + "metadata": {}, + "outputs": [], + "source": [ + "add_symbolic('J0', torch.special.bessel_j0, c=1)" + ] + }, + { + "cell_type": "markdown", + "id": "bda24c6d", + "metadata": {}, + "source": [ + "After adding Bessel, we check suggest_symbolic again" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "83e5cfdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " function fitting r2 r2 loss complexity complexity loss total loss\n", + "0 0 0.000000 0.000014 0 0 0.000003\n", + "1 J0 0.198505 -0.319216 1 1 0.736157\n", + "2 x 0.001602 -0.002298 1 1 0.799540\n", + "3 sin 0.161428 -0.253977 2 2 1.549205\n", + "4 cos 0.161428 -0.253977 2 2 1.549205\n" + ] + }, + { + "data": { + "text/plain": [ + "('0',\n", + " ((x)>,\n", + " (x)>,\n", + " 0,\n", + " (x, y_th)>),\n", + " 0.0,\n", + " 0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# J0 fitting is not very good\n", + "model.suggest_symbolic(0,0,0)" + ] + }, + { + "cell_type": "markdown", + "id": "4180de14", + "metadata": {}, + "source": [ + "The fitting r2 is still not high, this is because the ground truth is J0(20x) which involves 20 which is too large. our default search is in (-10,10). so we need to set the search range bigger in order to include 20. now J0 appears at the top of the list\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e78f4674", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " function fitting r2 r2 loss complexity complexity loss total loss\n", + "0 J0 0.998912 -9.830484 1 1 -1.166097\n", + "1 0 0.000000 0.000014 0 0 0.000003\n", + "2 x 0.001602 -0.002298 1 1 0.799540\n", + "3 cos 0.583964 -1.265186 2 2 1.346963\n", + "4 sin 0.583964 -1.265186 2 2 1.346963\n" + ] + }, + { + "data": { + "text/plain": [ + "('J0',\n", + " (,\n", + " J0,\n", + " 1,\n", + " ),\n", + " 0.9989116787910461,\n", + " 1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.suggest_symbolic(0,0,0,a_range=(-40,40))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_6_PDE_interpretation-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_6_PDE_interpretation-checkpoint.ipynb new file mode 100644 index 00000000..a85aa118 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_6_PDE_interpretation-checkpoint.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 6: Solving Partial Differential Equation (PDE)" + ] + }, + { + "cell_type": "markdown", + "id": "7d568912", + "metadata": {}, + "source": [ + "We aim to solve a 2D poisson equation $\\nabla^2 f(x,y) = -2\\pi^2{\\rm sin}(\\pi x){\\rm sin}(\\pi y)$, with boundary condition $f(-1,y)=f(1,y)=f(x,-1)=f(x,1)=0$. The ground truth solution is $f(x,y)={\\rm sin}(\\pi x){\\rm sin}(\\pi y)$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0e2bc449", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pde loss: 2.23e+00 | bc loss: 5.99e-03 | l2: 3.78e-03 : 100%|███████| 20/20 [00:22<00:00, 1.11s/it]\n" + ] + } + ], + "source": [ + "from kan import *\n", + "import matplotlib.pyplot as plt\n", + "from torch import autograd\n", + "from tqdm import tqdm\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "dim = 2\n", + "np_i = 21 # number of interior points (along each dimension)\n", + "np_b = 21 # number of boundary points (along each dimension)\n", + "ranges = [-1, 1]\n", + "\n", + "model = KAN(width=[2,2,1], grid=5, k=3, seed=1, device=device)\n", + "\n", + "def batch_jacobian(func, x, create_graph=False):\n", + " # x in shape (Batch, Length)\n", + " def _func_sum(x):\n", + " return func(x).sum(dim=0)\n", + " return autograd.functional.jacobian(_func_sum, x, create_graph=create_graph).permute(1,0,2)\n", + "\n", + "# define solution\n", + "sol_fun = lambda x: torch.sin(torch.pi*x[:,[0]])*torch.sin(torch.pi*x[:,[1]])\n", + "source_fun = lambda x: -2*torch.pi**2 * torch.sin(torch.pi*x[:,[0]])*torch.sin(torch.pi*x[:,[1]])\n", + "\n", + "# interior\n", + "sampling_mode = 'random' # 'radnom' or 'mesh'\n", + "\n", + "x_mesh = torch.linspace(ranges[0],ranges[1],steps=np_i)\n", + "y_mesh = torch.linspace(ranges[0],ranges[1],steps=np_i)\n", + "X, Y = torch.meshgrid(x_mesh, y_mesh, indexing=\"ij\")\n", + "if sampling_mode == 'mesh':\n", + " #mesh\n", + " x_i = torch.stack([X.reshape(-1,), Y.reshape(-1,)]).permute(1,0)\n", + "else:\n", + " #random\n", + " x_i = torch.rand((np_i**2,2))*2-1\n", + " \n", + "x_i = x_i.to(device)\n", + "\n", + "# boundary, 4 sides\n", + "helper = lambda X, Y: torch.stack([X.reshape(-1,), Y.reshape(-1,)]).permute(1,0)\n", + "xb1 = helper(X[0], Y[0])\n", + "xb2 = helper(X[-1], Y[0])\n", + "xb3 = helper(X[:,0], Y[:,0])\n", + "xb4 = helper(X[:,0], Y[:,-1])\n", + "x_b = torch.cat([xb1, xb2, xb3, xb4], dim=0)\n", + "\n", + "x_b = x_b.to(device)\n", + "\n", + "steps = 20\n", + "alpha = 0.01\n", + "log = 1\n", + "\n", + "def train():\n", + " optimizer = LBFGS(model.parameters(), lr=1, history_size=10, line_search_fn=\"strong_wolfe\", tolerance_grad=1e-32, tolerance_change=1e-32, tolerance_ys=1e-32)\n", + "\n", + " pbar = tqdm(range(steps), desc='description', ncols=100)\n", + "\n", + " for _ in pbar:\n", + " def closure():\n", + " global pde_loss, bc_loss\n", + " optimizer.zero_grad()\n", + " # interior loss\n", + " sol = sol_fun(x_i)\n", + " sol_D1_fun = lambda x: batch_jacobian(model, x, create_graph=True)[:,0,:]\n", + " sol_D1 = sol_D1_fun(x_i)\n", + " sol_D2 = batch_jacobian(sol_D1_fun, x_i, create_graph=True)[:,:,:]\n", + " lap = torch.sum(torch.diagonal(sol_D2, dim1=1, dim2=2), dim=1, keepdim=True)\n", + " source = source_fun(x_i)\n", + " pde_loss = torch.mean((lap - source)**2)\n", + "\n", + " # boundary loss\n", + " bc_true = sol_fun(x_b)\n", + " bc_pred = model(x_b)\n", + " bc_loss = torch.mean((bc_pred-bc_true)**2)\n", + "\n", + " loss = alpha * pde_loss + bc_loss\n", + " loss.backward()\n", + " return loss\n", + "\n", + " if _ % 5 == 0 and _ < 50:\n", + " model.update_grid_from_samples(x_i)\n", + "\n", + " optimizer.step(closure)\n", + " sol = sol_fun(x_i)\n", + " loss = alpha * pde_loss + bc_loss\n", + " l2 = torch.mean((model(x_i) - sol)**2)\n", + "\n", + " if _ % log == 0:\n", + " pbar.set_description(\"pde loss: %.2e | bc loss: %.2e | l2: %.2e \" % (pde_loss.cpu().detach().numpy(), bc_loss.cpu().detach().numpy(), l2.cpu().detach().numpy()))\n", + "\n", + "train()" + ] + }, + { + "cell_type": "markdown", + "id": "e2246bab", + "metadata": {}, + "source": [ + "Plot the trained KAN" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "02e2a0ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot(beta=10)" + ] + }, + { + "cell_type": "markdown", + "id": "64d2573b", + "metadata": {}, + "source": [ + "Fix the first layer activation to be linear function, and the second layer to be sine functions (caveat: this is quite sensitive to hypreparams)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e2e78752", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is 0.8357976675033569\n", + "r2 is not very high, please double check if you are choosing the correct symbolic function.\n", + "saving model version 0.1\n", + "r2 is 0.8300805687904358\n", + "r2 is not very high, please double check if you are choosing the correct symbolic function.\n", + "saving model version 0.2\n", + "r2 is 0.8376883268356323\n", + "r2 is not very high, please double check if you are choosing the correct symbolic function.\n", + "saving model version 0.3\n", + "r2 is 0.8372848629951477\n", + "r2 is not very high, please double check if you are choosing the correct symbolic function.\n", + "saving model version 0.4\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(0.8373)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fix_symbolic(0,0,0,'x')\n", + "model.fix_symbolic(0,0,1,'x')\n", + "model.fix_symbolic(0,1,0,'x')\n", + "model.fix_symbolic(0,1,1,'x')" + ] + }, + { + "cell_type": "markdown", + "id": "3fae3f32", + "metadata": {}, + "source": [ + "After setting all to be symbolic, we further train the model (affine parameters are still trainable). The model can now reach machine precision!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "308b72af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pde loss: 1.71e+01 | bc loss: 1.14e-02 | l2: 1.37e-01 : 50%|███▌ | 10/20 [00:11<00:11, 1.20s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/6j/b6y80djd4nb5hl73rv3sv8y80000gn/T/ipykernel_75424/3364925475.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/var/folders/6j/b6y80djd4nb5hl73rv3sv8y80000gn/T/ipykernel_75424/2545871995.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_grid_from_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0msol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msol_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_i\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mpde_loss\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbc_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 383\u001b[0m )\n\u001b[1;32m 384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 386\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mobj_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_directional_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m loss, flat_grad, t, ls_func_evals = _strong_wolfe(\n\u001b[0m\u001b[1;32m 444\u001b[0m obj_func, x_init, t, d, loss, flat_grad, gtd)\n\u001b[1;32m 445\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36m_strong_wolfe\u001b[0;34m(obj_func, x, t, d, f, g, gtd, c1, c2, tolerance_change, max_ls)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;31m# evaluate objective and gradient using initial step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mf_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m \u001b[0mls_func_evals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mgtd_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36mobj_func\u001b[0;34m(x, t, d)\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mobj_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_directional_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 443\u001b[0m loss, flat_grad, t, ls_func_evals = _strong_wolfe(\n\u001b[1;32m 444\u001b[0m obj_func, x_init, t, d, loss, flat_grad, gtd)\n", + "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36m_directional_evaluate\u001b[0;34m(self, closure, x, t, d)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_directional_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0mflat_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gather_flat_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_param\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/var/folders/6j/b6y80djd4nb5hl73rv3sv8y80000gn/T/ipykernel_75424/2545871995.py\u001b[0m in \u001b[0;36mclosure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mpde_loss\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbc_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0;31m torch.autograd.backward(\n\u001b[0m\u001b[1;32m 523\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m )\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "train()" + ] + }, + { + "cell_type": "markdown", + "id": "35985ae9", + "metadata": {}, + "source": [ + "Print out the symbolic formula" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f0ec310e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - 0.5 \\sin{\\left(3.141592 x_{1} + 3.141593 x_{2} - 4.712389 \\right)} + 0.5 \\sin{\\left(3.141593 x_{1} - 3.141592 x_{2} + 1.570797 \\right)}$" + ], + "text/plain": [ + "-0.5*sin(3.141592*x_1 + 3.141593*x_2 - 4.712389) + 0.5*sin(3.141593*x_1 - 3.141592*x_2 + 1.570797)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "formula = model.symbolic_formula()[0][0]\n", + "ex_round(formula,6)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c3e90ca", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_7_PDE_accuracy-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_7_PDE_accuracy-checkpoint.ipynb new file mode 100644 index 00000000..0d7f0391 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_7_PDE_accuracy-checkpoint.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5d904dee", + "metadata": {}, + "source": [ + "# Example 7: Solving Partial Differential Equation (PDE)" + ] + }, + { + "cell_type": "markdown", + "id": "7d568912", + "metadata": {}, + "source": [ + "We aim to solve a 2D poisson equation $\\nabla^2 f(x,y) = -2\\pi^2{\\rm sin}(\\pi x){\\rm sin}(\\pi y)$, with boundary condition $f(-1,y)=f(1,y)=f(x,-1)=f(x,1)=0$. The ground truth solution is $f(x,y)={\\rm sin}(\\pi x){\\rm sin}(\\pi y)$." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0e2bc449", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pde loss: 2.13e+00 | bc loss: 1.80e-03 | l2: 3.11e-03 : 100%|███████| 50/50 [00:35<00:00, 1.43it/s]\n", + "pde loss: 5.68e-01 | bc loss: 5.30e-04 | l2: 1.03e-03 : 100%|███████| 50/50 [00:35<00:00, 1.43it/s]\n", + "pde loss: 1.23e-01 | bc loss: 1.51e-04 | l2: 1.74e-04 : 100%|███████| 50/50 [00:35<00:00, 1.42it/s]\n" + ] + } + ], + "source": [ + "from kan import KAN, LBFGS\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "from torch import autograd\n", + "from tqdm import tqdm\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "\n", + "dim = 2\n", + "np_i = 51 # number of interior points (along each dimension)\n", + "np_b = 51 # number of boundary points (along each dimension)\n", + "ranges = [-1, 1]\n", + "\n", + "\n", + "def batch_jacobian(func, x, create_graph=False):\n", + " # x in shape (Batch, Length)\n", + " def _func_sum(x):\n", + " return func(x).sum(dim=0)\n", + " return autograd.functional.jacobian(_func_sum, x, create_graph=create_graph).permute(1,0,2)\n", + "\n", + "# define solution\n", + "sol_fun = lambda x: torch.sin(torch.pi*x[:,[0]])*torch.sin(torch.pi*x[:,[1]])\n", + "source_fun = lambda x: -2*torch.pi**2 * torch.sin(torch.pi*x[:,[0]])*torch.sin(torch.pi*x[:,[1]])\n", + "\n", + "# interior\n", + "sampling_mode = 'mesh' # 'radnom' or 'mesh'\n", + "\n", + "x_mesh = torch.linspace(ranges[0],ranges[1],steps=np_i)\n", + "y_mesh = torch.linspace(ranges[0],ranges[1],steps=np_i)\n", + "X, Y = torch.meshgrid(x_mesh, y_mesh, indexing=\"ij\")\n", + "if sampling_mode == 'mesh':\n", + " #mesh\n", + " x_i = torch.stack([X.reshape(-1,), Y.reshape(-1,)]).permute(1,0)\n", + "else:\n", + " #random\n", + " x_i = torch.rand((np_i**2,2))*2-1\n", + " \n", + "x_i = x_i.to(device)\n", + "\n", + "# boundary, 4 sides\n", + "helper = lambda X, Y: torch.stack([X.reshape(-1,), Y.reshape(-1,)]).permute(1,0)\n", + "xb1 = helper(X[0], Y[0])\n", + "xb2 = helper(X[-1], Y[0])\n", + "xb3 = helper(X[:,0], Y[:,0])\n", + "xb4 = helper(X[:,0], Y[:,-1])\n", + "x_b = torch.cat([xb1, xb2, xb3, xb4], dim=0)\n", + "\n", + "x_b = x_b.to(device)\n", + "\n", + "alpha = 0.01\n", + "log = 1\n", + "\n", + "\n", + "grids = [5,10,20]\n", + "steps = 50\n", + "\n", + "pde_losses = []\n", + "bc_losses = []\n", + "l2_losses = []\n", + "\n", + "for grid in grids:\n", + " if grid == grids[0]:\n", + " model = KAN(width=[2,2,1], grid=grid, k=3, seed=1, device=device)\n", + " model = model.speed()\n", + " else:\n", + " model.save_act = True\n", + " model.get_act(x_i)\n", + " model = model.refine(grid)\n", + " model = model.speed()\n", + "\n", + " def train():\n", + " optimizer = LBFGS(model.parameters(), lr=1, history_size=10, line_search_fn=\"strong_wolfe\", tolerance_grad=1e-32, tolerance_change=1e-32, tolerance_ys=1e-32)\n", + "\n", + " pbar = tqdm(range(steps), desc='description', ncols=100)\n", + "\n", + " for _ in pbar:\n", + " def closure():\n", + " global pde_loss, bc_loss\n", + " optimizer.zero_grad()\n", + " # interior loss\n", + " sol = sol_fun(x_i)\n", + " sol_D1_fun = lambda x: batch_jacobian(model, x, create_graph=True)[:,0,:]\n", + " sol_D1 = sol_D1_fun(x_i)\n", + " sol_D2 = batch_jacobian(sol_D1_fun, x_i, create_graph=True)[:,:,:]\n", + " lap = torch.sum(torch.diagonal(sol_D2, dim1=1, dim2=2), dim=1, keepdim=True)\n", + " source = source_fun(x_i)\n", + " pde_loss = torch.mean((lap - source)**2)\n", + "\n", + " # boundary loss\n", + " bc_true = sol_fun(x_b)\n", + " bc_pred = model(x_b)\n", + " bc_loss = torch.mean((bc_pred-bc_true)**2)\n", + "\n", + " loss = alpha * pde_loss + bc_loss\n", + " loss.backward()\n", + " return loss\n", + "\n", + " if _ % 5 == 0 and _ < 20:\n", + " model.update_grid_from_samples(x_i)\n", + "\n", + " optimizer.step(closure)\n", + " sol = sol_fun(x_i)\n", + " loss = alpha * pde_loss + bc_loss\n", + " l2 = torch.mean((model(x_i) - sol)**2)\n", + "\n", + " if _ % log == 0:\n", + " pbar.set_description(\"pde loss: %.2e | bc loss: %.2e | l2: %.2e \" % (pde_loss.cpu().detach().numpy(), bc_loss.cpu().detach().numpy(), l2.cpu().detach().numpy()))\n", + "\n", + " pde_losses.append(pde_loss.cpu().detach().numpy())\n", + " bc_losses.append(bc_loss.cpu().detach().numpy())\n", + " l2_losses.append(l2.cpu().detach().numpy())\n", + " \n", + " \n", + " train()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dcbfa677", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pde_losses, marker='o')\n", + "plt.plot(bc_losses, marker='o')\n", + "plt.plot(l2_losses, marker='o')\n", + "plt.yscale('log')\n", + "plt.xlabel('steps')\n", + "plt.legend(['PDE loss', 'BC loss', 'L2 squared'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bce40477", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_8_continual_learning-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_8_continual_learning-checkpoint.ipynb new file mode 100644 index 00000000..6df4a261 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_8_continual_learning-checkpoint.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "134e7f9d", + "metadata": {}, + "source": [ + "# Example 8: Continual Learning" + ] + }, + { + "cell_type": "markdown", + "id": "2571d531", + "metadata": {}, + "source": [ + "Setup: Our goal is to learn a 1D function from samples. The 1D function has 5 Gaussian peaks. Instead of presenting all samples to NN all at once, we have five phases of learning. In each phase only samples around one peak is presented to KAN. We find that KANs can do continual learning thanks to locality of splines." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2075ef56", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1x0lEQVR4nO2deXwU9f3/X7ubY3MuCUcSIEBEQEK8CIZL8EY8UFu/VascWrQibT1Qq1RtiK1FW0vpIXgi9ahFq1j5qVGschSwyClLQBCCIGwISch973x+f2xms5vsMbM785nP7r6fjwePh04+O/P5PPezn3nPZ2Y+bxNjjIEgCIIgCMIgzEZXgCAIgiCI2IaCEYIgCIIgDIWCEYIgCIIgDIWCEYIgCIIgDIWCEYIgCIIgDIWCEYIgCIIgDIWCEYIgCIIgDIWCEYIgCIIgDCXO6AooQZIknDhxAmlpaTCZTEZXhyAIgiAIBTDG0NDQgIEDB8Js9j//ERHByIkTJ5Cbm2t0NQiCIAiCCIFjx45h8ODBfv8eEcFIWloaAFdj0tPTDa4NQRAEQRBKqK+vR25urvs87o+ICEbkWzPp6ekUjBAEQRBEhBHsEQt6gJUgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCEOhYIQgCIIgCENRHYxs2LABM2bMwMCBA2EymfD+++8H/cz69etRWFgIq9WKM844A88//3wodY15nBLDpoNVePaT/Xj2k2+w6dsqOCVmdLWiDvLMB/LMj/ZOCa9sPIxf/9uOVzYeRnunZHSVohLq06FjYoypMvXxxx9j06ZNGDt2LG688UasXr0aN9xwg9/y5eXlKCgowF133YW7774bmzZtwvz58/HWW2/hxhtvVHTM+vp62Gw21NXVxexy8KV2Bx59bw9qmzu8tvdJjsfTPzwb0wtyDKpZdEGe+UCe+bH4ozK8tLEcnudEswm4a0oeFl6db1zFogzq075Rev5WHYx4fdhkChqMPPLII/jggw+wb98+97Z58+Zh9+7d2LJli6LjxHowUmp3YN4bOwKWeX7m2Jjt7FpBnvlAnvmx+KMyvLCh3O/f755KAYkWUJ/2j9Lzt+7PjGzZsgXTpk3z2nbllVdi27Zt6Ojo8PmZtrY21NfXe/2LVZwSw6IP9gYtV7KmjKYDw4A884E886O9U8JLG/0HIgDw0sZyumUTJtSntUH3YKSiogJZWVle27KystDZ2Ymqqiqfn1m8eDFsNpv7X25urt7VFJat5TWoqG8LWs5R14qt5TUcahSdkGc+kGd+vL7lCIKd+yTmKkeEDvVpbeDyNk3P1MHynSF/KYUXLlyIuro6979jx47pXkdR+aysQnHZtSrKEt6QZz6QZ35sPHhKUbkNB31fFBLKoD6tDboHI9nZ2aio8P4CKisrERcXh759+/r8TGJiItLT073+xSJOiWH1ruOKy/971wmaBgwB8swH8swPp8Sw/ehpRWV3Hj1NnkOE+rR26B6MTJw4EWvXrvXa9umnn2LcuHGIj4/X+/ARzdbyGtQ0+X6uxhfVTe00DRgC5JkP5JkfW8tr0NDqVFS2vrWTPIcI9WntUB2MNDY2YteuXdi1axcA16u7u3btwtGjRwG4brHMnj3bXX7evHn47rvvsGDBAuzbtw8rVqzAK6+8goceekibFkQxlQ2tXD4T65BnPpBnfqj1Rp5Dg/q0dsSp/cC2bdtwySWXuP9/wYIFAIA5c+Zg5cqVcDgc7sAEAPLy8vDRRx/hgQcewHPPPYeBAwfiL3/5i+I1RmKZAWlWLp+JdcgzH8gzP9R6I8+hQX1aO1QHIxdffDECLU2ycuXKXtsuuugi7NgR+B1sojdFeZnIsVlRUdcKJXcZc2xWFOVl6l6vaIM884E880N27agLfhVOnkNHTZ82Acgm136h3DQCYzGbcN25OYoGbgC47twcWMy+31Ai/EOe+WAxm1A8I588c0Du00ogz6GjZuxgAIpn5JNrP1AwIjCldgdeDLB6Yk9e3FCOUrtDxxpFJ+RZTMhz6Kjp0+Q5dNSOHYR/KBgRFKfEULKmTPFVpAyt8qcO8swP2bUayLN6QunT5Fk9aj2bQJ4DQcGIoGwtr1F0v9cTBlrlTy3kmR9qXZPn0CDPfCDP2kLBiKCE8/oXvTqmHPLMj1B9kWd1kGc+kGdtoWBEUMJ5/YteHVMOeeZHqL7IszrIMx/Is7ZQMCIo8itjap67NoFe01MLeeaHWtfkOTTIMx/Is7ZQMCIo8muQSpF/EPTqmDrIMz/UuCbPoUOe+UCetYWCEYGZXpCD524di9RE77Xp+iTHo0+yd16fjJR4PHfr+ZheoGxtAaIb2bMtydspedYe2XVPeo7P5Dk8ZM89T3zkWVtkzwlx3qfSnp77JMeR5yBQMCIwpXYHfvNhGRrbOt3bMlPi8bsbCvC7G85GvKW7x9c0deA3H+6j9QJCQPZc19Kd8MrTszW++2dCnsOj1O5Ayf/b67UtMyUeP5k0DCmJFvc28hwecp/2fI1U9pye1H1xQ57DQ/bc3im5t8meMzwuZE43d5LnIJhYoLXdBaG+vh42mw11dXVIT083ujpcKLU7cM8bO3q9w24C/L7XLocmy2eOpQhcIeSZH/5c+4M8hwZ55gN5VobS8zfNjAhIoMV0AnV8+W+0sI4yyDM/QlmIizyrhzzzgTxrDwUjAhLKQlwytLCOcsgzP0J1TZ7VQZ75QJ61h4IRAdFiURxaWCc45Jkf4Xoiz8ogz3wgz9pDwYiAaLEoDi2sExzyzI9wPZFnZZBnPpBn7aFgREBCWYhLhhbWUQ555keorsmzOsgzH8iz9lAwIiCBFtMJ1PlpYR11eHruacvk5789/588K0ft4nIAeQ4F8swH8qw9FIwIyvSCHCyfORYD0hK9tmfbrHh+5lg8P3MsstN7/y3WXhsLF9lzts172tTLs4+/kWf1yK57LhCVY7Pi7ql5yCHPmiB7TkmweG0nz9oie+65WCJ5Dg1aZ0Rwyk7U4eq//BepiXF4afY4FOVluqNqp8SQ/+tStHVK+NNN5+G68wZSxB0inU4Jox4vhZMxPHfrWEwvyPbyfOXSDfi2shEPXjES8y85kzyHwbV/3Qj78Xr8dGoeLhmV5e7TTonhrte24fP9lfhR4WA8feM55DkMHnpnN/61/Xtce04Obhs/1Mvzbz8sw6ubjqBoWCbe+ukE8hwGL6w/hMUf70fh0Aw8NG2Ul+c3vvwOxR/sxYC0RGxZeFlMeqZ1RqKE+lbX6qtZ6YmYOLyvV2e2mE3o3zVzMqRvckx2dK1o7nDC2RWXXzZ6QC/PQzKTAQAD0hPJc5icbnKtdDu9IMerT1vMJozKTgMApCTGkecwOd3UDgCYfGa/Xp7HDskAAJhMIM9hcrrZ1Z/PHdynl+cLR/QDALR0OMlzECgYERinxLD1sOt9dDnS7ok8RfjJXge2HKqmxXRCpKbRNXDHW0zYebS2l0c5R83Gg1XkOQycEkN1UxsA4Luq5t6eu/rznuN15DkMnBLD0ZpmAMCp+rZeHtOtriXhj9Y0k+cwcEoMB042AAAaWjt6eUzryivW0NqJ/x6sIs8BoNs0glJqd6BkTZnXwjo5NiuKZ+S77zmW2h24961daHdKfssQwSm1O/DYajuqu64kAW+PpXYHHnx7N5ranT7/Tiij1O7Aog/2oqK+zb2tp+dH3v0adS2dPv9OKCPY2FFqd+Dx9+2oavTd3wllKPEcqL/HCkrP3xSMCEigfCmAK7cBgKBlYqnDh0ow1z+dmocXN5ST5zAhz3wgz3wgz8qhYCRCcUoMFz7zud+lhk1wPT8CmFBR779Mts2K/z5yKd2nDEAw14ArFbi/mVXyrAzyzAfyzAfyrA56gDVCCZbzgAGoqG/zG4jIZSj/QXCU5JcIdIuXPCuDPPOBPPOBPOsDBSOCoWXOAsp/EBit/JDnwJBnPpBnPpBnfaBgRDC0zFlA+Q8Co5Uf8hwY8swH8swH8qwPFIwIRrCcByYA2emJyE4PXIbyHwRHSX4Js8n/EvzkWRnkmQ/kmQ/kWR8oGBEMJflSFl03BouuC5y7hvIfBCdYDiATgLum5Pn9O0CelaDGM+UBCh3yzAfyrA8UjAhIoHwp8ithcpl+qQl+yxDBcedLsXgPDLLHhVfnu74LygMUFv7ypfTyTPk8wqI7X0qc13byrC2y574pvsdf8qweerVXYNo7JYx8/GMAwAszC3F5flavaPpIVRMufnYd4i0mvPaT8V65awjlTF+6AfsrGnDPxcMxdUT/Xh6dEsOY4lK0dlAeoHD45b924+1t3+Pqs7Mxa8Iwn56v/vNGfHOyAQ9cPhI/v5TyAIWCv3wpMk6J4Z43tuPTspP44fmD8IcfnUueQ2DdN5W4/dWvMLCPFX/80Xk+PT/98X68tPEwzs/tg3/dMynmPNOrvVFAi8eKn5ecNcBnJ87oisw7nAznD+kTcx1dKxq6cgBNH5PdKwcQ4JqazUh2uT6jfwp5DpHGNpfnomGZfj0PykgC4LqvTp5DQ+7PYwam+/U8IisVAJCeFE+eQ0T2nJuR7Nfzubk2AEC8xUyeA0DBiMA0tLkSMCXGmXulXZdJS4yD3L/rWjp4VS3qqO9yl2aN81tGzgNU30qeQ0UevNN7pF33RM6bQp5DR3bXM729J+nWrv5M40bI1Ha5k3NX+SKty3NDW6ffMgQFI0IjX0UGOkGazabuzk6Dd0g4JeYeKAKfJF1/o6AvdOQs1HKf9YX8HdBJMnTkPpquxHMrnSRDRR5zA/Xn1K5keY1t1J8DQcGIwMhXkXJn9keqR2ZIQj2NHlcsgQI/efCmYCR0ugfvAJ6tdJIMF7mPBpoZSaMZqLBpbA1+wSjP9NH4HBgKRgTFKTF81bVcsMlk8pt62ikxmLvu02wtr6EU1SFQ2+zKXhpvMWHHd7V+HaZ3vaGw5VA1pV0PAafEUNOVKba8qsmvv9SuwXvviTryHAJOieH46RYAQEVdq3/PXRcxx083k+cQcEoM31Y2AgDqmjv8+kvqeoOsvqUDm7+tIs9+oLdpBCRYamq15Qj/KE2nXmp3YMHbu9Hs8VAxuVaOmj79yLtfo66lM2A5wjdqPD+22o7qpsD9nvCNGs/FH+zFyfq2gOWiGcraG6EES00tv6OutBzhH3LNB/LMB/LMB/KsDnq1NwJxSgwla8p6dV4A7m0la8rQ3ikpKkfTgf4h13wgz3wgz3wgz/pBwYhABEtNLaeefn3LEUXlKEW1f8g1H8gzH8gzH8izflAwIhBKU0p/V9Os6f5iEXLNB/LMB/LMB/KsHxSMCITSlNJDM5M13V8sQq75QJ75QJ75QJ71g4IRgQiWmlpOPT1r4jBF5ShFtX/INR/IMx/IMx/Is35QMCIQnqmpA6WeTogzKypHeRD8EywNONDbdaBy5No35JkPoXimsUM95Fk/KBgRDDk1dbDU00rLEf6RHVrjvX8G/lz3T/OdLpxcB0b2l271nda+p+cc6tMhIfvL6JEnhcYObZH9ZaUlem0nz+FB64wIilNimPz056iob0XxtfmYPWmYzyjaKTG8vPEwFn+8H7mZyVj30MUUbavkzr9/hc/2VeLmcYNxw/mDe6UBlznV0IYLnvoMAPDm3PGY4CNLJ+GfpWsPYOl/DmLS8L74xaUj/Hp2SgwXPPUZapra8dQNBbilaAh5VsH7O4/j/lW7cOaAVPzm+oKAnm96YQu2f3cad07Jw8KrRpNnFRypasLFz65DgsWMv/+kKKDne9/aiQ/3OHDtOTn48y3nx5RnWmckwrGYTejsegc90EnPYja57ztKEoupTq4V8qqqk87s5zMNuIy8HDwAFAy2kWuVyDmAzh5kC+jZYjYhM8U1C3VG/1TyrJKmdpfnvH4pQT3nZiQBALLTreRZJfK4YUuOD+p5ZFYaAFdCPfLsGwpGBEZOKhYsUZ6cpKmRUlSHRFPXoJKSENhzYpwFCRbXT4Zcq6dBQVIxme5Mp+RZLUqSt8mkUJLNkHFnVQ8yPgM0RiuBghFBae+U0NYpAQicBhwAUhNdf29s60QE3HUTjqauASI50RK0rJzErYkGFdU0tAVPty5DaddDR81JUvZM/Vk9ct9MVRJcuzP3Un/2BwUjguIZQacEOUnKHd0pMbR2SLrWKxpp7nIdbGYE6B686UpSPaHNjDiDlCR60u1ZedAn39ohlCN7DjZzDcD98HYjjRt+oWBEUOROmxRvQZwl8NeUHG+Bqes2ZANdSarGfZtGycwI3T4ImXo1J0kavEPGfZKk2zS6Io8BSoIRz9lrwjcUjAhKXYsrqIi3mLDlUHXAhEoMgDXOdSL974EqSr6kAqfE3APEfkdDUHdywPLF/sqg3wvRjVNiOFXvWvr6aE1zUG/JCS7PO4+eJs8qcEoM31U3AXC9/aXU8+FTTeRZBU6JYe/xegBAS4dTseeT9a3k2Q/0aq+AlNod+NVqO2qa2t3bcmxWFM/I7/VueqndgZI1ZV5JmfyVJbwptTuw6IO9qKhvc28L5K7U7sB9/9zlfpYnWHnChdo+Wmp34KF3dnvdoiHPwQnF86Pv7UFtc4ei8oSLUDw/8b4dpxqDj+fRiNLzNwUjglFqd+CeN3b0Sj0tvwzmuViOmrKEN2rdkevQIM98IM98IM/qoXVGIhCnxFCypqxXxwXg3laypgxOiakqS3ij1h25Dg3yzAfyzAfyrC8UjAjE1vIar6m/njAAjrpWbC2vUVWW8EatO3IdGuSZD+SZD+RZX0IKRpYtW4a8vDxYrVYUFhZi48aNAcu/+eabOPfcc5GcnIycnBzccccdqK6uDqnC0Uxlg/+O27OcmrKEN2rdkevQIM98IM98IM/6ojoYWbVqFe6//3489thj2LlzJ6ZMmYKrrroKR48e9Vn+v//9L2bPno25c+di7969eOedd/DVV1/hzjvvDLvy0caANGvwQl3l1JQlvFHrjlyHBnnmA3nmA3nWF9XByJIlSzB37lzceeedGD16NJYuXYrc3FwsX77cZ/kvv/wSw4YNw7333ou8vDxceOGFuPvuu7Ft27awKx9tFOVlIsdm7ZVyWsYE11PYRXmZqsoS3qh1R65DgzzzgTzzgTzri6pgpL29Hdu3b8e0adO8tk+bNg2bN2/2+ZlJkybh+++/x0cffQTGGE6ePIl//etfuOaaa0KvdZRiMZtQPCPf59/kDl08Ix8Ws8mrbM/O3rMs4Y0az6GUJ1yQZz6E45nGDuWo9Uae1aEqGKmqqoLT6URWVpbX9qysLFRUVPj8zKRJk/Dmm2/i5ptvRkJCArKzs9GnTx/89a9/9XuctrY21NfXe/2LFaYX5GD5zLFIjPP+arJt1l6vgclls23WoGUJb2R36T1WqfTnTi6fmRyvqDzhQvaWodCbXH5AWqKi8oQLtd5o7AgNtd7Is3JUrTNy4sQJDBo0CJs3b8bEiRPd25966im8/vrr2L9/f6/PlJWV4fLLL8cDDzyAK6+8Eg6HAw8//DAuuOACvPLKKz6Ps2jRIpSUlPTaHgvrjMjc/MIW/K+8BnMmDcX0MTkoysv0G0E7JYanP96PlzYextghffDOvEkUbSvk+fWH8PTH+zFuaAYenDYqoGcA2HakBv/3/Bb0TUnA324dG7Q84eLd7d/jwXd2Y2RWKkquKwjqraG1A2cv+hQA8OrtF2DqyP7kWQEHKhowbekGJCdY8MqcC4J6dkoMExf/B5UNbSi5bgxmThhKnhXQ3ilh5OMfAwBemFmIy/Ozgnqe9cr/sPlQNWZNGIpF142JGc+6rDPSr18/WCyWXrMglZWVvWZLZBYvXozJkyfj4YcfxjnnnIMrr7wSy5Ytw4oVK+BwOHx+ZuHChairq3P/O3bsmJpqRgXyEuUXjxqAicP7Buy4FrMJ5+baAABxFnPMdHItaOnKS3NWTlpQzwBgS3Jd4UuMKSpPuGjpcHke1jdFkbfUxDjIRcYMTCfPCmntdHnOSE5Q5NliNqFvqms2ZVi/FPKsELk/A8DFZwUPlC1mE4b1SwEA9E1NIM8+UBWMJCQkoLCwEGvXrvXavnbtWkyaNMnnZ5qbm2E2ex/GYnGt0+9vUiYxMRHp6ele/2KNZjl5m4JMsoBHAjdKeKWK5nblGXsBjwRubZ1++y/RG7dnBUnFAMBkMnVnSKbkYopp6lpCX86FooQ0OXMveVaMfLGYEGdGYpwy13J/li+ACG9Uv02zYMECvPzyy1ixYgX27duHBx54AEePHsW8efMAuGY1Zs+e7S4/Y8YMvPfee1i+fDkOHz6MTZs24d5770VRUREGDhyoXUuiDHlgUDqopFkpm2woyBl7k1UGfR1O5pWjhghMKCfJVDpJqkYO+pIVBn1Ad/JHupBRjuwqTYVnue/TGO0b5Sa7uPnmm1FdXY0nn3wSDocDBQUF+OijjzB06FAAgMPh8Fpz5Pbbb0dDQwP+9re/4cEHH0SfPn1w6aWX4plnntGuFVGIe2ZEYWeX07I3tHYEKUl4Ip/o5AE5GJ4zKE1tnbDGKz+5xjJqZ0aArlmoOjpJqqHJPaOqvF/K3wmdJJXT2OYaZ9X0Z3nsaKaZEZ+oDkYAYP78+Zg/f77Pv61cubLXtl/84hf4xS9+EcqhYhLGmMftA4UnSfkqkjq6Krqv2JX9FMxmE1ISLGhqd6KxrdN9v50ITPcMFJ0k9aTZPaOqIuijGSjVyBmlVQUj5DkglJtGQNo6Jci5k5ROt8pBS3unhA4n3T5QSvcVu4rbB123xBroil0x8klS6bM5gMdzUDR4K8Y9M6KmP5Nn1XT3ZzXBtasszYz4hoIRAfGMnJMU3gbwvBKizq6cJpUPCgM0eIeCe2YkhJMkXUkqJ5SZEZqBUk93f1bzzAh5DgQFIwIiX3EnWMzYWl6jKMW0xWyCxeR6XWzjgVOUlloBTomhqqENAHCkukmRM88y246cJs8KcEoMjtoWAMCJ2lbFzuST5P/Ka7DlUDW5DoJTYjhY2QgAqGvpUOxLvnW2v6KBPCvAKTHsPVEHAGjtcCr2ZY13nW4r6lvJsw9ULXpmFEoXTYkGSu0OPP6+HVWN7e5tOTYrimfk+12tr9TuQMmaMq901cE+E+uE4ow8qydUZ6V2Bxa8vdtrlo9c+yccz4+8+zXqWjpVfS5WCcfzY6vtqG5SPq5HC0rP3xSMCESp3YF73tiBnl+IvDyOr+WDQ/lMrEOe+RCqM3KtDvLMB/IcGrqswEroh1NiKFlT1qvDAnBvK1lT5jW1F8pnYh3yzIdQnZFrdZBnPpBn/aFgRBC2ltd4Tf31hAFw1LVia3lNWJ+JdcgzH0J1Rq7VQZ75QJ71h4IRQahs8N9h/ZUL5TOxDnnmQ6jOyLU6yDMfyLP+UDAiCAPSrMEL9SgXymdiHfLMh1CdkWt1kGc+kGf9oWBEEIryMpFjs8JfLkcTXE9fF+VlhvWZWIc88yFUZ+RaHeSZD+RZfygYEQSL2YTiGfk+/yZ35OIZ+V6ppz0/07Oz+/tMrEOe+RCK53A+F6uE2jfJszq08ExjR2AoGBGI6QU5WD5zbK8lhrNtVr+vf8mfybZZFX8m1pGdZabEe20nz9oiO+ufmuC1PZgz+XP9VH4uVgm1b7o/l56o6nOxStieaewICK0zIiAL3/sab209hqsKsjF74jAU5WUGjZydEsND7+zG6p3HMS0/C8tnFlK0HYRSewXmvbEdw/omY/EPz1Hs+W+ff4s/fXYAo7LS8NF9U8hzEHYdq8UNz21Cn6R4LJ9ZqMgzABypasLFz65DQpwZf7+jSPHnYpWOTgkjHv8YAPDCzEJcnp+lyJdTYigo/gQtHU4s+dG5uP78QeQ5AE6JYfzvPkNVYzt+e0MBflw0RLHn6Us34GBlIx68YiTmX3JmTHimdUYimJauVScLh2Zg4vC+ijqsxWzCyKw0AEB6UnxMdPJwae1weR6ckazK8/lD+rj/mzwHR/bcNzVBsWegOyFhe6eE8RSIBKXdI0HmRaP6K/ZlMZuQnuRyPTI7jTwHwWI2obNrXRA1/dJiNiEr3TU7kpuZTJ57QMGIgMhJmJJUZIQEPLNCUiImJTS6k4qRZz3pzoysPKkY4J28sKWDkj8Go6nLs9kEJMapG9opvb06mtvUJ8oDuseaJho7ekHBiIC4B28VmTeB7qyQTW00cCsh1JOk2zNlR1aE3B/VBn3WeDO6cj/S4K0A+QSZkhAHk0ndVbc81lDG7+B0OCX3LFTP5/uCIY81zTRG94KCEQGRBwTVV+wJdMWuhlBPknL5ZrqKVESowbXJZHJ/poVOkkGRA7bkRHX9GaArdjV4BmzJKvu0PKvaSGNHLygYERD3FY7aK/ZEmhlRg3ySTA1xZqS5wwmJckoEpSnEKW3A4yRJfToo8klSbdAH0BW7GuRxI95iQoLa22HuGSgKRnpCwYiAuK9waGZEV5rcM1ChXd0wBrR20uAdjO6ZkdCv2KlPB0d+3oNmRvSle0Y1lOCabvH6g4IRAXFf4dCzDLoiD94pKgdva5zF/SwD3WMPTqhBn+dnqE8HpzkMz/TMiHLCCa7lsYYeFO4NBSMC0hTuWx7U0RUR6hWO2WxCcrzsmgbvYDSHGPR5fob6dHDcwXUoM1B0klSMPG6ofdsR8HxricaNnlAwIhidTgltnfKT2vQsg550v00TyuAtX7HT4B0MLWZG6Io9OO6ZkRCezUlxv4lH/TkYLR2hvYUH0G3HQFAwIhieT1nvOV4Hp4qgwhrv+joZA9YfOKXqs7GGU2I4We9K232spkWVK6fEYOm6T7O1vIY8B8ApMRyraQYAVNa3qnYlD95bDldjy6Fqcu0Hp8Swz1EPAGhq7VTtSb7K31fRQJ4D4JQYdh2tBQB0Opl6z10zqsdPt5DnHtBy8AJRanfg1//ei8qGNve2HJsVxTPyg+YvKLU7sOiDvaioV//ZWKPU7kDJmjI46lrd29R4DvWzsUa4rkrtDjywahdaOrpXFiXXvdHC8y//9TXqW7svhMhzb7Tw/KvVe1DT1BHS5yMVpedvCkYEodTuwD1v7EDPL0NeuihQQqVwPhtrkGc+hOuKXCuDPPOBPIcO5aaJIJwSQ8masl4dFYB7W8maMp9TeuF8NtYgz3wI1xW5VgZ55gN55gMFIwKwtbzGa+qvJwyAo64VW8trNP1srEGe+RCuK3KtDPLMB/LMBwpGBKCywX9HDVYunM/GGuSZD+G6ItfKIM98IM98oGBEAAakWUMuF85nYw3yzIdwXZFrZZBnPpBnPlAwIgBFeZnIsVnhL8+mCa6nrovyMjX9bKxBnvkQrityrQzyzAfyzAcKRgTAYjaheEa+z7/JHbh4Rj4s5t7d2fOzPf8a7LOxBnnmQzietfh8rBBunyTPytDSM40d/qFgRBCmF+Rg+cyxSOuxql+2zRr0tS/5s9k272k+JZ+NNWRXfVMSvLaTZ22RXQ1IS/TartSV/PnMlPiQPh8rhNsn5c9npYf2PcUKWnmmscM/tM6IYDxTuh/L1x3C1JH9cM9FZ6IoL1NxxOyUGO55Yzs+LTuJH5w/CM/+6NyYj7b9seHAKcxesRXZNiv+dNN5qj0//fF+vLTxMAqHZuDtuyeSZz8cPtWIS/+4HolxZqy8o0iVZwDYefQ0frBsMzKS47HstkLVn48VnBJDQfEnaOlwYslN5+L68wap8tTRKWHk4x+DAVh+21hMG5NNnn3glBgu++M6HKluxiPTz8JPp56hypNTYrj42XU4VtOMX109GnMvzIt6z7TOSITS0pVf4uxBNkwc3ldVR7WYTTijfyoAICM5Ieo7eTi0drg859isIXkuGOT6USXGmclzAFq7Vk9NT4pX7RkAUj1mCkP5fKxgNgFtna4+feGZ/VR7io8zu5feH52TTp79YDGbYOpKBTFuWIZqTxazCf1TXbOyQzKTybMHFIwIhpxAKZSkYkB3xk45mRPhGzmpmNpkhDIplNpeEeGkWwc8ExKS50C0dUqQ18wKJVEe4JFRlpK4BUTu03KeGbXInmmM9oaCEcFocp8kwxy8KUV1QJrcQV+onim1vRLCydgLdP8O2jsldDilIKVjF89su8lhniQpQ3JgmrvG1lCy9gLdYw6N0d5QMCIY8skt5KsbSlGtCHnwDnVASaHU9opw9+dQgz6PIIZc+6fZHfRZYA5x6r/7JEljhz8YY+4LmVAvGLvHDvLsCQUjgtEU5u0DmhlRhuwn1JNkSiIFfUpwz4yEGPQlxJkRb3GdXMm1f5rCvL0LUICtBC1uh8mzqjRGe0PBiGC4nxlJDDXqppOkEmQ/qSFPtdKzDEoI95kRoPvePJ0k/dPkvnUQuufukySNHf7wdBPqMyPJNDPiEwpGBMN9PzLUmRE6SSoi/GcZXJ+jZxkC0z0DFcYVu/wsA11J+iXcB98BmhlRguwmKd4S8psw7tth5NkLCkYEw/Pebyik0IOVimh2PzMSmuckj++HBm//uGdGwrlidw/e1Kf94Z4ZCWMGijwHJ9zxGfAI+miM9oKCEcFwPxxFtw90pTHMK/aEODMSLK6fD023+kfTmRHy7Jfu27s0A6UnTWHeRvf8LF3EeEPBiEB0OiX3PcmyE/VwSuoXx7XGu77SxrZObDlUHdI+oh2nxOCoawEAnKhtCcmRU2KIj3NN0276ljz7wikxlFc1AQCqG9tCdiTfm19/4BT1aR84JQb78ToArsX8wvW853gtefZDY6trfO50spAdyZ6PVDeRZw9oOXhBKLU7sOiDvaiob3Nvy7FZUTwjX3HeglK7A8Uf7MXJMPYR7ZTaHShZUwZHXat7Wyiew91HtKOVo1K7A/f9cxfaOrufyyHX3Wjp+eF3vkaDx60D8uxNqd2Bhe/twenmDve2UMaOcPcRaSg9f1MwIgCldgfueWMHen4R8uNRShIpabGPaIc880ErR+Q6MOSZHzR2hA7lpokQnBJDyZqyXh0UgHtbyZqygFN5Wuwj2iHPfNDKEbkODHnmB40dfKBgxGC2ltd4TbH2hAFw1LVia3mNrvuIdsgzH7RyRK4DQ575QWMHHygYMZjKBv8dVGk5LfYR7ZBnPmjliFwHhjzzg8YOPlAwYjAD0qxhl9NiH9EOeeaDVo7IdWDIMz9o7OADBSMGU5SXiRybFf7W8jPB9bR1UV6mrvuIdsgzH7RyRK4DQ575QWMHHygYMRiL2YTiGfk+/yZ33OIZ+QGXHvbcR89SSvcR7ZBnPmjlSIvvK5ohz/zQeuwIdR/RDgUjAjC9IAfLZ45FutV79cRsm1Xx617yPrJt3tN8avYR7ciO+qUmeG0nz9oiO8pKT/TartaRvJ8+yfFh7Sda0aovyvsZkBbe9xXNyI4S47xPmaGMHTk0dviE1hkRiBfWH8Lij/dj3NAMPDhtFIryMlVHyk6JYfaKrdj0bRVmjh+KkuvHxHS07Yvt353Gjcs3IzM5Hs/dVhiy58dX78FbXx3DRSP7Y8XtF5DnHlQ3tqHwt58BAF6fW4RJw/uF5Og/+05i7t+3YXBGEv7wf+eG9H1FM06JYfzvPkNVYzt+e0MBflw0JCQ/dS0dOLfkUwDA3+8owoUjQvu+opnbXv4Sm76txqwJQ3D12QNDHjsueOoz1DS146kbCnBLiN9XpEDrjEQgLR2uXAWjstMwcXjfkDqoxWzC0L7JAID+aYlR3clDpbXLc7+0xLA8jx7o+mGlJsaRZx+0dq2amhBnxpQR/UN2lGZ1zYwkWMwhf1/RjMVsQmfX+hQTzgjdT6pHXpuCQenk2QctXflkpozoH9bYIc/2DR+QSp67oGBEIOTESaEmyZNJjpcTMVFiMV/I+X/CSd7m+XnKcuobd2bkMDKcApRNVglycrtwsiNbzCZ33pQmSpbnk+6sveGNHe7MvdSn3VAwIhDdJ8kwB+9EOkkGQh5QUsMM+uSTLGU59U2TRgO3/HugLKe+ae+U0O50zUKFfZKksSMgWmTtBTwCbBo73FAwIhDumZGwo27q6IFwDygU9OmKe2YkzIHbndq+3YkIeMSNOy0eQVq4fTolkWZVA+GegdIo6GuhANtNSMHIsmXLkJeXB6vVisLCQmzcuDFg+ba2Njz22GMYOnQoEhMTMXz4cKxYsSKkCkczzVpF3Yk0BRiIJvdJUqOZERpQfKL1zIhTYl7ZewkXcjCcEGdGvCW860v3rUe6kPGJZhcydOuxF6pHiVWrVuH+++/HsmXLMHnyZLzwwgu46qqrUFZWhiFDhvj8zE033YSTJ0/ilVdewZlnnonKykp0dtKX0BOtZ0boJOkbeaANf0CRB27qy76Qg+FwZ0Y8g5nmdies8eHtL9pwew6zP3vugy5keuOUGFo7XMFw+Bcy3bN9hAvVRpcsWYK5c+fizjvvBAAsXboUn3zyCZYvX47Fixf3Kl9aWor169fj8OHDyMx0rS43bNiw8GodpWj2zAh19IB0nyTDnWqloC8Q3UFfeJ4tZhMS48xo65TQ1NaJzJSE4B+KIbTyDHjceqSZkV7IbzsCWtzilW+lU9Ano2pOr729Hdu3b8e0adO8tk+bNg2bN2/2+ZkPPvgA48aNw+9//3sMGjQII0eOxEMPPYSWlha/x2lra0N9fb3Xv1hAsye1qaMHpEmjGagkj6lWepahN5pescv32DvoJNmTJo1moACaGQmE/AyUHByHA82M9EbVaFxVVQWn04msrCyv7VlZWaioqPD5mcOHD+O///0vrFYrVq9ejaqqKsyfPx81NTV+nxtZvHgxSkpK1FQtKtDuSW3q6IHQ7MHKLs+MAa0dkjs4IVy4r9jDnIECXFeiNU0UYPuiWcuZEffr6jR29MT9DFS8BSZTeGuD0MxIb0IK73p+EYwxv1+OJEkwmUx48803UVRUhKuvvhpLlizBypUr/c6OLFy4EHV1de5/x44dC6WaEYVTYqhr7gAAfHuyEU4p9CvtpHjX11rT1I4th6rD2le04ZQYvj/t6ncVda1huUnweFhww4FT5NkDp8RwsLIBAFDX3BGWG6fE3Pk7th05TZ49cEoMu47VAgA6nVLYbuTbDzuPnqaxowcNra7x2Ww2he1GXs/lYGUjee5C1XLw7e3tSE5OxjvvvIMf/OAH7u333Xcfdu3ahfXr1/f6zJw5c7Bp0yZ8++237m379u1Dfn4+Dhw4gBEjRgQ9brQvB19qd6BkTRkcda3ubTk2K4pn5KvOV1Bqd+CJf9txqqE97H1FG1p71mpf0QZ55oPWbkrtDjz4zm6v50XItYtSuwOPrbajuin8cbXU7sAj736NupbuWZFo9qzLcvAJCQkoLCzE2rVrvbavXbsWkyZN8vmZyZMn48SJE2hsbHRvO3DgAMxmMwYPHqzm8FFJqd2Be97Y4TWgAK6r9nve2IFSu0P1vjwDkVD3FW3o4VmLfUUb5JkPWruR99fzwVVy3e3GMxABwuvTnoFIqPuKNlTfplmwYAFefvllrFixAvv27cMDDzyAo0ePYt68eQBct1hmz57tLn/rrbeib9++uOOOO1BWVoYNGzbg4Ycfxk9+8hMkJSVp15IIxCkxlKwpg6+pKXlbyZoyRVN4Wu4r2iDPfCDPfNDaDbn2D/VpfqgORm6++WYsXboUTz75JM477zxs2LABH330EYYOHQoAcDgcOHr0qLt8amoq1q5di9raWowbNw633XYbZsyYgb/85S/atSJC2Vpe0+vKxhMGwFHXiq3lNVz3FW2QZz6QZz5o7YZc+4f6ND9Cevx6/vz5mD9/vs+/rVy5ste2s846q9etHQKobPDfMdWW03Jf0QZ55gN55oPWbsi1f6hP84Ny0xjIgDSrZuW03Fe0QZ75QJ75oLUbcu0f6tP8oGDEQIryMpFjs8LfG+smuJ6yLsrL5LqvaIM884E880FrN+TaP9Sn+UHBiIFYzCYUz8j3+Te5wxbPyIfFHHyBHc999Sytdl/RBnnmg5ZuyLN/tHaj5e8j2tBr7Ah3X9EIBSMGM70gB8tnjkWf5Hiv7dk2K5bPHKvqvXN5X9k272m+UPYVbchuBqQlem0nz9qipRvy7B+t3cj765/qnfeHXHe7SeqRoDGsPp0e/jgUbaha9Mwoon3RMwBY9dVRPPLuHuTnpOOJa/NRlJcZcoTslBh+uGwTdn9fh3kXDcfDV46K2Wi7J/sd9Zj+541ISbDg5TkXhO35/n/uxJqvHbj67Bz89cfnk+cu2jsljHz8YwDAC7MKcfnorLA8//HTb7Bs3SGcPciG9382mTx34ZQYLn52HY7VNONXV4/G3AvzwnJz/HQLJj/zOcwm4I254zH+jL7kuouf/2MH/t/XDvxw7CD8qDA37LEj/9elaOuU8Oebz8O15w6MWs+6LHpG6IecmjqvfwomDg9vALCYTRiU4VrDZWAfa9R28lBo7XR57pOcoInnEVlpAABbUjx59qCts3vxrItG9g/b87m5fQAA8RYTefbA5cJ1PTluWEbYbtKSXC9YSgwo1GB/0URLV26aCXl9NRk70qwu16Ny0sgzKBgRhiYNM5wCHgmvKBW4F00aJcmTkXN5tFCWUy/kJI1aZDgFKMtpIOREeeFmoQZcSeB67pdwIY/RWiXEpDHaGwpGBEHLzJsApQL3hxyMaOY5kbKc+qLbc/gZTgGPLKfUn3vhzvatwUkyzmKGtSvRJrn2Rg6Etb6QoTHaBQUjguCeGdGooydR1O0TeUBJ1SCtPUADij/cA7dmwXXXzAj1Zy+cEnPf4k3RqE+n0NjhE90uZMgzAApGhIFmRvig5VUkQAO3P9wDt8ZXkXS17o3n71uzK3aahfKJ1gE2Xch4Q8GIIGh9kkxOpHvsvnDfX9dqZiSRBhRfaD4z0vV9tXZIMZtIzBey5zizCQkWbYZzmoXyjexaqwDbfSFDYzQACkaEQftpbTpJ+qKxjWZGeKB5cO2xH+rT3Wj9bI68L4BmRnrS7H7JQOMLmTbyDFAwIgyaT2vT/UifuAcUre6v08yIT7SegUqMM7tff6TZvm66H6rUxrPnvqhPd9PeKaHD6ZqRo5kRfaBgRBBoZoQP8g9fuyt2GlB8ofXMiMlk6r5ipytJN00az/R57osuZLrxHEeT47V9NodmRlxQMCIATomhuqkNAFBe1aTJPXFr1w+mor4VWw5V0312uDx/V90MAKhqaNfEibyGRnunhI0HT5FnuDzvdzQAcJ0stXDilBjiu56J+PJwDXnuoqHrRNbulDT7nctv4n11pIbGji4aWl2e48wmfHXktDae41xjdJmjnjyDloM3nFK7AyVryuCoa3Vvy7FZUTwjP+Q8BaV2Bx5bbUd1U7tm+4x09PK86IO9qKhv02yfkY5enrXeZzRQanfg0Xf3oLalw71NC9cL3t7tdSss1l2X2h14/H07qhq1G09L7Q48/K+v3UGOFvsUFaXnbwpGDKTU7sA9b+xAzy9AfgwtlMRJeuwz0iHPfCDP/CDXfCDP4UO5aQTHKTGUrCnr1SEBuLeVrClTNXWnxz4jHfLMB/LMD3LNB/LMFwpGDGJreY3XtHNPGABHXSu2ltcYus9IhzzzgTzzg1zzgTzzhYIRg6hs8N8hQymn1z4jHfLMB/LMD3LNB/LMFwpGDGJAmlXTcnrtM9Ihz3wgz/wg13wgz3yhYMQgivIykWOzwt+aiSa4nq4uyss0dJ+RDnnmA3nmB7nmA3nmCwUjBmExm1A8I9/n3+SOWjwj373qpNp99vxUqPuMdMgzH/RwQp59o3ef1mqfkQ555gsFIwYyvSAHy2eORWZKvNf2bJs15Ne75H1m27yn+cLZZ6QjO8lKS/TaTp61RQ8n5Nk3shd50T0ZLVz3TUnQbJ+RjuykZ0ZkLTxrOR5FA7TOiAB8srcCd7++HUP7JuPpH56DorzMsCNjp8Qw7U/rcehUEx6eNgrzLh4ek9G2J8dqmjHl918gzmzC63PHa+b5rte24fP9lbhpXC4W//DsmPfslBgKij9BS4cTS246F9efN0gTz7/5f2VYufkIxudl4h93TYh5zwDwo+c346sjp3HH5GGYlp+tSZ/+pqIBVy7dgOQEC16Zc4Em+4x0nnjfjte//A5XjsnC7ZPyNHHS0u7E6F+XAgBenj0Ol5w1ICo90zojEURL12qHuRnJmDi8ryYd0mI2uR+Cyu2bHJWdXC2tHS7PadY4TT0P65sCAOibmkCeAZhNQGuny/WFZ/bTzPOYga6BLCnBQp67kFdKvXjUAM36dJrVtRx8p5Npts9IR/Y8dkiGZk6SEiyIt7j2M2ZQesx7pmBEALROKiaTQomYvOhOkqddhlOAPPektUOCPN+arEc2WUrg5qY7waZ2Y4ecrLPdKaG9U9Jsv5GMnChPy/4MeCTapD5NwYgIyINrql4dnTLKAujOcNrz/m+4kGdvPDOcJmmU4RTwyCZLmajddGft1W7sSPb4fbRQnwbgcSGjYX8GKLu6JxSMCECjPKBofpKkK3ZP9Bi4AY+ZERpQAHRfrSfFa3s7xT0zQidIN+6ZEQ3HjniLGQldD8ZS4OeiWacLmSQ5wKaZEQpGREA+iaVofJKkK3Zv9Bi4gW7PdJJ0IZ/AtPcsD9x0ggQAxpjHLV6NA2y6YveiyT12aH0hI48d5JmCEQFobNOro7sGlBbq6AA8TpJ6Ddx0dQOg+ypP+xMkBX2eeD6bo1eA3Uh9GoB+s6rdtx7JMwUjAtCs0wOsNDPiTbNOQZ/8UBtNabvQrT8ndj8zEgErEuiO3N9MJsAaRw+/60mzTrN97gCbPFMwIgJNOj3ASs8yeKPbW0vuKW0K+oDu/qz5TF/XwM2Ya1Yg1pGv1pPiLTBr/FooXch4Iz/Xp/mt9ETyLEPBiME4JYYTtS0AgOO1LXBK2l3xWbue/C6vasKWQ9Wa7jvScEoMB082AgDqWjp08Vzd2EaeJYavv68FAHR0Spq6SLB0D1frD5yKac8A0NDqOkHGmU2a9junxNDpdAV7u4/Vxrzn9k7JHfyWOeo19SG/bbbju5qYHztoBVYDKbU7ULKmDI667nTROTYrimfkh70kcKndgUff24Pa5g7N9x1p6O35ifftONXYrvm+Iw29Peu170ik1O7AY6vtqG7Stt+RZ29K7Q4Uf7AXJ+vb3Nu07NMPvr3ba1YkGl0rPX9TMGIQpXYH7nljB3rKlydbw8lRoOe+Iw3yzAfyzA+9fJBnb6hPawMtBy8wTomhZE1Zr44IwL2tZE1ZSFN2eu470iDPfCDP/NDLB3n2hvo0fygYMYCt5TVe06A9YQAcda3YWl4j1L4jDfLMB/LMD718kGdvqE/zh4IRA6hs8N8RQynHa9+RBnnmA3nmh14+yLM31Kf5Q8GIAcjZdLUqx2vfkQZ55gN55odePsizN9Sn+UPBiAEU5WUix2aFv5UBTHA9VV2UlynUviMN8swH8swPvXyQZ2+oT/OHghEDsJhNKJ6R7/NvcgctnpEfUpIxPfcdaXi66NlaLT1rve9IgzzzQ6/fN3n2hsZo/lAwYhDTC3KwfOZY9EtN8NqebbOG/VqXvO8cm/c0nxb7jjRkF1npiV7btfScTZ51dUGevZF9JMV7D9/h+iDP3sg+0qzeq65q2af1GP8jFVpnxGC2ltfgphe2oG9KAv5261gU5WVqFhE7JYbzf7MW9S0deObGc/B/hYNjLtqWaW7rRH7xJwCAV+aMw8WjBmjq+QfLNuHr7+sw/+LheHDaqJj17JQYJj/9OSrqW1E8Ix+zJw7T1PMDq3bhg90ncHVBNv5669iY9QwAP31tGz4tO4kfFQ7GD8cO1mzscEoMK/5bjqc+2ofcjCSse/iSmPb8p7UH8Of/HMTkM/vi55eM0HSMPnSqEZf9cT0S48xYeUeRpvsWBVpnJEJo7XCtvjcg3YqJw/tq2hEtZhP6JMUDAM4ckBp1nVwNLR3dqxxqGYgALs+D+iQBAHL6JMW0Z4vZhM6u9RHG52nfn0cMSAUA2JLjY9oz0N2nJ53ZV9Oxw2I2YdywDACu10xj3bOc26tgoE3zMVqedWl3ShgfhYGIGigYMRh3NkiNk7fJJCdQsjygO3lbUrxFlx98MmXfdCMncNM68SPgkViMUtvrlrwN6E5ySMkfu5PYJevh2TP5Y2dsu6ZgxGAadcpwKpNCgzcAj4FbN89yevvY9uyUmPuKXet064BnhmQK+pp07NPyRUwTBdcenrXvz3KiPNdxYnvsoGDEYNwzIzp0dIAGFZmmdvlqXS/PNDMCeAcJupwkKbh206TjhYx8xd7WKbkz+MYqeno2m000e90FBSMGI3d0PaYAge5BJdY7ut4zI+6gL8ZnRuT+bDGbkBin/fBCMyPdNOl4i9fzd9LcEet9Wu+xg26JARSMGI6e99cBILXrAamGGL9i139AoZMk4PkcgwUmk37P5sR60AcAzTpesSfEmRFvMXkdJ1bR+7k+eVY81scOCkYMRr66Sdapo6e6p7Vju6PrHfTRszku9PfcNXDHeH9u75TQ3nX7RO8r9qYYP0nKAbZes9duzzE+dlAwYjB6X7G7H6yM8Y7efTuMns3RE3dwTbfDdMWzn+l2xS7P9sX42CHfPtEtwKZZVQAUjBiKU2I4WtMMAKisb4NT0n79OTnqLnPUY8uhal2OITpOiWGfox6AKyjRw4H8bM73tc0x6xkAGlpdA2pHp6SLh8Q418Dd2NZJngHEW0z46shpzT04Jea+zba1PHY9OyWGupYOAMCBkw26eEjqCkY2HqyK6T5NK7AaRKndgZI1ZXDUdaeJzrFZUTwjX7OlgEvtDvzyX1+jvrU74tb6GKLDy/OvVttR09Su2zEigVK7A4++uwe1XYM3oK2HUrsDxR/sxcn6Nl32HymU2h14/H07qhr16W88fjORQKndgUUf7EWFjv2t1O7A/f/chdbO7jeWos210vM3BSMGUGp34J43dqCnePlxPy1yE/A4huiQZ37o7YE8uyDPfKCxQztoOXhBcUoMJWvKenVAAO5tJWvKwpqq43EM0SHP/NDbA3l2QZ75QGOHMYQUjCxbtgx5eXmwWq0oLCzExo0bFX1u06ZNiIuLw3nnnRfKYaOCreU1XtOfPWEAHHWt2FpeI/QxRIc880NvD+TZBXnmA40dxqA6GFm1ahXuv/9+PPbYY9i5cyemTJmCq666CkePHg34ubq6OsyePRuXXXZZyJWNBiob/HfAUMoZdQzRIc/80NsDeXZBnvlAY4cxqA5GlixZgrlz5+LOO+/E6NGjsXTpUuTm5mL58uUBP3f33Xfj1ltvxcSJE0OubDQwIM2qaTmjjiE65Jkfensgzy7IMx9o7DAGVcFIe3s7tm/fjmnTpnltnzZtGjZv3uz3c6+++ioOHTqE4uJiRcdpa2tDfX29179ooSgvEzk2K/ytTWmC62nqorxMoY8hOuSZH3p7IM8uyDMfaOwwBlXBSFVVFZxOJ7Kysry2Z2VloaKiwudnDh48iEcffRRvvvkm4uKULRqzePFi2Gw297/c3Fw11RQai9mE4hn5Pv8md8ziGflhpbnncQzR8XTQs5V6eNbrGJGA3v2NPLsgz3ygMdoYQnqAtWfOCcaYzzwUTqcTt956K0pKSjBy5EjF+1+4cCHq6urc/44dOxZKNYVlekEOls8ciwFpiV7bs21WzV7nko+Rna7fMUTH7cDmPdWpi2cdjxEJyB4SLN7jgFYeyLML2UPPlYTJs7bIHmxJ3hfQeowdGcnxuh0jklC1zkh7ezuSk5Pxzjvv4Ac/+IF7+3333Yddu3Zh/fr1XuVra2uRkZEBi6X7hyNJEhhjsFgs+PTTT3HppZcGPW60rTMi821lAy5fsgGJcWasvKMIRXmZmkfCTolhTHEpWjskLL35PMw4d2BMRdsA0OmUMOLxj8EYsOy2sbhyTLYunqcv3YCDlY1YcPlI/OzSM2POMwBc85eN2HuiHndPPQMXjxqgeZ92Sgw/fW0b/rO/Ev9XOBjP3HhOTHp+6J3d+Nf273HtOTm4bfxQXTz/8dNvsGzdIZwz2IbV8yfHpOcV/y3Hk/+vDOfl9sEj08/SZYze9G0Vbnv5f8hKT8TSm8/X5RhGoss6IwkJCSgsLMTatWu9tq9duxaTJk3qVT49PR179uzBrl273P/mzZuHUaNGYdeuXRg/fryaw0cdcs6DzJQETBzeV5cOaDGbkG51Rd5nDkiNqk6ulA4ngxxyTx3ZXzfPOX2SAACDMpJi0jPQnTPl8vwsXfq0xWzC8AGpAICM5PiY9dzSNXaMG5qhm+dzBvcBAMRbzLHrucPleWRWqm5jtC3JNT6bYNLtGJGA6sw/CxYswKxZszBu3DhMnDgRL774Io4ePYp58+YBcN1iOX78OF577TWYzWYUFBR4fX7AgAGwWq29tscija36ZjiVSU2MQ2VDW8wmcWv0aHdyvD5JxQAgNbE7b0qs0qhzQkKg+/fSGMMJ3Bp1TrDp2jclf2zSOWMv4NmfY9czEEIwcvPNN6O6uhpPPvkkHA4HCgoK8NFHH2Ho0KEAAIfDEXTNEcJFg5xu3apvMOJObx+jWSHdmZETLDDreNVBg0p35lE9A+wU8uzu03p6lk/AzTGcIbk7q7qOwbW1uz9LEtN1jBKZkHry/PnzMX/+fJ9/W7lyZcDPLlq0CIsWLQrlsFEHz5kRIHavJHlcRXruP1ZPkpLE3CcuPV2nyZ5bO4KUjF6aujwnc5gZieXU9vIFY5o1PkjJ0PEc/5vaO3U9lshQbhoDaXR3dE4zIzF6kuRxFem5/5j17HHS4jEz0hSjwTXg2af1u2JPSSDPPC4YE+PMiO96Cy1WL2QACkYMpZHbSbLrWYbW2Ozo8klS75mRWL9NI5+0zCbXAKsX8rR2Q4x6BrpnK/R8lkH+vbR0OGMqYZsnPC4YTSZT99gRo2M0QMGIYTglhm8qGgAADa2duv7Y5QFr+3c12HKoOqYGFqfEsOtorfu/9fXsCvoOnmyMOc8A0NB12yQxzowvD9fo1n7Zc2V9a0x6dkoMdS0u1wcqGnRrv9UjoFz/zamY9FzRlczu+5oW3drvlBjiLS7XsdifZVStM2IU0bbOSKndgZI1ZV5ZG3NsVhTPyNd8oZtSuwMPvr3bfY9Zz2OJBm/Pj767B7Ut3c8xxIpnwNX+x1bbUd3U7t6mR/tL7Q48/r4dVY36HkdUSu0OLPpgLyrq29zb9PLM4ziiwmvs4DlGGYXS8zcFI5wptTtwzxs70FO6/Py0livv8TyWaJBnfvBqP3kmzzwgz9qiy6JnRHg4JYaSNWW9Oh8A97aSNWWaTNPxPJZokGd+8Go/eSbPPCDPxkHBCEe2ltd4Tcf1hAFw1LVia3lNRB1LNMgzP3i1nzyTZx6QZ+OgYIQjlQ3+O18o5UQ5lmiQZ37waj95Js88IM/GQcEIRwakWYMXUlFOlGOJBnnmB6/2k2fyzAPybBwUjHCkKC8TOTYr/C32a4LrSeqivMyIOpZokGd+8Go/eSbPPCDPxkHBCEcsZhOKZ+QDQK9OKP9/8Yx8TbI2eh6rJ1ofSzTIMz94tZ/ndyoi5JkP5Nk4KBjhzPSCHCyfORZZ6Yle27NtVs1f5ZKP1T8tQfdjiYbc9myb9zSnnp5zOBxLROT2W+O9hxOt28/zOxURuf1pPVYSJs/aIrc/I9k7Rwx51hdaZ8QgTjW04YKnPgMAvDF3PCYO76tbFFzd2IbC3/I5lmg4JYbxv/sMVY3t+M31Y3Dr+KG6td0pMRQ99Rmqm9rx2xsK8OOiITHjGQDueHUrvvjmFG65IBfXnzcIRXmZurTfKTH86Pkt2HH0NO6ckoeFV42OKc9Pf7wPz68/jItG9sO8i87U1fOCt3fh37tOYPqYbDx329iY8rx653E8sGoXRgxIxZPXF+jq+W+ff4s/fXYAo7LS8NF9U6LKM60zIjgtXSuiJsVbcOGIfrp2vvSk7gj/7EG2qOrowbCYTWjvlAAAE4fr69liNiEzxTULdUa/lJjyDHTn8Zg6sr+uAa/FbMKQzCQAQFaaNWY9n5ubobvns7JdJ4+UxLiY8ywnI8zrl6K758KhGQAAkwkx51mGghGDaGhzLRueqnPGXgCIt5iR0JVnojHG0oEzxtyDdzoH17GcxK2hlU8WaiC2PcvJ1HrertED+buU8w7FEu5Epjz7MyXKI3jDc0DxPE6sZYVsandCXsQwzRofuLAGpLrT28eWZ6B7INU7CzXQnVE2Fj3zyCQrkxbDJ0meY3SsZ/wGKBgxDJ5RNxC7Vzhye+PMpl4PWOpBLA8qsmseQV+sBtcAUN/Kb+xI7/ou5ZncWILnGC2Pz41tnYiAxzh1gYIRg3B3dF4zI12DSn3MBSPdV5Emk/73YlNiNBjhfTssVj0DHlfsPIK+GJ4ZqecYXMvnAafE0Noh6X48EaFgxCB4TmkDQHpSbA4qPK/WAY+ZkRjz3OxxO4zLPfYYDkbcz5txeWaka2Ykxvoz0P0b5uE5OcEC+VopFmehAApGDMEpMew9UQ8AaOlwcsnMKP+gNh6swpZD1TGTDbK22fXDlhjj0m7Z89ff18WkZ7MJ2HW0Vvd2pyS4PB+taY4pz06J4XSTy/Whykbd252cYAEA1LV0YPO3VTHl+XhtCwCgoq5V93ZLzPVmJQD890DsePaE1hnhTKndgZI1ZV4ZG3NsVhTPyNdtkZtSuwMPrNqNlg4nt2OKQKndgUff3YPalu4rDT3bXWp34OF/fe11FRkrnh9/346qxnb3Nr09/+q9Pahp5vO9ikKp3YFFH+xFRX2be5venos/2IuTnI4nCrzHaCPOCTxRev6mYIQjpXYH7nljB3oKl59k0GPVPSOOKQK8202evSHP2kKe+UCetYcWPRMMp8RQsqasV6cD4N5WsqZM0+k5I44pArzbTZ57Q561gzzzgTwbCwUjnNhaXuM1DdcTBsBR14qt5TURfUwR4N1u8uwb8qwN5JkP5NlYKBjhRGWD/04XSjlRjykCvNtNnrUpJ9rxRIE884E8GwsFI5wYkGYNXkhFOVGPKQK8202etSkn2vFEgTzzgTwbCwUjnCjKy0SOzQp/y26Z4HqCuigvM6KPKQK8202efUOetYE884E8GwsFI5ywmE0onpEPAL06n/z/xTPyNc3Y6HnMnuh1TBHg3W7y3Bu9PfP6DYkAeeYDeTYWCkY4Mr0gB8tnjkW2zXvaLdtm1e0VLvmY/dMSuB1TBOR2x/X4IevVbvl4ORy/WxGQ2y0v2CSjt2eevyERkNvdc6l98qwtcrv7pvAZL2PVsy9onREDaGl3YvSvSwEAL80ah0tHD9A9+q1ubEPhbz8DALw+twiThveLiYh7yjOf49jpFtx/+QiMz+uLorxMXdvtlBjG/XYtTjd34Hc/OBs3X5AbE55/+to2fFp2Ej8qHIwfjh3MxfMPl23C7u/rMO+i4Xj4ylEx4fnPnx3Enz47gIln9MW9l43g4vmBVbvwwe4TuKogG3+7dWxMeP6s7CTufG0bcjOS8Pv/O5eL59+X7scLGw7jvNw+ePeeSVHjmdYZERjPfBo8AhEAsCV152YZM9AWNR09GA1drq85OwcTh/fVvd0Wswn9UhMBAMP6JseM56Z2l+cLR/Tj5nlI3xQAQP+0xJjzXDAonZvnUdlpAFypDmLNc25mMjfPY4dmAABMJsSMZ08oGDGAuhY5eRu/H3ecxYyUrjwT9S2xkYiJMeaRtZdPojwASE+KvQzJvBM/At3ZgWOlPwMwpj/HYObeegP6s3zBWBdD/dkTCkYMQD5Jec5W8CDWMnB6JiFM45BJVqb7JBkbngGDTpIxGfTxy9grI2dhjqVssnKAy3OMlo8VS+OGJxSMGIAc+aZzHLgBID2p6yQZI4O3fIK0mE3u7KM8iM2TpByM8Az6Ym/wlm/x8vSclhhbFzEAUNvsSvrIMxhxjxstHYiARzk1h4IRzjglhu1HTnv9Py9SuwaV/+w7GRNp1+W09olxZnx5uIZbe+Wr1q+O1MSEZ6fEUNfiGrwPnmzg7vnAyfqY8Xz8tCutvaNW/7T2Mildnh11rTHj+eDJRgCu2zW8+3O7U8L6A6ei3nNP6G0ajhiZKrrU7sB9/9yFtk6J+7GNoNTuwGOr7ahu4pPW3vO4D76zG01tTq7HNQreae09j/vou3tQ63F/Pdo9GzF2lNodePx9O6oa+f6OjMJIz0b8jnig9PxNwQgnjEwVHQtpqj0xqr3k2QV51hbyzAfyrA/0aq9AGJkqOtbSVBvVXvLcDXnWDvLMB/JsPBSMcMDIVNGxlqbaqPaSZ2/IszaQZz6QZ+OhYIQDRqaKjrU01Ua1lzyHV0704xoFeeYDeTYeCkY4YGSq6FhLU21Ue8lzeOVEP65RkGc+kGfjoWCEA0amio61NNVGtZc8e0OetYE884E8Gw8FIxwwMlV0rKW3N6q95LkbXp5jIe06eeYDeTYeCkY4YWSqaN5psY1Gbm9CnHf31ru97u84PZHrcY1Cbm/P1W25eY6RtOtye21J3quukmdtMWqcjDXP/qB1RjjT3ilh5OMfAwCen1mIK/KzuEW9+x31mP7njUhOsOCVORfonhbbaK75y0bsPVGPu6eegYtHDeDWXqfEcNYTH6PDyfCXW87HNefkRLXn+1ftwvs7j+O6cwfix0VDuHqevnQDDlY2YsEVI/GzS86Mas/L1x3CM6X7ccGwDCy4YhRXzz99bRv+s78SN44djN//3zlR7fmL/ZW4Y+VXGNTHimd/dB5Xz4s/3oeXN5Zj7JAMvDNvYlR4pnVGBKW5vTu/w6VnDeDa2TK6Iv7WDifGR3kgAnQvBz+9IJtLGnAZi9kEW5LL9Yis1Kj3LCcVm3xmX+6eB2UkAXBdRUa7ZzlJ3piBNu6eR2anAeCbadwo5JxSuZnJ3D2fn5sBAIgzm6Lec08oGOGMnNQrKd7S6zaC3shJnyQWG0mv5ISEGckJQUpqjzspYQykA+9OKmaAZ2t3crFoR176vk8y3wSbAJCRHDvp7Y3I2CsjHzMWPPeEghHO1HQN3AlxZu5Jp+ItZiR2BUCf76+M6lX9Wjuc7gynB082cm+rfJJcWxbdSQmdEkNFvWsNhOO1LdzbKWevjfakhE6J4fApV/K2003thvXnbyoaot7znuP1AIC2Dol7O1O7+nNFfWwkJfSEnhnhSKndgV+t3oOaJv7JvYxM0sebUrsDv/73XlQ2GJN0KlaSEhrdp2IlKaEInhe+twenm6M7KaEInp/4tx2nGqIrKSElyhMMSpTHB6PbavTxeWF0O40+Pi+MbqfRx+eF0e00+vh6Qg+wCgQlyuOD0W01+vi8MLqdRh+fF0a30+jj88Lodhp9fFGgYIQDlCiPD0a31ejj88Lodhp9fF4Y3U6jj88Lo9tp9PFFgYIRDlCiPD4Y3Vajj88Lo9tp9PF5YXQ7jT4+L4xup9HHFwUKRjhAifL4YHRbjT4+L4xup9HH54XR7TT6+Lwwup1GH18UKBjhACXK44PRbTX6+Lwwup1GH58XRrfT6OPzwuh2Gn18UaBghANGJlGLpURMRierM/r4vDC6nUYfnxdGtzNWxg7yLAYhBSPLli1DXl4erFYrCgsLsXHjRr9l33vvPVxxxRXo378/0tPTMXHiRHzyySchVzhSkZMhxVu8OxTPRHmxkIhJbqs1nm+SvJ7HH5AW3cny5HamJfJN3tbz+DlR3qfldmb2WHWVt+doHzvkdmYZ9LuNFc+BUL3OyKpVqzBr1iwsW7YMkydPxgsvvICXX34ZZWVlGDJkSK/y999/PwYOHIhLLrkEffr0wauvvopnn30W//vf/3D++ecrOmY0rDMiM/npz3G8tgUPXDECRcP6ck1W55QY3tp6FI+/b0dmSgK+euzyqI22b3v5S2z6thqzJgzB1WcP5J4UsLmtE/nFrqD75dnjcAnnPES8eOrDfXhp42FcPKo/7p46nLtnp8Qw+enPUVHfiuJr8zF70rCo9Pz+zuO4f9UunDkgFb+5vsAQz7e+9CX+V16D2ycNwxPXRueV+reVjbh8yXokxpmx8o4iQzw/+PZuvL/rOKblZ2H5zMKI96zbOiNLlizB3Llzceedd2L06NFYunQpcnNzsXz5cp/lly5dil/+8pe44IILMGLECPzud7/DiBEjsGbNGrWHjgpOdy0Hf8N5g7gmYQJc04FTR/QH4ErYF+mdPBBybodLR2dx9wwAyYlxSEmwAADOHBC9yfLkPB7jhmYY4tliNmFgH9fVZE6fpKj1LCfJO7N/qmGez+ifCsCV6ylaPctJ8vqlJhrm+ZzBNgBAfJw5aj37QlUw0t7eju3bt2PatGle26dNm4bNmzcr2ockSWhoaEBmpv+Hcdra2lBfX+/1Lxpo7XCiud21dLWcQZc3fVLiu+oiobXDGaR05CJn7O1jQLIrmT5dCfrkfETRSG1LV5I8A5IRymR2/ZZOR7HnOgOTt8nICfrk7zwaOd3kalumQeMzAPRN7Ro3GqPXsy9UBSNVVVVwOp3Iysry2p6VlYWKigpF+/jjH/+IpqYm3HTTTX7LLF68GDabzf0vNzdXTTWFpbqrc5lNgP37OkNW1EuOt8BickXbn5WdjMpV/ZwSc7sur2oyrI0ZXYFfqd0RlUmvnBLD0ZpmAMCp+lbD2icHfZu+rYpaz2WOBgBAU3unYe2Tk+Xt+b4uaj1/dcS1sJjZBOP6c1fAeaS6KSo9+0PVMyMnTpzAoEGDsHnzZkycONG9/amnnsLrr7+O/fv3B/z8W2+9hTvvvBP//ve/cfnll/st19bWhra27iRn9fX1yM3NjehnRkrtDjz+vh1VjcYlQTI6ERQPSu0OLPpgLyrqjUmS51mPe/+5C+1RmixPlL5Uanfgwbd3o6k9OpPlieT5kXe/Rl1Lp6H10AuRPBt9ntAaXZ4Z6devHywWS69ZkMrKyl6zJT1ZtWoV5s6di7fffjtgIAIAiYmJSE9P9/oXychJkKp6TLtV1LXinjd2oNTu4FaHnssO86yD3sht9AxEAP5tlOvhGYgYUQ+9EKUvyfXwDESMqIdeiObZMxAxoh56IZpnI88TRqIqGElISEBhYSHWrl3rtX3t2rWYNGmS38+99dZbuP322/GPf/wD11xzTWg1jVBESIIkQh30RpQ2ilIPvRClfaLUQy9EaZ8o9dALUdonSj2MRPXbNAsWLMDLL7+MFStWYN++fXjggQdw9OhRzJs3DwCwcOFCzJ49213+rbfewuzZs/HHP/4REyZMQEVFBSoqKlBXV6ddKwRGhCRIItRBb0Rpoyj10AtR2idKPfRClPaJUg+9EKV9otTDSOKCF/Hm5ptvRnV1NZ588kk4HA4UFBTgo48+wtChQwEADocDR48edZd/4YUX0NnZiZ/97Gf42c9+5t4+Z84crFy5MvwWCI4ISZBEqIPeiNJGUeqhF6K0T5R66IUo7ROlHnohSvtEqYeRqA5GAGD+/PmYP3++z7/1DDDWrVsXyiGiBhGSIIlQB70RpY2i1EMvRGmfKPXQC1HaJ0o99EKU9olSDyOh3DQ6I0ISJBHqoDeitFGUeuiFKO0TpR56IUr7RKmHXojSPlHqYSQUjOiM0UmYetYhWhMxieBZpHrohSjtE6UeeiFK+6J97CDP4kDBCAfkJEhxPToSzyRIsZCIyegkeT3rEa3J8oxOktezHtGaLE9uX4ZBSfJ61iNaxw5Rfq/R7jkYqhPlGUE0JMpzSgznPfkpGlo7ccsFuZhx7kBMOIN/7gOnxPD65iNY9P/KkGaNw/MzCw2ph144JYbr/vZf7D1RjytGZ+H2ycMMa199SwfOKfkUAPDwtJG4a+pwJMRFR/zvlBh+/uYOfLy3Aufn9sFDV44yzLNTYpj6+y9wvLYFN5w3ED8alxs1fdopMfyhdD+e33AYuRlJePrGcwz1LCfLmzKiH+ZdNDyqPK/aehS/et+OpHgLXp4zzlDPv37fjje3HsWIAalYdN2YiPas9PxNwQgHRFkVVK7LE/+241RD9KzwJyPKKoqi1UVrRGtbqd2Be9/aiXZn91AWDa5F9Lzg7d3u/FpG10crRPT8yLt73PmIjK5PuFAwIgjyqno9JcsxLs/pN5HqojUitU2kumiNaG0TrT5aIVq7RKuPVojWLtHqowW6LAdPqEOkVfVEqovWiNQ2keqiNaK1TbT6aIVo7RKtPlohWrtEqw9vKBjREZFW1ROpLlojUttEqovWiNY20eqjFaK1S7T6aIVo7RKtPryhYERHRFpVT6S6aI1IbROpLlojWttEq49WiNYu0eqjFaK1S7T68IaCER0RaVU9keqiNSK1TaS6aI1obROtPlohWrtEq49WiNYu0erDGwpGdESkVfVEqovWiNQ2keqiNaK1TbT6aIVo7RKtPlohWrtEqw9vKBjREVFW9+tZl2hb4Y8880EkzyLWRytEa1e09mnR2iVafXhDwYjOyKvqJViMXRXUsy7RuMKf3LaUBIvXdvKsLXLbbEnGrr7asz7Z6dG12q3crn6pCV7bDfccZX1ableWIP0nWj0rgdYZ4YBTYpjy+89xorZViBUinRLDc59/iyWfHUD/tEQsvfm8iF7hT8YpMdz20pf4srwGF57ZD/dcbOwKkU6JYc2uE7j/7V0wm4DXflKEicP7RYXnhe9+jbe3f4+zstPwxLX5hvcfp8RwzqJP0NTuxK1FQ3DNOTmG1ylcnBLDC+sP4feffIN+qQn48y3nG94mp8Rw7V83Yp+jAdPyszBnknErHGvJ0ZpmTP39FzAB+NXVZ2HOpDxDV0t2Sgzz39iOT8pOYuyQPnhwmnErHIcLLXomCKKt7ifX6fH37ahqjJ5VWEX1LMrKu1ohome5Xj//x050eqzBIEK9QkVkz/ev2oXWDkmoeoVDqd2Bx1bbUd0kznhYanfg4X99jYbWTmHqFCoUjAiAiKvpiVincBGxTSLWKVxEbZOo9QoVUdsjar3CQcQ2iVincKAVWA1GxNX0RKxTuIjYJhHrFC6itknUeoWKqO0RtV7hIGKbRKwTLygY0QkRV9MTsU7hImKbRKxTuIjaJlHrFSqitkfUeoWDiG0SsU68oGBEJ0RcTU/EOoWLiG0SsU7hImqbRK1XqIjaHlHrFQ4itknEOvGCghGdEHE1PRHrFC4itknEOoWLqG0StV6hImp7RK1XOIjYJhHrxAsKRnRCxNX0RKxTuIjYJhHrFC6itknUeoWKqO0RtV7hIGKbRKwTLygY0QnRVlHsWSdfR2UG1ClcyDMfRPQMBK6XTCS5jgTP0danfT0KSmMHfygY0RlbcnyvbX2S4w17Pcu9gqafekUq5JkPonkGXK5/OjWv1+BtNgE/nZoXUa9ByojqORr7dHqP1YQBGjuMgIIRnZDfFa9t7uj1t9M+tvGmzkcd6po7cM8bO1BqdxhQo9Agz3wQ2XOp3YEXN5T3usJlDHhxQzl51pho69P1LZ29/iaC62jxrBQKRnQg0LvigGv6zah3xaPpPXbyzAfyzAeRPQPkmhfR5FkNFIzogMjviotcN7WI3BaR66YWkdsict3UInpbRK+fGkRui8h10xMKRnRA5HfFRa6bWkRui8h1U4vIbRG5bmoRvS2i108NIrdF5LrpCQUjOiDyu+Ii100tIrdF5LqpReS2iFw3tYjeFtHrpwaR2yJy3fSEghEdKMrLDPjUs5Hvigd7jx1wPbEdCe+xk2c+kGc+yG0JhJFrTESba+rTYkHBiA6sLavw+TS8jJHvigd6t16mtrkDa8squNUpVMgzH8gzHyxmE647N/CrpNedm2PYGhPR5Jr6tHhQMKIx8pPQgeiTHI8r8rM51ag3V+RnB70qEP1pbfLMB/LMD6fE8MHuwK9sfrDbYWg7osE19WkxoWBEY4I9CQ24olojn4TeWl4T9KpA9Ke1yTMfyDM/lLg2uh3R4Jr6tJhQMKIxkfAkdCTUMRiR0IZIqGMwIqENkVBHJURCOyKhjsGIhDZEQh21hoIRjYmEJ6EjoY7BiIQ2REIdgxEJbYiEOiohEtoRCXUMRiS0IRLqqDUUjGiMyE9py0TD09rkmQ/kmR+R5DoQRtcxGJHkOdL7tBooGNEYkZ/SlomGp7XJMx/IMz8ixbXIb/woIVI8R0OfVgMFIxoSCU9py0Ty09rkmQ/kmR+R4joS3vgJRKR4BiK/T6uFghENiYSntGUi+Wlt8swH8syPSHEdCW/8BCJSPAOR36fVQsGIhkTSE9CRVNeeRFLdI6muPYmkukdSXX0RKfWPlHr6I5LqH0l11QIKRjQkkp6AjqS69iSS6h5Jde1JJNU9kurqi0ipf6TU0x+RVP9IqqsWUDCiIaeb2oKWMfopbRklT8UDwOmmdg61UQd55kOwtw4AIEOQJ/oj2TMQOX2aPPMj0l2rhYIRjXBKDL/5cF/Qck9cY+xT2jIWswlPXDM6aLnffCjWA1LkWSxEqXEke46kPk2e+RHJrkOBghGNUPJgFABkpCRwqI0yMlISg5YR7QEp8syPYA/QAeI87AdEtudI6tPkmR+R6joUKBjRiEh82IjqzIdIrDMQefWOtPrKRFq9I62+MpFY70isc6hQMKIRR6qaFJUT6WEjpXU5UtWsc02UQ575EWmuyTMfyDM/YukhVgpGNMApMby19WjQcqI8GCVTlJeJ7PTg04D//OqoEPckyTM/ItE1eeYDeeZH4dAMBHt8xWxylYt0KBjRgK3lNaioD/6U9i0XDBHiwSgZi9mEHxcNCVpOlHuS5JkfkeiaPPOBPPNj+3enESyek5irXKRDwYgGfKYwP8Cwfsk610Q9w/qlKConQg4EpfdFyXP4RGqfJs98IM98UDrmieI5HCgYCROnxPDPbccUlRXxvp7SOr297XvDp1z7pQafGlZTjieR5DmS+zR55gN55kMkeQ4XCkbC5G+fH0RTmzNoub4pCULdi5QpystEZkrgha0AoLGtE3/7/FsONfLP1vJqZQUF/E1GkudI7tPkmQ/kmQ+R5DlcKBgJA6fE8OqmI4rKXn/eQKHuRcpYzCb84LxBisq+urncsOjbKTG8tLFcUdkqBass8kaN5xc2HDLU8wsbDisqK2KfJs98IM98iBTPWkDBSBhsLa9BbUvgRaFkREhJ7Y/LFdbNyAWu/vb5QTS3B7+6AcSbapVR6rm53WnYVY4az6L2afLMB/LMh0jwrAUUjIRBRb2yh4v6JImRv8MfRXmZsFnjFJWtqGvRuTa9UXN100eQXCm+KMrLRJ+k4FOugDFXObHo+fn135LnECHPfBDds1ZQMBIGq7Z+p6jc5aMHCDf954nFbMIV+VmKyq5S8K6+1tz71g7FVzd3TMoT1rXFbMIdk4cpKmvEVU4sem7pkHDvWzv0rVAPyDMf7v8neY4kKBhRiVNi2HSwCtf/bSO+LFf2bvfkM/vpXKvwmTyiv6JyXx45jRv+thGbvq3SNQL39PzhHmWvrSUnWPDzS8/UrU5a8PNLRyA5waKo7NL/HMDGA6fIcwio8fzhngrMf2Ob7leUTonhnte3xbTnm5/fjPZOSdc6OSWGJZ9+gzVfx65nHmO01iibm49CnBLDl4eqsenQKRw/7f/WA2MMVY3taO10oqnNicNVTehwqvuCs21J4VZXd7LTlT9nsev7etz28v9gATB8QApSrXGwxlnQLzURJh8XF5Ik4XBVMxrbO5GaEIe8/imw+CoIl+9vTjbi21ONcKocs+6eOlzYqxsZi9mEu6eegT99djBoWcaAWSu2Is4EnDekDwbarO6+6Mu33FdbOjrR3smQGGeGNd7/93KitgW7vq9T3Z+jzTMAfGQ/idJffYSxQ/tgUB/X79Xzt+/pu+f2vikJqG7y/70ALtfbvqtV9aJXNHr+35HTGPn4xzijXxLOHtTH629Kvfr7XgCX553HaqEm3olGz/IYbTYBY3NtGJTRe/0UT49J8XE4d3AfTB7RDxPO6GuIDxNjTHXotGzZMvzhD3+Aw+HAmDFjsHTpUkyZMsVv+fXr12PBggXYu3cvBg4ciF/+8peYN2+e4uPV19fDZrOhrq4O6enpaqvbi1K7A4++tydoRlIt6JMcj+2PXyF8Z3dKDIW/Wav4gVzRSE6wYM+iK4X3DLhc5/+6FG06XyHqQaR5PnvRJ4qn6kWCPPOBPPemT3I8nv7h2ZhekKPJ/pSev1Xfplm1ahXuv/9+PPbYY9i5cyemTJmCq666CkeP+n6WoLy8HFdffTWmTJmCnTt34le/+hXuvfdevPvuu2oPrQmldgfmvbGDSyACiH0v0hM19yVFJBKubmQsZhMuPWuA0dUIiUjzfPfUM4yuRkiQZz6Q597UNndg3hs7UGp36H4sT1QHI0uWLMHcuXNx5513YvTo0Vi6dClyc3OxfPlyn+Wff/55DBkyBEuXLsXo0aNx55134ic/+QmeffbZsCuvFqfEsOiDvdyOl5oYJ/y9SE9+fukIpCQquy8pEpHmGQBmThhqdBVUE4meI7FPk2c+WOPM5DkAJWvKuD5zoioYaW9vx/bt2zFt2jSv7dOmTcPmzZt9fmbLli29yl955ZXYtm0bOjp8z060tbWhvr7e658WKE2WpBW/v/GciIm6AVfk/YcbzzG6GqqJNM8AMOGMvhE3eEei50js0+SZD/MuipxZERmennknOlQVjFRVVcHpdCIry/s10KysLFRU+H5yuaKiwmf5zs5OVFVV+fzM4sWLYbPZ3P9yc3PVVNMvSpMOacFdU/Jw9Tna3HPjydXnDMRdU4YZXQ3FRKrnSBu8I9Uz4OrT15yt7NV1o4l0z5EydljjzPjFZSOMrkZI8PTM85wZ0qu9ph6PijPGem0LVt7XdpmFCxeirq7O/e/YMWVJjoLBa2XOuRcOw2PX5HM5lh48ds0YzL1wmNHVCEqke46UwTvSPQPAX35ciOR4sVcyuGNy5HuOlLFjyU3nRdysiCe8PPNczVrVr7Nfv36wWCy9ZkEqKyt7zX7IZGdn+ywfFxeHvn37+vxMYmIi0tPTvf5pQVFeJrLT9c3oeteUYXji2jG6HoMHT1w7BndNyTO6Gn6JFs+PXUOeeWAxm7Dk5vOMroZfLh/dH8UzIt8zIP7YcffUyJ198kRvzzk2K9dVaVUFIwkJCSgsLMTatWu9tq9duxaTJk3y+ZmJEyf2Kv/pp59i3LhxiI9XtsStVljMJiy6Tp8ffGqiBctuPR+PXRMdAwoAPHZNPpbdOhapieIsR0Oe+WCNN0ed5+kFOXh+5ljFS2vz4q4pw/DynCKjq6Epcp+OF2j2QR47Fl4d2bNPnug5dhTPyOc6e6R6nZFVq1Zh1qxZeP755zFx4kS8+OKLeOmll7B3714MHToUCxcuxPHjx/Haa68BcL3aW1BQgLvvvht33XUXtmzZgnnz5uGtt97CjTfeqOiYoq4zEm8GLhudhVkThxm2UAwP5AXiXvvyCD7fX6l6kaxwsZiBK8gzFxItJtxz8XD84rKR5FlH4syuLLGLf3guEuLEvn0UDk6J4c9rD2D5hkOGeI61MfoPn+7HrmN1Ye0rIzkeiw1YZyTkRc9+//vfw+FwoKCgAH/6058wdepUAMDtt9+OI0eOYN26de7y69evxwMPPOBe9OyRRx4xdNEzILQVWK1xFvRPs2JwZhImDTdupToj8fT2fU1zwBVBZdSuwFrV2I42p4TcjGTcOHYwJp3ZL6Y9y/0z0MqTnn9XugKrjMlkwqCM2OzTgcYBLVdgBchzsPFWixVYgdj2DADtnRL+vrkcW8tr0NzWib5++iOvFVh1DUZ4o0cwQhAEQRCEvui2AitBEARBEISWUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShiJOZKwDyIrH19fUG14QgCIIgCKXI5+1gi71HRDDS0NAAAMjNzTW4JgRBEARBqKWhoQE2m83v3yMiN40kSThx4gTS0tJgCpTxSyX19fXIzc3FsWPHojbnTbS3kdoX+UR7G6O9fUD0t5HaFzqMMTQ0NGDgwIEwm/0/GRIRMyNmsxmDBw/Wbf/p6elR2cE8ifY2Uvsin2hvY7S3D4j+NlL7QiPQjIgMPcBKEARBEIShUDBCEARBEIShxHQwkpiYiOLiYiQmJhpdFd2I9jZS+yKfaG9jtLcPiP42Uvv0JyIeYCUIgiAIInqJ6ZkRgiAIgiCMh4IRgiAIgiAMhYIRgiAIgiAMhYIRgiAIgiAMJeqDkaeeegqTJk1CcnIy+vTpo+gzjDEsWrQIAwcORFJSEi6++GLs3bvXq0xbWxt+8YtfoF+/fkhJScF1112H77//XocWBOb06dOYNWsWbDYbbDYbZs2ahdra2oCfMZlMPv/94Q9/cJe5+OKLe/39lltu0bk1vQmlfbfffnuvuk+YMMGrjCjfH6C+jR0dHXjkkUdw9tlnIyUlBQMHDsTs2bNx4sQJr3JGfYfLli1DXl4erFYrCgsLsXHjxoDl169fj8LCQlitVpxxxhl4/vnne5V59913kZ+fj8TEROTn52P16tV6VV8Ratr43nvv4YorrkD//v2Rnp6OiRMn4pNPPvEqs3LlSp+/ydbWVr2b4hM17Vu3bp3Puu/fv9+rnEjfoZr2+RpPTCYTxowZ4y4j0ve3YcMGzJgxAwMHDoTJZML7778f9DNC/AZZlPPrX/+aLVmyhC1YsIDZbDZFn3n66adZWloae/fdd9mePXvYzTffzHJyclh9fb27zLx589igQYPY2rVr2Y4dO9gll1zCzj33XNbZ2alTS3wzffp0VlBQwDZv3sw2b97MCgoK2LXXXhvwMw6Hw+vfihUrmMlkYocOHXKXueiii9hdd93lVa62tlbv5vQilPbNmTOHTZ8+3avu1dXVXmVE+f4YU9/G2tpadvnll7NVq1ax/fv3sy1btrDx48ezwsJCr3JGfIf//Oc/WXx8PHvppZdYWVkZu++++1hKSgr77rvvfJY/fPgwS05OZvfddx8rKytjL730EouPj2f/+te/3GU2b97MLBYL+93vfsf27dvHfve737G4uDj25Zdf6toWf6ht43333ceeeeYZtnXrVnbgwAG2cOFCFh8fz3bs2OEu8+qrr7L09PRev00jUNu+L774ggFg33zzjVfdPX9LIn2HattXW1vr1a5jx46xzMxMVlxc7C4j0vf30Ucfsccee4y9++67DABbvXp1wPKi/AajPhiRefXVVxUFI5IksezsbPb000+7t7W2tjKbzcaef/55xpirc8bHx7N//vOf7jLHjx9nZrOZlZaWal53f5SVlTEAXh1iy5YtDADbv3+/4v1cf/317NJLL/XadtFFF7H77rtPq6qGRKjtmzNnDrv++uv9/l2U748x7b7DrVu3MgBeA6oR32FRURGbN2+e17azzjqLPfrooz7L//KXv2RnnXWW17a7776bTZgwwf3/N910E5s+fbpXmSuvvJLdcsstGtVaHWrb6Iv8/HxWUlLi/n+l4xMP1LZPDkZOnz7td58ifYfhfn+rV69mJpOJHTlyxL1NpO/PEyXBiCi/wai/TaOW8vJyVFRUYNq0ae5tiYmJuOiii7B582YAwPbt29HR0eFVZuDAgSgoKHCX4cGWLVtgs9kwfvx497YJEybAZrMprsfJkyfx4YcfYu7cub3+9uabb6Jfv34YM2YMHnroIXf2ZF6E075169ZhwIABGDlyJO666y5UVla6/ybK9wdo8x0CQF1dHUwmU69bkTy/w/b2dmzfvt3LKwBMmzbNb1u2bNnSq/yVV16Jbdu2oaOjI2AZ3t8VEFobeyJJEhoaGpCZmem1vbGxEUOHDsXgwYNx7bXXYufOnZrVWynhtO/8889HTk4OLrvsMnzxxRdefxPlO9Ti+3vllVdw+eWXY+jQoV7bRfj+QkGU32BEJMrjSUVFBQAgKyvLa3tWVha+++47d5mEhARkZGT0KiN/ngcVFRUYMGBAr+0DBgxQXI+///3vSEtLww9/+EOv7bfddhvy8vKQnZ0Nu92OhQsXYvfu3Vi7dq0mdVdCqO276qqr8KMf/QhDhw5FeXk5nnjiCVx66aXYvn07EhMThfn+AG2+w9bWVjz66KO49dZbvZJc8f4Oq6qq4HQ6ff52/LWloqLCZ/nOzk5UVVUhJyfHbxne3xUQWht78sc//hFNTU246aab3NvOOussrFy5EmeffTbq6+vx5z//GZMnT8bu3bsxYsQITdsQiFDal5OTgxdffBGFhYVoa2vD66+/jssuuwzr1q3D1KlTAfj/nnl/h+F+fw6HAx9//DH+8Y9/eG0X5fsLBVF+gxEZjCxatAglJSUBy3z11VcYN25cyMcwmUxe/88Y67WtJ0rKKEFp+4De9VRbjxUrVuC2226D1Wr12n7XXXe5/7ugoAAjRozAuHHjsGPHDowdO1bRvv2hd/tuvvlm938XFBRg3LhxGDp0KD788MNeQZea/aqB13fY0dGBW265BZIkYdmyZV5/0/M7DITa346v8j23h/J71JNQ6/PWW29h0aJF+Pe//+0VhE6YMMHrIevJkydj7Nix+Otf/4q//OUv2lVcIWraN2rUKIwaNcr9/xMnTsSxY8fw7LPPuoMRtfvUm1DrsnLlSvTp0wc33HCD13bRvj+1iPAbjMhg5Oc//3nQtwKGDRsW0r6zs7MBuKLFnJwc9/bKykp3ZJidnY329nacPn3a6+q6srISkyZNCum4niht39dff42TJ0/2+tupU6d6RbG+2LhxI7755husWrUqaNmxY8ciPj4eBw8eDPtExqt9Mjk5ORg6dCgOHjwIQP/vD+DTxo6ODtx0000oLy/H559/HjT1t5bfoS/69esHi8XS62rJ87fTk+zsbJ/l4+Li0Ldv34Bl1PQBrQiljTKrVq3C3Llz8c477+Dyyy8PWNZsNuOCCy5w91lehNM+TyZMmIA33njD/f+ifIfhtI8xhhUrVmDWrFlISEgIWNao7y8UhPkNavb0ieCofYD1mWeecW9ra2vz+QDrqlWr3GVOnDhh2AOs//vf/9zbvvzyS8UPP86ZM6fXGxj+2LNnDwPA1q9fH3J91RJu+2SqqqpYYmIi+/vf/84YE+f7Yyz0Nra3t7MbbriBjRkzhlVWVio6Fo/vsKioiN1zzz1e20aPHh3wAdbRo0d7bZs3b16vh+euuuoqrzLTp0839AFWNW1kjLF//OMfzGq1Bn2YUEaSJDZu3Dh2xx13hFPVkAilfT258cYb2SWXXOL+f5G+w1DbJz+ou2fPnqDHMPL78wQKH2AV4TcY9cHId999x3bu3MlKSkpYamoq27lzJ9u5cydraGhwlxk1ahR777333P//9NNPM5vNxt577z22Z88e9uMf/9jnq72DBw9mn332GduxYwe79NJLDXu195xzzmFbtmxhW7ZsYWeffXav10J7to8xxurq6lhycjJbvnx5r31+++23rKSkhH311VesvLycffjhh+yss85i559/vvDta2hoYA8++CDbvHkzKy8vZ1988QWbOHEiGzRokJDfH2Pq29jR0cGuu+46NnjwYLZr1y6vVwnb2toYY8Z9h/Jrk6+88gorKytj999/P0tJSXG/efDoo4+yWbNmucvLrxU+8MADrKysjL3yyiu9XivctGkTs1gs7Omnn2b79u1jTz/9tBCv9ipt4z/+8Q8WFxfHnnvuOb+vWS9atIiVlpayQ4cOsZ07d7I77riDxcXFeQWporbvT3/6E1u9ejU7cOAAs9vt7NFHH2UA2LvvvusuI9J3qLZ9MjNnzmTjx4/3uU+Rvr+Ghgb3eQ4AW7JkCdu5c6f7TTtRf4NRH4zMmTOHAej174svvnCXAcBeffVV9/9LksSKi4tZdnY2S0xMZFOnTu0VDbe0tLCf//znLDMzkyUlJbFrr72WHT16lFOruqmurma33XYbS0tLY2lpaey2227r9Ypdz/YxxtgLL7zAkpKSfK47cfToUTZ16lSWmZnJEhIS2PDhw9m9997ba60OHqhtX3NzM5s2bRrr378/i4+PZ0OGDGFz5szp9d2I8v0xpr6N5eXlPvu0Z7828jt87rnn2NChQ1lCQgIbO3as10zMnDlz2EUXXeRVft26dez8889nCQkJbNiwYT4D5HfeeYeNGjWKxcfHs7POOsvrRGcEatp40UUX+fyu5syZ4y5z//33syFDhrCEhATWv39/Nm3aNLZ582aOLfJGTfueeeYZNnz4cGa1WllGRga78MIL2YcffthrnyJ9h2r7aG1tLUtKSmIvvviiz/2J9P3JMzj++puov0ETY11PqhAEQRAEQRgArTNCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEIShUDBCEARBEISh/H/8jUceLbfJQQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kan import *\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "datasets = []\n", + "\n", + "n_peak = 5\n", + "n_num_per_peak = 100\n", + "n_sample = n_peak * n_num_per_peak\n", + "\n", + "x_grid = torch.linspace(-1,1,steps=n_sample)\n", + "\n", + "x_centers = 2/n_peak * (np.arange(n_peak) - n_peak/2+0.5)\n", + "\n", + "x_sample = torch.stack([torch.linspace(-1/n_peak,1/n_peak,steps=n_num_per_peak)+center for center in x_centers]).reshape(-1,)\n", + "\n", + "\n", + "y = 0.\n", + "for center in x_centers:\n", + " y += torch.exp(-(x_grid-center)**2*300)\n", + " \n", + "y_sample = 0.\n", + "for center in x_centers:\n", + " y_sample += torch.exp(-(x_sample-center)**2*300)\n", + " \n", + "\n", + "plt.plot(x_grid.detach().numpy(), y.detach().numpy())\n", + "plt.scatter(x_sample.detach().numpy(), y_sample.detach().numpy())" + ] + }, + { + "cell_type": "markdown", + "id": "19477c89", + "metadata": {}, + "source": [ + "Sequentially prensenting different peaks to KAN" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "831a9456", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(1, 5, figsize=(15, 2))\n", + "plt.subplots_adjust(wspace=0, hspace=0)\n", + "\n", + "for i in range(1,6):\n", + " plt.subplot(1,5,i)\n", + " group_id = i - 1\n", + " plt.plot(x_grid.detach().numpy(), y.detach().numpy(), color='black', alpha=0.1)\n", + " plt.scatter(x_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak].detach().numpy(), y_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak].detach().numpy(), color=\"black\", s=2)\n", + " plt.xlim(-1,1)\n", + " plt.ylim(-1,2)" + ] + }, + { + "cell_type": "markdown", + "id": "3e487a84", + "metadata": {}, + "source": [ + "Training KAN" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "11a1d129", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 89.80\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 76.04\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 92.65\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 79.18\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 87.63\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.5\n" + ] + } + ], + "source": [ + "ys = []\n", + "\n", + "# setting bias_trainable=False, sp_trainable=False, sb_trainable=False is important.\n", + "# otherwise KAN will have random scaling and shift for samples in previous stages\n", + "\n", + "model = KAN(width=[1,1], grid=200, k=3, noise_scale=0.1, sp_trainable=False, sb_trainable=False, base_fun='zero')\n", + "\n", + "for group_id in range(n_peak):\n", + " dataset = {}\n", + " dataset['train_input'] = x_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None]\n", + " dataset['train_label'] = y_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None]\n", + " dataset['test_input'] = x_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None]\n", + " dataset['test_label'] = y_sample[group_id*n_num_per_peak:(group_id+1)*n_num_per_peak][:,None]\n", + " model.fit(dataset, opt = 'LBFGS', steps=100, update_grid=False);\n", + " y_pred = model(x_grid[:,None])\n", + " ys.append(y_pred.detach().numpy()[:,0])" + ] + }, + { + "cell_type": "markdown", + "id": "dbb9a1b7", + "metadata": {}, + "source": [ + "Prediction of KAN after each stage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12379f4a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(1, 5, figsize=(15, 2))\n", + "plt.subplots_adjust(wspace=0, hspace=0)\n", + "\n", + "for i in range(1,6):\n", + " plt.subplot(1,5,i)\n", + " group_id = i - 1\n", + " plt.plot(x_grid.detach().numpy(), y.detach().numpy(), color='black', alpha=0.1)\n", + " plt.plot(x_grid.detach().numpy(), ys[i-1], color='black')\n", + " plt.xlim(-1,1)\n", + " plt.ylim(-1,2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2002726", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/.ipynb_checkpoints/Example_9_singularity-checkpoint.ipynb b/tutorials/.ipynb_checkpoints/Example_9_singularity-checkpoint.ipynb new file mode 100644 index 00000000..5fd01166 --- /dev/null +++ b/tutorials/.ipynb_checkpoints/Example_9_singularity-checkpoint.ipynb @@ -0,0 +1,459 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "134e7f9d", + "metadata": {}, + "source": [ + "# Example 9: Singularity" + ] + }, + { + "cell_type": "markdown", + "id": "2571d531", + "metadata": {}, + "source": [ + "Let's construct a dataset which contains singularity $f(x,y)=sin(log(x)+log(y))\n", + " (x>0,y>0)$" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2075ef56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.14e-01 | test_loss: 1.29e-01 | reg: 6.34e+00 | : 100%|█| 20/20 [00:03<00:00, 5.03it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from kan import *\n", + "import torch\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", + "model = KAN(width=[2,1,1], grid=5, k=3, seed=2, device=device)\n", + "f = lambda x: torch.sin(2*(torch.log(x[:,[0]])+torch.log(x[:,[1]])))\n", + "dataset = create_dataset(f, n_var=2, ranges=[0.2,5], device=device)\n", + "\n", + "# train the model\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3f95fcdd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ccb7ec43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is 0.9974619150161743\n", + "saving model version 0.2\n", + "r2 is 0.997527003288269\n", + "saving model version 0.3\n", + "r2 is 0.9740613698959351\n", + "saving model version 0.4\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(0.9741, device='cuda:0')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fix_symbolic(0,0,0,'log')\n", + "model.fix_symbolic(0,1,0,'log')\n", + "model.fix_symbolic(1,0,0,'sin')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0937db67", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 2.66e-07 | test_loss: 2.75e-07 | reg: 0.00e+00 | : 100%|█| 20/20 [00:01<00:00, 15.69it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e959cda3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - 1.0 \\sin{\\left(2.0 \\log{\\left(5.017 x_{1} \\right)} + 2.0 \\log{\\left(1.512 x_{2} \\right)} - 7.194 \\right)}$" + ], + "text/plain": [ + "-1.0*sin(2.0*log(5.017*x_1) + 2.0*log(1.512*x_2) - 7.194)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ex_round(model.symbolic_formula()[0][0], 3)" + ] + }, + { + "cell_type": "markdown", + "id": "16e4da06", + "metadata": {}, + "source": [ + "We were lucky -- singularity does not seem to be a problem in this case. But let's instead consider $f(x,y)=\\sqrt{x^2+y^2}$. $x=y=0$ is a singularity point." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1ce52cec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 3.65e-03 | test_loss: 3.97e-03 | reg: 4.84e+00 | : 100%|█| 20/20 [00:03<00:00, 5.36it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + } + ], + "source": [ + "from kan import *\n", + "import torch\n", + "\n", + "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", + "model = KAN(width=[2,1,1], grid=5, k=3, seed=0)\n", + "f = lambda x: torch.sqrt(x[:,[0]]**2+x[:,[1]]**2)\n", + "dataset = create_dataset(f, n_var=2)\n", + "\n", + "# train the model\n", + "model.fit(dataset, opt=\"LBFGS\", steps=20);" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3a69ec41", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "abef7aa9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is 0.9999973773956299\n", + "saving model version 0.2\n", + "r2 is 0.9999948740005493\n", + "saving model version 0.3\n", + "r2 is 0.9998846650123596\n", + "saving model version 0.4\n" + ] + }, + { + "data": { + "text/plain": [ + "tensor(0.9999)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fix_symbolic(0,0,0,'x^2')\n", + "model.fix_symbolic(0,1,0,'x^2')\n", + "model.fix_symbolic(1,0,0,'sqrt')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aa71848c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rewind to model version 0.4, renamed as 1.4\n" + ] + } + ], + "source": [ + "model = model.rewind('0.4')\n", + "model.get_act(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e14000d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 1.00775534257195 \\sqrt{0.999962771771901 \\left(6.10769914067904 \\cdot 10^{-5} - x_{1}\\right)^{2} + \\left(9.20887777110479 \\cdot 10^{-5} - x_{2}\\right)^{2} + 0.00441348508007971} - 0.00955450534820557$" + ], + "text/plain": [ + "1.00775534257195*sqrt(0.999962771771901*(6.10769914067904e-5 - x_1)**2 + (9.20887777110479e-5 - x_2)**2 + 0.00441348508007971) - 0.00955450534820557" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "formula = model.symbolic_formula()[0][0]\n", + "formula" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c56ee3d5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 1.01 \\sqrt{1.0 x_{1}^{2} + x_{2}^{2}} - 0.01$" + ], + "text/plain": [ + "1.01*sqrt(1.0*x_1**2 + x_2**2) - 0.e-2" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ex_round(formula, 2)" + ] + }, + { + "cell_type": "markdown", + "id": "1fd57d41", + "metadata": {}, + "source": [ + "w/ singularity avoiding (LBFGS may still get nan because of line search, but Adam won't get nan)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "de708f21", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 5.11e-04 | test_loss: 5.64e-04 | reg: 0.00e+00 | : 100%|█| 1000/1000 [00:14<00:00, 70.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 1.5\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"Adam\", steps=1000, lr=1e-3, update_grid=False, singularity_avoiding=True);" + ] + }, + { + "cell_type": "markdown", + "id": "6fd34c4c", + "metadata": {}, + "source": [ + "w/o singularity avoiding, nan may appear" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "031fabd6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: nan | test_loss: nan | reg: nan | : 100%|█████████| 1000/1000 [00:17<00:00, 57.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 1.6\n" + ] + } + ], + "source": [ + "model.fit(dataset, opt=\"Adam\", steps=1000, lr=1e-3, update_grid=False);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "124c9ca4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/API_9_video.ipynb b/tutorials/API_9_video.ipynb index 27c39d53..c5a723c9 100644 --- a/tutorials/API_9_video.ipynb +++ b/tutorials/API_9_video.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "2075ef56", "metadata": { "tags": [] @@ -31,7 +31,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.90e-01 | test_loss: 3.15e-01 | reg: 1.18e+01 | : 100%|█| 5/5 [00:09<00:00, 1.98s/it" + "| train_loss: 2.89e-01 | test_loss: 2.96e-01 | reg: 1.31e+01 | : 100%|█| 5/5 [00:09<00:00, 1.94s/it" ] }, { @@ -57,7 +57,7 @@ "print(device)\n", "\n", "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", - "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=2, device=device)\n", + "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1, device=device)\n", "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n", "dataset = create_dataset(f, n_var=4, train_num=3000, device=device)\n", "\n", diff --git a/tutorials/Example_10_relativity-addition.ipynb b/tutorials/Example_10_relativity-addition.ipynb index 36761ea7..50d49d0e 100644 --- a/tutorials/Example_10_relativity-addition.ipynb +++ b/tutorials/Example_10_relativity-addition.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "id": "0a59179d", "metadata": {}, "outputs": [ @@ -34,6 +34,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -42,12 +43,15 @@ "source": [ "from kan import *\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "# initialize KAN with G=3\n", - "model = KAN(width=[2,1,1], grid=10, k=3)\n", + "model = KAN(width=[2,1,1], grid=10, k=3, device=device)\n", "\n", "# create dataset\n", "f = lambda x: (x[:,[0]]+x[:,[1]])/(1+x[:,[0]]*x[:,[1]])\n", - "dataset = create_dataset(f, n_var=2, ranges=[-0.9,0.9])" + "dataset = create_dataset(f, n_var=2, ranges=[-0.9,0.9], device=device)" ] }, { @@ -60,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "id": "a87b97b0", "metadata": {}, "outputs": [ @@ -68,7 +72,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 9.06e-04 | test_loss: 9.09e-04 | reg: 6.62e+00 | : 100%|█| 20/20 [00:02<00:00, 7.45it" + "| train_loss: 2.28e-03 | test_loss: 2.31e-03 | reg: 6.50e+00 | : 100%|█| 20/20 [00:03<00:00, 5.88it" ] }, { @@ -92,13 +96,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "3f1cfc9d", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -121,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "2ccb7048", "metadata": {}, "outputs": [ @@ -130,11 +134,11 @@ "output_type": "stream", "text": [ " function fitting r2 r2 loss complexity complexity loss total loss\n", - "0 arctanh 0.999999 -16.478226 4 4 -16.478226\n", - "1 tan 0.999841 -12.526917 3 3 -12.526917\n", - "2 arcsin 0.998865 -9.770447 4 4 -9.770447\n", - "3 arccos 0.998865 -9.770447 4 4 -9.770447\n", - "4 x^0.5 0.982241 -5.814472 2 2 -5.814472\n" + "0 arctanh 0.999992 -15.786788 4 4 -15.786788\n", + "1 tan 0.999825 -12.397871 3 3 -12.397871\n", + "2 arccos 0.998852 -9.753944 4 4 -9.753944\n", + "3 arcsin 0.998852 -9.753944 4 4 -9.753944\n", + "4 sqrt 0.982166 -5.808383 2 2 -5.808383\n" ] }, { @@ -145,11 +149,11 @@ " (x)>,\n", " 4,\n", " (x, y_th)>),\n", - " 0.9999990463256836,\n", + " 0.999992311000824,\n", " 4)" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -168,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "1bb96fe1", "metadata": {}, "outputs": [ @@ -176,19 +180,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "r2 is 0.9999994039535522\n", + "r2 is 0.9999759197235107\n", "saving model version 0.2\n", - "r2 is 0.9999990463256836\n", + "r2 is 0.999992311000824\n", "saving model version 0.3\n" ] }, { "data": { "text/plain": [ - "tensor(1.0000)" + "tensor(1.0000, device='cuda:0')" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "83b852a3", "metadata": {}, "outputs": [ @@ -208,7 +212,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 7.43e-04 | test_loss: 7.97e-04 | reg: 5.39e+00 | : 100%|█| 20/20 [00:02<00:00, 6.78it" + "| train_loss: 7.94e-04 | test_loss: 9.43e-04 | reg: 4.12e+00 | : 100%|█| 20/20 [00:04<00:00, 4.34it" ] }, { @@ -232,13 +236,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "9ccd0923", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -261,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "99ad38b9", "metadata": {}, "outputs": [ @@ -269,27 +273,27 @@ "name": "stdout", "output_type": "stream", "text": [ - " function fitting r2 r2 loss complexity complexity loss total loss\n", - "0 gaussian 0.990241 -6.677572 3 3 -6.677572\n", - "1 tanh 0.974454 -5.290217 3 3 -5.290217\n", - "2 arctan 0.971844 -5.149921 4 4 -5.149921\n", - "3 cos 0.966063 -4.880588 2 2 -4.880588\n", - "4 sin 0.966063 -4.880560 2 2 -4.880560\n" + " function fitting r2 r2 loss complexity complexity loss total loss\n", + "0 tanh 0.999998 -16.336284 3 3 -16.336284\n", + "1 arctan 0.999435 -10.764618 4 4 -10.764618\n", + "2 cos 0.995899 -7.926177 2 2 -7.926177\n", + "3 sin 0.995899 -7.926177 2 2 -7.926177\n", + "4 gaussian 0.994457 -7.492519 3 3 -7.492519\n" ] }, { "data": { "text/plain": [ - "('gaussian',\n", + "('tanh',\n", " ((x)>,\n", " (x)>,\n", " 3,\n", " (x, y_th)>),\n", - " 0.9902409911155701,\n", + " 0.9999979138374329,\n", " 3)" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -300,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "id": "af24c80d", "metadata": {}, "outputs": [ @@ -308,18 +312,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best value at boundary.\n", - "r2 is 0.974454402923584\n", + "r2 is 0.9999979138374329\n", "saving model version 0.5\n" ] }, { "data": { "text/plain": [ - "tensor(0.9745)" + "tensor(1.0000, device='cuda:0')" ] }, - "execution_count": 20, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "id": "01936f17", "metadata": {}, "outputs": [ @@ -338,14 +341,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 5.23e-07 | test_loss: 5.28e-07 | reg: 0.00e+00 | : 100%|█| 2000/2000 [00:25<00:00, 79.\n" + "| train_loss: 1.97e-06 | test_loss: 2.06e-06 | reg: 0.00e+00 | : 100%|█| 2000/2000 [00:21<00:00, 93.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "saving model version 0.7\n" + "saving model version 0.6\n" ] } ], @@ -355,13 +358,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "id": "76bcc188", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -376,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "id": "b62b0246", "metadata": {}, "outputs": [ @@ -389,7 +392,7 @@ "tanh(atanh(x_1) + atanh(x_2))" ] }, - "execution_count": 25, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -416,7 +419,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_11_encouraing_linear.ipynb b/tutorials/Example_11_encouraing_linear.ipynb index d49514e1..ee212632 100644 --- a/tutorials/Example_11_encouraing_linear.ipynb +++ b/tutorials/Example_11_encouraing_linear.ipynb @@ -27,8 +27,6 @@ "id": "91301ca0", "metadata": {}, "source": [ - "### Case 1: 1D function \n", - "\n", "$f(x)={\\rm sin}(\\pi x)$. Although we know a [1,1] KAN suffices, we suppose we don't know that and use a [1,1,1,1] KAN instead." ] }, @@ -42,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "c881665b", "metadata": {}, "outputs": [ @@ -50,6 +48,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -58,7 +57,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.43e-03 | test_loss: 1.47e-03 | reg: 8.40e+00 | : 100%|█| 20/20 [00:03<00:00, 5.80it" + "| train_loss: 3.74e-04 | test_loss: 3.84e-04 | reg: 8.88e+00 | : 100%|█| 20/20 [00:05<00:00, 3.79it" ] }, { @@ -79,24 +78,27 @@ "source": [ "from kan import *\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "# create dataset f(x,y) = sin(pi*x). This task can be achieved by a [1,1] KAN\n", "f = lambda x: torch.sin(torch.pi*x[:,[0]])\n", - "dataset = create_dataset(f, n_var=1)\n", + "dataset = create_dataset(f, n_var=1, device=device)\n", "\n", - "model = KAN(width=[1,1,1,1], grid=5, k=3, seed=0, noise_scale=0.1)\n", + "model = KAN(width=[1,1,1,1], grid=5, k=3, seed=0, noise_scale=0.1, device=device)\n", "\n", "model.fit(dataset, opt=\"LBFGS\", steps=20);" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "201ceacf", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -119,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "id": "a22ffff3", "metadata": {}, "outputs": [ @@ -135,7 +137,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 5.23e-03 | test_loss: 4.93e-03 | reg: 1.19e+01 | : 100%|█| 20/20 [00:04<00:00, 4.68it" + "| train_loss: 8.89e-03 | test_loss: 8.40e-03 | reg: 1.83e+01 | : 100%|█| 20/20 [00:04<00:00, 4.20it" ] }, { @@ -158,10 +160,10 @@ "\n", "# create dataset f(x,y) = sin(pi*x). This task can be achieved by a [1,1] KAN\n", "f = lambda x: torch.sin(torch.pi*x[:,[0]])\n", - "dataset = create_dataset(f, n_var=1)\n", + "dataset = create_dataset(f, n_var=1, device=device)\n", "\n", "# set base_fun to be linear\n", - "model = KAN(width=[1,1,1,1], grid=5, k=3, seed=0, base_fun='identity', noise_scale=0.1)\n", + "model = KAN(width=[1,1,1,1], grid=5, k=3, seed=0, base_fun='identity', noise_scale=0.1, device=device)\n", "\n", "# penality spline coefficients\n", "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=1e-4, lamb_coef=10.0);" @@ -169,13 +171,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "id": "c82c8db5", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -188,198 +190,6 @@ "model.plot(beta=10)" ] }, - { - "cell_type": "markdown", - "id": "af370a4c", - "metadata": {}, - "source": [ - "### Case 2: 2D function \n", - "\n", - "$f(x,y)={\\rm exp}({\\rm sin}(\\pi x)+y^2)$. We know a [2,1,1] KAN represents it. Let's suppose we don't know about that and use a [2,3,3,3,1] KAN instead." - ] - }, - { - "cell_type": "markdown", - "id": "fdba8357", - "metadata": {}, - "source": [ - "without tricks" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "5920bdaf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "checkpoint directory created: ./model\n", - "saving model version 0.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "| train_loss: 3.11e-02 | test_loss: 3.13e-02 | reg: 3.66e+00 | : 100%|█| 50/50 [00:24<00:00, 2.07it" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saving model version 0.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# create dataset f(x,y) = exp(sin(pi*x)+y^2)\n", - "f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)\n", - "dataset = create_dataset(f, n_var=2)\n", - "\n", - "model = KAN(width=[2,3,3,3,1], grid=3, k=3, noise_scale=0.5)\n", - "model.fit(dataset, opt=\"LBFGS\", steps=50, lamb=0.01, reg_metric='edge_forward_spline_u');" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "26af5d19", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "b0316bee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saving model version 0.2\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = model.prune(node_th=1e-1)\n", - "model.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "9df646f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rewind to model version 0.2, renamed as 1.2\n" - ] - } - ], - "source": [ - "model = model.rewind('0.2')" - ] - }, - { - "cell_type": "markdown", - "id": "ca1c5e86", - "metadata": {}, - "source": [ - "with tricks" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1f82e8c0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "| train_loss: 5.05e-01 | test_loss: 5.94e-01 | reg: 4.57e+01 | : 100%|█| 20/20 [00:07<00:00, 2.75it" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saving model version 1.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=0.001, lamb_l1=0., lamb_entropy=0., lamb_coef=1.0);" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e09861b6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.plot()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -405,7 +215,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_12_unsupervised_learning.ipynb b/tutorials/Example_12_unsupervised_learning.ipynb index 52ffaa8a..943ae09b 100644 --- a/tutorials/Example_12_unsupervised_learning.ipynb +++ b/tutorials/Example_12_unsupervised_learning.ipynb @@ -36,6 +36,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -46,10 +47,12 @@ "import torch\n", "import copy\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", "\n", "seed = 1\n", "\n", - "model = KAN(width=[6,1,1], grid=3, k=3, seed=seed)\n", + "model = KAN(width=[6,1,1], grid=3, k=3, seed=seed, device=device)\n", "\n", "# create dataset\n", "\n", @@ -81,10 +84,10 @@ " x_test, y_test = generate_contrastive(x)\n", " \n", " dataset = {}\n", - " dataset['train_input'] = x_train\n", - " dataset['test_input'] = x_test\n", - " dataset['train_label'] = y_train\n", - " dataset['test_label'] = y_test\n", + " dataset['train_input'] = x_train.to(device)\n", + " dataset['test_input'] = x_test.to(device)\n", + " dataset['train_label'] = y_train.to(device)\n", + " dataset['test_label'] = y_test.to(device)\n", " return dataset\n", "\n", "dataset = create_dataset()" @@ -98,7 +101,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAFICAYAAACcDrP3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABFZUlEQVR4nO3deViU5eI+8PudDdkRVDYB2dzBcDvllrumotYxPZaWaWLHrfJoZR3TX7snbdHQ1PbNTK085IZpaqZmCYq4saiALMqO7LM8vz86M18RF8B3mBm4P9fl1WUOM8/78M7c8+ySEEKAiIhIRgpLF4CIiJoehgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGC9EdxMXFYcaMGfDw8IBGo4GHhwdmzJiBuLg4SxeNyGpJPOaY6OZ0Oh3mzJmD9evXQ6VSQafTmf7N+PeoqChER0dDpVJZsKRE1ofhQnQLs2bNwoYNG3C7t4gkSZg5cybWrVvXiCUjsn4MF6KbiIuLQ48ePer8+OPHj6N79+5mLBGRbeGYC9FN1KerS6VSYc2aNWYuEZFtYcuF6CY8PDxQUFBQ58e7u7sjPz/fjCUisi0MF6Kb0Gg00Gq1dX68Wq1GdXW1GUtEZFvYLUZ0E87OzmZ9PFFTx3Ahuonx48fXa8zlwQcfNHOJiGwLu8WIboKzxYjuDlsuRDfRvXt3REVFQZKk2z5OkiRERUUxWIhuwHAhuoXo6GjMnDkTAGp1kRn/PnPmTERHRzd62YisHbvFiO4gLi4Oa9aswebNm1FSUgIXFxc8/PDDmD17NlssRLfAcCGqI+M4DMdXiO6M3WJERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREslNZugBE1q6goADx8fGIjY0FAOzYsQN2dnZo37491Gq1hUtHZJ0kIYSwdCGIrFF5eTk+/fRTREdHo6ioCP7+/rC3t0dBQQHy8/PRu3dvLFmyBBEREZYuKpHVYbgQ3URRURHmzp2Lffv2Yd68eZg0aRJ0Oh30er0pYKKjo7Fv3z68++67GDduHCRJsnSxiawGw4XoBjqdDs888wy2b9+OL7/8En369EFlZSUGDhyIc+fO4cknn8TKlSuh1Wqxfv16rFixAps3b0avXr0sXXQiq8EBfaIbHDp0CBs3bsTKlSvRt29fKBQKCCFQVlaGa9euobKyEgCg0Wgwa9YsjB8/HkuXLkVVVZWFS05kPRguRNcRQuCzzz5Djx49MHr06Dt2danVasybNw9nzpxBQkJCI5WSyPpxthjRdcrKynDs2DHMmDEDsbGxyMjIAABUV1ejoKAAAHD69GmsXbsWACBJEkaOHIlOnTrh6NGj7Boj+h+GC9F1SktLUVhYiICAAERHR2P37t21HnPw4EEcPHgQAKBUKrFjxw4EBATg8uXLjV1cIqvFcCG6jlKphEqlQlVVFZydndGyZUvTvxUXF8NgMMDOzg4ODg61Hq/RaCxVbCKrw3Ahuo6Liwt8fX1x8uRJREdHmwbvKyoqEBkZieTkZEycOBGvvfaa6WecnJywePFiDBs2zFLFJrI6HNAnuo6dnR0eeOABxMTEAAD8/f3h7++Ptm3bmlbjOzk5wc/PD/7+/vDz88PJkydx9epV9OnTx5JFJ7IqDBeiGzz++OOorKzEypUrUV1dfcvHCSGQl5eHZcuWYeLEiQgICGjEUhJZN3aLEd2gXbt2eOuttzB37lzY2dnhX//6FzQaDdq1awedTgdPT08AwMWLF/HMM89AqVRi0aJFXKFPdB2u0Ce6CYPBgI0bN+LFF1+Ej48PpkyZgs6dO8PBwQGFhYU4fPgwNm7ciC5dumD16tXw8/OzdJGJrArDheg2kpOTsX79euzatQs5OTkoKyuDm5sbwsPD8fjjj2PcuHGmmWNE9H8YLkR1UFpaigMHDmDMmDGIjY3FoEGDoFKxV5noVjigT1QHTk5O8Pb2BgB4eHgwWIjugOFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyU4SQghLF4LIFgghIISAJEmQJMnSxSGyaipLF4DIHMzxncn4nHI/N4OKmiKGCzU5Qghs3LgRqampVv3BLYRAcHAwJk+ebNXlJGoIhgs1SYmJiZg5cyacnJzu6nkMBgOysrKQmJiI/Px8+Pj4ICIiAm5ubnddxtLSUmzYsOGun4fIGjFcqElSKBTw8PCAi4tLg37eYDAgMTERq1evxvbt25Gbmwu9Xg+1Wo0OHTpg6dKlGD9+PJRKZYPLaGdnB4WCc2qoaWK4EF1HCIGCggK8//77WLNmDQoLC9G2bVtMnjwZbdu2xalTp7B371489thjWL58OWbPns2AILoJhgvR/+j1euzfvx8vvPAC4uLi0KZNG/y///f/8MQTT8Db2xuSJEGr1eKHH37A7Nmz8fzzz8PHxwcPPvggx0yIbsBwoWZPCIGioiKsWLECq1evRmVlJcaNG4dXX30VnTt3rhEcGo0GEydOhMFgwIwZMzB//nyEh4cjODiYAUN0HbbnqVkzGAw4cuQIRo8ejbfeegsuLi6Ijo7GN998UytYjCRJwsSJEzF37lxkZWVh0aJF0Gq1Fig9kfViuFCzJIRAcXExXn/9dYwZMwZ//PEHRo0ahT179uDJJ59EixYtbtsSUSqVePHFF9GtWzds374dP/zwg1nW1hDZKoYLNTt6vR4HDx7E6NGjsWzZMqjVaqxYsQKbNm1Cx44d69y95erqiuXLl0OhUGDp0qUoLCw0c8mJbAfDhZoNIQTS0tKwYMECREZG4vfff8fw4cOxe/duzJ8/Hw4ODvUaN5EkCYMHD8aDDz6IpKQkfPjhh2y9EP0Pw4WaPCEE8vLy8N5772HgwIFYvXo13N3d8cEHH2DLli3o1q1bgwfjlUolXn75Zbi6umLVqlXIyMiQufREtonhQk1afn4+1q5di4EDB2LhwoUoLCzEnDlzsH//fkRFRcHR0fGuZnlJkoQOHTpg+vTpuHr1Kt59910YDAYZr4DINjFcqMnasWMHBg4ciPnz5+PSpUuYMGEC9uzZg/feew/t2rWTbeqwQqHAs88+C09PT3z22Wc4f/68LM9LZMsYLtRkKRQKZGRkYPz48di5cye++uor9OzZ8662bLkVX19fzJkzB8XFxXjrrbeg1+tlfw0iW8JwoSZryJAh2LdvH7755hv069cParXabAsdJUnCU089hXbt2mHLli2Ii4vj4D41awwXarLUajW6d+8OjUbTKKvnPTw8sGjRIlRWVuLVV1+FTqcz+2sSWSuGC5FMJEnClClT0KVLF+zevRsHDhxg64WaLYYLkYycnJywePFi6PV6vP7666iurrZ0kYgsguFCJCNJkjB+/Hj06NEDv/32G/bu3cvWCzVLDBcimbVo0QIvvPAChBB4++23uaklNUsMFyKZSZKEkSNH4p577sHhw4dx+PBhtl6o2WG4EJlBixYt8PTTT0On02H16tVctU/NDsOFyAwkSUJkZCQCAwMRGxuLlJQUSxeJqFExXIjMxMXFBVOnTkVZWRm+/PJLdo1Rs8JwITITSZLw6KOPwsnJCd9++y1KS0stXSSiRsNwITKjwMBA9OvXD5cuXcKRI0csXRyiRsNwITIjhUKBqVOnwmAw4Ouvv2bXGDUbDBciM5IkCUOGDEGrVq3w888/o6ioyNJFImoUDBciM2vVqhUGDBiAnJwcHD161NLFIWoUDBciM5MkCQ899BCEENi2bRu7xqhZYLgQmZkkSRgwYACcnZ2xd+9eVFRUWLpIRGbHcCFqBN7e3ujWrRvS09ORlJRk6eIQmR3DhagRKBQKDB8+HFqtFvv27WPXGDV5DBeiRiBJEoYOHQqlUok9e/YwXKjJY7gQNZJOnTqhTZs2iI+PR3FxsaWLQ2RWDBeiRuLs7IyIiAjk5eXh7Nmzli4OkVkxXIgaiSRJGDx4MAwGAw4ePGjp4hCZFcOFqJFIkoS+fftCqVTi119/5bgLNWkMF6JG1L59e7i7uyMhIQFlZWWWLg6R2TBciBqRq6srunTpgitXruDChQuWLg6R2TBciBqRJEno168f9Ho94uLiLF0cIrNRWboAROZgMBhw5coVq+x6GjhwINzd3XHPPfdg165dli4OkVkwXKhJioiIwNatWyFJkqWLckuHDx9GRESEpYtBZBaS4JQVaoJs6ba25gAkaii2XKhJMscH9vWBxUAguj0O6BPVUXx8PBQKBeLj4y1dFCKrx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXojuIi4vDjBkzMGjQIADAoEGDMGPGDMTFxVm4ZETWSxJCCEsXgsga6XQ6zJkzB+vXr4dKpYJOpzP9m/HvUVFRiI6OhkqlsmBJiawPw4XoFmbNmoUNGzbgdm8RSZIwc+ZMrFu3rhFLRmT9GC5ENxEXF4cePXrU+fHHjx9H9+7dzVgiItvCMReim6hPV5dKpcKaNWvMXCIi28KWC9FNeHh4oKCgoM6Pd3d3R35+vhlLRGRbGC5EN6HRaKDVauv8eLVajerqajOWiMi2sFuM6CacnZ3N+niipo7hQnQT48ePr9eYy4MPPmjmEhHZFnaLEd0EZ4sR3R22XIhuonv37oiKioIkSbd9nCRJiIqKYrAQ3YDhQnQL0dHRmDlzJgDU6iIz/n3mzJmIjo5u9LIRWTt2ixHdQVxcHNasWYPNmzejpKQELi4uePjhhzF79my2WIhugeFCVEfGcRiOrxDdGbvFiIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGSnsnQBiKxdQUEB4uPjERsbCwDYsWMH7Ozs0L59e6jVaguXjsg6SUIIYelCEFmj8vJyfPrpp4iOjkZRURH8/f1hb2+PgoIC5Ofno3fv3liyZAkiIiIsXVQiq8NwIbqJoqIizJ07F/v27cO8efMwadIk6HQ66PV6U8BER0dj3759ePfddzFu3DhIkmTpYhNZDYYL0Q10Oh2eeeYZbN++HV9++SX69OmDyspKDBw4EOfOncOTTz6JlStXQqvVYv369VixYgU2b96MXr16WbroRFaDA/pENzh06BA2btyIlStXom/fvlAoFBBCoKysDNeuXUNlZSUAQKPRYNasWRg/fjyWLl2KqqoqC5ecyHowXIiuI4TAZ599hh49emD06NF37OpSq9WYN28ezpw5g4SEhEYqJZH142wxouuUlZXh2LFjmDFjBmJjY5GRkQEAqK6uRkFBAQDg9OnTWLt2LQBAkiSMHDkSnTp1wtGjR9k1RvQ/DBei65SWlqKwsBABAQGIjo7G7t27az3m4MGDOHjwIABAqVRix44dCAgIwOXLlxu7uERWi+FCBKCyshLp6ek4efIkqqqqUFVVBWdnZ7Rs2dL0mOLiYhgMBtjZ2cHBwQHAX+GiUqlQWVmJwsJCXLp0CQEBAZw5Rs0eZ4tRsyKEwJUrV5CWllbjT3Z2NioqKnDt2jUkJCRg1qxZWLhwoWnwvqKiApGRkUhOTsbUqVPx2muvmZ7TyckJw4cPR1xcHJRKJZydnREWFoawsDCEh4cjLCwMXbt2hbOzs6Uum6jRseVCTVZFRYUpPC5duoS0tDSkp6ebAsPBwQHOzs5QKpVo06YNHB0d4ePjA39/f8TExGDhwoXw9/cH8NdYjHE1vpOTE/z8/CBJEoQQ2L9/P5KSkiCEgE6ng6OjI4qLixEbG4uPPvoIer0eABAYGGgKm7CwMHTr1g2BgYFQKDivhpoehgvZPIPBgJycnFqtkatXrwL4q+vK19cX7dq1Q5cuXSCEQHl5OfLy8iCEgKenJ0JDQxEaGgpvb2+kpaWhT58+ePvtt/H6669Do9Hc9HWFEMjLy8OyZcvwz3/+E4sWLcKuXbsQExODXbt2oaSkBG3btsV9990Hf39/6PV6nD17FuvWrUNubi6AvwKua9euprAxtnLc3Nwaq/qIzILdYmRTysrKarREjK2R6upqAICbmxsCAgIQEBCAdu3awd/fH0IIXLx4EcnJycjLy4NSqUS7du1MgeLi4lLjNbZt24aHH34YarUaCxYswMKFC6HRaDBhwgSkpKTgkUcewcsvv4yLFy9i/vz5OHnyJH777TdTKwf4a3bZr7/+ipiYGMTExODChQtwdHTEsGHDEBkZid69eyMnJwcJCQk4deoUEhIScPbsWWi1WgCAv79/jVZOeHg4QkJCoFQqG6+yie4Cw4Wskl6vR3Z2dq3WSF5eHgBApVLBz8/PFCTGP66urqiqqsKFCxeQnJyMlJQUVFRUwNHRESEhIQgNDUVgYOAtWyMbNmzAU089hQcffBCjR4/GsmXL4OPjgylTpqBz585wcHBAYWEhDh8+jI0bN8LHxweVlZXo0KED3nvvPbi7u9d6TiEEzp49i//+97+IiYnBkSNHAAC9e/dGZGQkIiMjERYWBp1Oh/Pnz5sCxxg62dnZAIAWLVqgS5cuprAJDw9H165d4eHhYabfAlHDMVzI4q5du1ajJZKWloaMjAzTt3h3d3dTS8QYIj4+PjW+xRcXFyMpKQnJyclIS0uDwWBAmzZtTK0THx+f287gEkLgtddew8svv4w5c+bg/fffh1KpRHJyMtavX49du3aZgk2j0aBTp054/PHHMW7cOGRkZGD+/PlwdHTE6tWr4evre9vrzc3NxY4dOxATE4Pdu3ejtLQUAQEBpqC5//77YWdnV+PxiYmJOHXqFE6ePIlTp07hzJkzph0BfH19a0weCA8PR2hoKHdsJotiuFCj0ev1yMzMrBEily5dQmFhIYC/Vrv7+/vX6ta62SwrIQSysrJMgZKbmwuFQlGju8vV1bXO5Zo3bx7Wrl2LV199FS+99FKtICotLUV+fj60Wi2cnZ3h4eEBler/hiwzMzMxb948lJWVYfXq1Wjfvn2dXruqqgoHDhwwdZ+lpaXByckJI0aMQGRkJEaNGoXWrVvX+jmdToeUlBRT2Bj/GBd9ajQadO7cuVbX2s2ei8gcGC5kFkVFRbW6tDIyMkwzp1q1alWjJRIQEABvb+/bzpyqrq6u0d1VXl4OBwcHU3dXUFDQLbu7bqWyshJTpkzBDz/8gHXr1uHJJ59s8DUXFBTgmWeeQVpaGlauXImePXvW6+eFEEhMTDQFze+//w4AuO+++0ytms6dO9+2BVZQUGBq5SQkJCAhIQGnT59GRUUFAMDLy6tG2ISHh6NDhw71rjeiO2G40F3RarU1WiPG7q3i4mIAgJ2dnak1YgwTf39/ODo61un5S0pKkJycjOTkZFy6dAl6vR6tWrVCaGgo2rdvD19f3wYvWCwuLsa4cePw+++/Y9OmTRg7dmyDnud65eXleO655xAXF4dXXnkFQ4cObfBzXblyBdu3b0dMTAxiY2NRXl6OwMBAU9AMGDCgTqGg1+tx4cIFU9gYgyctLQ3AX+NXHTt2NM1WM/7x8vLiYlBqMIYL1YkQAoWFhbVaI5mZmabWiKenZ42WSLt27eDp6VmvDyghBLKzs02BcuXKFSgUCvj7+5sCRY5pullZWXjggQeQkZGBmJgY9O3b966f00ir1eKVV17B7t27sXDhQkycOPGun7OyshK//PKLqVVz+fJluLi4YOTIkYiMjMQDDzxQ74H94uLiGq0cY9daWVkZAKB169a1FoN26tQJLVq0uOvroaaP4UK1VFdX4/Lly7UG2a9duwYAsLe3rzVLy3hKY0NotVrTVOGUlBSUlpbC3t4ewcHBaN++PYKCgmoMcN+t8+fPY8SIEdDr9di1axe6dOki23MbGQwGrFq1Cl9//TWmT5+Op556SrZWgBACJ0+eNAXNH3/8AYVCgb59+5paNR06dGjQ6xkMBly6dKlG2Jw8eRIXLlwA8Neaofbt29eYPBAWFnZXLUhqmhguzZgQAvn5+bW6tLKysiCEgCRJ8PLyqjVTq3Xr1nf9QVJSUoKUlBRTd5dOp4OHh0eN7i5zrFw/duwYRo0ahTZt2mD37t3w8/OT/TWu99VXX+H999/H2LFj8eKLL5plnUp2drap+2zPnj2oqKhASEiIKWj69et31zPHSktLcfr06Roz1k6dOoWSkhIAQMuWLWuM44SFhZmmblPzxHBpJqqqqpCenl6rW8vYBeLo6FirS8vPz0+2FoMQAjk5OaburpycHEiSZOruCg0NvekaETnt3LkTEyZMQLdu3fDTTz+Z/fWMduzYgVdeeQV9+vTBG2+8YdZupYqKCuzduxcxMTH46aefkJWVBTc3txrdZ9dvxnk3hBBIT0+vtS4nOTkZQggoFAqEhITUmrHm7+/PVk4zwHBpYoQQyM3NrbWKPScnx9Qa8fHxqTVTy8PDQ/Y3vE6nq9Hdde3aNbRo0QLBwcEIDQ1FcHBwo/Xff/HFF5gxYwZGjhyJTZs2Nfo36iNHjuC5555DaGgo3nvvvVq7ApiDEAJxcXGm7jPjxpr9+vXD2LFjERkZidDQUNlft7y8HGfOnKkxY+3UqVOmKecuLi419lcLCwtDly5d4OTkJHtZyHIYLjasoqKiRmvk0qVLSE9PN007dXJyMoWI8b9t27Y167TT0tJSU+vk4sWL0Ol0cHd3N7VO/Pz8GnWjRiEEVqxYgeeeew7Tp0/HunXraqxPaUynT5/GM888g5YtW2L16tXw9PRs1NfPzMzETz/9hJiYGOzdu9e0s4Cx+6xPnz5mqxshBDIzM2tMHkhISEBSUpJpQkhQUFCNGWvh4eFo164dN/a0UQwXG2DcJv7GAfYrV64A+L+NGW8cZG/ZsmWjdD9c392VnZ0NSZLQtm1btG/fHqGhoRbbnsRgMGDhwoV499138dJLL+HVV1+1eHdMeno65s6dC51Ohw8++ABBQUEWKUdZWVmN7rOcnBy4u7vjgQceQGRkJEaOHFnnRah3o7KyEmfPnq0VOsbdEJycnEwbe16/5U1jtPzo7jBcrExZWZmpNXL9NvHGrT5cXV1rdWn5+vo26lYfOp0OaWlpSEpKQkpKCkpKSmBnZ4egoCC0b98ewcHBDZ45Jpfq6mo88cQT2LhxI1atWoW5c+datDzXy83NxdNPP42cnBy8++676Natm0XLYzAY8Oeff5q6z06ePAmVSoUBAwaYWjXBwcGNVh7j+Nz14zgJCQk4d+4cdDodACAgIKDG5IHw8HAEBQVxY08rwnCxEIPBUGtjxkuXLpm+sSmVyptuzGiprdjLyspMs7suXLgArVYLNzc3U+vEz8/Pat7Y165dw4QJE7B//3589dVXePjhhy1dpFpKS0vxr3/9C4mJiXjzzTcxYMAASxfJJD093dR9tm/fPlRXV6NTp06mcZp7773XIr/r6upq08ae108iyMnJAfDX8QXXb+xp7F6TawID1Q/DpRFcu3at1iyt9PT0Whsz3tgasfSH9dWrV03dXZmZmZAkCb6+vqZAadWqlUXLdzNXr17FqFGjkJSUhG3btmHQoEGWLtItVVdXY8mSJdi/fz8WL16M8ePHW7pItZSWlmLPnj2IiYnB9u3bcfXqVbRq1QqjRo1CZGQkhg8fbvEuqqtXr9bYXy0hIQFnzpwxHcPg5+dXa8ubkJAQi429NRcMFxnp9XpkZWXVmqlVUFAA4K+NGW/WGrH0m9NIr9cjLS3NFCjFxcXQaDQICgpCaGgoQkJCrHrdwoULFzBixAiUlpZi586duOeeeyxdpDsyGAx4++23sWXLFjz11FOYPn26xceFbsVgMODYsWOm7rNTp05BrVZj4MCBpu6zdu3aWbqYAP5amJucnFxjHOfUqVPIzMwE8Ne2RJ07d64xYy0sLMwqvzDZKoZLAxUXF9fq0rp8+bKpT7hVq1a1QsTb29virZEblZeX1+juqq6uhqurq2l2V0BAgNWV+Wbi4+PxwAMPwNnZGbGxsQgMDLR0kepMCIGPP/4Y69atw4QJE7Bo0SKbmCF16dIlU9Ds378fWq0WXbt2NQVN7969re7eyc/PR2JiYo2utdOnT5uOvvb29q61LqdDhw48vqABGC53oNPpkJmZWWumVlFREYC/tja/cZv4gICAOm/MaAm5ubmm1snly5cB/HUmiDFQ2rRpY+ES1s++ffswfvx4dOjQAdu3b7e58hv9+OOPePPNNzFo0CC88sorNrVTcUlJCWJjY03dZ/n5+WjdujXGjBmDyMhIDBs2zGrXsej1eqSkpNRaDJqeng7grx6HTp061dryprGnktsahsv/CCFqbBNvDJPrN2Zs06ZNrdaIl5eX1X/L1Ov1SE9PNwVKUVER1Gp1je4uaw7D2/nuu+8wZcoUDBo0CFu3brXaD7C6OnDgAF588UV07doVK1eutMnr0ev1OHr0qKlVc+bMGWg0GgwePNjUqjH3tjtyKCoqqnVIW2JiIsrLywH89Xlw4yFtHTt2lHUfPFvWLMNFq9UiIyOjVreWcWPGFi1a3HSbeGseb7hRRUVFje6uqqoquLi41OjusvUBzdWrV+Ppp5/GI488gk8++cSmvunfzokTJ7BgwQJ4eXnh/ffft/kDvlJTU02zzw4cOACdTodu3bqZgqZnz55W/wXNyGAw4OLFizX2V0tISMDFixcB/HV8QYcOHWp1rXl7e1vtWJq5NOlwEUKgoKCg1gB7VlYWDAYDAJg2Zry+W6tNmzY2eSPk5+ebTma8fPkyhBDw8fExBUpTacYLIfDSSy/hzTffxIIFC/D222/bzIdTXaWmpmLevHlQqVT44IMP4O/vb+kiyaK4uBi7du1CTEwMduzYgcLCQnh5eWH06NGIjIzE0KFDbbIVXVJSYtrY8/rxnNLSUgCAh4dHrS1vOnXqZPH1YObUZMKlqqqqRmvEGCbGjRmN28RfvwDR39/fps+mMBgMyMjIQHJyMpKSklBYWAiVSlWju8sWu1VuR6fTYdasWfjkk0/w9ttvY+HChZYuktnk5ORg/vz5KCwsxHvvvWeWowEsSafT4fDhw6bus/Pnz6NFixYYMmQIIiMjMWbMGPj6+lq6mA1mMBhMG3teP2stNTXVtLFnaGhorUPa/Pz8bPLL7Y1sLlyEEMjLy6s1wJ6dnW3amNHb27vWAHurVq2axC+ssrISqampSEpKwoULF1BZWQlnZ2dT66Rdu3Y23911K+Xl5Zg0aRJ27dqFTz75BFOnTrV0kcyuuLgYzz77LJKTk/Gf//wH9913n6WLZDbJycmmoPn111+h1+vRvXt3U/dZ9+7dm8R7uKysrEYrx/hf4+mtbm5utdbldO7c2eZadDYRLsnJydi/f78pSIwbMzo6OtbaCkXObeKtyR9//IFz584hIyMDQgh4eXmZAsXb29vSxTO7/Px8REZGIiEhAVu2bMHIkSMtXaRGU1lZiRdffBGHDx/Gyy+/jFGjRlm6SGZXWFho6j7buXMnioqK4OPjgzFjxmD+/PlNrhUnhEBGRkatLW+Sk5NhMBggSRKCg4MRHh6Oe+65By+88ILVB61NhEteXh4uX74MBwcHODg4wNHREQ4ODtBoNFZfwXI5efIkJEmCh4cHWrVq1SQD9HaEEKaWaXP5nV9PCGEaK2zbtm2zqgPjR5Txv83pHjDe99f/AWAT627MEi42kFcm5rhJm/v1A+apA2O4yI11wPeBLd0D5iJ3HcjeOS+EwKFDh3DlyhWr/nYhhICnpyf69esnazmFEDhz5ozpYCRr1rJlS3Tu3Fn235MQAhs3bkRqaqrV3wPBwcGYPHmyWepg9+7dpj3ZrJUQAr6+vhgxYoTs7wPeAwKbNm2ymTqYNGmSrOU0y8hveno6hg4dapUzsS5fvozk5GR06NABcXFxZnmNq1evIiIiol5N16KiImRnZ6NVq1aNsq5Bq9UiPj4enTt3NsvzJyYmYvTo0fjll1/Qr18/dOzY0eq2AiktLcWGDRvM9vypqakYP3682ddHabVaJCQkoLS0FBEREfXaq668vBw//vijWcqVmJiIkSNH4siRIwgMDERgYCC8vLys6nPB3PdAYmIiJk2ahC+//BJTp06Fl5eX2V6roUpLS/Hxxx9j0qRJsj6vWcJFoVDA2dnZ6hYdCiFw4sQJbNu2DY8//rjZ1kZIkgR7e/s6jYsIIZCWloaYmBjTMcAPPPAAOnbsaNZvO1VVVWZ9foVCgVOnTmHp0qVwcXHBI488gqVLl6J169ZW8y3Ozs7OrOtjJEmCq6urWaeDV1dX47333sO2bdug1+vRqVMnvP7663VetKdWq832+1AoFDh//jwWL14MlUoFFxcXBAcH4/7778fo0aPRo0cPODo6WvR+MPc9oFAosHPnTqxatQq7du3CypUrMWTIEKtal2WuOrD4FQohoNfrUVFRAb1eb9Y+Sp1Oh/j4eKhUKnTs2NFsr1MfpaWl2L59O8rKytChQwfodDrs2bMHJSUlli7aXQsPD8dzzz0Hd3d3fPjhh3jooYdw6dIlm+qHtmZCCGzfvh3ff/892rRpg169euHMmTP4z3/+Y9pu3tI6deqEJUuW4O9//zt8fX1x7tw5rFixAiNHjsSQIUOwfv16FBQUNOl7YsyYMXjkkUeQmpqKf/zjH1i9erXV/H7MyaLhYjxxLjo6GosXL8a7776LrKwss91oeXl5yM7Oho+Pj1VsbiiEwO+//47i4mKEh4dj3Lhx6N69O0pLS3Hs2DGbf8N17doVb7zxBvbt24cxY8bgyJEjmDRpEjIzM23+2qxBUVERPv30U2g0GixduhRvvPEGOnfujKNHj2L//v1WUcfh4eFYtmwZvv76a/z22284fPgw3n//fdx7771ITEzEnDlzMGjQIHz77beorKy0ijLLrW3btvjwww+xevVqqNVqLF68GM8//zxKS0ub5PUaWSxchBDIzc3F22+/jUOHDqGkpATHjh3DypUrTTsOy/16Z8+eRXV1NcLCwqyi///atWs4deoU7O3t0bdvX6hUKvTu3Rv29vY4ffq0aXcBWyZJEvz9/fH555/jwQcfxJ9//onp06ebFoxRwwghsHPnTuTk5GDIkCEIDw+Hk5MTZs+eDYVCgS+++MJ0NLalSZIEhUIBJycndOnSBXPnzsXOnTuxY8cOjBs3DsnJyZg2bRoee+wxXLx4sUl+4Go0GjzxxBPYtGkT/P39sWbNGsycORP5+flN8noBC4aLTqfDJ598gsuXL2PAgAF444030LdvX2RkZGDLli2mvb/kFB8fD0mSrOIQKSEETp8+jYqKCnTt2tU0COvs7Iz27dujvLwcycnJTeLGM449rFu3DgMHDsTevXvx/PPPN4uuAXOpqqrCtm3boNFoMHnyZCgUCkiShIiICHTr1g2pqak4fvy4Vd4/kiShRYsWGDBgADZu3IjNmzejS5cu2Lp1K0aMGIHdu3eb5f1vaZIkYcCAAfj+++8RERGB77//Hv/4xz+abKBaJFyM3UHx8fEIDAzEtGnT4OnpialTp8Ld3R0HDx5Edna2rK9ZWVmJ5ORkODk5WcVBUjqdDqdOnYJKpUK3bt1Mg5qSJCE8PBwKhQKJiYlN5qaTJAnu7u745JNPEBISgk8//RRr165tkh8ijeH06dNIS0tDWFgYgoKCTP9fqVRi4sSJEELghx9+sOr7R5IkaDQajBo1Crt378bs2bORkZGBf/zjH9iwYYPp4L2mRJIkdOrUCVu2bMGwYcPw66+/Yvz48Th69KhV/64awiLhUllZie+//x4KhQKPPvqoacZIy5YtMXz4cFRWVuLnn3+WtbKzsrJQVFQEf39/q9jM8cqVKygoKIC3t3eto1W9vLzg5uaGnJycJjGwbyRJEgICAvDxxx/DyckJL7/8Mvbt29fk3lTmJoTArl27YDAYMHr06BpdvJIkoVevXvDy8sLx48dx9epVC5a0biRJQuvWrfHOO+9g1apVkCQJzzzzDFauXAmtVmvp4slOkiT4+vriq6++wrRp05CcnIyHHnoI69atQ0VFxU3fD8bV+UVFRTh69Cg++eQTLF++HB9++CGOHTuGqqoqq3sfNXq4CCHw559/4vLlywgLC6uxiM/YbHR0dMThw4dlG3Mwjrfo9XqEhYVZfCqssTwGgwFdunSpVR6VSoWQkBBotdom12SWJAl9+/bFW2+9hcrKSsyaNQsXLlxoUtdobmVlZThy5AhcXFxw77331rp/HB0dMWDAAJSVleHXX3+1mbpVq9V48skn8cUXX8DFxQVLly7FO++802RbMK6urnj//ffx2muvQavVYsGCBZg0aRIOHDiA0tJS6PV66HQ65OXlYe/evViwYAEGDBiA4cOH46mnnsKSJUswf/58DBs2DJMmTcLp06et6nfd6OGi0+mwY8cOKBQKjB07ttbAuvHcg8LCQtkqSwiBhIQESJKErl27WjxcdDodUlJSoFarERwcXKs8kiQhNDQUkiQhOTnZQqU0H0mS8MQTTyAqKgqXLl3CzJkzzTKJo6k6e/Ys8vLyEBYWBnd391r/LkkSRowYAaVSidjYWNNJqrZAoVBgzJgx+Oqrr+Dq6oqlS5di3bp1TbL71Dj29Oyzz+L7779H9+7dERsbi7Fjx2LgwIGYOHEiIiMj0adPH4wbNw5r1qzB1atX0bdvXyxevBirVq3Cv//9b3To0AG7du3C2LFjcfjwYasJmEYPl5SUFFy8eBFBQUHo0KHDTT9Y77//fgCQ7VtXZWUlLl26BBcXF7Rt2/aun+9uXb16FcXFxfD29oazs/NNH+Pp6QlHR0dkZWWZdoFuStRqNV577TUMHjwYBw4cwIIFC1BZWWnpYlk9IQT2798Pg8GAwYMH3/KLUmhoKPz9/XH+/Hnk5OQ0cinvjiRJGDp0KD777DM4ODjg+eefx5YtW6zmQ1NuCoUC/fv3x/bt27FmzRr06NEDGRkZiI2NxZEjR6DT6TB8+HCsXr0ahw4dwrZt27Bs2TJT6yU2NhYLFixATk4Opk2bhpSUFKuoq0YNF4PBgD179kCv12PYsGE3PXdEkiR07NgRrq6uOHPmjOno4buRnZ2N4uJi+Pv7W/zkNyEEUlJSYDAY0L59+1t+ONjZ2cHHxwfl5eU20W/eEC4uLvjoo4/QsWNHfPnll3jttdeaZBeInKqrq3Hs2DHY29ujZ8+et7x/NBoN+vXrh8rKShw5csQqPmzqQ5IkjBw5Eh988AGEEJgzZ45VfSuXm7Gb7IknnsDOnTtx+PBh7Nu3DwcPHsSRI0ewefNmzJw5EyEhITV2g5ckCW5ubli2bBn++c9/Ij09Hc8++6xVfFFr1HApLi7GiRMn4Obmhh49etzyjeHo6IguXbqgtLQUSUlJd/WaQgicO3fuluMbjc1gMCA1NRVKpRJBQUG3LU9ISAgANNkxCeMamC+//BJeXl54++23ER0dbVPdOI0tIyMDWVlZCA4Ovu0edMYeAIVCgX379tlkt5IkSZg0aRKWLl2KoqIiTJ8+vcnv8GDsKgsODkavXr0QHh4OT09PqFSq235WaDQavPzyy+jVqxd+/vlnfPvttxavp0YLF+NA/rVr19CzZ89bdgcZ/e1vf4MQQpaV6qdOnYIkSWbZAbi+SkpKkJeXB3d3d7i5ud3ycZIkwc/PD0qlEmlpaRa/UczFuDbj008/hZOTE1588UV88cUXNvlhaG5CCPzxxx/QarW477777rgQODQ0FF5eXjh37hzy8/MbqZTyUiqVeOaZZzB9+nSkpKQgKipKlt6MpsjFxQXLly+HnZ0d3nzzTeTl5Vm0PI0WLgaDAQcPHoRSqcT9999/2w95Y9eYg4MDTp8+fVcrjSsrK3Hx4kU4OTlZfLxFCIH09HTodDoEBgbe8cPB1dUVrq6uyM/PR2lpaSOVsvEZ+9jXrVsHpVKJp59+ukn3sTeUEAKHDx+GQqG46SyxG7Vo0QJ/+9vfUFZWhri4OJutT41Gg+XLl6N///7Yt28fli1bxu7Tm5AkCffeey8mTJiAS5cu4aOPPrLo77zRwiU7Oxupqanw8fGp0yJGFxcXBAYGoqCgAJcvX27w6+bk5JjGW6zhDGrj7K+bzRK7kVKpRNu2bVFdXW1zg7L1JUkSHnroIaxatQoGgwGzZs1CTEyMzX4gmkNJSQnOnTsHDw+PGgsnb8XYNSZJEg4cONAIJTQfV1dXbNiwAX5+flizZg02bdrEe+MmlEolFi1aBGdnZ6xfvx65ubkWK0ujhItxRb6xOa/RaO74MwqFAhEREdDr9UhISGjQjWQcb9Hr9VYx3lJVVYWsrCw4ODjU+VyHdu3aAUCT72sG/vqdT506Ff/5z39QWVmJ6dOnY9euXU3+uusqKSkJJSUlCAsLq/NxFp07d4abmxtOnDhh061fSZIQEhKC6OhoqFQq/Otf/2pSO1jIqX379njooYeQmZmJb775xmJ11CjhotPpcPjwYajV6jo154H/2wZFqVTixIkTDe6DT0xMtJrxltzcXJSVlcHb27tOByZJkgQfHx+oVCpkZGQ0i3EIpVKJWbNm4Y033kBpaSkef/xx2XdrsEVCCNMWIffdd1+df87FxcW0buzcuXNmLKH5SZKEBx54AAsXLkRubi6eeuopboB6EwqFAvPmzYO9vT02bNhgsS8VjRIuaWlpyMzMRLt27eDt7V3nn/Px8YGHhwfS0tIatA1KVVUVLly4AEdHR/j5+dX75+UkhDDN+goODq7zz7m4uMDFxQUFBQVNYpfkulAqlZg3bx5eeeUVlJSUYOrUqc1+mxi9Xo8///wTGo0G99xzT52/KBm7xgwGAw4cOGDzdahQKLBo0SKMGDECR48e5fjLLXTp0gWDBw9GSkoKdu/ebZHfu9nDxTgIqdfr0bdv33ptda/RaNChQwdUVFQgNTW13q995coVFBYWws/Pz+LjLUIIXLx4EUqlEgEBAXX+cDCOu2i12iY/7nI9lUqFBQsWYOnSpSgsLMSUKVOadcDk5eUhLS0NPj4+9ToqV5Ik9OjRA/b29vj999+bxE7UDg4O+OCDD+Dn54cPP/wQ33//fbO9L25FoVDgn//8JxQKBdatW2eRADZ7uFRVVeHYsWNo0aLFbRd93UpERASEEIiPj6/XDSSEwPnz56HX69G5c2eLHytaWlqK3NxcuLq6omXLlnX+OeNmjwDuakqyEAJlZWXIy8uzmc0AVSoVFi5ciCVLlqCgoABTpkyxmkOwGltiYiIqKipwzz331GnM8npt2rRBSEgIMjMzkZaWZqYSNh5JkhAYGIj3338fCoUCCxYsaDLHU8hFkiT0798fHTt2xNGjR3HmzJlGL4PZP3FTUlKQm5uL9u3b19r9904kSUKHDh1gZ2eHM2fO1Dt9jetbrGEw//Lly9BqtfD396/3QWW+vr6mcZeGTmxISkrC559/jk8//RRbt261mbUCarUazz//PP7973+joKAAjzzyCH755Zdm9UFiHG8BgPvuu6/e97JSqcSAAQOg0+lw6NChJlF3kiRhzJgxmDdvHrKzszFnzpxm021cVy1atMDjjz+OqqoqfP75543+ezdruAghcPDgQRgMBvTv379BrQd3d3f4+PjgypUr9ZpWV1VVhZSUFDg4OMDf37/erysnIYSpWy8kJKTeHw53M+4ihEBmZia2b9+O0tJSuLi44OLFizY1uKtWq/HCCy+YWjCPPvpos+oiq66uRnx8PBwcHNC5c+d6/7wkSejXrx/UajUOHDjQZHZAUCqVeOmll9CvXz/s27cPy5cvbzLXJgdJkjBhwgS4ubnh+++/R2FhYaO+vlnDpbS0FPHx8XB2dq7XIOT1lEolunbtCq1Wi7Nnz9b5A8U43mIN57dotVqkp6eb9gurL6VSCV9fX1RXV+PKlSv1fu2ff/4ZVVVVGDhwIKZMmYKhQ4ciIiKi3uWwJGML5uWXXzYFzJ49e2wiYIxncTRUZmYmcnJyEBgYCA8PjwY9h5+fHwICApCamorMzMwGl8XaODs7Y+3atfD09MQ777zDqes38PHxwbBhw5Cdnd3o7xezhktCQgKKi4sRHh5uOsa3viRJMp3UGB8fX6efEULgzJkz0Ov16Nq1q8XHW/Lz83Ht2jV4enrWeX3C9a4fd6nPehfjuTFZWVkICAhA9+7d4eDggJ49e95001Brp1ar8dxzz5n2mpo6darFZsLUVWVlJX788UcUFBQ06OeN441arRa9evWqd5eqkVqtRv/+/VFdXY2DBw9adZ3Vh/FkxxUrVkCv12PevHnNYk1YXUmShGnTpkGSJHz++eeNupzBbJ+6Qgj88ssvAHDH7V7uJDAwEE5OTkhOTq7Tbp9CCJw8eRKSJFn8cDBjl5jBYGhQl5iRr68vlEplvcZdtFotfv/9dyiVSvTv3x9KpRKSJFl8/OluGAPmlVdeQXFxMaZOnYrt27db7YfJtm3bsHz5cqxfv75BXTb13fLlViRJwpAhQ6BWq/Hzzz83qem7kiRh4sSJiIqKQlpaGubMmYPy8nJLF8sqSJKEPn36ICQkBIcPH27QrNuGMlu45OTk4Ny5c/D09ETHjh3v6gPNeO59UVERMjIy7vh449RlFxcXq1jfkpKSUqddkG/H1dUVLi4uyM/Pr9O4izHU8vPzERAQAF9fX5sOlesZpym//vrrpoWWP/74o1UuMh0yZAg8PT2xfft2nDx5st4hWFpaitOnT8PNzc20S3ZDBQYGIigoCKmpqbh48eJdPZe1UalUeOWVV3Dvvfdi9+7dHH+5joODAx5++GGUl5c36p59ZguXxMRE6HQ69O/fv06r0W9HkiR0794dBoOhTm/Q9PR0FBcXIygoqEHdUHIqLi5Gbm4u3NzcbnpqYF0plUr4+fmhuroa2dnZd3y8wWDA8ePHAQC9evVqMsFipFKp8PTTT2P58uWoqKjA9OnTsXnzZqsLmFatWmHmzJnQ6XRYs2ZNvTdhTU5ORlFREbp06XLXY4cqlQojRoxAdXU1du7cabWtvYZydXXFunXr4OnpiZUrV+Knn35qctfYEJIk4eGHH4a9vT02bdrUaGe9mC1c+vXrh0WLFmHIkCF3/cFmPJ5YpVIhPj7+th8gQgicOHECQghERERYvEvswoUL0Gq1CAkJaXB/OfB/c/uBup3vkpubi8zMTLRq1Qr+/v5NLlyAvz4s58yZg5UrV0Kr1SIqKgpff/211QXMsGHDEB4ejsTExHrNchNC4LfffoPBYEC/fv1keR8NHjwYTk5O2LNnT5PbOsW47OCdd96BwWDAvHnzkJSUxIDBX/uN9ezZE0lJSfjzzz8b5TXNFi729vbo3r37XX1bv563tzc8PT2Rnp5+27Mp9Ho9Tpw4AbVabRXjLWfPnjWt17nbsrRt2xZqtRrp6em3bfIbA1av16Nbt242OXhfV0qlElFRUVi1ahWEEJg9ezY++eQTq+oS0Wg0mDVrFlQqFT799NM67/Wk1Wpx5MgR2NnZNWgB8s14eXmhX79+uHr1Knbv3n3Xz2dtjN/S582bh8zMTMyaNatBW0c1NUqlElOnToVer8dXX33VKIFr1mlUcg4eq9VqhIeHo6qqCqdPn75l5eTm5uLy5cvw8vKCp6enLK/dUEVFRcjOzkbLli1lKYuTkxM8PDxQWFiIoqKiWz6uvLwc58+fh729PTp16tQkWy3XUyqVmDZtGtauXWs6D2bNmjVWM2htnPHYr18/pKenY9u2bXV6c2dkZCA9PR2BgYH12pPvTmV55JFHYGdnh40bN972PrJVSqUSS5YsweDBg/Hrr79iyZIlVnMvWIpx089WrVphx44djXJ4nGXn6NaDJEmmsQPj7rA3Ms4Sq66uRkREhMW/sZ87dw5arRYdO3aUpSwKhQJBQUHQ6/W4ePHiLevg/PnzKCsrQ2hoqMXX+DQWhUKByZMn4+OPP0aLFi3w3HPP4Z133rGarW6USiWefPJJ2NvbY+PGjXc8JVAIgV9//RVarRb333//XXWpXk+SJISGhmLo0KHIyclBbGysLM9rbZycnPDhhx8iICAA69atwxdffNHsu8fatGmDYcOG4erVq42y07jNhAvw1wFb7u7uOH/+/E37iw0GA44ePQqFQoHevXtb9Bu7VqvFqVOnoFKpZNvu33imhSRJt9xLydgtaDwPp6m3Wq6nUCjw0EMP4fPPP4ezszOWLFmCN954wyo2a5QkCUFBQRg1ahRyc3OxcePG2765dTod9u3bB7VajQEDBsj6ezR2Jc6ePRsjR46U7XmtibG+165dC41Gg+eee06WI9Nt3dSpUyFJUqMcJW5T4WJvb4977rkHZWVlN501lpubi5SUFHh6epoO2bKUCxcuoKCgAP7+/vXeU+122rRpAxcXF2RlZdXquxdC4PLly7h69Sq8vb3rtXtuUyFJEkaPHo1vvvkGHh4eeP311/Hyyy832gyZ2zEehtayZUv8+OOPt2x9AsDFixeRmpqK4OBgs9zLXl5emDp1aoMXN9sCSZIwbNgwLF26FMXFxXjyySeRnZ3dbANGkiTcd999CA4OxpEjR3DhwgWzvp5NhYtxp0+FQoH9+/fXSF7jYrOqqirce++99d45Vm729vYICAiQvQWlVqsREhKC6upqpKSk1HijCCHwxx9/wGAwoEePHhbfmcBSjAsGv/vuO/j4+GDlypVYtGiRVSys8/LywuTJk1FWVoa1a9fedCxACIHt27dDq9VixIgRZunetfXFtHVlPDhr8uTJOH36NObOnYuKigpLF8tiHBwcMGHCBJSVlWHz5s1mDVqb+/QJCQmBj48PkpKSkJ6ebvr/lZWVOHDgADQaDfr372/xN46/vz8mTpyIdu3ayVoW43RLhUKBU6dOmQJWCIErV67g4sWLaNmyJUJDQy1eB5YkSRL69u2LLVu2IDAwEGvXrsXcuXNRUlJi0W+uxs0Eg4ODcejQoZtOTS4oKEBsbCxcXFwwdOjQZv17lINGo8HKlSvRs2dP/Pe//8Vbb71lVbMJG5MkSZg0aRLs7e3x7bffmjVobS5cNBoNBg8eDK1Wi507d8JgMEAIgWPHjiE7OxtdunRp0OaQ5qBSqczSevDy8kKbNm2QnZ2NrKwsCCFgMBhw6NAh6HQ69OrVy+ItN2tgPChr69at6NixIz7//HNERUWhqKjIogHj6OiIefPmQalUYtWqVUhPTzeVRwiBH374Afn5+Rg0aBBat25tsXI2FZIkoVWrVvjoo4/Qpk0brFixAlu3bm223WPt27fHvffei+TkZBw5csRsr2Nz4SJJEgYMGAAPDw8cPnwY58+fR2FhIbZs2QKlUonx48c3+e4gpVKJnj171giUs2fPIjU1Fa1bt0bXrl35bfd/jAtwf/jhB0RERGDz5s147LHH6nV8gznK1Lt3b0ycOBF5eXlYtmyZ6UvCqVOn8O2338LFxQWPPvpok7+XG4txn8EPPvgAkiRh/vz5dd4It6lRKpV44oknYDAY8PHHH5stZG3yznVxccHf//53aLVaREdHY8WKFcjJyUG/fv1kWaxo7SRJQseOHeHj44NLly7hhx9+wO7du6FUKjF48GDY2dlZuohWxTjLbuvWrejTpw+2b9+OyZMnIysry2JlUiqVmDFjBgYMGIAzZ85g3rx5ePPNN7F48WKUlpZi2rRppp2wSR6SJGHcuHF44YUXkJeXh+nTpzero8ONJEnCiBEj4Ovriz179tRpv8aGsMlwkSQJAwcOxPDhw1FQUIALFy4gLCwMU6ZMkW09gLVTq9UYOXIk3NzcTDudDho06K42x2zKJEmCv78/vvvuOwwdOhQHDhzA5s2bLVomBwcHLFmyBOPGjUNBQQG2bduG8vJyPPbYY5g4cSJ/j2agVCqxcOFCTJgwAadPn8a3335r6SJZhJubGyZOnIhr165h586dZnkNs6wyNBgMKC4urvcmffU1duxYdO7cGZWVlejQoQMMBkOdT1urrKw02zxv43n15l7A5+TkhPHjx5t2AWjVqlWdT6qsrq42a5+zwWDAlStXrPLo2ZUrV+KLL77A2LFjsWnTJrO9jhACBQUFd5wGPWPGDAwcOBBZWVnw9fVFUFBQo21ZUl5ebrb7wJrvgcWLFyMwMBBjx47Fd999Z7bXseY6GDt2LNzd3TFgwADExMTI/vySkPnOMp73nZOTY9XfvIQQ8PLyuqszMm71vOfOnUNhYaHVX3/Lli3v+jiEWz33li1bkJqaarV1YLztg4ODMWHCBLPUwd69e5GZmWm1dQD8VU5fX19ZNpi98Xmt/R4A/iqnOe+BrVu34sKFC1ZfB0FBQfj73/8u78xWucMFgE3NwjDHL725Xz/AOgBYB839+oHmXQdmCRe56fV6VFRUwN7evtmMqdxIr9ejvLwcDg4OzbIOrr9NrflboDkZDAZUVVXBzs6u2c4ia+73gRACQgibWARrE3doWloapk2bhrS0NEsXxWJyc3OxatUqi06htaT4+HgoFIpmO30UAJKSkjBgwAAkJSVZuigW09zvgxMnTsDOzg4nTpywdFHuyCbChYiIbAvDhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiOQnrNjx48fF9OnThaurq5AkSbi6uorp06eL48ePW7pojcZYB25ubkKhUAg3N7dmVQfG63dxcREAhIuLS7O6fiH4PhCC94EtXr9VhotWqxVRUVECgFCpVAKA6Y/x71FRUUKr1Vq6qGbT3OuguV+/EKwDIVgHtnz9VhkuUVFRQpKkGhV54x9JkkRUVJSli2o2zb0Omvv1C8E6EIJ1YMvXb3Xhcvz48dtW5I1/rLlZ2FDNvQ6a+/ULwToQgnVg69dvdQP60dHRUKlUdXqsSqXCmjVrzFyixtfc66C5Xz/AOgBYB7Z+/ZIQQli6ENfz8PBAQUFBnR/v7u6O/Px8M5ao8TX3Omju1w+wDgDWga1fv9WFi0ajgVarrfPj1Wo1qqurzViixtfc66C5Xz/AOgBYB7Z+/VbXLebs7GzWx9uC5l4Hzf36AdYBwDqw9eu3unAZP358vfoZH3zwQTOXqPE19zpo7tcPsA4A1oHNX79l5xPUZuszJOTQ3OuguV+/EKwDIVgHtn79VhcuQtj23G65NPc6aO7XLwTrQAjWgS1fv1WGiy2vSpVLc6+D5n79QrAOhGAd2PL1W2W4GB0/flzMmDFDuLu7C7VaLdzd3cWMGTOsrvlnTs29Dpr79QvBOhCCdWCL1291U5GJiMj2Wd1sMSIisn0MFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2f1/blzVf9RhEP8AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -140,7 +143,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -164,7 +167,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 5.00e-01 | test_loss: 5.00e-01 | reg: 8.60e-01 | : 100%|█| 50/50 [00:16<00:00, 3.12it" + "| train_loss: 1.80e-01 | test_loss: 1.78e-01 | reg: 3.77e+01 | : 100%|█| 50/50 [00:13<00:00, 3.76it" ] }, { @@ -194,7 +197,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -215,144 +218,6 @@ "This gives the dependence among $(x_4,x_5)$. Another random seed can give dependence among $(x_1,x_2,x_3)$." ] }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e3c31cf5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "checkpoint directory created: ./model\n", - "saving model version 0.0\n" - ] - } - ], - "source": [ - "seed = 6\n", - "model = KAN(width=[6,1,1], grid=3, k=3, seed=seed)\n", - "dataset = create_dataset()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e1d5046a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAFICAYAAACcDrP3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABEeklEQVR4nO3deViU9d4/8Pc9C/uigBsICCgCLqm4a7mlKWhaImXpMSW1o1meMqvjL+08mXaOkh096HE5tujJErVUwC1xJ8PAVEBcQFERVDYRZJuZ7++PnplHAhX1HmYG3q/r8iplYD73zcy87/u7SkIIASIiIhkpTF0AERE1PAwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBeih0hOTkZERARcXV1hZWUFV1dXREREIDk52dSlEZktidscE9VOo9Fg5syZWLNmDVQqFTQajeFr+r9PmzYNUVFRUKlUJqyUyPwwXIjuY/r06Vi7di0e9BaRJAlTp07F6tWr67EyIvPHcCGqRXJyMoKDg+v8+KSkJHTr1s2IFRFZFva5ENXiUZq6VCoVVq5caeSKiCwL71yIauHq6oqCgoI6P97FxQX5+flGrIjIsjBciGphZWWFqqqqOj9erVajsrLSiBURWRY2ixHVwtHR0aiPJ2roGC5EtRgzZswj9bm88MILRq6IyLKwWYyoFhwtRvRkeOdCVItu3bph2rRpkCTpgY+TJAnTpk1jsBD9AcOF6D6ioqIwdepUAKjRRKb/+9SpUxEVFVXvtRGZOzaLET1EcnIyVq5ciejoaBQXF8PJyQnjxo3DjBkzeMdCdB8MF6I60vfDsH+F6OHYLEZERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO5WpCyAydwUFBTh58iT27t0LAIiLi4O1tTX8/f2hVqtNXB2ReZKEEMLURRCZo7t37+LLL79EVFQUioqK4OXlBVtbWxQUFCA/Px89e/bERx99hK5du5q6VCKzw3AhqkVRURHefPNNxMfHY9asWXjppZeg0Wig1WoNARMVFYX4+HgsW7YMo0ePhiRJpi6byGwwXIj+QKPRYPbs2YiNjcWGDRvQt29flJeXY+DAgUhPT8frr7+OyMhIVFVVYc2aNVi6dCmio6PRo0cPU5dOZDbYoU/0B0ePHsWmTZsQGRmJfv36QaFQQAiB0tJS3LlzB+Xl5QAAKysrTJ8+HWPGjMGCBQtQUVFh4sqJzAfDhegeQgh89dVXCA4ORmho6EObutRqNWbNmoW0tDScPn26nqokMn8cLUZ0j9LSUiQmJiIiIgJ79+7F1atXAQCVlZUoKCgAAKSmpmLVqlUAAEmSMHz4cAQGBuL48eNsGiP6XwwXonuUlJSgsLAQ3t7eiIqKwp49e2o85vDhwzh8+DAAQKlUIi4uDt7e3rh27Vp9l0tkthguRPdQKpVQqVSoqKiAo6MjmjZtavja7du3odPpYG1tDTs7uxqPt7KyMlXZRGaH4UJ0DycnJ3h4eODUqVOIiooydN6XlZVh1KhRuHDhAsLDw7Fw4ULD9zg4OODDDz/E0KFDTVU2kdlhhz7RPaytrTFixAjs3LkTAODl5QUvLy+0bt3aMBvfwcEBnp6e8PLygqenJ06dOoWbN2+ib9++piydyKwwXIj+YNKkSSgvL0dkZCQqKyvv+zghBPLy8vDxxx8jPDwc3t7e9VglkXljsxjRH7Rp0wafffYZ3nzzTVhbW+Pdd9+FlZUV2rRpA41GgxYtWgAALl26hNmzZ0OpVOK9997jDH2ie3CGPlEtdDodNm3ahL/+9a9wd3fHhAkTEBQUBDs7OxQWFiIhIQGbNm1Chw4dsGLFCnh6epq6ZCKzwnAheoALFy5gzZo12L17N3Jzc1FaWoomTZqgc+fOmDRpEkaPHm0YOUZE/4fhQlQHJSUlOHToEEaOHIm9e/di0KBBUKnYqkx0P+zQJ6oDBwcHtGrVCgDg6urKYCF6CIYLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDtJCCFMXQQRETUsKlMXQGQslnTdJEmSqUsgkhXDhRqsffv24ciRI1CpzPNlLoSAu7s7pk6daupSiGRnnu86IhlkZGRg/Pjx8PT0NGkdN27cwNatW9GxY0f0798fCsXvXZ0VFRX47LPPTFobkbEwXKhBs7e3h6Ojo8mev7S0FBERETh8+DDs7e2xefNmjBgxApIkQa1WG4KGqKHhK5vISIQQiI6OxpEjR9C1a1doNBrMmTMHpaWlpi6NyOgYLkRGotFosGrVKqjVaqxbtw5jxoxBeno6YmJiLGqwAdHjYLgQGUlGRgZ+++03dOvWDZ07d8Y777wDlUqFqKgoaLVaU5dHZFQMFyIjEEJg165dqKysRFhYGJRKJbp27Ypu3bohMTERaWlppi6RyKgYLkRGIIRAbGwsVCoVhg8fDkmSoFKpEBERgcrKSnz55ZemLpHIqBguREZQXFyM3377Da1bt4afnx+A3ydKjhkzBm5ubvjuu+9QWFho4iqJjIfhQmQEZ8+eRWFhIXr37g1ra2vDv7u5uWHs2LHIzc3Fjh07TFghkXExXIhkJoTA0aNHodPpMHjw4GpLu0iShOnTp+Opp56CWq02YZVExsVwITKCw4cPQ6lUonfv3jW+9tRTT+Ho0aN45ZVXTFAZUf1guBDJrLy8HKdPn4abmxvatGlT4+sKhQIODg71XxhRPWK4EMksJycHubm5CAgIgL29vanLITIJhguRzE6fPo3Kykr07t2bS+lTo8VwIZKREALHjx8HAPTp04fhQo0Ww4VIZomJibCyskLHjh1NXQqRyTBciGRUVlaGc+fOoVmzZmjVqpWpyyEyGYYLkYyuX7+OW7duoX379rC1tTV1OUQmw3AhklFKSgqqqqrQo0cPU5dCZFIMFyKZCCGQmJgIAOjVqxc786lRY7gQyejEiRNQq9XszKdGj+FCJJPy8nKkp6fD1dUV7u7upi6HyKQYLkQyyc3Nxc2bN+Hv78/OfGr0GC5EMklNTUVlZSV69OjB/hZq9BguRDK4d2Y+l30hYrgQyeaXX36BWq1G586dTV0KkcmpTF0AkTkRQqCiogI3b96Era0tXF1doVA8/BqsrKwMaWlpaNasGTw8POqhUiLzxjsXov+l1Wqxe/duPPvss+jSpQu6deuGBQsWoKys7KHfe+3aNdy6dQtBQUGws7Orh2qJzBvDhQhAVVUVlixZgrFjx+LXX39F27ZtodPp8Omnn+L999+HRqN54PcnJSWhqqoK/fr1q6eKicwbw4UaPI1GAyHEfb9eVVWFTz75BB999BFcXFywefNmHDlyBAcPHoS/vz9WrVqFHTt23PdnCCFw6NAhAED//v3ZmU8Ehgs1cKWlpZgyZQoOHz4MnU5X4+tVVVVYtGgRFi9eDA8PD+zYsQOjRo2CtbU12rZti9WrV0OlUuH9999HUVFRrc+h1WqRkJAABwcHdOrUychHRGQZGC7UoB06dAibN2/GqFGj8Pnnn6O8vBxCCAghUFZWho8//hgLFy6Eu7s7tm3bhq5duxruPCRJwtNPP42JEyfi4sWLWLlyZa13L7m5ucjIyEC7du3g6upa34dIZJYYLtSgDRs2DGvXroWdnR3ef/99hIWFYcuWLdi5cyfCw8Px2WefoXXr1jWCRU+hUGDevHlwcXHB8uXLcf369RrPcfz4cdy9exeDBg2CUqmsr0MjMmsMF2rQVCoVJkyYgH379qFfv37YvXs3XnrpJYwZMwZxcXHo378/du3ahW7dut23r8TLywszZ87EzZs3ERkZWe3uRQiBHTt2QJIkhISEsL+F6H9xngs1eJIkoWPHjti1axcOHDiAo0ePoqKiAv369cPw4cNhb2//wFCQJAmzZs3C+vXr8Z///AfTpk1DQEAAAODOnTvYv38/mjVrhuDg4Po6JCKzx3ChRkGSJNjb22PkyJEIDQ2t9u914ebmhg8++ABvvfUWPvzwQ2zevBkqlQoHDhxATk4Oxo8fD2dnZ2OVT2Rx2CxGjY4kSYY/j/I9kydPRo8ePbBz506sXbsWpaWl+Oyzz6BUKjF9+nQ2iRHdg+FCVEd2dnZYuXIlnJ2dMWfOHDz33HP45ZdfMHToUPTp08fU5RGZFYYLUR1JkoRu3bph48aNcHd3R3JyMnr16oWVK1dCpWILM9G9+I6gBksIgfT0dNy+fVvWn+vh4YG1a9fi+vXr8PHxQXFxMc6cOfPIP6eyshJVVVWy1kZkLiTxoHUxiCzYmTNnkJSUZNZ9IZ6enhg0aJBZ10j0OBguREQkO/a5EBGR7BguREQkO4YLUR0lJydDkiQkJyebuhQis8dwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMF6KHSE5ORkREBAYNGgQAGDRoECIiIpCcnGziyojMlySEEKYugsgcaTQazJw5E2vWrIFKpYJGozF8Tf/3adOmISoqCiqVyoSVEpkfhgvRfUyfPh1r167Fg94ikiRh6tSpWL16dT1WRmT+GC5EtUhOTkZwcHCdH5+UlIRu3boZsSIiy8I+F6JaPEpTl0qlwsqVK41cEZFl4Z0LUS1cXV1RUFBQ58e7uLggPz/fiBURWRaGC1EtrKysUFVVVefHq9VqVFZWGrEiIsvCZjGiWjg6Ohr18UQNHcOFqBZjxox5pD6XF154wcgVEVkWNosR1YKjxYieDO9ciGrRrVs3TJs2DZIkPfBxkiRh2rRpDBaiP2C4EN1HVFQUpk6dCgA1msj0f586dSqioqLqvTYic8dmMaKHSE5OxsqVKxEdHY3i4mI4OTlh3LhxmDFjBu9YiO6D4UJUR/p+GPavED0cm8WIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZKcydQFE5q6goAAnT57E3r17AQBxcXGwtraGv78/1Gq1iasjMk+SEEKYuggic3T37l18+eWXiIqKQlFREby8vGBra4uCggLk5+ejZ8+e+Oijj9C1a1dTl0pkdhguRLUoKirCm2++ifj4eMyaNQsvvfQSNBoNtFqtIWCioqIQHx+PZcuWYfTo0ZAkydRlE5kNhgvRH2g0GsyePRuxsbHYsGED+vbti/LycgwcOBDp6el4/fXXERkZiaqqKqxZswZLly5FdHQ0evToYerSicwGO/SJ/uDo0aPYtGkTIiMj0a9fPygUCgghUFpaijt37qC8vBwAYGVlhenTp2PMmDFYsGABKioqTFw5kflguBDdQwiBr776CsHBwQgNDX1oU5darcasWbOQlpaG06dP11OVROaPo8WI7lFaWorExERERERg7969uHr1KgCgsrISBQUFAIDU1FSsWrUKACBJEoYPH47AwEAcP36cTWNE/4vhQnSPkpISFBYWwtvbG1FRUdizZ0+Nxxw+fBiHDx8GACiVSsTFxcHb2xvXrl2r73KJzBbDhQhAWVkZLl26hKSkJJSXl6OiogKOjo5o2rSp4TG3b9+GTqeDtbU17OzsAPweLiqVCuXl5bh9+zauXLkCT09PjhyjRo+jxahR0el0yM3NRUZGBjIyMnDx4kVkZGQgOzsbxcXFKCgowPXr1/HOO+9gzpw5hs77srIyjBo1ChcuXMDEiROxcOFCw890cHDAsGHDkJKSAnt7ezg5OSEoKAgdOnQw/DcwMBAODg6mOmyiesc7F2qwSktLkZmZaQiSjIwMZGZmoqysDABgZ2cHpVKJvLw8nD9/Hnfv3kXLli3RtWtX7Ny5E3PmzIGXl5fhZ+ln4zs4OBjuToQQOHjwIC5cuAAAuHPnDpydnZGXl4effvoJ33zzDbRaLQDA29u7WuB06NAB3t7eUCg4roYaHoYLWTydTofs7OxqIZKRkYHc3FwAvzddeXt7o23btvD390d2djZOnjyJo0ePQqPRoHPnzpg9ezZCQ0PRtWtXXLlyBX379sWSJUvw6aefwsrKqtbnFUIgLy8PH3/8Md544w3MnTsXe/fuRWxsLPbt24fi4mJ4eHigd+/ehpA6f/48vvzyS+Tn5wMAbG1tERQUVC1wAgMD4ezsXD8nj8hI2CxGFuXOnTuGOxB9s9alS5cMc0xcXFzg5+dn+NOmTRvk5uZiz549iIuLQ3p6OqysrDBw4ECEhIQgJCQEnp6e1Z5j+/btGDduHNRqtaF5zMrKCmFhYbh48SJeeeUVzJ8/H5cuXcJbb72FEydOIDExEd7e3oafUVlZiWPHjiEuLg6xsbG4dOkS7O3tMXjwYISGhqJHjx64efMmUlNTkZaWhtTUVJw/fx5VVVUAgNatW1e7ywkKCoKvry+USmX9nWyiJ8BwIbOk1Wpx7do1XLx4sVrT1s2bNwH8Pr+kTZs21YLE19cXTZs2RXFxMfbv34/Y2Fjs2rULBQUFaNasGUaMGIHQ0FAMHjz4vv0fa9euxRtvvIEXXngBoaGh+Pjjj+Hu7o4JEyYgKCgIdnZ2KCwsREJCAjZt2oSWLVvi0qVLCAgIwHfffQc3N7caP1MIgfT0dMTGxiI2Nha//PILAKB79+4IDQ1FSEgIOnbsCI1Gg4sXL1YLnNTUVNy4cQMAYG1tjcDAwBp3OS4uLkb6LRA9PoYLmdzt27drNGldunTJcBXv5uYGPz8/tG3b1hAinp6eUKn+r1U3KysLsbGxiIuLw+HDh1FVVYWOHTsiJCQEoaGh6N69+wP7NoQQWLhwIebPn4+ZM2fin//8J5RKJS5cuIA1a9Zg9+7dyMvLA/D7zPzAwEBMmjQJo0ePRkZGBsLDw+Hg4IAtW7ZUu4OpTV5eHnbv3o3Y2Fj89NNPKCkpgZeXlyFonn76aVhbWxsen5+fj7S0NKSlpSElJQVpaWlIT09HZWUlAKBVq1YICgpCx44dDU1sfn5+XLGZTIrhQvVGo9HgypUrNYJE3/9gZWUFHx8fw51I27Zt4evrCycnpxo/S6vV4tdffzUESmpqKtRqNQYMGGBo7nrYh/y9P2vWrFlYtWoVPvnkE8ybN6/GUOKSkhLk5+ejqqoKjo6OcHV1rRFuYWFhKCkpQXR0NDp27Fin566oqMCRI0cMzWdXrlyBg4MDnn32WYSGhmL48OG13g1pNBpkZmZWu8tJS0tDdna24Vy2b9/e0KSmDx9XV9c61UX0pBguZBSFhYXVhvpmZmbi8uXL0Gg0AIAWLVpUa9Ly8/ND69atH3h3UVJSUq25Ky8vD66urobmriFDhsDR0fGR6iwvL8eECRPwww8/YPXq1Xj99dcf+5jz8vLw0ksvISMjAxs3bkT//v0f6fuFEEhNTTUEzYkTJwAAvXr1MtzVBAYGPnAOTWFhIc6ePVutWe3s2bOGIdXNmzevMWKtbdu29x20QPS4GC70RKqqqpCVlVVt3khmZiYKCwsBADY2NvD19YWvr2+1Zq26zvm4evUq4uLiEBcXh4MHD6KyshKBgYGG5q6ePXs+dif37du3MXr0aPzyyy/4/vvv8fzzzz/Wz7lXSUkJXnvtNSQkJGDVqlUYPXr0Y/+smzdvYteuXYiNjcX+/ftx9+5dtGnTxhA0/fv3r1MoaLVaXL58uVqzWmpqqmFpG5VKhXbt2hma1fTh07x5c04GpcfGcKE6EUIgPz+/RpPWlStXDPM43N3dDeGhb9Zq1arVI83j0Ol0SE5ONjR3nT59GiqVCk8//bQhUHx8fJ74eK5fv44RI0bg6tWr2LlzJ/r16/fEP1OvsrISs2bNwrZt27B48eInuhvSKy8vx6FDhwx3NdnZ2XBycsLQoUMRGhqK55577pE79ouLi3H27FlDk5o+dO7evQsAcHV1rTFirX379tX6g4juh+FCNVRUVODy5cs1gqS4uBjA75MP7w0RPz8/+Pj4wN7e/rGer7S0FPHx8YiLi8OuXbtw48YNNG3aFMOHD0doaCieffZZWed9nDt3Ds899xy0Wi12796NDh06yPaz9XQ6HT7++GOsXLkS77zzDj788EPZ7gKEEDhz5oxh9FlSUhIUCgX69OljuKvx9/d/rOfT6XS4cuVKtcBJSUnB5cuXAfw+Z6ht27bV+nGCgoLQqlUr3uVQNQyXRkwIgZs3bxr6RPT9I9euXYNOp4MkSfDw8KjRN9KiRYsnnlWenZ1drbmrvLwc/v7+CA0NRWhoKHr16lWtw1wuiYmJCAkJQfPmzbFnz54ac1zkFhUVhQULFuDVV19FZGSkUY4pNzcXu3btQlxcHPbv34+ysjL4+fkZ7vT69u37xCPHSktLa/TlpKWl4c6dOwCAJk2aVOvHCQoKQkBAAGxtbeU4RLJADJdGory8HJmZmTWWQykpKQHw+5ImfwwRHx8f2NjYyPL8Op0Ov/32m+Fq+9SpU1AqlejXr5/hartt27ayPNf97Nq1C2FhYXjqqacQExNTb/NDNm/ejLfeegtDhgzBunXrjPqBW1ZWhgMHDhiCOycnB87Ozhg2bJih+axJkyayPJcQAteuXasxYi0jIwNCCCgUCvj6+tZYZ61169a8y2kEGC4NjE6nw40bN2o0aWVnZxve8K1bt67Wue7n52eUztv7fdA999xzCA0NxbBhw2T7oHuYb775BhERERg+fDi+//57w6rG9WX//v147bXX0LFjR3z77bfVVls2FiFEtUD/7bffoFQq0bdvX4wcOdJogV5WVob09PQadzlFRUUAAEdHxxp3OYGBgY/drErmieFiwe7evVvrwoz6DlknJ6cac0batGlj1A7Z3NxcQ5jEx8ejrKwMbdu2NTTR9OnTp14n9wkhsHTpUsydOxdTpkzB6tWrjdI0VRfJyckYP348XF1dER0dDQ8Pj3p9/uvXrxt+NwcOHDA0Rep/N7179zbauRFCICcnp9qItbS0NFy8eNEwIKRNmzbVJoJ26NABXl5eXNjTQjFcLIBOp0NOTk61OSMZGRm4fv06gN87Wb28vGo0a7m6uhq9+UEIgdOnTxuujpOTk6t1LoeGhsLf39+oNdyPTqfDnDlzsGzZMsybNw+ffPKJyZtjMjIyEBYWBo1Gg+joaAQEBJikjtLS0mp3lfpBFPq7yqFDh9bL4pkVFRU4d+5ctWa1lJQUw66f9vb2hrDRDyAIDAx85PlMVP8YLmampKSk2qKM+n4S/SS4pk2b1ggRb2/ver0b0A+L1Q8XlmNYrNwqKysxefJkbNq0CcuXL8ebb75p0nrulZubi5deegnXrl3Dt99+i169epm0nvsN/+7fv7+hP8zX17fe6tEPNLm3WS01NRUXLlwwTML19PQ0NKvpm9batGnDhT3NCMPFRHQ6Ha5du1ajb0S/SKFKpUKbNm2qTT708/Orl7b62tw7oS8+Ph6lpaXw8fExfPj069fPbGZ537lzB2FhYTh48CA2btyIcePGmbqkGoqLizFhwgQkJSXhP//5D4YPH27qkgxqm7gaEBBguBN9komrT6KystKwsOe9gwj0i5na2tpWW9hTf7dTX/16VB3DpR4UFxfXujCjfuFBNze3anNG2rZtW2NhxvomhEBKSoph0t6vv/4KSZKqLUUSEBBg8mamP7p58yZCQkJw/vx5bN++HYMGDTJ1SfdVUVGBN954A7GxsYiMjMTEiRNNXVINJSUliI+PNyy5c+vWLbi6uhrmIA0ZMqTWtd/qU15eXo2JoOfOnTO8vzw8PGoMIPD19TXp+6sxYLjISKPR4OrVq9XmjGRkZBhW01Wr1dUWZtSP1jKXK6uKigocPnzYcNVa10UUzUVmZiaee+45lJSUYNeuXejSpYupS3oorVaLDz74AF9++SU+/PBDvPPOO2YX2Ho6na7aYqEpKSlQq9V45plnDIMC6rpYqLFVVVUhIyOjxmrSOTk5AH5f2DMgIKDaRNAOHTqYvDm3IWG4PKbCwsJqI7UuXryIrKwswzLxzZs3r3VhRnNrE87Ly6u2ftXDln83VydPnsSIESPg6OiIvXv3yrJETH0RQiAyMhKfffYZpkyZgsWLF5vd66Q2tW1z0KFDh2rbHJjbcRQUFBiWvNHf7Zw9e9aw2VyLFi2q3eHoF/bk9gWPjuHyEFVVVbUuE68fzWJtbV2tSUv//+Y6mkUIgbNnzxqau/QbV/Xo0cMQKB06dDDbq+faxMfHY8yYMWjfvj1iY2PRvHlzU5f0WDZs2IB3330XI0eOxKpVqywi1PVq26DNzc3NEDQP2qDN1LRaLTIzM6uNWEtNTcW1a9cA/N7i4O/vX2M16WbNmpm4cvPGcPlfQggUFBTUCJGsrCzDOPyWLVtWmzPi5+cHDw8Psx+HX1lZiaNHjxoC5fLly7C3t8eQIUMMzV2W+oG8efNmTJgwAYMGDcLWrVvN9gOsrnbt2oXXX38dwcHB2Lhxo8n7Mx6HVqtFYmKi4a7m7Nmz1baWDg0NRevWrU1d5kPdvn27xl1OWloaysrKAPzeV/rHuxx/f3+zGdhiao0yXCorKw0LM9477Pf27dsAfh91cu/diP6OxJJmEBcUFFTb7bC4uBgeHh6Gu5MBAwbItrSLqaxYsQJvv/02XnnlFaxfv77BvKmPHz+OV199Fa1bt8b333+Pli1bmrqkJ5KZmWnoxzty5Ag0Gg06d+5sCJpu3bqZ/QWank6nQ1ZWVo0Ra1lZWQD+b/uCeyeCdujQAS1atLCo1gA5NOhwEUIgLy+v2pwR/TLxOp0OAGpdmLFly5YW82LXE0Lg/PnzhsmMx48fh06nQ3BwsGEIaadOnRrEC1wIgXnz5mHx4sV45513sGTJEov7fT3M2bNnER4eDpVKhS1btsDPz8/UJcni9u3b2LdvH2JjY7F7924UFRWhRYsWGDFiBEJCQjB48GCLuojTu3PnDtLT06tNBE1LS0NpaSkAwMXFpcZK0u3bt7f4C7wHaTDhUl5eXusy8fpVW+3t7WvMGfHx8bHoVVurqqqQkJBgaH7IyMiAra0thgwZgpCQEIwYMcLir3r/SKPRYPr06Vi/fj2WLFmCOXPmmLoko7l27RrCw8ORn5+PTZs2oVu3bqYuSVYajQbHjx83vH7Pnz8PGxsbDBo0yLBVtbu7u6nLfGz6uWz3TgRNS0vDpUuXDOv8+fn5VWtaCwoKgoeHR4O4CLS4cNHP3r13qG9mZma1ZeI9PT2rbVhlrIUZTaGoqAh79uxBbGws9u7di9u3b6NVq1aG5q6BAwdadGA+yN27d/HSSy9h9+7dWL9+vVnOC5FbQUEBXn31VaSkpOCrr77CkCFDTF2S0Vy8eNHQL3js2DFotVp06dLFcOfdpUuXBvEevnv3rmH7gntXIdDvl+Ts7FxjJemAgIB6X2z1SVlEuKSlpWHPnj2GINHfajo6OtZo0mrTpk2DvNWMiorC9u3bkZCQ0GDfdA+Sn5+PUaNG4fTp09iyZYtZzWg3trKyMkRERCA+Ph7Lly9HeHi4qUsyuqKiIuzduxexsbHYs2eP4SIqJCQEM2bMQFBQkKlLlJUQAtnZ2TXm5WRkZBgumn18fBAUFITOnTtj9uzZZv+et4hwKSwsxI0bN2BrawsbGxvDf9VqtdmfYLnox+ErlUoolcpGc9x6QggIISBJUqM7duD34y8tLYUQAg4ODo3qHAghoNPpoNVqodVqYWVlZXbzZ4xFCAGtVguNRmP4rxDCbCZeP4jRwsUCMsvAGG/Uxn78gPznQB8uxsBzwPeBpbwGjEnOc2C0xXV+/fVXnD592myvMIQQcHNzw8iRI43y8/fv349jx46Z7fpFQgi0atUKU6ZMMdpz7Nu3D0ePHjXr14C7uzumTp1qtOc4ePAgjh8/btavg5YtWxqt/2r//v1ISEgw69eAsd8HjfU1YLSjzc7OxpAhQ2SZnKfVanHp0iWcOXMGSqUSXbt2feKtUisrK/Htt98+cW33k5mZifDw8CeaLCaEwI0bN3D06FFkZ2ejdevWeOaZZ2SZGVxRUYGlS5c+8c95kIyMDLz88sv33ae+oKAAmzdvRs+ePU0yEqqiogJ///vfjfocly9fxosvvmjUjcFKS0sRGxsLSZIQEhLySEN5KyoqsHz5cqPVdunSJYwbN072SZO5ubmIj49HYWEh+vTpgy5dujzWcPSKigpERkbKWtsf1cdr4GGEEDh58iSSkpIwYMCAanssGes1YNQotbGxeeIRDmVlZdi4cSN27NhRbYfFqVOnIjQ09LGviOqj38Le3v6xl4GpqKjAunXrsGzZMly/ft3QHOLr64vFixcjNDT0ieZ2qNXqepkbcr9zoNVqMXXqVGzevBnu7u44fvx4vc/arq9zYGdnZ7SVA8rLy/Hhhx8iJiYGAHD06FFERUXVeVCLSqUy+jl4kvfBH+l0OsTFxeG9997DlStXIISAvb095syZg3ffffeR7w4awmvgYYQQiI+Px7Rp01BcXIwvv/wS27ZtM2xxbazXgFnPPKuoqMDnn3+O77//Hk2aNMH06dMxefJkAMDy5ctx4MABi2rPrKs7d+5g1qxZmDt3LsrKyvDGG29g1apVmDx5MnJycjB58mT88MMPFn3saWlp2L59O2xsbHD9+nWsW7fOoo/HFIQQiI6ORkxMDLp164auXbsiJibG4l8b9yOEwJYtWzB58mTcunUL06dPx9///nc0adIEn376Kb799tsGedxPqri4GH/9619RUVGBMWPGIDc3F4sXLzZMJDcWsw0XnU6HjRs3Yt++ffD398fnn3+O8ePHY9KkSViwYAFUKhX+9a9/GTbXaijKysowa9YsbNy4EU899RTi4uIQGRmJ1157DStWrMC6desgSRLefvttpKenW+SbSQiB77//HhUVFXjvvffg4OCALVu2GFaUpropLS3F8uXLYWtri88//xxLly6FtbU1li9fbrjLbyiEEDh+/DjefvttKBQKrFu3DkuXLsWbb76JjRs3wtbWFgsWLEBubq6pSzUrQgj88MMPyMzMxNixY7Fs2TL4+vpi//79hm3SjcUsw0UIgeTkZHz33Xdo1qwZ5s+fj5YtWxqGoQYHB+OVV15BYWEhvvnmG6MncH3RaDT429/+hs2bN6N79+6Ijo5G586dDc13CoUCY8aMwfz581FYWIi5c+da5AdyVVUVduzYAXt7e0RERCA4OBgXLlxARkaGqUuzGPqmjqysLIwYMQIBAQHo0KEDnn32WWRkZODIkSMWeeFxPzdv3sSf//xn3L17F0uWLMHo0aOhUCggSRJ69uyJ6dOn48aNG1izZk2DOu4nVVlZia+++go2NjaYMWMG7O3t8eKLL6K0tBTx8fFGPVdmGS6lpaX417/+BZ1Oh9mzZ9dYDkGSJLz44otwd3dHfHy80RO4Puiv5leuXAkfHx988803cHd3r9EvJEkSpk6dij59+uDAgQPYt2+fxb2ZLl++jPPnz6NLly7w8PBASEgIqqqqcPDgQYs7FlMRQuC///0vFAoFJk+eDIVCAYVCgSlTpkCSJGzYsKHBnEuNRoN58+bh/PnzmDJlCl555ZUanwd//vOf4erqiq+//hpFRUWmK9bMpKWl4dy5c+jRowfatm0LSZIwfPhwKJVK7Nmzx6jPbXbhor+Nu3TpEoYMGYLevXvX2vGuT+CysjLs3LnTot9I+j1WPvjgA9jY2GDdunXw9va+74ADa2trLFiwAAqFAv/4xz8s6u5FCIH9+/ejsrLSMChh8ODBUCqV2Lt3r6nLsxg3btxAYmIifHx88NRTTxn+PTg4GN7e3vj5558Ne8tbMiEEduzYgc2bN6NTp06YP39+rZ32LVu2xOjRo5Gbm4s9e/ZY9OeBXIQQ2L59OzQaDcLDww2d9n5+fmjWrBlOnz5t1OZTswuXmzdvYvPmzXB2dsaUKVPuOxpMkiTD/t3x8fGGPRYsUUlJCWbOnImCggJ89NFH9w1UPUmS0KdPH/Tt2xdJSUlITEysx2ofjRCi2gKi+g8LlUqF4cOHQ5Ik+Pv7w83NDb/++qtF/x7rixAChw4dQklJCUJCQqptKmZjY4ORI0fizp07Rm/2qA/Xr1/H+++/D7VajWXLlt13ZrokSZg8eTJUKhU2bNjQYJrKn0RlZSX27NkDR0dHDBo0yPCZYmdnh6CgIOTl5Rk2RDMGswoXIQS+++47FBcXY+zYsWjRosUDH9+0aVP06tULt27dwunTp+upSnlptVosWrQIx48fx8iRIzFt2rQ6DZFWKpWYOXMmdDodVq9ebbYfIkeOHMHTTz+NRYsWQafTIS8vD7/88gu8vLwQEBAAAHBwcEDXrl1x48YNZGZmmrhiyxATEwOlUomQkJAaTUTPP/88lEoltm/fbravi7qoqqrChx9+iOzsbMycOfOhF10dO3aEv78/EhMTG0RT+ZPKzMxEVlYWunTpUmNuXI8ePaDRaJCammq05zercMnJycGePXvg5uaG0aNHP/RDVpIkDBs2DADw008/WdwbSQiBXbt2YdWqVfD29kZkZGSdN7ySJAmDBg2Cp6cnfvrpJ7NtAvHz84NKpcKKFSuQkJCAPXv2oKioCKGhodWuuAcPHgyNRoOjR4+asFrLUFxcjKSkJLRs2dIQ0PcKCAiAl5cXfv31V+Tl5Zmgwpoe9b2pv9D84Ycf0KVLF8yZM+ehczGsrKwwZswYlJaWYu/evRb3eSAnIQQOHDiAqqoqQwuBniRJ6NKlCwDg5MmTRqvBbMJFCIGtW7eitLQUYWFhcHZ2rtP3BQUFoWnTpkhKSjLL4ZdVVVW13qILIXDx4kXD0MoVK1Y88j4O9vb2GDNmjGEFWXN8M3l4eGDp0qWorKxEREQEFi9eDLVajYkTJxqOVZIkDBgwAAqFwiIvEupbamoq8vPz0adPn1onKVtbW2Po0KEoLi7Gzz//XOv5LC8vx82bN6HRaIxer06nQ2xsLAoLC+v0uxVC4NSpU/jrX/8Ke3t7rFixok6TMPV3bSqVCtu2bWvUryMhBPbt2we1Wo2BAwfW+Fxp27YtrKysGsedy61bt7B37164urpixIgRdf6Qtbe3R5cuXVBYWIgLFy4YucpHc/XqVUyaNAmxsbHVXuhCCNy6dQsRERHIzc3Fe++9hyFDhjzyigGSJBl2K9y8ebPZvplefPFFvPnmm8jMzMS5c+cQFhZmuHLSCwgIQPPmzfHLL7+Y5UWCudAPQRZCYNiwYbW+ZiRJwsiRI6FQKLB9+/Zaf05CQgIGDhyI9evXG7tkHDp0CJMmTcLEiRORnZ39wNepEAJXrlxBREQEioqKsGDBAnTr1q3O7w1/f3+0adMGSUlJuHXrllyHYHEKCwtx5swZtG7dGt7e3jW+7ubmBmdnZ2RlZRltQJBZhIu+k7e4uBjPP/98ne9agN/fSM888wyEEEhISDCrD9jr169j165dmDNnDrKysgzLxufk5GDSpEk4ceIEwsLC8Je//OWxl1/o0KEDfHx8kJiYaLZNYyqVCosXL8a3336Lf//731i5cmWNET8ODg7o3bs3cnJyLLb/rD7odDocOnQItra26NGjx30f16lTJ7Ro0QI///yzYROqe6WkpODWrVtwcXExZrmGWgYOHIgDBw4gNDQU+/btMywdfy8hBM6cOYPw8HCkp6cjIiICr7/++iNddFlbW+PZZ59FcXExfvnlF7kPxWKcOXMGxcXF6Nu3b61N7ba2tmjVqhXy8vJw+/Zto9RgFuFSVFSEmJgYODs7Y9SoUY98Bd+pUyfY2trixIkT0Gq1Rqry0fXo0QMzZszA1atXERYWhi1btuCrr75CaGgoDh06hJCQECxfvvyJNjeztrbG8OHDUVxcjGPHjslYvbysra0xbtw4TJ069b4XD2PHjoVOp0N0dLRZXSSYk7y8PFy4cAE+Pj4PHPBib2+PAQMGID8/HydOnKj2Nf2HuEKhqLaAobG4ubnhq6++wsyZM3HlyhW8/PLLeO2117Bv3z7k5OQgPz8faWlp+PTTTzFy5EikpaVh8uTJWLRoEdRq9SM9l/6uTZIki5+i8Lj0/S1CiPu2iCgUCvj6+qK8vNxoqxqYPFyEEIb22BEjRjzWlVTTpk3h4+ODa9eumdWtsEKhwLx58zBlyhScP38ekyZNwowZM5CVlYVp06Zh/fr1cHJyeqLn0LczKxQK/Pjjjxb7ZpIkCUOHDoWLiwuio6ONdjVl6U6fPo3S0lL06dPngYs06l8XALBjx45qrwutVouzZ8/C3t7+vitWy83R0RGLFi3Cxo0b0b59e/z4448YO3YsevXqhV69emHAgAFYtGgRFAoFFi9ejMjIyMde9LZr165wcXHBkSNHUF5eLvORmD+tVosjR47Azs4OwcHBtT5GkiS0a9cOOp0OWVlZRqnD5BsM3LlzBz/88APs7e3xwgsvPNZKxQqFAt27d0daWhpOnz6NVq1aGaHSx2NnZ4dly5Zh9OjROHjwIKytrTFs2DB0795dtv0dOnfujObNmyMhIQElJSWyrUBb35o1a4axY8di7dq1+Prrr/HWW281qh0XH0Y/vwUABgwY8NBz07NnT7i5uSE+Pr7a66KgoADXrl1DmzZtnvji5lGoVCqEhITg6aefxv79+xETE4P09HRUVFTA398fAwYMQHh4ONq0afNEv3dnZ2cEBwcjPj4eFy5cQOfOnWU8CvN369YtZGRkwNfX94FbnuhXRb506ZJR6jDpnYt+KG5eXh6GDh360Hkt96NfX0iSJBw/ftzsrt7VajWGDh2KhQsXGiZJyrlxkKOjI/r06YPc3Fyjjv4wNkmS8O6778LZ2RmLFy+22IU5jUWr1SIhIQF2dnY1BkTUxsnJCYMGDcLNmzdx7Ngxw7lMT0/H3bt30bVr13rfxEuSJDg5OWHMmDFYt24d4uPjceTIEezYsQNz586Fj4/PE19QSJKEESNGQKPRNIiJpI8qOTkZd+/eRf/+/R/4++3duzdWrlyJkJAQo9Rh0nApLi5GdHQ07O3tER4e/kQvKv1VWGpqKiorK2WsUj7G2v9dkiSEhoZCp9Nh9+7dFv1mateuHT744APk5eVh/PjxyMjIsOjjkVN+fj4yMzPh7e1dpw3jJEnC+PHjIUkSNm7caBhQom+PHzBgQD1Uff/aJEmCtbU1bG1toVKpZHtv6Af5qNVqsx2ibyz60YT6eXAPOqfu7u4YN24cfH19jVKLycJFv4bYrVu38Nxzz8Hd3f2Jfp69vT3atWuHvLy8Rjk7t3///rCxscFPP/1kVoMaHpUkSZg9ezZef/11pKSkYNSoUTh16lSj+oC4n5SUFJSWlqJ37951vuMIDg6Gn58fjhw5goyMDJSWlmLnzp1wcnJC7969jVyx6fj4+MDT0xOnTp1CYWGhqcupN1VVVUhISICjo6PJmwONHi6FhYU15i0IIXD9+nVs3boVzs7OhqurJyFJEnr06AGtVtsoP4w8PDzg7++Ps2fPWvyeFtbW1li2bBneeustXLx4EaNGjcKhQ4cs+nealZX1RHfUQgjD6gVPP/10nd8vNjY2iIiIwN27dxEZGYn9+/fjypUrsm1Bbq6sra3Rr18/FBUV4dSpU6Yup95kZ2fj6tWrCAwMRNOmTU1ai1HDJSUlBTNmzMDWrVurfTBoNBqsWrUKd+7cwfjx42XZE16SJHTt2hUKhcKsF3I0FqVSicGDB+Pu3bs4fvy4qct5Yra2tvjss8/w8ccf49atWwgLC8MPP/xgkQsSpqSkYPTo0ViyZMljT1jT6XRISEiAjY1Nnfpb9PQTbTt06IAff/wR7777LpRKpWGZ/oZKv7S8EKLRNI0JIfDzzz+joqICAwcONPnv16jP3qxZM5SUlCA6Oho5OTkQQkCn0+HHH3/EsWPHEBAQUKc1xOrK09MTLi4uSE9Pb3Sr6+rfTJIkIS4urkG8maysrPDBBx9gxYoVqKiowKRJk7B69ep6WbJETlZWVoadUzdt2vRYvxv9ChReXl6PPPDFwcEBy5cvR0BAABwdHfHOO+88cAJmQ9GrVy/Y29vjwIEDFt1U/Ch++uknKBSKh/a31Aejhkvz5s0RHh6O4uJiLFq0COnp6fjuu++wZs0aODk5Yc6cObC1tZXt+WxtbdG+fXsUFhYadSlpc9W5c2e4urri2LFjDWZ8v1KpREREBL755hvY2NjgL3/5CxYuXIiKigpTl1Zn/v7+WLt2Lezs7LBo0aLHmldw9uxZlJSUoEePHo81sbBTp07Yvn079u3bhzlz5sg6WtFcNW/eHP7+/rh48SJycnJMXY7RlZaWIjExEc2aNUP79u1NXY5xw0WSJIwbNw7PPPMMUlJSMGvWLKxZswb29vaYN28e/Pz8ZE/Xnj17QqfT4eTJkw3i6v1RNGnSBMHBwcjOzja7ddaehH57523btqFVq1ZYuHAh3nrrLdy5c8difsddunTB22+/jfz8fCxZsuSRmveEEIZti+syv6U2kiTB2dkZbm5uJr+irS9KpRJDhgxBWVkZEhISTF2O0emX9OnZsyfs7e1NXY7xO/Stra3xwQcfYObMmejZsydeeOEFfPHFF+jRo4fsL3JJkvDUU09BqVTixIkTFvPBI6fhw4dDq9Vi//79Der4JUlC//79ERMTg44dO2LdunV49dVXcfPmTYs4Tv1mVn5+fti5cyfS0tLq/L06nQ7Hjh2DtbU1unXrZsQqGxb9lhySJGHXrl0W8Tp5XPo5gzqdDiNHjjR1OQDqIVwkSYKdnR3CwsIMV5xPOgP3Qdzd3eHm5obz58+jtLTUKM9hriRJwsCBA6FWqy1+vkttJElCUFAQYmJiMHjwYMTGxuL555/HhQsXLOJYHRwc8NZbb6G8vBwrVqyo891LYWEh0tPT4enpaVarT1iCTp06wc3NDQkJCQ26H7aiogJ79uyBg4MD+vXrZxZ3p/U2nECSJCgUCqMftJWVFTp27Iji4uJGuauhj48PvL29cerUKeTn55u6HNlJkgQPDw9ER0djwoQJSEpKQkhIiNmtiF0b/XpfPj4+2L17d52X3UhJScGdO3fQq1evR+5vaeycnJzQvXt35OTkID093dTlGE1aWhouX76MHj16yDL6Vg4NbiyiJEno3bs3hBBITEw0+w8cuVlZWWHQoEG4fft2gx2Sre8/+Pe//425c+fi6tWrGDNmDLZu3Wr2Q5Xt7e0N807Wr1//0NfnvTPqBw8ebBZXpJZm5MiR0Gq12LNnT4P8PBBCYPPmzdBqtRg3bpzZvEYaXLgAv4+asra2Nrsl+OuDJEmGbQssfQ/1B5EkCba2tvif//kffPHFF4ahyl988YXRNj+SgyRJGDt2LJo1a4atW7c+dBVvrVaLw4cPw87ODt27d6+nKhsOfVOxjY0N4uLiGuTnQVFREXbu3Ak3NzezugBpkOHi5uYGLy8vXL58uUE2DT1M9+7d4erqigMHDjT4XR1VKhWmT5+Ob7/9Fk5OTvjggw8wZ84clJSUmG2wuri44KWXXkJ+fj62bNnywDqvX7+OjIwMtGvXrkHPqDcmT09PBAQEIC0trcFNURBCYOfOnbh58yZGjRpVL5u/1VWDDBelUolevXqhoqICycnJZvshYyxNmjRB3759cf369Uaxq6NCoUBoaChiYmLQtm1b/Otf/8LEiRPNdiSZJEn405/+BHt7e3z11Vf3HXgihMCxY8dQVlaGIUOG1PsKxg2FUqnEyJEjUVZW1uBm65eWlmL16tWwtbXF5MmTzeauBWig4aIftqpQKCx+TarHIUkSwsLCGtWujpIkoVu3bti1axcGDBiAHTt2YNSoUWa7bL+3tzeGDRuGy5cvP/ADLyYmBkql0jCklh6dfiCFWq3Gli1bzL5frq6EEPjuu+9w4cIFDB8+3CwmTt6rQYYLAPj6+qJly5Y4c+YMioqKTF1OvRs4cCBcXFwQExPTaIZkS5IELy8vbN26Fa+++iqSk5MREhJilhcYCoUCf/7zn6FWqxEVFVXrigP5+flITEyEh4cHAgMDTVBlw+Hv74+AgAAkJSUZbefF+iSEwOXLl/H555/DwcEBc+bMMflaYn9kXtXIyMrKCv369UNJSUm1UWP6PS0aOn3n3rVr1wyzuxsDSZLQtGlTrFmzBvPmzUNOTg5efPFFbNiwwew6czt16oT+/fsjJSWlxqZW+l0ni4qKMGzYMFmXSWqM1Go1wsPDUVZWhs2bN5v8/SCEQElJCU6dOoVt27bh66+/xpYtW/Dzzz8jJycHVVVV961RCIHCwkK8/fbbyMvLw6xZs9CuXbt6PoKHa7Dhop+dq1KpEBMTY/hgKS4uRkxMTIPv6JckCVOmTIEkSVi9erWhKUCn06G4uNjE1RmfjY0N5s+fj3//+98QQmD69On45JNPzGrNNaVSidmzZ0OpVGLp0qXVBl9otVps2LABKpXKrIaXWir9UlSOjo7YuHGjye7mhRAoLS3F119/jVGjRiE0NBTTp0/HnDlz8Oc//xkvvvgiBg0ahLFjx+Lzzz/HiRMncPv2bWi1WgghoNFokJaWhilTpuDnn3/G8OHD8cYbb5jl66NBr17n6+tr2OMkPT0dHTp0wJEjRxAZGYmXX37Z1OUZXZ8+fdCxY0ccPHgQJ0+eRHBwME6fPo3XX38db775pqnLMzqlUolJkybBy8sLU6ZMwcKFC3H58mUsW7YMTZo0MXV5hj2Ihg0bhri4OPz3v//F1KlTAQCnT5/GiRMn0LFjR3Ts2NHElTYMrVu3xogRIxAdHY0ff/wRr776ar0+vxAC2dnZePfdd3Hw4EHY2dmhX79+CA4ORtOmTVFSUoKLFy8iNTUVp06dws8//4zPP/8c7u7uaNeuHVxcXHDjxg0kJyejpKQEw4cPxxdffAEbG5t6PY66atDholQq8fLLL+Pjjz/Gf/7zHyxYsADbtm2DSqXCwIEDceDAAVOXaFTW1tZ49913MXnyZHz00Uf47rvvsGTJEqSlpTWYTs2H0W/3GhcXh0mTJmHDhg3IysrC+vXrzWIpFaVSiXnz5iEhIQH/+Mc/0KlTJ3Tu3BmLFi2CRqPBjBkzOCtfJgqFAn/5y1+wc+dOLFq0CEOGDKnXDbWuXLmCP/3pTzh79iwGDhyI//f//h+CgoKgVCohSZKhGaysrAxZWVlISEjAgQMHkJKSgqNHj0Kj0UCtVsPb2xsTJ07EK6+8Ajs7O7O8awEaeLhIkoQ+ffqgS5cuOHnyJN555x1kZmbimWeeQZs2bUxdntHpR8nogzQ0NBSnTp1Cp06dMHr0aERGRpq6xHohSRICAwMRExODN954Azt27EBISAjWrFlj6tIgSRLatm2L+fPnY+7cuZg8eTLatm2LxMRE9OvXDyNGjDDbDw9L1KlTJ0yZMgVRUVEIDw/H+++/Xy/PW1RUhLlz5+Ls2bOYOHEi/va3v8He3r7a71b//3Z2dggMDERAQABee+013L59Gzdu3EBpaSkcHR3h4eFR43vNUYPtc9FTq9V47733EBAQgKtXryIgIAAzZ840+1+MXKytrbFq1Sr07dsXqamp8PX1xapVq8xiSe76JEkSmjdvjo0bN+Ltt9/GpUuX8NNPP5m6LAC/1zZ+/Hj87W9/g1KpxOnTp9G7d2/885//hLW1tanLa1AUCgXmz5+PsLAwnDlzBhkZGfXyvL/99hvS0tLw/PPPY+HChXBwcHjoZ5AkSVAqlXBxcUFgYCC6d++O9u3b1+l7zYFR71yuXLliNsNgZ86ciRs3bqBFixYoKSlBYWGhUUcPCSFw7tw53L5922jP8Sg+/fRTXL58Ga1atYJarUZqaqrRd3QUQiA9Pd1szoHehAkT4ObmhmeeeQbbtm0z6nMJIXDhwoU6DaLo27cv/Pz8UFRUBG9vb9y5c+eRluZ/HFVVVUZdLsfc3gd6s2fPxtNPPw0fHx9kZ2cb9bmEEHB3d8eHH36ITp06md2CusZ6DUjCSGPyMjMzce7cObNO2ObNm6Nr165GqTElJQUnT5406+Nv3br1Y28+VRdnzpxBUlKSWZ8DT09Po24Jm5aWhlOnTpn1OfDw8ED//v35PuBrQNYajRYuRETUeDX4PhciIqp/FhEuOp0Od+/ebTTDZ2sjhIBOpzP5zGJT0a+s0FiPH4BhEl1jPweN/X1gKa8BiwiXixcvYsSIEbh48aKpSzGZU6dOwd7eHqdOnTJ1KSZx8uRJKBQKnDx50tSlmMyZM2cM6+U1Vo39fXDmzBm0atXKIl4DFhEuRERkWRguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJDuGCxERyY7hQkREsmO4EBGR7BguREQkO4YLERHJjuFCRESyY7gQEZHsGC5ERCQ7hgsREcmO4UJERLJjuBARkewYLkREJD9hxpKSksSUKVOEs7OzkCRJODs7iylTpoikpCRTl1Zv9OfAyclJABBOTk6N6hw09uMXgudACJ4DSzx+swyXqqoqMW3aNAFAqFQqAcDwR//3adOmiaqqKlOXajSN/Rw09uMXgudACJ4DSz5+swyXadOmCUmSqp3IP/6RJElMmzbN1KUaTWM/B439+IXgORCC58CSj9/swiUpKemBJ/KPf8z5tvBxNfZz0NiPXwieAyF4Diz9+M2uQz8qKgoqlapOj1WpVFi5cqWRK6p/jf0cNPbjB3gOAJ4DSz9+SQghTF3EvVxdXVFQUFDnx7u4uCA/P9+IFdW/xn4OGvvxAzwHAM+BpR+/2YWLlZUVqqqq6vx4tVqNyspKI1ZU/xr7OWjsxw/wHAA8B5Z+/GbXLObo6GjUx1uCxn4OGvvxAzwHAM+BpR+/2YXLmDFjHqmd8YUXXjByRfWvsZ+Dxn78AM8BwHNg8cdv2vEENVn6CAk5NPZz0NiPXwieAyF4Diz9+M0uXISw7LHdcmns56CxH78QPAdC8BxY8vGbZbhY8qxUuTT2c9DYj18IngMheA4s+fjNMlz0kpKSREREhHBxcRFqtVq4uLiIiIgIs7v9M6bGfg4a+/ELwXMgBM+BJR6/2Q1FJiIiy2d2o8WIiMjyMVyIiEh2DBciIpIdw4WIiGTHcCEiItkxXIiISHYMFyIikh3DhYiIZMdwISIi2TFciIhIdgwXIiKSHcOFiIhkx3AhIiLZMVyIiEh2DBciIpIdw4WIiGT3/wF0utawj2L+lwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model(dataset['train_input'])\n", - "model.plot(beta=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "52ec328b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saving model version 0.1\n" - ] - } - ], - "source": [ - "# set the (1,0,0) activation to be gausssian\n", - "#model.fix_symbolic(1,0,0,lambda x: torch.exp(-x**2/10),fit_params_bool=False)\n", - "model.fix_symbolic(1,0,0,'gaussian',fit_params_bool=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "79fff8e1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model(dataset['train_input'])\n", - "model.plot(beta=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "818d76e2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "| train_loss: 2.22e-01 | test_loss: 2.25e-01 | reg: 6.02e+00 | : 100%|█| 100/100 [00:18<00:00, 5.43" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "saving model version 0.2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "model.fit(dataset, opt=\"LBFGS\", steps=100, lamb=0.01, lamb_coef=1.0);" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c5cb7884", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.plot(in_vars=[r'$x_{}$'.format(i) for i in range(1,7)])" - ] - }, { "cell_type": "code", "execution_count": null, @@ -378,7 +243,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_13_phase_transition.ipynb b/tutorials/Example_13_phase_transition.ipynb index a38d43c5..ae7a4241 100644 --- a/tutorials/Example_13_phase_transition.ipynb +++ b/tutorials/Example_13_phase_transition.ipynb @@ -34,6 +34,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -43,12 +44,14 @@ "from kan import KAN, create_dataset\n", "import torch\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", "\n", - "model = KAN(width=[3,1,1], grid=3, k=3)\n", + "model = KAN(width=[3,1,1], grid=3, k=3, device=device)\n", "\n", "# create dataset\n", "f = lambda x: (torch.sin(torch.pi*x[:,[0]]) + torch.cos(torch.pi*x[:,[1]]) + torch.tan(torch.pi/2*x[:,[2]]) > 0).float()\n", - "dataset = create_dataset(f, n_var=3)\n" + "dataset = create_dataset(f, n_var=3, device=device)\n" ] }, { @@ -62,7 +65,7 @@ { "data": { "text/plain": [ - "tensor(0.5060)" + "tensor(0.5060, device='cuda:0')" ] }, "execution_count": 2, @@ -82,7 +85,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -123,7 +126,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAFICAYAAACcDrP3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAuDUlEQVR4nO3de1jUZd4/8Pc9IDicRBDPWUDkWVFTezx3ecDEytOm2W5R+nhYD6tPWWprnnNtMwUtS7TS0mivRMvDij5a+tNqUQItD4QiGuJZhoCBYYa5f3+MM8+ASAjf4cvMvF/XxbULw9iH4eb7nvv4FVJKCSIiIgVp1C6AiIhcD8OFiIgUx3AhIiLFMVyIiEhxDBciIlIcw4WIiBTHcCEiIsUxXIiISHEMFyIiUhzDhYiIFMdwISIixTFciIhIcQwXIiJSHMOFiIgUx3AhIiLFeapdAJEzkFLi9u3bKCgogJ+fH4KDgyGEULssojqLPReiSuh0OsTGxiIiIgIhISEIDQ1FSEgIIiIiEBsbC51Op3aJRHWS4J0oiSqWlJSE0aNHQ6/XA7D0XqysvRYfHx9s374dUVFRqtRIVFcxXIgqkJSUhOjoaEgpYTab7/t9Go0GQgjs2bOHAUNkh+FCVI5Op0PLli1RVFRUabBYaTQaaLVaZGdnIzAw0PEFEjkBzrkQlbN582bo9foqBQsAmM1m6PV6bNmyxcGVETkP9lyI7EgpERERgczMTDzIn4YQAmFhYcjIyOAqMiIwXIjKuHXrFkJCQmr0/ODgYAUrInJOHBYjslNQUFCj5+fn5ytUCZFzY7gQ2fHz86vR8/39/RWqhMi5MVyI7AQHByM8PPyB502EEAgPD0dQUJCDKiNyLgwXIjtCCMyYMaNaz505cyYn84nu4oQ+UTnc50JUc+y5EJUTGBiI7du3QwgBjabyPxHrDv3ExEQGC5EdhgtRBaKiorBnzx5otVoIIe4Z7rJ+TavVYu/evRgyZIhKlRLVTQwXovuIiopCdnY21qxZg7CwsDKPhYWFYc2aNbhy5QqDhagCnHMhqgIpJb799lsMHDgQBw8exJNPPsnJe6JKsOdCVAVCCNucSmBgIIOF6A8wXIiISHEMFyIiUhzDhYiIFMdwISIixTFciIhIcQwXIiJSHMOFiIgUx3AhIiLFMVyIiEhxDBciIlIcw4WIiBTHcCEiIsUxXIiISHEMFyIiUhzDhYiIFMdwISIixTFciP6A0WjElStXcPbsWQDAhQsXcOfOHZjNZpUrI6q7eJtjovvQ6XTYvn07tm7ditOnTyM/Px8lJSWoX78+QkJC0LdvX0yYMAG9e/eGp6en2uUS1SkMF6IK/PDDD5g9ezZOnTqF7t27Izo6Gp06dYKfnx90Oh1SUlKwa9cunD9/HmPHjsWyZcsQEhKidtlEdQbDhaic/fv3IyYmBn5+flixYgWGDRuGkpISJCQkwGAwICAgAOPGjYPRaERCQgIWLVqE9u3b47PPPkOTJk3ULp+oTmC4ENn59ddfMXToUPj6+iIhIQHt2rWDEAKZmZno2rUr8vLyEBoaipSUFDRs2BBSShw9ehTjx4/HgAEDsHHjRnh7e6v9YxCpjhP6RHeVlpbi7bffRm5uLtatW2cLlsoIIdCnTx+88847+Prrr7Fv375aqpaobmO4EN11/vx57Nq1C6NGjUKfPn3+MFishBAYMWIEnnjiCcTHx8NkMjm4UqK6j0tciO76/vvvUVBQgNGjRyMrKwuFhYW2x7Kzs1FaWgoAKCkpwenTpxEQEGB7vHnz5hg1ahQWLVqEa9euoWXLlrVeP1FdwnAhuuvcuXPw8fFBWFgYJk+ejGPHjtkek1LCYDAAAHJycjB48GDbY0IIrFq1Ch07doRer0dOTg7Dhdwew4XorqKiInh6esLb2xsGgwHFxcUVfp+U8p7HTCYTtFptmRAicmcMF6K7GjdujKKiIuh0OvTs2RO+vr62x4qKivD999/bQqRXr162jZNCCLRq1Qo3btyARqNBw4YN1foRiOoMhgvRXd26dYPRaERycjJWrlxZ5rHMzEx0794deXl5aNKkCb788ksEBgbaHhdCYP78+WjatCmHxIjA1WJENj169EBYWBg2b96MwsJCeHh4lPmwEkJAo9HYvq7RaHD16lV89dVXiI6ORoMGDVT8KYjqBoYL0V3BwcGYPn06fvrpJ8TFxVV5SbHBYMDSpUtRVFSEyZMnV3kJM5Er47AYkZ2YmBgcOXIEK1euhI+PD6ZOnYr69esDADw9PeHp6WnrxUgpkZ+fj+XLlyMhIQGrV69G69at1SyfqM7g8S9E5dy8eRPTpk3D7t27ERUVhdmzZ6Nt27ZIT0+H2WyGl5cXHn30USQnJ+Pdd99FWloalixZgqlTp5YZPiNyZwwXogoUFhYiPj4ecXFxuH79OsLCwhAREQF/f3/k5uYiPT0dOTk56NatGxYuXIj+/ftDo+EoM5EVw4WoEteuXcPBgwdx+PBhZJ48ieLkZDTs2xcdevfGkCFD0LNnT/j4+KhdJlGdw3AhqqLS48che/SA5vhxaB5/XO1yiOo0TugTVZFtPoXDX0R/iH8lRESkOIYLEREpjuFCRESKY7gQEZHiGC5ERKQ4hgsRESmO4UJERIpjuBARkeIYLkREpDiGCxERKY7hQkREimO4EBGR4hguRESkOIYLEREpjvdzIaoqKS0fQlg+iOi+2HMhehAMFaIq4c3CyCWYjEbkXr4MaTarXUqNCSEQ2KoV6nl5qV0KUbUxXMgl6LKzkTx1KgKd+fbDej3g44O8EyfQff16hISHq10RUbUxXMglSCkR0Lkzei1frnYp1fPdd8DChcDq1ThqMlnmdoicGMOFXI5wpnmR0lIgMRGYPh24eRPyzTeB1q3VroqoxjihT6QWkwn46CNg4kTg5k2gUyfgH/8AvL3VroyoxhguRGowGCxB8tprQH4+0L8/sGMH0LkzV6SRS+CwGFFtkhIoKgLeeguIi7MMi40YAXz4IRASonZ1RIphz4Wotkhp6aXMmgXExgJmMxATA3zyiSVY2GMhF8JwIaoNUgI6HTBlCrBpkyVIZs609F4CAhgs5HI4LEbkaFICt24BkyYB33wD1KsHvPEGMG8eUL++2tUROQTDhciRpASuXwdeeQXYt88SJgsXArNnA9yBTy6M4ULkKFICV68CL70EHDwI+PgAK1YAU6cCnvzTI9fGFk7kCFICV64Af/kLcPgw4OsLrFoFTJgAeHioXR2RwzFciJQmJZCdDfz5z8D/+3+Avz+wZg3w4osMFnIbDBciJUkJ/PYb8MILwLFjlpVga9daPtdwcSa5D4YLkVIqCpb33weef57BQm6H4UKkBPuhMGuwfPABMG4cg4XcEls9UU1ZJ+///Gfg6FEGCxEYLkQ1Y11u/Je//N/k/bp1DBZye2z9RNVl3SD54ouW5cZ+fpbJ+/HjGSzk9vgXQFQdUlruwRITAxw6ZNnHsmYNV4UR3cW/AqIHJSVw+7ZlQ+T+/Zad9++9Z9mJz30sRAAYLkQPRkogN9dyCOWePYBWC7zzjuXsMAYLkQ3DhaiqpATy8ixng339teUQyrfftgQNg4WoDIYLUVVYb/Q1Ywbw1VeWE40XLwamTeMhlEQVYLgQ/REpgcJCyzH527ZZ7sfy979b7ijJYCGqEMOFqDJSAno9MGcOsHmzJUzmzrV8Xq+e2tUR1VkMF6LKFBdb7hgZH29ZYvzqq5bPeaMvokoxXIjup7gYWLAAWL/eco/7GTOAt94CvL3VroyozmO4EFXEYACWLgXi4ixDY1OmAMuW8Z73RFXEcCEqr6TEcjvid98FzGZg4kTgH/+w7GkhoiphuBDZMxotmyJXrABMJsu5Ye++a9mFT0RVxnAhsjIaLce4LFtmCZbx4y3nhfn6WuZciKjKGC5EgCVMYmOBRYssIfPcc5aj8/39GSxE1cBwITKZLEflv/WWZb5l1CjLzb4CAhgsRNXEcCH3ZjJZeihvvmlZIfbss8BHHwGBgQwWohpguJD7MpmA998H5s+3BMvTT1s2SzZsyGAhqiGGC7kna49l3jzLZsnhw4GNG4GgIAYLkQIYLuR+TCbL5kj7HsumTUBwMIOFSCEMF3Iv1uXG1jkWBguRQ/C8cHIfJSWWDZFLl1r+/4gRwIYNHAojcgCGC7kHg8Gy6/4f/7D0XsaMsRxIycl7IodguJDrKyqybI5cvRooLbXsvF+7FmjQgMFC5CAMF3Jd1jtIzpsHfPih5fOXX7bMuXDnPZFDMVzINUkJ5OVZbk382WeWIJk61TIs5uPDYCFyMIYLuR4pgZs3LWGyc6fl1sSzZwMLF/LYfKJawnAh13P5MjBhAnDokOWukX//O/Daa7yDJFEtYriQazlzBnjpJeCnnwA/P8sKscmTLb0XIqo1/Isj12EwWHooP/1k2bsSFweMHQt4eKhdGZHbYbiQ6/D2BhYvhtTpLEuPBw+2TNxLqXZlRG6H4UIuQQiB/FOn8KOPD9CrF/D998APP6hdVrXkp6VxNRs5PSEl39aR8zOWlOBOZiZkaanapdSY0GgQFB6Oel5eapdCVG0MFyIiUhyHxYiqyv59GIetiCrFI/eJqio1FdBoLP9LRJViuBARkeIYLkREpDiGCxERKY7hQkREimO4EBGR4hguRESkOIYLEREpjuFCRESKY7gQEZHiGC5ERKQ4hgsRESmO4UJERIpjuBARkeIYLkREpDiGCxERKY7hQlQFUkrk5uYCAHJzc8EbuBJVjuFCVAmdTofY2FhERERg4KBBAICBgwYhIiICsbGx0Ol06hZIVEcJybdgRBVKSkrC6NGjodfrAQCRUuInAF0BpN29zbGPjw+2b9+OqKgo9QolqoPYcyGqQFJSEqKjo1FUVAQp5T3DYNavFRUVITo6GklJSSpVSlQ3sedCVI5Op0PLli1RVFQEs9ls+3oXwNZzSbX7fo1GA61Wi+zsbAQGBtZusUR1FHsuROVs3rwZer2+TLBUxmw2Q6/XY8uWLQ6ujMh5sOdCZEdKiYiICGRmZt4zFHa/ngsACCEQFhaGjIwMiLvzMUTujD0XIju3b9/GhQsXHnipsZQSFy5cwJ07dxxUGZFzYbgQ2SkoKKjR8/Pz8xWqhMi5MVyI7Pj5+d33sXOwDImdq+T5/v7+SpdE5JQYLkR2goODER4eXuG8SREscy1FFTxPCIHw8HAEBQU5ukQip8BwIbIjhMCMGTOq9dyZM2dyMp/oLq4WIyrnfvtc7of7XIjuxZ4LUTmBgYHYvn07hBDQaCr/E9FoNBBCIDExkcFCZIfhQlSBqKgo7NmzB1qtFkKIe4a7rF/TarXYu3cvhgwZolKlRHUTw4XoPqKiopCdnY01a9YgLCyszGNhYWFYs2YNrly5wmAhqgDnXIiqQEqJb7/9FgMHDsTBgwfx5JNPcvKeqBLsuRBVgRDCNqcSGBjIYCH6AwwXIiJSHMOFiIgUx3AhIiLFMVyIiEhxDBciIlIcw4WIiBTHcCEiIsUxXIiISHEMFyIiUhzDhYiIFMdwISIixTFciIhIcQwXIiJSHMOFiIgUx3AhIiLFMVyIiEhxDBeiP2A0GnHlyhWcPXsWAHDhwgXcuXMHZrNZ5cqI6i7e5pjoPnQ6HbZv346tW7fi9OnTyM/PR0lJCerXr4+QkBD07dsXEyZMQO/eveHp6al2uUR1CsOFqAI//PADZs+ejVOnTqF79+6Ijo5Gp06d4OfnB51Oh5SUFOzatQvnz5/H2LFjsWzZMoSEhKhdNlGdwXAhKmf//v2IiYmBn58fVqxYgWHDhqGkpAQJCQkwGAwICAjAuHHjYDQakZCQgEWLFqF9+/b47LPP0KRJE7XLJ6oTGC5Edn799VcMHToUvr6+SEhIQLt27SCEQGZmJrp27Yq8vDyEhoYiJSUFDRs2hJQSR48exfjx4zFgwABs3LgR3t7eav8YRKrjhD7RXaWlpXj77beRm5uLdevW2YKlMkII9OnTB++88w6+/vpr7Nu3r5aqJarbGC5Ed50/fx67du3CqFGj0KdPnz8MFishBEaMGIEnnngC8fHxMJlMDq6UqO7jEheiu77//nsUFBRg9OjRyMrKQmFhoe2x7OxslJaWAgBKSkpw+vRpBAQE2B5v3rw5Ro0ahUWLFuHatWto2bJlrddPVJcwXIjuOnfuHHx8fBAWFobJkyfj2LFjtseklDAYDACAnJwcDB482PaYEAKrVq1Cx44dodfrkZOTw3Aht8dwIQJgNptx9epVeHp6wtvbGwaDAcXFxRV+r5TynsdMJhO0Wm2ZECJyZwwXcjsGgwG//PIL0tLSbB8nT55Efn4+tFotdDodevbsCV9fX9tzioqK8P3339tCpFevXraNk0IItGrVCjdu3IBer8f48ePRs2dPdOnSBZGRkejSpQuaNWtW5TkcIlfApcjk0nQ6XZkQSU1NxdmzZ2EymSCEQOvWrREZGYnIyEgYjUYsWbIEcXFxmDhxYpl/JzMzE927d0deXh4eeeQRnDhxAoGBgbbHhRCYP38+Nm/ejBEjRuDcuXNITU1FXl4eACAkJARdunQpEziPPvooPDw8avPlIKo1DBdyCVJKXLlyBampqUhNTcXJkyeRmpqKrKwsAED9+vXRsWNHW5B06dIFHTt2LNM7uX37Nvr06YOGDRti3759ZSbs77fPxfrfzsnJQf/+/fHMM89g1apVEEJASolLly4hNTXVFmypqanIzs4GAPj6+qJTp062eiIjI9GxY0fUr1+/9l44IgdhuJDTKS0tRXp6+j09ktu3bwMAGjZsaOsldO7cGV26dEHr1q2rdP7X+++/j1dffRV///vfMXfuXNtzKguX4uJizJo1C7t27cKhQ4fQunXrSv8bt27dKlN3Wloazp07B7PZDA8PD7Rt27ZM4ERGRiIoKKiGrxpR7eKcC9Vper0eP//8c5mL8c8//4yioiIAwMMPP4zIyEhMnz7ddkF+6KGHqj2/ERMTgyNHjmDlypXw8fHB1KlTbT0JT09PeHp62oaypJTIz8/H8uXLkZCQgNWrV/9hsABAo0aNMGjQIAwaNKjCn9MaONu3b7f9nK1atSozpFbTn5PI0dhzoTrD+o7eflgrPT29zDt6+95I586dHfKO/ubNm5g2bRp2796NqKgozJ49G23btrXV4uXlhUcffRTJycl49913kZaWhiVLlmDq1KmKzqGYTCZkZGTcM6xm7aEFBQWVGeaLjIxEmzZteEIz1QkMF6p1UkpkZWXdM6xlnYvw8fEpEyBdunRB+/btodVqa63GwsJCxMfHIy4uDtevX0dYWBgiIiLg7++P3NxcpKenIycnB926dcPChQvRv39/aDSOP/DCfm7Jvpdz8eJFAIC3tzc6duxYZvFAp06dyswtEdUGhgs5lNFoxNmzZ8v0RtLS0u67iioyMrJOraK6du0aDh48iMOHD+PkyZNITk5G37590bt3bwwZMgQ9e/aEj4+P2mVCp9PZXl/ra3zmzBnbqrjHHnuszLBaZGQkGjdurHbZ5MIYLqSY/Px8nDp1qkyQ/PLLLygpKQEAhIeHl+mNREZGOtX+j+PHj6NHjx44fvw4Hn/8cbXL+UPFxcU4c+ZMmV7OyZMnUVBQAMByZI39HE5kZCTCwsKc5vdBdRvDharl2rVrZYZl0tLScP78eUgpUa9ePbRv3/6e+RH7pb3O6KeffkK3bt2QkpKCrl27ql1OtZjNZly4cKHMHE5aWhquXbsGAAgICCgT/l26dEG7du3g5eWlcuXkbBguVCnrxaj8sJb1YuTv73/PsllXvRi5QrjcT/k3C6mpqcjIyACAMm8WrL9rV3izQI7FcCEbg8GA06dPlwmS8sMo9iESGRmJ0NDQWpnIrgtcOVwqYj/MaQ2c8sOc5YfVnGmYkxyL4eKmyh+LUtkEsPXD3SeA3S1cKmJdoFG+l2NdoNG4ceMyPVkec+O+GC4urvzSVeuHdelq+WNRuHT1/hguFbNfWm4fOOWPubEPnA4dOvCYGxfHcHEhFR2LkpaWhlu3bgH4v2NR7DfeVfVYFGK4PKjyx9xUtCmWx9y4LoaLkyp/LEpaWhpOnTp1z7Eo9kHC40JqhuFScxUdc2Pfbq3H3Nj3cthunRPDxQncvn27zLDW/d4B2n/wHaDyGC6OYX/Mjf2w2v2OuWGP2zkwXOqQqh6LYv+HVtvHorgzhkvtqeiYm/vdQoHH3NRNDBeV2K+6sR8iqOhYFOumNq66URfDRX32qxwrO+bGflgtJCRE7bLdEsOlFhQUFJTZN3K//QL278K4X6DuYbjUTVU55qb8uWo85sbxGC4Ks9/pbA2Syo5F6dSpExo0aKB22VQFDBfnYX+yhH0vx/6Ym/Jv6Fz1ZAm1MFyq6UGORbEGCRuvc2O4OD/7N3/Wv1kec+MYDJcqMBqN+Pnnn3ksiptjuLimqhxzYx84Xbt2RdOmTVWuuu5juFRBVlaWbYzWOmFof3Ksux+L4i4YLu7D/j5E5RfcxMTE4JNPPlG7xDqP4VIF9i8RJwHdl5QSUkoIIdgO3BCvAw+Gu5CqgA2JrNgW3Bd/9w/G6cPFaDTi8uXLMJvNapdSY0IItGrVipP+1cB2QGwDdYvTh0t2djamTp3qFLedrUhpaSkAwMPDAydOnMD69esRHh6uclXOx9nbgT22g+qxtoFu3brBYDA49anLrtAGnD5cpJTo3Lkzli9frnYp1fKvf/0LCQkJmDRpEoxGIzgFVj3VaQdZWVnYsWMHzp49i2bNmmHkyJHo3Lmz6sMfc+fOZTuoBmsbGDlyJN544w3MmzcPAwcOdMpVm67QBpw+XOypfVF4UMXFxVi/fj2OHDkCrVaL5s2bq12SS/ijdmA0GrFlyxYsXLgQOTk5tq+vW7cOCxcuxLRp01Q7FNHZLyhqKykpwbJly3D48GGkpKTg9ddfx6uvvor69es7zfXBVdqA80W6i5BS4scff8R//vMfeHl54eWXX+a5YbXAaDTin//8J6ZPn46cnBy0atUK48aNQ4cOHZCXl4d58+Zhy5YtLvMH7m7q1auHt956C/369UNhYSEWL16MWbNmIT8/n7/TWsZwUYnZbMamTZtgMBjQrVs39O7dW+2SXJ7ZbEZ8fDyWLl2KkpISjBw5EkeOHMHWrVtx8OBBDB8+HMXFxViwYAHOnTvHi5ETEkKgW7du2LFjB6ZMmQIhBDZu3Ijp06czYGoZw0UlGRkZ2Lt3LzQaDSZMmMBj8x1MSolDhw7hzTffhMFgwKhRo7Bp0ybbjagaNWqEuLg4hIeH4+rVq1i5cqVtsQU5n4YNG2LVqlWYN28ePD09sXXrVrz66qu2m5KR4zFcVCClxNatW5Gbm4vQ0FA8/fTTapfk0qSUyM7Oxt/+9jfk5eWhR48eWLt2LRo0aGAbh7cu/XzjjTeg0WiwY8cOpKWl8Z2uE6tfvz7efPNNzJ07Fx4eHvj000+xZMkS27Eu5FgMFxXcuHED27ZtAwCMHz8ejRo1Urki12Y0GrFgwQKcPXsWjRs3xvvvv48mTZrcM8ErhMCf/vQndOzYEfn5+diwYQPDxcl5eXlh/vz5mDZtGqSUWLNmDdavX89eaS1guNQyKSW+/vprZGVlITg4GC+88ILTrGJxRlJKbN++HQkJCfD09MTChQvRtWvX+77mAQEBmDhxIoQQ+Oabb3Dp0qVarpiU5u3tjaVLl+K5555DSUkJ3nrrLXzzzTd84+BgDJdaptfr8fHHH0NKieHDhzv1Jqm6TkqJS5cuYcGCBSgpKcHw4cMRExNTaZgLITBy5Ei0aNECN27cwM6dO3kRcgG+vr6IjY1Fv379kJ+fjxkzZiAlJYW/WwdiuNQiKSW+++47pKamQqvVYsKECU65wctZGI1GLFy4EJmZmWjevDmWL19epV3bTZs2xfDhwwFYNrlyEtj5WRdtbNiwAY899hhycnIwceJE/PbbbwwYB+GVrRaZTCZs2LABRqMRvXv3Rvfu3Tkk5iBSSiQmJuLLL7+Ep6cnFixYgDZt2lTp9RZCYOzYsfDy8sLJkydx6tSpWqiYHE0IgYiICGzYsAGNGjXCqVOn8Ne//hV5eXkMGAdguNQSKSXS0tJw6NAheHh4YNKkSU59KF1dJqXExYsX8eabb6KkpATDhg3Diy++WOUgt+6VaNu2LYqLizk05kKEEOjbty9WrVoFrVaLf//737bl6aQshkstMZvN2LhxIwoLC9GhQwcMGTKEvRYHKSoqwhtvvIGLFy+iRYsWWLFixQMfYujr62tbIr53717bXUfJ+Qkh8Pzzz+P111+HRqNBfHw83nvvPZhMJrVLcykMl1qSmZmJHTt2QAiBCRMmwN/fX+2SXJLZbEZcXBx27twJLy8vLF26tMrDYfaEEBg+fDi0Wi0yMjJw8uRJB1VMavD09MTrr7+OF198EaWlpVi+fDk+/vhjlziuv65guNQCKSU2b96MW7du4eGHH8aYMWPYa3GQxMREvP322zCbzYiJicH48eOr/Vq3b98ebdq0gcFgwJ49ezg05mLq16+Pd999F9HR0SgqKsKcOXOwdetWBoxCGC614OrVq/j8888BAH/5y1/QpEkTlStyTQaDAQkJCSgsLMSAAQOwfPly1KtXr9r/no+PD4YOHQoA2L9/P/R6vVKlUh0ghEBgYCA++ugj9O/fH/n5+Zg+fTo++eQTDpEpgOHiYFJKfPHFF/jtt9/QuHHjB5pYpgfj7e2Njz76CHPnzkV8fDyCgoJq9FoLITB06FB4eXnh3LlzSE9PV7BaqguEEGjatCm2bNli2wMzc+ZMLFu2DAUFBeyt1gDDxcHu3LmDTZs2QUqJP/3pTwgNDVW7JJcWHByMZcuWITQ0VJEQ79SpE0JDQ1FUVIQDBw7wYuOChBBo2bIltm3bhujoaBQXF2PZsmUYM2YMTpw4gdLSUv7eq4Hh4kDWo0d+/fVXBAYGYtKkSey11AIhhGKvc0BAAAYMGAAASEpKgtFoVOTfpbpFCIFmzZrh888/x6xZs+Dt7Y39+/dj8ODBmDJlCk6cOAGDwcCQeQAMFwfKy8vD+vXrYTabMWLECLRr147h4oSGDh0KDw8PnDx5EtnZ2WqXQw4ihECDBg2wcuVKfPnll+jatSsKCgqwadMmPPnkkxg6dCg++OADZGRkoKSkhEHzBxguDiKlxM6dO/Hzzz/D398ff/3rX3nUixMSQqBHjx5o3LgxdDodjh49youKi/P09ER0dDT+93//F+vXr0fXrl1hNBpx+PBhzJgxAz169MBTTz2FtWvXIiMjAyaTiW2iArzaOcjvv/+OtWvXwmw24+mnn0ZkZCR7LU6qcePG6NGjB6SU2LdvHy8kbsC6kmzixIn47rvvsGfPHvz3f/83HnnkERQUFODbb7/FrFmz8MQTT2DcuHHYv38/iouL2TbsMFwcwHqu1cmTJ+Hn54eZM2fCw8ND7bKomjQaDaKiogAAP/zwA+7cuaNyRVRbhBDw8/PDwIEDsX79ehw/fhy7du3C5MmTERoairy8PCQmJuLZZ5/Fs88+i6NHj/JeMXcxXBxAp9MhNjYWZrMZzz77bKX3D6G6TwiB/v37w9/fH1euXEFaWpraJVEtE0JAo9EgODgYUVFR+OCDD5CcnIwvvvgCgwcPhhACBw4cQHR0NBYsWMDDMMFwUZz1Fsa//PILAgICMGvWLPZaXEBoaCjatWsHk8mE/fv3u/2Fw51ZVyMGBwdjzJgx+Oabb7Bjxw488cQTKCwsxMqVKzFu3DhcvnzZrdsJw0Vh165dQ1xcHMxmM8aOHcu5Fhfh7e2NQYMGAQAOHTrEe7wQAEvQeHt7IyoqCnv37sX8+fOh1WqRlJSEMWPGICMjw20DhuGiILPZjA8//BAXLlxASEgIZs+ezRViLkIIgUGDBqFevXpIT0/H+fPn1S6J6hDrAoBFixZhw4YNCAoKQkpKCsaNG4fMzEy3DBhe+RQipcTZs2fx4YcfQkqJSZMm4bHHHmOvxYV06tQJrVq1QmFhIb777ju3vGBQ5Tw8PDBu3Dh8+umnaNSoEdLS0hATE4Pr16+7XXthuCjEaDTi7bffxs2bNxEREcF9LS6oQYMG6NOnDwDLbn2uCqKKaDQaREdH48MPP0RAQACOHTuGmTNnorCwUO3SahWvfgqQUmLv3r1ITEyEp6cn5s2bh6ZNm6pdFilMCIGoqCgIIZCSkoJr166pXRLVUUIIjBgxAitWrICXlxcSExPxz3/+063ekDBcakhKievXr2PBggUwGAwYNGgQnnvuOQ6HuSAhBP7rv/4LwcHBuHXrFn744Qe3G+qgqtNoNJg4cSKmTZsGKSVWrVqF3bt3u02bYbjUkMlkwvLly3H69GnbibxarVbtsshBWrRogW7dusFsNmPv3r1uc6Gg6qlXrx4WLFiAJ598Enq9Hq+99hqysrLcot0wXGrAen7Ypk2boNFo8Nprr3HpsYvz8PDAsGHDAACHDx9Gbm6uyhVRXdegQQOsWbMGzZs3x4ULFzB//nwYDAa1y3I4hks1SSlx5swZzJkzB8XFxRgyZAimTZvGSXwXJ4TAwIED4e/vj+zsbBw/ftwt3oVS9Qkh0L59eyxZsgT16tVDYmIiEhISXL7d8EpYDVJK3Lx5E1OmTMHly5fxyCOP4L333oOvr6/apVEtCA8PR+fOnWEymbBr1y61yyEnIITA+PHjMXLkSBiNRixevNjlh8cYLg9ISon8/HzMmDEDx44dg7+/P+Li4tC6dWsOh7kJLy8vDB8+HABw4MAB6HQ6dQsip+Dt7Y0lS5agRYsWuHTpEpYvXw6TyaR2WQ7DcHkAUkoUFhbif/7nf/DVV1/By8sLy5Ytw7BhwxgsbkQIgaeeegp+fn7IysrCjz/+6NLvQEkZQghERERg7ty58PDwwBdffIFDhw65bNthuFSRlBI6nQ7Tpk3Dp59+Cg8PD8ydOxdTpkzhPIsbeuyxx9CtWzeYTCZ89dVXLnuBIGUJIfDSSy+hb9++KCoqwuLFi/H777+rXZZD8KpYBVJKXLx4EePHj8fnn38ODw8PzJkzB/PmzUO9evXULo9U4OXlhTFjxkAIgaSkpAfaUCmlhNFoxKlTp7B582Zs27YNmZmZMJvNDqyY6gpfX18sWrQIfn5+SE5OxmeffeaSb04YLpWQUsJkMmH37t146qmnkJSUBK1Wi8WLF2PhwoXw9vZWu0RSiRAC0dHRaNSoEa5evVrlPS9SSmRlZeGVV15Bnz598PLLL+PPf/4zevXqhQ8++MClx+DJQgiBXr164fnnn4fZbMZ7772HK1euqF2W4hgu92E2m5GRkYGpU6di3LhxyMjIQLNmzbBx40bMmTMHXl5eapdIKnvooYcQFRUFKSU2b978h8fwSylx7NgxPPXUU9i6dSuKiooQHh6OFi1a4MaNG5gzZw4++ugj9mDcgIeHB1577TU0bdoUWVlZWLduncv93hkudqzDFb/88gvmzJmDfv36YdOmTSgpKcHgwYOxb98+jB07ljf/IgCW4z1efvlleHt74/jx4zhy5Mh9ey9SSuzfvx/PPfccfv31VzRr1gwbNmzAiRMn8OOPP2LkyJEwmUxISUmB0Wis5Z+EapsQAo8++iimTp0KIQQ+/vhjnDt3zqWGx9w6XKSUkFKiuLgYGRkZ+PjjjzFixAj06dMHq1evxs2bNxEeHo5169YhMTERHTp04KowsrGeNdarVy+UlJQgNja2wp3XUkr8+9//xksvvYRr166hTZs22LlzJ2JiYtCgQQM0b94c69evR1xcHNauXcvhVjchhMCkSZMQERGBW7duYfXq1S51sKWn2gXUBuu7AbPZDIPBgLy8PGRnZ+PMmTM4ceIEkpOTkZ6ejvz8fEgp4eHhgbZt29rGw5s0acJQoQp5e3tj1qxZOHbsGA4dOoQdO3Zg3LhxtvZiNpvx9ddfY/Lkybh16xY6duyIbdu2oV27drbvEUIgJCQEU6ZMUfNHIRU0btwYf/vb3zBjxgz861//wqRJk/D444+rXZYiXCpcpJQoLS2FXq/HnTt3cPXqVfz222+4ePEiLl68iEuXLiE7OxvXr1/H77//jpKSEttzPT098dBDD6Ffv34YM2YM+vXrhwYNGjBUqFJCCAwePBjDhg3Dzp07MX/+fLRp0waRkZHQ6/WIj4/HokWL8Pvvv6Nz58744osvKtxwa/3clYZF6I8JIfD8888jPj4eaWlpeO+997Blyxa1y1KEy4TLN998gwMHDuDMmTO4fPkybt++Db1eX+H4tYeHB3x8fNCqVSu0bt0aPXr0QJ8+fdCxY0cEBQVBCMFQoSrz9vbG8uXLkZqaikuXLuGZZ57BoEGDcPbsWaSkpKC0tBS9e/fGJ598gvDwcLYtKqNBgwaYPXs2XnnlFezevRvJyclql6QIlwmXvXv3Ij4+3vZ5vXr14Ovri4YNG6Jp06Zo1aoVQkNDERoairCwMDz88MNo3LgxfH19odFo+AdP1SaEQJs2bfDJJ59g4sSJyMzMxObNmwEAPj4+eOGFF7B06VKEhISwndE9rDcWe//995GcnIy4uDi0bNlS7bJqzGXCZcCAAbh58yY6dOiAtm3bIiwsDM2aNUNgYCC0Wi08PSv/UTkc4TrU+l32798fBw8exLZt23DmzBk0b94czzzzDHr06GFrf2xntcPZXmc/Pz+8+uqriI+PR0xMDA4dOqR2STXm9OEihMCpU6eg1WrRoUMHaDQapKenIz09Xe3SHlhaWhrf2VaTtR0sXrxY7VIAAGFhYRBC4MCBAzhw4MADPZftoHrqWht4UCaTCY8//jj+85//4NSpU07fBoR0togvp6SkBJmZmS6xhE+j0SA8PJwbNKuB7YDYBuoWpw8XIiKqe5x+WKy22Gews3dXqXrYBohtoOrceof+g0hNTYWHhwdSU1PVLoVUkpqaCo1GwzbgxtgGqo7hQkREimO4EBGR4hguRESkOIYLEREpjuFCRESKY7gQEZHiGC5ERKQ4hgsRESmO4UJERIpjuBARkeIYLkREpDiGCxERKY7hQkREimO4EBGR4hguRESkOIZLFUgpkZubCwDIzc0Fb97pftgGiG3gwTBcKqHT6RAbG4uIiAgMGjQIUkoMGjQIERERiI2NhU6nU7tEcrDybQAA24CbYRuoHiEZvxVKSkrC6NGjodfrAVR8e1MfHx9s374dUVFRqtRIjsU2QGwD1cdwqUBSUhKio6MhpYTZbL7v92k0GgghsGfPHjYsF8M2QGwDNcNwKUen06Fly5YoKiqqtEFZaTQaaLVaZGdnIzAw0PEFksOxDRDbQM1xzqWczZs3Q6/XV6lBAYDZbIZer8eWLVscXBnVFrYBYhuoOfZc7EgpERERgczMzAdaCSKEQFhYGDIyMmzjsOSc2AaIbUAZDBc7t27dQkhISI2eHxwcrGBFVNvYBohtQBkcFrNTUFBQo+fn5+crVAmphW2A2AaUwXCx4+fnV6Pn+/v7K1QJqYVtgNgGlMFwsRMcHIzw8PAHHi8VQiA8PBxBQUEOqoxqC9sAsQ0og+FiRwiBGTNmVOu5M2fO5CSeC2AbILYBZXBCvxyubye2AWIbqDn2XMoJDAzE9u3bIYSARlP5y2PdmZuYmMgG5ULYBohtoOYYLhWIiorCnj17oNVqIYS4p5tr/ZpWq8XevXsxZMgQlSolR2EbILaBmmG43EdUVBSys7OxZs0ahIWFlXksLCwMa9aswZUrV9igXBjbALENVB/nXKpASok7d+4gPz8f/v7+CAoK4qSdm2EbILaBB8NwISIixXFYjIiIFMdwISIixTFciIhIcQwXIiJSHMOFiIgUx3AhIiLFMVyIiEhxDBciIlIcw4WIiBTHcCEiIsUxXIiISHEMFyIiUhzDhYiIFMdwISIixf1/2qhVBBFFUzkAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -147,7 +150,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 7.72e-02 | test_loss: 1.37e-01 | reg: 2.37e+02 | : 100%|█| 50/50 [00:04<00:00, 10.44it" + "| train_loss: 7.71e-02 | test_loss: 1.17e-01 | reg: 2.43e+02 | : 100%|█| 50/50 [00:09<00:00, 5.32it\n" ] }, { @@ -156,13 +159,6 @@ "text": [ "saving model version 0.2\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -177,7 +173,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -215,7 +211,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_14_knot_supervised.ipynb b/tutorials/Example_14_knot_supervised.ipynb index a3d81529..11bbfbef 100644 --- a/tutorials/Example_14_knot_supervised.ipynb +++ b/tutorials/Example_14_knot_supervised.ipynb @@ -1,5 +1,4 @@ { - "cells": [ { "cell_type": "markdown", @@ -40,6 +39,8 @@ "from kan import *\n", "import copy\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", "\n", "seed = 42\n", "torch.manual_seed(seed)\n", @@ -73,10 +74,10 @@ "test_id_ = np.array(list(set(range(num))-set(train_id_)))\n", "\n", "dtype = torch.get_default_dtype()\n", - "dataset['train_input'] = torch.from_numpy(X[train_id_]).type(dtype)\n", - "dataset['train_label'] = torch.from_numpy(Y[train_id_][:,0]).type(torch.long)\n", - "dataset['test_input'] = torch.from_numpy(X[test_id_]).type(dtype)\n", - "dataset['test_label'] = torch.from_numpy(Y[test_id_][:,0]).type(torch.long)\n" + "dataset['train_input'] = torch.from_numpy(X[train_id_]).type(dtype).to(device)\n", + "dataset['train_label'] = torch.from_numpy(Y[train_id_][:,0]).type(torch.long).to(device)\n", + "dataset['test_input'] = torch.from_numpy(X[test_id_]).type(dtype).to(device)\n", + "dataset['test_label'] = torch.from_numpy(Y[test_id_][:,0]).type(torch.long).to(device)\n" ] }, { @@ -94,7 +95,7 @@ "def test_acc():\n", " return torch.mean((torch.argmax(model(dataset['test_input']), dim=1) == dataset['test_label']).float())\n", "\n", - "model = KAN(width=[n_feature,1,n_class], grid=5, k=3, seed=seed)\n", + "model = KAN(width=[n_feature,1,n_class], grid=5, k=3, seed=seed, device=device)\n", "model.fit(dataset, lamb=0.005, batch=1024, loss_fn = nn.CrossEntropyLoss(), metrics=[train_acc, test_acc], display_metrics=['train_loss', 'reg', 'train_acc', 'test_acc']);" ] }, @@ -145,7 +146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_15_knot_unsupervised.ipynb b/tutorials/Example_15_knot_unsupervised.ipynb index 4f443d4f..95ecd68b 100644 --- a/tutorials/Example_15_knot_unsupervised.ipynb +++ b/tutorials/Example_15_knot_unsupervised.ipynb @@ -39,6 +39,8 @@ "from kan import *\n", "import copy\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", "\n", "seed = 2024\n", "torch.manual_seed(seed)\n", @@ -79,11 +81,11 @@ "dataset['contrastive_train_input'] = construct_contrastive_dataset(dataset['train_input'])\n", "dataset['contrastive_test_input'] = construct_contrastive_dataset(dataset['test_input'])\n", "\n", - "dataset['train_label'] = torch.cat([torch.ones(dataset['train_input'].shape[0],1), torch.zeros(dataset['contrastive_train_input'].shape[0],1)], dim=0)\n", - "dataset['train_input'] = torch.cat([dataset['train_input'], dataset['contrastive_train_input']], dim=0)\n", + "dataset['train_label'] = torch.cat([torch.ones(dataset['train_input'].shape[0],1), torch.zeros(dataset['contrastive_train_input'].shape[0],1)], dim=0).to(device)\n", + "dataset['train_input'] = torch.cat([dataset['train_input'], dataset['contrastive_train_input']], dim=0).to(device)\n", "\n", - "dataset['test_label'] = torch.cat([torch.ones(dataset['test_input'].shape[0],1), torch.zeros(dataset['contrastive_test_input'].shape[0],1)], dim=0)\n", - "dataset['test_input'] = torch.cat([dataset['test_input'], dataset['contrastive_test_input']], dim=0)\n" + "dataset['test_label'] = torch.cat([torch.ones(dataset['test_input'].shape[0],1), torch.zeros(dataset['contrastive_test_input'].shape[0],1)], dim=0).to(device)\n", + "dataset['test_input'] = torch.cat([dataset['test_input'], dataset['contrastive_test_input']], dim=0).to(device)\n" ] }, { @@ -101,7 +103,7 @@ "def test_acc():\n", " return torch.mean(((model(dataset['test_input']) > 0.5) == dataset['test_label']).float())\n", "\n", - "model = KAN(width=[n_feature,1,1], grid=5, k=3, seed=seed)\n", + "model = KAN(width=[n_feature,1,1], grid=5, k=3, seed=seed, device=device)\n", "model.fix_symbolic(1,0,0,'gaussian',fit_params_bool=False)\n", "model.fit(dataset, lamb=0.001, batch=1024, metrics=[train_acc, test_acc], display_metrics=['train_loss', 'reg', 'train_acc', 'test_acc']);" ] @@ -153,7 +155,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_1_function_fitting.ipynb b/tutorials/Example_1_function_fitting.ipynb index 6cfe89ff..ba369ab8 100644 --- a/tutorials/Example_1_function_fitting.ipynb +++ b/tutorials/Example_1_function_fitting.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "id": "0a59179d", "metadata": {}, "outputs": [ @@ -28,6 +28,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -36,21 +37,16 @@ "source": [ "from kan import *\n", "\n", - "seed = 0\n", - "torch.manual_seed(seed)\n", "\n", - "# high precision\n", - "torch.set_default_dtype(torch.float64)\n", - "\n", - "# for reproducibility, the following line should be included (otherwise LBFGS is non-deterministic)\n", - "torch.use_deterministic_algorithms(True)\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", "\n", "# initialize KAN with G=3\n", - "model = KAN(width=[2,1,1], grid=3, k=3, seed=seed)\n", + "model = KAN(width=[2,1,1], grid=3, k=3, seed=1, device=device)\n", "\n", "# create dataset\n", "f = lambda x: torch.exp(torch.sin(torch.pi*x[:,[0]]) + x[:,[1]]**2)\n", - "dataset = create_dataset(f, n_var=2)" + "dataset = create_dataset(f, n_var=2, device=device)" ] }, { @@ -63,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "id": "a87b97b0", "metadata": {}, "outputs": [ @@ -71,7 +67,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.58e-02 | test_loss: 1.51e-02 | reg: 9.10e+00 | : 100%|█| 20/20 [00:03<00:00, 6.53it" + "| train_loss: 4.16e-02 | test_loss: 4.35e-02 | reg: 9.79e+00 | : 100%|█| 20/20 [00:03<00:00, 6.03it" ] }, { @@ -138,7 +134,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.87e-04 | test_loss: 3.21e-04 | reg: 9.62e+00 | : 100%|█| 20/20 [00:02<00:00, 7.53it" + "| train_loss: 6.96e-03 | test_loss: 6.10e-03 | reg: 9.75e+00 | : 100%|█| 20/20 [00:02<00:00, 7.32it" ] }, { @@ -170,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "a1c25e8a", "metadata": {}, "outputs": [ @@ -186,7 +182,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.44e-02 | test_loss: 1.50e-02 | reg: 8.89e+00 | : 100%|█| 200/200 [00:11<00:00, 18.06\n" + "| train_loss: 1.46e-02 | test_loss: 1.53e-02 | reg: 8.83e+00 | : 100%|█| 200/200 [00:10<00:00, 19.67\n" ] }, { @@ -201,7 +197,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.74e-04 | test_loss: 3.09e-04 | reg: 8.90e+00 | : 100%|█| 200/200 [00:11<00:00, 18.04\n" + "| train_loss: 2.84e-04 | test_loss: 3.29e-04 | reg: 8.84e+00 | : 100%|█| 200/200 [00:15<00:00, 13.09\n" ] }, { @@ -216,7 +212,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.74e-05 | test_loss: 5.64e-05 | reg: 8.89e+00 | : 100%|█| 200/200 [00:21<00:00, 9.12\n" + "| train_loss: 4.21e-05 | test_loss: 4.04e-05 | reg: 8.84e+00 | : 100%|█| 200/200 [00:09<00:00, 21.22\n" ] }, { @@ -231,7 +227,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.63e-04 | test_loss: 1.04e-03 | reg: 8.91e+00 | : 100%|█| 200/200 [00:54<00:00, 3.69\n" + "| train_loss: 1.02e-05 | test_loss: 1.24e-05 | reg: 8.84e+00 | : 100%|█| 200/200 [00:10<00:00, 18.76\n" ] }, { @@ -246,7 +242,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 8.62e-05 | test_loss: 2.12e-03 | reg: 8.92e+00 | : 100%|█| 200/200 [01:03<00:00, 3.14" + "| train_loss: 1.64e-04 | test_loss: 1.74e-03 | reg: 8.86e+00 | : 100%|█| 200/200 [00:17<00:00, 11.72" ] }, { @@ -267,8 +263,6 @@ "source": [ "grids = np.array([3,10,20,50,100])\n", "\n", - "seed = 1\n", - "torch.manual_seed(seed)\n", "\n", "train_losses = []\n", "test_losses = []\n", @@ -277,7 +271,7 @@ "\n", "for i in range(grids.shape[0]):\n", " if i == 0:\n", - " model = KAN(width=[2,1,1], grid=grids[i], k=k, seed=seed)\n", + " model = KAN(width=[2,1,1], grid=grids[i], k=k, seed=1, device=device)\n", " if i != 0:\n", " model = model.refine(grids[i])\n", " results = model.fit(dataset, opt=\"LBFGS\", steps=steps)\n", @@ -296,13 +290,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "156f68a2", "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGwCAYAAABFFQqPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABSDklEQVR4nO3deXxU5d0+/uvMmn2yBwIkgIISA1gCKuICakFQVKhWfCyLS3+1xCrl0Vq1X1upQh8Xitpo1T5FrfURbXFpXSBWBJQ9AiJREFkSICFknawzmZn798eZmWSSDJDknDkzZ67365VXMjMnM58cMXPlc9/nviUhhAARERFRFDJoXQARERGRVhiEiIiIKGoxCBEREVHUYhAiIiKiqMUgRERERFGLQYiIiIiiFoMQERERRS2T1gWEO4/Hg+PHjyMxMRGSJGldDhEREZ0BIQQaGxuRnZ0NgyF434dB6DSOHz+OIUOGaF0GERER9UF5eTkGDx4c9HEGodNITEwEIJ/IpKQkjashIiKiM2G32zFkyBD/+3gwDEKn4RsOS0pKYhAiIiKKMKeb1sLJ0kRERBS1GISIiIgoajEIERERUdTiHCEiIiKNuN1utLe3a11GRDKbzTAajf1+HgYhIiKiEBNCoLKyEvX19VqXEtGSk5MxYMCAfq3zxyAURFFREYqKiuB2u7UuhYiIdMYXgjIzMxEXF8cFe3tJCIGWlhZUVVUBAAYOHNjn55KEEEKpwvTIbrfDZrOhoaGBl88TEVG/ud1u7N+/H5mZmUhLS9O6nIhWU1ODqqoqjBw5stsw2Zm+f3OyNBERUQj55gTFxcVpXEnk853D/syzYhAiIiLSAIfD+k+Jc8ggRERERFGLQYiIiIiiFoMQERERhdzQoUOxYsUKrcvg5fNaqWlyoMXpRkq8BQlW/mcgIqLwN3nyZJx//vmKBJjt27cjPj6+/0X1EztCGrnv7d249Il1+GhPhdalEBERKUIIAZfLdUbHZmRkhMWVcwxCGplf+wz+Y/lvDDherHUpRESkMSEEWpwuTT7OdDnBBQsWYP369XjmmWcgSRIkScIrr7wCSZKwZs0ajB8/HlarFRs3bsT333+P66+/HllZWUhISMCECRPwySefBDxf16ExSZLwl7/8BbNmzUJcXBxGjBiB999/X8nT3COOyWgk1V2DswwVqHLUa10KERFprLXdjbxH1mjy2qVLpiHOcvo48Mwzz2D//v3Iz8/HkiVLAAB79+4FAPzqV7/CU089heHDhyM5ORlHjx7FjBkz8NhjjyEmJgavvvoqZs6ciX379iEnJyfoazz66KN44okn8OSTT+K5557DrbfeiiNHjiA1NVWZH7YH7AhpxGPw/qNzn1kLkYiISEs2mw0WiwVxcXEYMGAABgwY4F/NecmSJfjhD3+Is846C2lpaRg7dix+9rOfYfTo0RgxYgQee+wxDB8+/LQdngULFuCWW27B2WefjaVLl6K5uRnbtm1T9ediR0gjQpJPvfAwCBERRbtYsxGlS6Zp9tr9NX78+IDbzc3NePTRR/Hvf/8bx48fh8vlQmtrK8rKyk75PGPGjPF/HR8fj8TERP9+YmphENKIMHj/4Xn6viw4ERHpgyRJZzQ8Fa66Xv11//33Y82aNXjqqadw9tlnIzY2FjfeeCOcTucpn8dsNgfcliQJHo9H8Xo7i9yzHuF8HSF4uLs9ERFFBovFArf79O9bGzduxIIFCzBr1iwAQFNTEw4fPqxydX3DOUIaEd45QsLNjhAREUWGoUOHYuvWrTh8+DCqq6uDdmvOPvtsrF69Grt27cLu3bvxX//1X6p3dvqKQUgrvsnSnCNEREQR4r777oPRaEReXh4yMjKCzvn54x//iJSUFFx88cWYOXMmpk2bhnHjxoW42jPDoTGNeBiEiIgowowcORKbN28OuG/BggXdjhs6dCg+/fTTgPsKCwsDbncdKutpPaP6+vo+1dkb7AhpRWIQIiIi0hqDkFa8V41JDEJERESaYRDSiDB6LxFkECIiItJMVAShWbNmISUlBTfeeKPWpXTwDo2xI0RERKSdqAhC99xzD1577TWtywjEoTEiIiLNRUUQmjJlChITE7UuI4DkGxoTXFCRiIhIK5oHoQ0bNmDmzJnIzs6GJEl49913ux3z/PPPY9iwYYiJiUFBQQE2btwY+kKVZuDQGBERkdY0D0LNzc0YO3Ys/vSnP/X4+KpVq7Bo0SI8/PDD2LlzJy699FJMnz49YBGngoIC5Ofnd/s4fvx4qH6MXpOMchAyCAYhIiIirWi+oOL06dMxffr0oI8vX74cd9xxB+68804AwIoVK7BmzRq88MILWLZsGQCgpKREsXocDgccDof/tt1uV+y5A3iHxiTuNUZERKQZzTtCp+J0OlFSUoKpU6cG3D916lRs2rRJlddctmwZbDab/2PIkCGqvI7kGxpjR4iIiCLE5MmTsWjRIsWeb8GCBbjhhhsUe76+COsgVF1dDbfbjaysrID7s7KyUFlZecbPM23aNNx000348MMPMXjwYGzfvj3osQ8++CAaGhr8H+Xl5X2u/5QMHBojIiLSWlgHIR9JkgJuCyG63Xcqa9aswcmTJ9HS0oKjR49iwoQJQY+1Wq1ISkoK+FCDweQdGuNVY0REFAEWLFiA9evX45lnnoEkSZAkCYcPH0ZpaSlmzJiBhIQEZGVlYe7cuaiurvZ/3z/+8Q+MHj0asbGxSEtLw1VXXYXm5mb87ne/w6uvvor33nvP/3yfffZZyH8uzecInUp6ejqMRmO37k9VVVW3LlGk8Q2NGXjVGBERCQG0t2jz2uY44AyaC8888wz279+P/Px8LFmyBADgdrtx+eWX46c//SmWL1+O1tZWPPDAA/jxj3+MTz/9FBUVFbjlllvwxBNPYNasWWhsbMTGjRshhMB9992Hb775Bna7HStXrgQApKamqvqj9iSsg5DFYkFBQQGKi4sxa9Ys//3FxcW4/vrrVX3toqIiFBUVwe1Wp2PTcdUYO0JERFGvvQVYmq3Naz90HLDEn/Ywm80Gi8WCuLg4DBgwAADwyCOPYNy4cVi6dKn/uL/+9a8YMmQI9u/fj6amJrhcLsyePRu5ubkAgNGjR/uPjY2NhcPh8D+fFjQPQk1NTThw4ID/9qFDh7Br1y6kpqYiJycHixcvxty5czF+/HhMnDgRL730EsrKynDXXXepWldhYSEKCwtht9ths9kUf37fgooMQkREFKlKSkqwbt06JCQkdHvs+++/x9SpU3HllVdi9OjRmDZtGqZOnYobb7wRKSkpGlTbM82D0I4dOzBlyhT/7cWLFwMA5s+fj1deeQU333wzampqsGTJElRUVCA/Px8ffvihP1lGKn8QAofGiIiinjlO7sxo9dp95PF4MHPmTPzP//xPt8cGDhwIo9GI4uJibNq0CWvXrsVzzz2Hhx9+GFu3bsWwYcP6U7ViNA9CkydPhhDilMcsXLgQCxcuDFFFoWFgR4iIiHwk6YyGp7RmsVgCpoyMGzcO//znPzF06FCYTD1HCkmSMGnSJEyaNAmPPPIIcnNz8c4772Dx4sXdnk8LEXHVmBaKioqQl5d3yivM+sPgnSNkZBAiIqIIMXToUGzduhWHDx9GdXU1CgsLUVtbi1tuuQXbtm3DwYMHsXbtWtx+++1wu93YunUrli5dih07dqCsrAyrV6/GyZMnMWrUKP/zffXVV9i3bx+qq6vR3t4e8p+JQSiIwsJClJaWnnLNof7wXT5v5NAYERFFiPvuuw9GoxF5eXnIyMiA0+nEF198AbfbjWnTpiE/Px/33nsvbDYbDAYDkpKSsGHDBsyYMQMjR47Eb37zGzz99NP+HSV++tOf4pxzzsH48eORkZGBL774IuQ/k+ZDY9GKk6WJiCjSjBw5Eps3b+52/+rVq3s8ftSoUfj444+DPl9GRgbWrl2rWH19wY6QRoy+jhCDEBERkWYYhDTinyMEBiEiIiKtMAgFofpkaZMFAIMQERGRlhiEglB7srTR3xHiZGkiIiKtMAhppGOOkEfjSoiISAunW0OPTk+Jc8ggpBGDmUNjRETRyGyW/xBuadFok1Ud8Z1D3zntC14+rxGTd2jMzCBERBRVjEYjkpOTUVVVBQCIi4uDdAa7v1MHIQRaWlpQVVWF5ORkGI3GPj8Xg5BGOhZUdEMIwf8JiIiiiG+3dV8Yor5JTk7u9871DEJBFBUVoaioSLU9UEzeq8ZMkgftbg/Mpr6nWSIiiiySJGHgwIHIzMzUZFsJPTCbzf3qBPlIgrO1Tslut8Nms6GhoQFJSUmKPW9zfTXiV5wFAGj79QnExMQo9txERETR7kzfvzlZWiPGTrv0trc7NayEiIgoejEIacTkvWoMANwuriVERESkBQYhjfjWEQIAt4sdISIiIi0wCGlEMnQMjbldnChHRESkBQYhrUgS2oU8293FjhAREZEmGISCUHvTVQBwe0+/h3OEiIiINMEgFITam64CgEuSh8dcHBojIiLSBIOQhtyQh8YEgxAREZEmGIQ05Bsac7sZhIiIiLTAIKQht3eHE141RkREpA0GIQ25JXlozMMgREREpAkGIQ15fHOE3LxqjIiISAsMQhrydYQ4R4iIiEgbDEIa4lVjRERE2mIQCiIUCyp6vOsIcfd5IiIibTAIBRGKBRWFUd6Bvrm5UbXXICIiouAYhDTUFpMBAHA1HNe4EiIioujEIKQhZ/xAAIChsULjSoiIiKITg5CGRGI2AMDSUqlxJURERNGJQUhD5mQ5CMU7qjSuhIiIKDoxCGkoNj0HAJDsOqlxJURERNGJQUhDiZm5AIB0Tw08bo/G1RAREUUfBiENpWTJQShecqC+rlrjaoiIiKIPg5CGLLHxqEcCAKC+6ojG1RAREUUfBiGNNUpJAABnY53GlRAREUUfBqEgQrHFBgC0GeIAAO2tdlVfh4iIiLpjEAoiFFtsAIDDKAchV0uDqq9DRERE3TEIacxpjAcAuNkRIiIiCjkGIY25TPJkaeFgECIiIgo1BiGNuUxyRwgO7kBPREQUagxCGvNY5I6Q5GQQIiIiCjUGIY15LIkAAIOzWeNKiIiIog+DkNaschAytjdpXAgREVH0YRDSmDFGXlDRxCBEREQUcgxCGrPGe4OQi0GIiIgo1BiENBabkAwAsLg5R4iIiCjUGIQ0Fm9LAQDEelo0roSIiCj6MAhpLDEpFQAQJ1rg8QiNqyEiIoouDEIaS7TJQSgerbC3OjWuhoiIKLowCGnMEidPljZKAnUN3HiViIgolBiEtGaJh9v7n6GxvlrjYoiIiKILg1AQRUVFyMvLw4QJE9R9IUlCkyRvs9HMIERERBRSDEJBFBYWorS0FNu3b1f9tZpNyQCAtoYq1V+LiIiIOjAIhQGHyQYAcDbWaFwJERFRdGEQCgPt1mQAgKeFQYiIiCiUGITCgDtGvoRetNRqXAkREVF0YRAKAyI+HQBgbeUcISIiolBiEAoDnpSzAADpbUc0roSIiCi6MAiFAWPWKADAYBeDEBERUSgxCIUBa3YeACBN1AGcJ0RERBQyJq0LICA9LQ2VIgUDpDq4X7sBxlEzAZMFcLcDbfWA0QrYjwODxgGSAXA2ARW7AWsSkP0DwH4MiM8ALPFA6nAg5yKtfyQiIqKIwCAUBpJizDhoGIgBog7Gyt1A5e6eD9z9Rvf7SlZ2v89gAgzmjtuSpEyhkSrtbOD2NYAlTutKiIgozDAIhYnNGT9CYmUdDops2KUExBoFbIZWGFxtMJhMiHM3wWmMQ7PHDEm4cQ7KkIkaVEnpyBTy1hweGGCAB/C45A+SVX4FVH0DDC7QuhIiIgozDEJh4vxpt2He2+NwrL61+4OOU3+vBA8S0IZGxCEFdsRLp/mGKLLKsgSDpBpUNbYiU+tiiIgo7DAIhYmJZ6Xhi19fgbZ2N2qbnf6PhBgTWp1uOF0emI0GuIVAeoIF1U1OSAAOnmxChb0NVXYHnC4PjIZs1LU4AQBCdDy/QMeNzvfrXftReYiwvolBiIiIumMQCjMxZiOyk2ORnRx7RsdfNjJD5YoiW9mjBkAAwuPWuhQiIgpDvHyedM3j/Sfu8Xg0roSIiMIRgxDpmpC8QcjNjhAREXXHIES6Jrz/xIVgECIiou4YhEjXfB0hwY4QERH1gEGIdM0/R4gdISIi6gGDEOmavyPEydJERNQDBiHSNV9HCLx8noiIeqD7IFReXo7JkycjLy8PY8aMwdtvv611SRRKEi+fJyKi4HS/oKLJZMKKFStw/vnno6qqCuPGjcOMGTMQHx+vdWkUAh0dIe69RkRE3ek+CA0cOBADBw4EAGRmZiI1NRW1tbUMQtFCkgCwI0RERD3TfGhsw4YNmDlzJrKzsyFJEt59991uxzz//PMYNmwYYmJiUFBQgI0bN/bptXbs2AGPx4MhQ4b0s2qKFB7JKH/Bq8aIiKgHmneEmpubMXbsWNx222340Y9+1O3xVatWYdGiRXj++ecxadIkvPjii5g+fTpKS0uRk5MDACgoKIDD0X3H9bVr1yI7OxsAUFNTg3nz5uEvf/nLKetxOBwBz2W32/vz45Hm5I4QrxojIqKeaB6Epk+fjunTpwd9fPny5bjjjjtw5513AgBWrFiBNWvW4IUXXsCyZcsAACUlJad8DYfDgVmzZuHBBx/ExRdffMpjly1bhkcffbSXPwWFK+HtCAnOESIioh5oPjR2Kk6nEyUlJZg6dWrA/VOnTsWmTZvO6DmEEFiwYAGuuOIKzJ0797THP/jgg2hoaPB/lJeX96l2Cg9cR4iIiE5F847QqVRXV8PtdiMrKyvg/qysLFRWVp7Rc3zxxRdYtWoVxowZ459/9Le//Q2jR4/u8Xir1Qqr1dqvuil8MAgREdGphHUQ8pG8V/74CCG63RfMJZdcwiuGophv01VOliaiXnG3A1WlQNZowBDWgyfUT2H9Xzc9PR1Go7Fb96eqqqpbl0hpRUVFyMvLw4QJE1R9HVKZxCBERH3w3t3Ai5cBny/XuhJSWVgHIYvFgoKCAhQXFwfcX1xcfNpJz/1VWFiI0tJSbN++XdXXIXVxaIyI+uSrN+XPG5/Wtg5SneZDY01NTThw4ID/9qFDh7Br1y6kpqYiJycHixcvxty5czF+/HhMnDgRL730EsrKynDXXXdpWDVFio6rxtgRIqI+cDu1roBUpnkQ2rFjB6ZMmeK/vXjxYgDA/Pnz8corr+Dmm29GTU0NlixZgoqKCuTn5+PDDz9Ebm6uViVTJPHNJRPsCBFRH3DpDd3TPAhNnjwZQohTHrNw4UIsXLgwRBWRnnR0hBiEiIiou7CeI6QlTpbWCU6WJiKiU2AQCoKTpfXB1xEC5wgREVEPGIRI13zrTQnOESIioh4wCJGu+TpCEjtCRETUAwYh0jffOkLsCBERUQ8YhILgZGl9EP7J0gxCRETUHYNQEJwsrQ8SgxAREZ0CgxDpmjDwqjHSmYajQPk2ravQvXZh1LoEChEGIdI1doRId/54HvC/PwQqvtK6El1zgUEoWjAIkb751hHigoqkN+Vbta5A19q133iBQoRBiPSNHSHSK+6Bpap2doSiBoNQELxqTB98c4QkBiHSG857UxWHxqIHg1AQvGpMH/xzhLjpKumNp13rCnSNQ2PRg0GI9I2brpJOlVc3al2CrjWK2I4b7L7pGoMQ6ZuBc4RIn2oaW7QuQddOiuSOG80nNauD1McgRPomcY4Q6ZPEydKqEpA6btiPaVcIqY5BiHRNMnCvMdIpDveqSoLouNFwVLtCSHUMQqRrBoN3wiPH+ElvOFlaVQFBaN/H2hVCqmMQCoKXz+uDySQPjXkYhEhnJP6bVlVAEDpQDAgR/GCKaAxCQfDyeX0wGdkRIp3i0JiqDJ2DUPNJoKFcu2JIVQxCpGtGX0eIc4RIZyQ3J0urSep6x8H1WpRBIcAgRLpmNFkBAAY351OQzggGITUZJPmPp288OfId336gYTWkJgYh0jWDNR4AYBFtGldCpCwuCREa6zzny18c2cQhdp1iECJdM8YkAACsnlaNKyFSlsSrxlTlmyy91zMUsCQCjgag6httiyJVMAiRrpliEgEAMYJBiPRF4mRpVfkmS7tgBLLPl+88vlO7gkg1DEKka76OUAyHxkhnePm8unwdIQEA2T+Q72QQ0iUGIdI1c6wchGLRBsF1QEhHJE6WVpUvCHlg6OgIVezSrB5SD4NQEFxQUR8scfLQWBwccLg4uZT0gx0hdRl66ghV7gFcTs1qInUwCAXBBRX1wRqXBACIRxscTr5xkH4YOEdIZb4gJAEpw4C4NMDtZFdIhxiESNdM3jlCBkmgra1J42qIlMOhMXUZ/ENjEiBJQO4k+YHv12lYFamBQYh0TTLH+7/2OJo1rIRIWVxHSF0de41515g+Z7r8edfrgMuhSU2kDgYh0jeDAQ5hBgB4nLyEnvSDHSF1+bbY8Pi+yrsBiM8E6suADU9pVRapgEGIdM8tyf/M3S4uQEf6YWAQUpWh8xwhALDEATOelL/+fDlwYq9GlZHSGIRI91yQd6D3cJNK0pGhraWAh8NjapG6BiEAyLseOPdawOMC1jysUWWkNAYh0j035B3o3bzslfSm5jutK9AtqfNkaf+dEjBtKSAZgIPrgD3/0Kg6UhKDEOme2/vPXLAjRDrQJGI6btR8r10hOtdjRwgAUnKB8XfIX3/8INDOuYeRrldBaNu2bXC7O9au6LpSr8PhwFtvvaVMZUQKcUvy0JibQYj0ppZBSC1BgxAgd4VsQ4DmKmDn6yGujJTWqyA0ceJE1NTU+G/bbDYcPHjQf7u+vh633HKLctURKcA3NCY4NEY60HFZN9gRUpF/srToIQiZLMCke+WvNy4H7MdDWBkprVdBqGsHqKe9m/SynxO32NAPj3+OEK8ao8gX8LZcc0CrMnRP8p7ooO9oP5gLpAwFGo8DL00BvvlXiCojpSk+R0iSekjPEYhbbOiHW/J2hDg0RnpTe/D0x1CfGCBfkefpaWgMAMwxwPx/AekjgaZKYNVPgFVzgaaqEFZJSuBkadI9XxDyuNkRosgXMDRmPwbUHdasFj3zxZ8e5wj5JOcAP9sAXPrfgMEEfPM+8JergOaa4N9DYcfU228oLS1FZWUlAHkY7Ntvv0VTk7yHU3V1tbLVESnANzTGjhDpgS8IVZgGYaDrGFD6Xsd8FVLMKSdLd2aOBa58BDhvFvDmrUD9EeDVa4HbPgJik9UvlPqt10HoyiuvDJgHdO211wKQh8SEELoZGiP98Pg6Qh4GIdKPryzj5CB07EutS9GlbitLn86A0cB/rQJeuwGoKgU+vB/40cvqFUiK6VUQOnTokFp1EKnGd/k83LxqjCKf7235gGkEpgFAxW4Nq9GvM+4IdZY5Cpjzd+B/fwjseUvuEp07Q6UKSSm9CkK5ublq1UGkGn9HiENjpCPlxsHyFw3lgMcNGIzaFqQz3TZdPVODxwMT7wY2PQsU/z9513qOlIS1Xk2Wrq2txdGjRwPu27t3L2677Tb8+Mc/xhtvvKFocURKELxqjHRF7lTUSimAZJT3veKVSoozSPJVY73qCPlc/gBgjpeXN9j2ksKVkdJ6FYQKCwuxfPly/+2qqipceuml2L59OxwOBxYsWIC//e1vihdJ1B8e79AYgxDpgW/IptUtAYkD5TvtxzSsSJ86rhrrA2tCxwT2j34FlG9TqCpSQ6+C0JYtW3Ddddf5b7/22mtITU3Frl278N5772Hp0qUoKipSvEii/vD45wjx8nnSD6fLA9gGyTcYhBTXMVm6j6vMXP4rYMwc+eu1/w/QyWLDetSr/8KVlZUYNmyY//ann36KWbNmwWSS32iuu+46fPcdd0Om8OIfGuNVY6QDvk6F0+0BkrLlGw0MQkrrmCzd1yeQgKt+C5higfItwFvzAHuFYvWRcnoVhJKSklBfX++/vW3bNlx00UX+25IkweFwKFYckRI8DEKkI743aIdLAEnsCKmlT1eNdZWULXeGAHmxxWd/AGwuYncozPTqqrELLrgAzz77LF5++WWsXr0ajY2NuOKKK/yP79+/H0OGDFG8SKL+EAYOjZH+OFwCsHmvHGMQUpxvaKzXV411deliYPAE4NPH5M7QmoeArS8CMUkKVKkjV/8PMHSSJi/dqyD0+9//HldddRVef/11uFwuPPTQQ0hJSfE//uabb+Lyyy9XvEii/hC+OUIet7aFECnA16lo59CYqs5oi40zNexS4PaPgS+eAdYtlVefpkCORs1euldB6Pzzz8c333yDTZs2YcCAAbjwwgsDHp8zZw7y8vIULZCov3wdIQ6NkZ60uTxAEjtCajFIHkDIHSFFdk2QJOCSRcDYOcCJvYrUqCsDx2r20r3eYiMjIwPXX399j49dc801/S4oXBQVFaGoqAhuN7sIkc43WVrycGiMIp9/oT8hdVw11lgJuF2Asde/0imIzh2hdreAxaTQooiJA+QPChu9+r/mtddeO6Pj5s2b16diwklhYSEKCwtht9ths9m0Lof6wWPg0Bjph0HqNNE2PkPe9dzjAppOdAQj6jep0/Vi7W4PLKY+XkZPYa9XQWjBggVISEiAyWQK2Hi1M0mSdBGESEf8c4Q4NEb6IQB5W43EbKChTB4eYxBSTMdkaYM8H4t0q1cRd9SoUbBYLJg3bx7Wr1+Purq6bh+1tbVq1UrUJ8LAoTHSJ49HcFFF1XSsI9Tu5uXuetarILR371588MEHaG1txWWXXYbx48fjhRdegN1uV6s+on4TBrP8BTtCFOk6deIFJC6qqCKJHaGo0etBzwsvvBAvvvgiKioqcM899+Ctt97CwIEDceutt3IxRQpP3jlCEtcRIh0RANra3VxUUSVSQEeIQUjP+jz7KzY2FvPmzcOjjz6KCy64AG+++SZaWlqUrI1IER6jFQBg8DCoU4TrMjfT4fJ0LKrYcFSDgvRLEh0rSzMI6VufgtCxY8ewdOlSjBgxAnPmzMGECROwd+/egMUVicKGLwixI0QRL3BozNHuAeLT5Tta6zSqSZ/8HSEhweniHCE969VVY2+99RZWrlyJ9evXY9q0aXj66adxzTXXwGg0qlUfUf+ZfB0hp8aFEClHQEKbyw1YvVs1ODhXU0md9xpjR0jfehWE5syZg5ycHPzyl79EVlYWDh8+jKKiom7H3XPPPYoVSNRvviDk5tAYRbiuQ2Ptno4g1MYgpCQGoejRqyCUk5MDSZLwxhtvBD1GkiQGIQorBm8QMrIjRDoiADhcbiA2Ub5Dw72a9EjqtOkqL5/Xt14FocOHD5/2mGPHeOUChRlfEBIMQhTpOr8hS2hr9wDJviDEjpCS2BGKHoqtGV5ZWYl77rkHZ599tlJPSaQIyRwDADBwQUWKdN2uGnMDMd6hMbcTcHH4VykMQtGjV0Govr4et956KzIyMpCdnY1nn30WHo8HjzzyCIYPH47Nmzfjr3/9q1q1EvWJwSx3hEwcGiMdEZDky+ctCR13cp6QYgwMQlGjV0NjDz30EDZs2ID58+fj448/xi9/+Ut8/PHHaGtrw0cffYTLL79crTqJ+sw3R8jEoTGKeIEdobZ2t7zfmCUBcDbJw2MJGRrVpiMBK3gDTs4R0rVedYQ++OADrFy5Ek899RTef/99CCEwcuRIfPrppwxBFLaMllgAgElwaIwimxAdnQl5srT3dkKW/LmhPPRF6VGnIOSBBKeLHSE961UQOn78OPLy8gAAw4cPR0xMDO68805VCiNSim9ozMyOEOmIvKCiW76Rca78+eQ+7QrSlcCFK9t855l0qVdByOPxwGw2+28bjUbEx8crXhSRkoz+IMSOEEU24ekyNObrVGScI38++W2IK9KpgM6bhBYnN2zWs17NERJCYMGCBbBa5TeWtrY23HXXXd3C0OrVq5WrkKiffENjZjAIkX74t9gAgLSz5M91R7QrSE9EYEeo2cGOkJ71KgjNnz8/4PZPfvITRYshUoPRe/m8GfyrjiKbQOAkXofL+wadnCN/ri8LfVG61DUI8XeHnvUqCK1cuVKtOohUY7b6OkIuwOO9yoYoEnVZR6jVN3fFNkT+XF8GeDyAQbEl4qJTp6ExDyQ0O9kR0jP+30K6Z4y1ddxoa9CuEKJ+EiLIJF7bYMAcB7gdwM6/aVSdjnQ5z5wjpG+6D0KNjY2YMGECzj//fIwePRovv/yy1iVRiFmsVthFnHyjpUbbYoj6JbAj1OLrVBjNwORfy1//+5fAF8/InSHqo8AhSM4R0rdeDY1Fori4OKxfvx5xcXFoaWlBfn4+Zs+ejbS0NK1LoxAxGw2oFYlIklogmqshpY/QuiSiPunaEWrtPGQz8W7gRCnw1ZtA8SPAN/8GLvj/5C04PC550cXkHMAcCyQO0KD6CBIwNGbgHCGd030QMhqNiIuTuwFtbW1wu90Bv0xI/+IsRhxDAoATcDRWI0brgoj6qOuvrtbO69sYjMCsPwO5E4GPHwSObpM/ehKbCqTkAgYzIEny5fcX3wPEJKtWe0RxNgbc5NCYvmkehDZs2IAnn3wSJSUlqKiowDvvvIMbbrgh4Jjnn38eTz75JCoqKnDeeedhxYoVuPTSS8/4Nerr63H55Zfju+++w5NPPon09HSFfwoKZ3EWI+ohb0zZUlfFIEQRLMjQmI8kAQULgBHTgM//CBzbIacnySBPpHY0Aq5WoLVW/vAp3wp8+Zr65UcgwcnSuqd5EGpubsbYsWNx22234Uc/+lG3x1etWoVFixbh+eefx6RJk/Diiy9i+vTpKC0tRU6OfMloQUEBHI7uuy6vXbsW2dnZSE5Oxu7du3HixAnMnj0bN954I7Kysnqsx+FwBDyX3c5NDCOdJEloNSUBHsBhP6l1OUR91nVorFsQ8kkaCMx4oufHmquB6u/kfcnc7YCzGdj1OnD484AhIQL+4/4BnDBxaEznNA9C06dPx/Tp04M+vnz5ctxxxx3+rTxWrFiBNWvW4IUXXsCyZcsAACUlJWf0WllZWRgzZgw2bNiAm266qcdjli1bhkcffbSXPwWFu3ZzIuAAHC28aoz0QZ4j1Ic36Ph0+aOzsTcrU5SOXPH0Zzh4shkAGIR0LqyvGnM6nSgpKcHUqVMD7p86dSo2bdp0Rs9x4sQJf1fHbrdjw4YNOOecc4Ie/+CDD6KhocH/UV7OTQx1wZIAAHC1ssNHkavr/MZW7oGlnk6nusXJuaV6pnlH6FSqq6vhdru7DWNlZWWhsrLyjJ7j6NGjuOOOOyCEgBACd999N8aMGRP0eKvV6t9ChPRDsiYCjYC7tfH0BxNFAIEe5giRYjrHHpdHwOn2wGriYqx6FNZByEeSpIDbQohu9wVTUFCAXbt2qVAVRRSr3BGSnE0aF0LUD6e6fJ4U1bUD1OxwMwjpVFgPjaWnp8NoNHbr/lRVVQWd7KyUoqIi5OXlYcKECaq+DoWG5B0aM7YzCFHk6vrm7PIIhiGVdB0I4zwh/QrrIGSxWFBQUIDi4uKA+4uLi3HxxRer+tqFhYUoLS3F9u3bVX0dCg0pJhEAYHQ1a1xJFGk8Aez5B7B/TfcFcKjfBOSueHVT9ytmSXkchtQvzYfGmpqacODAAf/tQ4cOYdeuXUhNTUVOTg4WL16MuXPnYvz48Zg4cSJeeukllJWV4a677tKwaoo0Bm8QMrtaNK4kSuz6P+D9XwCedvn2zX8HRl2rbU06IDpd3p4WZ8bJFjeqGh0YkhqnYVX61DW7N3NRRd3SPAjt2LEDU6ZM8d9evHgxAGD+/Pl45ZVXcPPNN6OmpgZLlixBRUUF8vPz8eGHHyI3N1erkikCmWPlIGTxMAiprvZQYAgCgNJ3GYQU0fHunJ4Yg5MtzewIqUR0GRxramMQ0ivNg9DkyZNPe1niwoULsXDhwhBVRHpkipNXlra6GYRU9+ljcggadrm8bcPffwRUfq11VbrQ+VdlemIMcKIZJxsZhNTgO9cxZgPa2j2ob20/9TdQxArrOUJa4mRpfbHEJQMAYgWDkKoqvwa+/gcACZj6+46F+9rqtaxKRzqSUEaCvMwHO0Lq8AWhtHj5PNfyPOsWg1AQnCytLzHxNgCAGS7AxV9oqvn6n/LnUdcCA8cCsSny7dY67WrSkc6984wkedc8doTUlZZgAQDUNjs1roTUwiBEUSEmPqnjhoOX0Kvm4Dr587kz5c++IORqA9pbtalJR6ROY2MZiewIhUJavByEahiEdItBiKJCQmwMWoX8Cw1Ori6tCo8bqPpG/nrwePmzNRGQvIvQsSvUb52vGusIQnyDVoNv7mqadwiSHSH9YhCiqBBvNaIJ8lCCu41BSBW1h+TOjykWSBkq3ydJHB5TkK8f5BGSf44Qh8bU4TvXvo4Qg5B+MQgFwcnS+hJvNaFZxAIA2pq5A70qqkrlzxnnAIZOWxHEJsufW+tDXZH+eLsUAkCqd+5KXQvfoNXgG4VMZRDSPQahIDhZWl+sJgNavB0hB4OQOnzDYlnnBd5vks873Oxc9FfnpUYSY8wAgCaHCx4PV+5WC4fG9I9BiKKCJEloNcir7zqa7RpXo1MnvUEo49zA+w3e5crcXJBOKQISEmPk8yoE0NLO7R+U5ltQMa1T542BU58YhChqOA3y0Fh7K+cIqaG59rj8hW1w4ANG7yR1N/+i7rdOb8SxZiNMBnm/scY2LvanNP/QWJz879cjwEUVdYpBiKKG2yD/Qmt3tGlcif4cq29F2TFvEPLNCfIxykM4AVtuUL8ISJAAf1eokds/KM4XOc1GA5K857m2mcO7esQgRFHDY5TH+tsdXF1aafsrG2GTmuUbMcmBD/qHxhiE+ss3XCMgD/f65gmxI6S8ztuZJHu7Qg3sCOkSg1AQvGpMf4TRe/m8kwv7Kc1iMsAGOQi5rcmBD/qHxvgm0n+Bc1R8HSE7O0IqkM+1JAFJsd7z3MrzrEcMQkHwqjH98XiHaISLc1WUZpVciJfkYYNWU2LggxwaU4yvS8GhsdCRJMAWK/8btrPzpksMQhQ1PAZ5aIx7jSnP6uq4Eq9Figt80BeE2BHqP/94jTxJOsU7ZFPHS7sV5zvVEiQkeYcgOTSmTwxCFDV8c4Tg4mRppUkO+Uo8u4hFt9EDA4OQcjqGxiQJSOcO9KrxR87OHSEGIV1iEKKo4QtCEhf2U5zwdtkcMKPF2WVNGw6NKUZ0WlkaYBBSU+fFK5P8Q2McgtQjBiGKGsI7aZdBSHnCJYccF0zBgxDXEeo/0bkjJCE9Uf43fbKR51ZpnQchfR2hhhaGeT1iEKKoIbwdIQODkOI83mEvlzCitWsQ8g+N8a/p/uq4fF6eI5QWz46Q2iSpY78xnmd9YhAKgpfP64/wD43xr2el+YbG2mFEa9ftHriytOJ8QSgjkW/QaulovknITJR/d1Q18jzrEYNQELx8Xn+Ed/NPdoRU4PENjRnR4uzS+TGaAo6hvus8bwUInCPU9THqH9/5lCQgM1H+3VHVyAst9IhBiKKHyTs05mFnQmke7xyhdpi6X1nDoTHFdF5ZGugIQm3tnu5zs6hfOsfKzCT5PJ9sdMDNjVd1h0GIoobkDUJGDztCShNuXxAyotLe5a9mDo0pRwTOEYq3mhBrNgLg8Jji/OsIAWnxFhgkeePVGu43pjsMQhQ9vENjJgYh5bk7rhqraOgahDg0phSph2ZEOucJqUqSJJiMBqR5u29Vdp5nvWEQoqghrAkAAKu7WeNK9Ed0miN0ImhHiEFIKb6OENAxPMZL6JUVuIY3Ok2Y5jwhvWEQoqjhikkHACS46rUtRIeES57/0y6MOFzdEvggV5ZWTE8Tormoojo6T5YGOgUhdoR0h0GIooYnVg5CsaIFaOcO9IryTkB3wYhj9a2o6fymzJWlFcQgFCpdz7TvyrFuc+Ao4jEIUfSISYRDeOerNJ/Utha96TRHCAC2HKzteIybriqm8+7zPhkJnCOkhs6brgLA2Zny0Pru8nqNKiK1MAgFwQUV9cdsMqIaNvkGg5CyvJfGOyFfwfTOzqMdj/mGxjy8fL7/eugIeYdsqjlHSBW+obFJZ8sd5S0Ha3kJvc4wCAXBBRX1x2I0oEqkyDfqjmhbjN54h73iYuThg0+/rcJXR+vlxwxG7zEMQv0lhEf+3Kkj5N8ZvY0dNyWJLqHznAGJiDEb0NruRlltS5DvokjEIERRw2wy4BvPEPlG5VfaFqM33mGvuNhYXDoiHR4BLFi5HSVH6gCD7/J5BqH+6hga6xBvkc9vMxdUVJR/aMybOY0GCSMyEwEA+yobNaqK1MAgRFEj1mzEXjFMvrH1JWDNw8DuVcDuN4F3fi5/TX0ieUOOkEx4/tZxGD3IhtpmJ/7r5S3Yd9L717PHo2GF+iD1MDQWZ5U7bi0OBk0l9TT4NcI7T+j7k02hLYZUZdK6AKJQSYmz4F/uibjNXIyz28uBzX8KPGD3G0BcGjDiKm0KjGTeVaM9BhMSY8x47fYLcOdrO1BypA5//M9B/NkMdoQUIPyfO4bGfB0hbrGhMH9HqONcD06JBQAcq+dVp3rCjhBFjZR4M+yIx7WOx+C54UVg/B3A4AlA9riOg964CdjzD+2KjFCSd46Q8A6DpcRb8OrtF+CqUVloF95fMwxCCui6zB8Q7+0INXfd7JYUIXX6epAvCNUxCOkJO0IUNZJj5cuM24QZDSNmIOX8OR0PtjUAr90AHP8SePfn8n2jbwx9kZHKG3I8vivEACRYTVg45Sw8u49BSCmih+HFOHaEVOGbLN2pIYRByXEAgKN1nCytJ+wIUdSwmAxItMpvGnUtXS41jrEBd34CjLpOHuZ552fA4c81qDIyde0I+QxNi4fbe0m9h7vPK6anydJOlwftbs7DUkoPi3h3dITqW3tc5ZsiEztCFFWS481odLhQ2+zE8IwuDxqMwE2vAv+8A9i7GnjlGkAyAJnnAWdfASQNAs66Qj6upbbn35RA4J+QHXcGOTZYpT080OPznuq5e1NHL567h2NjnfICiqJTRwgAUuLMsJjlTly7qx3WIK9CZ6anBRVjLUb/1y1ON2yx/PtWCR2DkB3neqBNXh6ird2D2manfyNWimwMQhRVsm2xKK9txeGaFowfmtr9AIMBuL5IXnDx8EZAeIATe+QPCirP+1lIgb9SJEmCLT4GaAHcLnaE+qunq8YsJgPMRgntboEWp8u/rhD1T9e9xgAgxmxEZqIVVY0OHKtvZRDSCQYhiiqjB9mw9VAtSo7U4saCwT0fZIkD5v9LDkP15cC+D+SvD34GNNcAEPLVZVJPf3n30CUK2kEP8kDQlntPz92LY1V87iaHCyecMTiYegkmd3nMbObK0koTXTp1cRYTGlrb0ezgPCGlde1/DkqJRVWjA2W1LRgzOFmLkkhhDEJBFBUVoaioCG43f7HoyYRhqfjL54fw5vZyNDncGDvYBrPRgKN1LbhyVBYuGp4mHyhJQEKm/DG4QNuiI8DT/9qLlV8cxsKEs7o9ZjDJQ2MMQv0ngmztEG8xoqG1HS28ckwx/jPdJQmNyEzAzrJ67KtsxLVjQl0VqYFBKIjCwkIUFhbCbrfDZrNpXQ4p5IejsvDj8YPx1o6j+Nfu4/jX7uP+x17eeAjXjB6IhVPOwnnZ/G/eG769l0yG7vOHLGauLK22OO9FAOwIKSdYQ1T+3XAUe4/bQ1oPqYdBiKKKwSDhiRvH4uYJOVi/rwrfVzdDCIHS43YcrmnBB3sq8MGeClw0PBW3TxqGxBgzhBDwCMAjBNxCoKymBZX2Nq1/lLBScqQOAGA0dB8uNJm8Q2OCb9L9JdB9rzFA7ggBYEdIBVKXc33+kGQAwNaDNXC43LCajD18F0USBiGKSgW5KSjITQm4b9uhWry88SDWfVuFLQdrseVgrUbVRa6EmO6/UnxzhCQPg1C/BWlTcC0hZXW+NL7rRZKjB9n8E6a/PFKPiWelhbg6UhqDEJHXBcNSccGwVByrb8WK4v34sqwOBkmCQZIgSZC/NgAxJiNGDUyCxcTLlDtLijHjxnHdJ6CbvJfPS+wI9VtPW2wAQBw7QqrpOthrMEgYM9iGT76pwv4TjQxCOsAgRNTFoORYPHnTWK3L0A1/R0jwTbq/JG+nomtfiHOElNW58Sb1sG7WiKxEfPJNFb6r4i70esA/aYlIVWZvR8jAobF+E/6dQAPv5xwhZZ1uzWjfLvT7T3AXej1gECIiVVl9HSEwCPWXCHJNt2+OUDPnCCkiYI5QD4+PzEoEAHx3opFbbegAgxARqco3NGbgHCEFBFlHyLsDfYuDHSEldD7LPe0+c1ZGAiQJqGtpR3WTs/sBFFEYhIhIVVard2gMAuhh93Q6c8EmS8d75wg1Mggpruvl84C8v9tg7wasB09yeCzSMQgRkap8m64C4FpC/RVksnRqvHyOa9idUETAaFeQ/YiHpcvzhA5WN6tfEKmKQYiIVGWxdgpCXF26f4LMR8lIlDf/PNnoCGU1uiVOO10aGJ4eDwD4voodoUjHIEREqrKaGYSU1nVoLMO7C3p1E4OQEgIvn+/5mLzsJADArvJ69QsiVTEIEZGqYqzmjhsMQv0jfHOsugQhb0eoptkJT5CNWalvguQgXDgsFQCw+2g92to55BvJGISISFVWS+eOEN8w+qfnkJMab4FBkje/PcmukKJ6WlARAHJS45CVZEW7W+DLsroQV0VKYhAiIlXFWsxwC++bCYNQv/iGbLoOjZmNBgxNk+esfMdF/votYGgsyDGSJOHCYfL2GtsOcV/CSMYgFERRURHy8vIwYcIErUshimgxZgNc8O7QzaGxfur5qjEAONu72vE3FfYQ1qNPZzJZGpD3JwQYhCIdg1AQhYWFKC0txfbt27UuhSiixZiNcDMIKeMUqxgX5KYAAFZ+cQjH6ltDVZEunclkaQC4aLgchHYcqUNjW7vKVZFaGISISFVyEJJ/1XjcDEL9caqNH35yUS6GpsXheEMbJv3hU0x/ZiO+PtYQyvJ0I3AZoeBJ6KyMBJyVEQ+ny4OPv65UvzBSBYMQEakq1mz0D405HOxU9MspRmzirSa88dOLkJsWB0AeIrv2uc/xoxc2YemH36Bo3QG8s/MoPtpTwauceuFUHSFJknDD+YMAAG/vOMp9xyKUSesCiEjfYsxG2CFfOeZsa0WsxvVEMuGfI9Tzu3N2cixW//xiPLV2P4pLK1HT7ETJkTqUHAm8qum2SUPx25nnqV5vpOpNoJldMBjPfXoA2w7X4s/rD+Lnk89SsTJSA4MQEanKaJDg8AchbkfQH9IpJkv7pCVYsWz2aCybPRoHTzbh3Z3H0NruRlltC0or7CivbcXKLw7jk29OYPLITNw0fjByUuOQHGc5xbNGl970dQYlx+KB6efi9/8uxR8/2Y+ZYwdicEqcarWR8hiEiEh1Tkl+k213tGhcSWTr7dDL8IwELJ56TsD3P/TOHvzftnKU17bib1uO4G9bjiDWbMS8ibnITIpRuuSI1Hno8FRDYz63TxqKT0pPYPPBGjy9dj/+ePP56hVHimMQIiLVOSQrIIB2doQUEWxo7HQkScKy2WNQOOVsrPjkO2z+vgYOlxvVTU68uOGgwlVGPrNRgvEMkpAkSXhoxijM/NPneGfnMUzNy8L00QNDUCEpgUGIiFTX7g1Cbk6W7peOjlDfgpDP4JQ4PHXTWADyatRv7yjHl2V1aGv3nOY7o8slI9JhMp7ZNUWjB9tw2cgMbNh/Ej//+5fY87upSIwxn/4bSXMMQkSkOpfBCngAF4fG+kcoH1SMBglzLsjBnAtyFH/uaPPA1edgw/6TAIDPv6tmVyhC8PJ5IlKdyyBvCup2siOkBF6kHZ7Oy7bhjkuGAQD+822VxtXQmWIQIiLVub1ByMMg1C9cpib8XXluJgDgs31VXFcoQjAIEZHqPCb5aiTRzqExZfRvjhCpp2BoCqwmA6qbnPj+JDfAjQQMQkSkOrfRF4TYEeof7zpCZ3JNN2nCajJiXI6879vmg9yMNRIwCBGR6oS3IwQGoX6RONQSES4angYA2HqwRuNK6EwwCBGR6oTJu7EGg1C/+HJQX9cRotDw7Uq/9VAt5wlFAAYhIlKd0yIPFVgd/Au5PwS4zk8kGDskGRaTAScbHThYzUVEwx2DEBGprjkuGwCQ2FahcSWRjt2FSBBjNmJcTjIAYCvnCYU9BiEiUp0jfhAAwOZkEOoXDo1FjAuHyfOEvjhQrXEldDoMQkSkOmEbAgCIczcCbQ0aVxPJfLvPMwiFuytHyesJFZeeQG2zU+Nq6FQYhIhIdfGJyagVCfKN+nJti4lgnHcbOcYMTkbewCQ43R6s3VupdTl0ClEThFpaWpCbm4v77rtP61KIok5SjBnHRLp8o75M22J0gR2hSDA9fwAAbrcR7qImCD3++OO48MILtS6DKCrZ4sw4KjLkGw3sCPWZCpuuknouGSGH/60Ha+D2sJ0XrqIiCH333Xf49ttvMWPGDK1LIYpKtlgzKoW8tgoaOWG6v/iWGhlGD7LBYjLA3ubC8XquoRWuNA9CGzZswMyZM5GdnQ1JkvDuu+92O+b555/HsGHDEBMTg4KCAmzcuLFXr3Hfffdh2bJlClVMRL2VFGOGHXEAANFm17iayCU4WTqimIwGDEmRFxM9UsN99sKV5kGoubkZY8eOxZ/+9KceH1+1ahUWLVqEhx9+GDt37sSll16K6dOno6ysY55BQUEB8vPzu30cP34c7733HkaOHImRI0eG6kcioi5ssWY0CjkIuVp41VifcbZ0xMlNiwcAlNUyCIUrk9YFTJ8+HdOnTw/6+PLly3HHHXfgzjvvBACsWLECa9aswQsvvODv8pSUlAT9/i1btuDNN9/E22+/jaamJrS3tyMpKQmPPPJIj8c7HA44HA7/bbudf70S9VeM2YAWSX5DcLfWw6xxPRGPm65GjNw0+Q+APcf4B0C40rwjdCpOpxMlJSWYOnVqwP1Tp07Fpk2bzug5li1bhvLychw+fBhPPfUUfvrTnwYNQb7jbTab/2PIkCH9+hmICJAkCW5LIgDA08o3hD4TvqExihRXnpsFAPjgq+N4acP32Huc//7DTVgHoerqarjdbmRlZQXcn5WVhcpKddZlePDBB9HQ0OD/KC/nFS5ESvBYkwBwjlD/MAJFmolnpSEz0Qp7mwtLP/wW1zz7udYlUReaD42dCalLG1gI0e2+M7FgwYLTHmO1WmG1Wnv93ER0alJMEtAKGJyNWpcSsTqmCHFoLFIYDRLmXJCDZ//znf++hpZ22OI4QBwuwrojlJ6eDqPR2K37U1VV1a1LREThTYqxAQBMTnaE+kriVWMRaeHks5CZ2PEH9obvTmpYDXUV1h0hi8WCgoICFBcXY9asWf77i4uLcf3116v62kVFRSgqKoLb7Vb1dYiihSdBXmXX7GoCWuuB2GRN64lEHBiLTDFmIz6451L818tb8F1VE37xfzvx+XfcjLWz+RcPRV52kiavrXkQampqwoEDB/y3Dx06hF27diE1NRU5OTlYvHgx5s6di/Hjx2PixIl46aWXUFZWhrvuukvVugoLC1FYWAi73Q6bzabqaxFFA2t8Ek6IZGRJ9UDt98CggsAD9q8BNj0HXLoYOOsKTWoMe/6xMXaEIk1GohWv3n4Brnj6M7S1e7BqB+efdjb1vKzoDUI7duzAlClT/LcXL14MAJg/fz5eeeUV3HzzzaipqcGSJUtQUVGB/Px8fPjhh8jNzdWqZCLqgziLCYfFADkIVX8XGIQcjcAbP5a/rj0I/HwTO0Y94TpCES07ORZ/v/MibDlYo3UpYeesjATNXlvzIDR58mSI0/zPvXDhQixcuDBEFRGRGmLMRuz1DMWFhm+Bss3A2DkdD+5f0/G1/Riw/gng6qWhLzLMdawsTZGqIDcFBbkpWpdBnYT1ZGktFRUVIS8vDxMmTNC6FCJdiDUb8bknX75xZHPggyf3yZ/jM+XPW4qA9+4GKr4KXYGRQPg+cWiMSCkMQkEUFhaitLQU27dv17oUIl2INRtwRHiv9mzsdCWoEPLcIACY2Knzu/NvwIf3h67AiMBeEJHSGISIKCRiLUbUCO9kSEcD4HICHg+w6VnA5d2ZO/M8YOYzHd9UfyT0hUYCbrFBpBjN5wgRUXSIMRvRgHi4YYARHqClBvjyVeAzec9ADLlQvlrMaAJGXg08fQ7QdALwuAGDUdviw4QQXEeISGnsCBFRSMSYjRAwoFHydoVaqoFDG+Wvcy8BbvtIDkEAEJ8BSAZAeIBmLj7nx6vGiBTHIEREIRFrlrs69b4g9OdLgCPefZemPR7Y9TEYOyZON1aEsMpwxyBEpDQGoSB41RiRsmItctA5IaUHPmBJADLO6f4NifJK1AETq6MdO0JEimMQCoJXjREpy98REvEdd17+a2De+4A5tvs3JA6UP7Mj1A3nCBEph0GIiEIixhuESj2dVoWf8iAwuKDnb/B3hE6oXFnk6OgHMQgRKYVXjRFRSMRb5SD0knMaFk3JgHTO1af+BnaEupE4R4hIcQxCRBQSybEWAECrx4jmy36DBOtpfv34OkL2YypXFjkEV5YmUhyHxogoJGItRlhN8q+cumbn6b8hxTuEVndYvaIiDvcaI1IaO0JBFBUVoaioCG63W+tSiHQjOc6ME3YHGlrbMeR0B6cMkz/XHABqD3FRRQDmNnnXcokrSxMphkEoiMLCQhQWFsJut8Nms2ldDpEupMRZcMLuQF3LGXSEbIMBowVwO4Fnz1e9tkhwtvczO0JEymEQIqKQSY4zAwDqW9pPf7DBCFz638CW5wGXQ+XKIoNbCLS6JHxhnoQxWhdDpBMMQkQUMqnx8oTpmqYzDDaTfy1/EADg8/0nMf+v25CXmoSfa10MkU5wsjQRhUy2TV448Whdq8aVRDZOESJSDoMQEYXMkNQ4AAxCfSW4xQaR4hiEiChkBqfIHaHyuhaNK4ls7AgRKYdBKAhuukqkPF9HqLyWQagvfP0giQsqEimGQSgIbrpKpDxfR8je5kJD6xlcOUZEpDIGISIKmTiLCWneK8eOcnis97wtIQ6NESmHQYiIQmqwf3iME6aJSHsMQkQUUkO8w2OfHziJumYn3tpeDoeLW9mcCeFtCbEhRKQcLqhIRCE1OEXuCL2+pQybvq/BwZPNKK2w43fXnadxZeGPV88TKY8dISIKKYup49fOwZPNAIBXNh3G9X/6HJ/tq9KqrMjCSUJEimEQIqKQunHc4B7v3320Afe9/RVe+Ox7vLG1LMRVRQZfR4gxiEg5HBoLoqioCEVFRXC7OXeBSEk5aXE4tGwGWtvdOFrXCoMEzHlpK6qbHKhucuB/Pv4WAHDNmIGwxZo1rpaI9I4doSC4jhCReiRJQpzFhJFZiTg7MxE7fnMVrhk9MOCY3eX12hQXxvwLKrIlRKQYBiEiCgv/PXVkwO1lH32LfZWNGlVDRNGCQYiIwsLwjATM/sEg/+1vKuz4f+99rWFF4ce36SobQkTKYRAiorDx5E1j8X8/vQj3XHE2AKCigYsudsar54mUxyBERGHDaJAw8aw0zPZeWVbb5NS4ovAkcZIQkWIYhIgo7KR49yNrdrrR1s4rN324oCKR8hiEiCjsJMWYYDLIXY/6Fu5S3xX7QUTKYRAiorAjSZK/K1TbzOGxDt7J0kxCRIphECKisJSeYAUAnLC3aVwJEekZgxARhaXcVHlz1iM1zRpXEj46tthgS4hIKQxCQRQVFSEvLw8TJkzQuhSiqJSbJgehwzUtGldCRHrGIBQEt9gg0lZuWjwA4PuTTRpXEj78F42xIUSkGG66SkRhKX9QEgBg0/c1KK9t4QRhADWcOE6kOAYhIgpL5w5IgtVkgMPlwaVPrNO6nLDCTEikHAYhIgpLFpMBj153Hp5Ysw/NDpfW5YQNk0HC9PwBWpdBpBsMQkQUtuZckIM5F+RoXQYR6RgnSxMREVHUYhAiIiKiqMUgRERERFGLQYiIiIiiFoMQERERRS0GISIiIopaDEJEREQUtRiEiIiIKGoxCBEREVHUYhAiIiKiqMUgFERRURHy8vIwYcIErUshIiIilUhCCKF1EeHMbrfDZrOhoaEBSUlJWpdDREREZ+BM37/ZESIiIqKoxSBEREREUcukdQHhzjdyaLfbNa6EiIiIzpTvfft0M4AYhE6jsbERADBkyBCNKyEiIqLeamxshM1mC/o4J0ufhsfjwfHjx5GYmAhJkhR7XrvdjiFDhqC8vJyTsFXGcx0aPM+hwfMcGjzPoaPWuRZCoLGxEdnZ2TAYgs8EYkfoNAwGAwYPHqza8yclJfF/shDhuQ4NnufQ4HkODZ7n0FHjXJ+qE+TDydJEREQUtRiEiIiIKGoxCGnEarXit7/9LaxWq9al6B7PdWjwPIcGz3No8DyHjtbnmpOliYiIKGqxI0RERERRi0GIiIiIohaDEBEREUUtBiEiIiKKWgxCGnn++ecxbNgwxMTEoKCgABs3btS6pIixbNkyTJgwAYmJicjMzMQNN9yAffv2BRwjhMDvfvc7ZGdnIzY2FpMnT8bevXsDjnE4HPjFL36B9PR0xMfH47rrrsPRo0dD+aNElGXLlkGSJCxatMh/H8+zco4dO4af/OQnSEtLQ1xcHM4//3yUlJT4H+e57j+Xy4Xf/OY3GDZsGGJjYzF8+HAsWbIEHo/HfwzPc99s2LABM2fORHZ2NiRJwrvvvhvwuFLnta6uDnPnzoXNZoPNZsPcuXNRX1/fv+IFhdybb74pzGazePnll0Vpaam49957RXx8vDhy5IjWpUWEadOmiZUrV4qvv/5a7Nq1S1xzzTUiJydHNDU1+Y/5wx/+IBITE8U///lPsWfPHnHzzTeLgQMHCrvd7j/mrrvuEoMGDRLFxcXiyy+/FFOmTBFjx44VLpdLix8rrG3btk0MHTpUjBkzRtx7773++3melVFbWytyc3PFggULxNatW8WhQ4fEJ598Ig4cOOA/hue6/x577DGRlpYm/v3vf4tDhw6Jt99+WyQkJIgVK1b4j+F57psPP/xQPPzww+Kf//ynACDeeeedgMeVOq9XX321yM/PF5s2bRKbNm0S+fn54tprr+1X7QxCGrjgggvEXXfdFXDfueeeK379619rVFFkq6qqEgDE+vXrhRBCeDweMWDAAPGHP/zBf0xbW5uw2Wziz3/+sxBCiPr6emE2m8Wbb77pP+bYsWPCYDCIjz/+OLQ/QJhrbGwUI0aMEMXFxeLyyy/3ByGeZ+U88MAD4pJLLgn6OM+1Mq655hpx++23B9w3e/Zs8ZOf/EQIwfOslK5BSKnzWlpaKgCILVu2+I/ZvHmzACC+/fbbPtfLobEQczqdKCkpwdSpUwPunzp1KjZt2qRRVZGtoaEBAJCamgoAOHToECorKwPOsdVqxeWXX+4/xyUlJWhvbw84Jjs7G/n5+fzv0EVhYSGuueYaXHXVVQH38zwr5/3338f48eNx0003ITMzEz/4wQ/w8ssv+x/nuVbGJZdcgv/85z/Yv38/AGD37t34/PPPMWPGDAA8z2pR6rxu3rwZNpsNF154of+Yiy66CDabrV/nnpuuhlh1dTXcbjeysrIC7s/KykJlZaVGVUUuIQQWL16MSy65BPn5+QDgP489neMjR474j7FYLEhJSel2DP87dHjzzTfx5ZdfYvv27d0e43lWzsGDB/HCCy9g8eLFeOihh7Bt2zbcc889sFqtmDdvHs+1Qh544AE0NDTg3HPPhdFohNvtxuOPP45bbrkFAP9Nq0Wp81pZWYnMzMxuz5+Zmdmvc88gpBFJkgJuCyG63Uend/fdd+Orr77C559/3u2xvpxj/nfoUF5ejnvvvRdr165FTExM0ON4nvvP4/Fg/PjxWLp0KQDgBz/4Afbu3YsXXngB8+bN8x/Hc90/q1atwuuvv4433ngD5513Hnbt2oVFixYhOzsb8+fP9x/H86wOJc5rT8f399xzaCzE0tPTYTQau6XXqqqqbmmZTu0Xv/gF3n//faxbtw6DBw/23z9gwAAAOOU5HjBgAJxOJ+rq6oIeE+1KSkpQVVWFgoICmEwmmEwmrF+/Hs8++yxMJpP/PPE899/AgQORl5cXcN+oUaNQVlYGgP+mlXL//ffj17/+NebMmYPRo0dj7ty5+OUvf4lly5YB4HlWi1LndcCAAThx4kS35z958mS/zj2DUIhZLBYUFBSguLg44P7i4mJcfPHFGlUVWYQQuPvuu7F69Wp8+umnGDZsWMDjw4YNw4ABAwLOsdPpxPr16/3nuKCgAGazOeCYiooKfP311/zv4HXllVdiz5492LVrl/9j/PjxuPXWW7Fr1y4MHz6c51khkyZN6rYExP79+5GbmwuA/6aV0tLSAoMh8G3PaDT6L5/neVaHUud14sSJaGhowLZt2/zHbN26FQ0NDf07932eZk195rt8/n//939FaWmpWLRokYiPjxeHDx/WurSI8POf/1zYbDbx2WefiYqKCv9HS0uL/5g//OEPwmazidWrV4s9e/aIW265pcdLNQcPHiw++eQT8eWXX4orrrgi6i+BPZ3OV40JwfOslG3btgmTySQef/xx8d1334m///3vIi4uTrz++uv+Y3iu+2/+/Pli0KBB/svnV69eLdLT08WvfvUr/zE8z33T2Ngodu7cKXbu3CkAiOXLl4udO3f6l4VR6rxeffXVYsyYMWLz5s1i8+bNYvTo0bx8PlIVFRWJ3NxcYbFYxLhx4/yXftPpAejxY+XKlf5jPB6P+O1vfysGDBggrFaruOyyy8SePXsCnqe1tVXcfffdIjU1VcTGxoprr71WlJWVhfiniSxdgxDPs3L+9a9/ifz8fGG1WsW5554rXnrppYDHea77z263i3vvvVfk5OSImJgYMXz4cPHwww8Lh8PhP4bnuW/WrVvX4+/l+fPnCyGUO681NTXi1ltvFYmJiSIxMVHceuutoq6url+1S0II0fd+EhEREVHk4hwhIiIiiloMQkRERBS1GISIiIgoajEIERERUdRiECIiIqKoxSBEREREUYtBiIiIiKIWgxARERFFLQYhIiIiiloMQkSkewsWLMANN9ygdRlEFIYYhIiIiChqMQgRkW784x//wOjRoxEbG4u0tDRcddVVuP/++/Hqq6/ivffegyRJkCQJn332GQDg2LFjuPnmm5GSkoK0tDRcf/31OHz4sP/5fJ2kRx99FJmZmUhKSsLPfvYzOJ1ObX5AIlKcSesCiIiUUFFRgVtuuQVPPPEEZs2ahcbGRmzcuBHz5s1DWVkZ7HY7Vq5cCQBITU1FS0sLpkyZgksvvRQbNmyAyWTCY489hquvvhpfffUVLBYLAOA///kPYmJisG7dOhw+fBi33XYb0tPT8fjjj2v54xKRQhiEiEgXKioq4HK5MHv2bOTm5gIARo8eDQCIjY2Fw+HAgAED/Me//vrrMBgM+Mtf/gJJkgAAK1euRHJyMj777DNMnToVAGCxWPDXv/4VcXFxOO+887BkyRLcf//9+P3vfw+DgU11okjH/4uJSBfGjh2LK6+8EqNHj8ZNN92El19+GXV1dUGPLykpwYEDB5CYmIiEhAQkJCQgNTUVbW1t+P777wOeNy4uzn974sSJaGpqQnl5uao/DxGFBjtCRKQLRqMRxcXF2LRpE9auXYvnnnsODz/8MLZu3drj8R6PBwUFBfj73//e7bGMjIzTvp6vi0REkY1BiIh0Q5IkTJo0CZMmTcIjjzyC3NxcvPPOO7BYLHC73QHHjhs3DqtWrfJPgg5m9+7daG1tRWxsLABgy5YtSEhIwODBg1X9WYgoNDg0RkS6sHXrVixduhQ7duxAWVkZVq9ejZMnT2LUqFEYOnQovvrqK+zbtw/V1dVob2/HrbfeivT0dFx//fXYuHEjDh06hPXr1+Pee+/F0aNH/c/rdDpxxx13oLS0FB999BF++9vf4u677+b8ICKdYEeIiHQhKSkJGzZswIoVK2C325Gbm4unn34a06dPx/jx4/HZZ59h/PjxaGpqwrp16zB58mRs2LABDzzwAGbPno3GxkYMGjQIV155ZUCH6Morr8SIESNw2WWXweFwYM6cOfjd736n3Q9KRIqShBBC6yKIiMLRggULUF9fj3fffVfrUohIJeztEhERUdRiECIiIqKoxaExIiIiilrsCBEREVHUYhAiIiKiqMUgRERERFGLQYiIiIiiFoMQERERRS0GISIiIopaDEJEREQUtRiEiIiIKGr9/0q6i4nFfsn0AAAAAElFTkSuQmCC\n", + "image/png": "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", "text/plain": [ "
" ] @@ -330,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "8301085c", "metadata": {}, "outputs": [ @@ -340,13 +334,13 @@ "Text(0, 0.5, 'RMSE')" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -394,7 +388,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_3_deep_formula.ipynb b/tutorials/Example_3_deep_formula.ipynb index 28ef41be..93346074 100644 --- a/tutorials/Example_3_deep_formula.ipynb +++ b/tutorials/Example_3_deep_formula.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "2075ef56", "metadata": {}, "outputs": [ @@ -28,6 +28,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -36,7 +37,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.43e-02 | test_loss: 2.57e-02 | reg: 1.09e+01 | : 100%|█| 20/20 [00:17<00:00, 1.12it" + "| train_loss: 1.76e-02 | test_loss: 1.79e-02 | reg: 1.05e+01 | : 100%|█| 20/20 [00:05<00:00, 3.60it" ] }, { @@ -57,10 +58,13 @@ "source": [ "from kan import *\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", - "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1)\n", + "model = KAN(width=[4,2,1,1], grid=3, k=3, seed=1, device=device)\n", "f = lambda x: torch.exp((torch.sin(torch.pi*(x[:,[0]]**2+x[:,[1]]**2))+torch.sin(torch.pi*(x[:,[2]]**2+x[:,[3]]**2)))/2)\n", - "dataset = create_dataset(f, n_var=4, train_num=3000)\n", + "dataset = create_dataset(f, n_var=4, train_num=3000, device=device)\n", "\n", "# train the model\n", "model.fit(dataset, opt=\"LBFGS\", steps=20, lamb=0.002, lamb_entropy=2.);" @@ -68,28 +72,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "d81e80f7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.plot(beta=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "b8c880c1", "metadata": {}, "outputs": [ @@ -107,13 +90,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "585b699c", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -128,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "ee39c97b", "metadata": {}, "outputs": [ @@ -144,7 +127,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 5.40e-03 | test_loss: 5.50e-03 | reg: 1.08e+01 | : 100%|█| 50/50 [00:38<00:00, 1.30it\n" + "| train_loss: 9.21e-03 | test_loss: 9.23e-03 | reg: 1.04e+01 | : 100%|█| 50/50 [00:10<00:00, 4.89it\n" ] }, { @@ -160,7 +143,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.10e-03 | test_loss: 2.13e-03 | reg: 1.09e+01 | : 100%|█| 50/50 [00:40<00:00, 1.25it\n" + "| train_loss: 3.33e-03 | test_loss: 3.25e-03 | reg: 1.05e+01 | : 100%|█| 50/50 [00:10<00:00, 4.72it\n" ] }, { @@ -176,7 +159,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.80e-04 | test_loss: 1.68e-04 | reg: 1.09e+01 | : 100%|█| 50/50 [01:01<00:00, 1.23s/\n" + "| train_loss: 1.13e-03 | test_loss: 1.07e-03 | reg: 1.04e+01 | : 100%|█| 50/50 [00:09<00:00, 5.33it\n" ] }, { @@ -192,7 +175,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 3.95e-05 | test_loss: 2.68e-05 | reg: 1.09e+01 | : 100%|█| 50/50 [01:06<00:00, 1.33s/\n" + "| train_loss: 3.93e-04 | test_loss: 3.75e-04 | reg: 1.04e+01 | : 100%|█| 50/50 [00:05<00:00, 9.74it\n" ] }, { @@ -208,7 +191,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.86e-05 | test_loss: 3.86e-05 | reg: 1.09e+01 | : 100%|█| 50/50 [01:00<00:00, 1.21s/" + "| train_loss: 3.60e-05 | test_loss: 3.78e-05 | reg: 1.04e+01 | : 100%|█| 50/50 [00:04<00:00, 10.01it" ] }, { @@ -234,15 +217,24 @@ "test_rmse = []\n", "\n", "for i in range(len(grids)):\n", - " model = KAN(width=[4,2,1,1], grid=grids[i], k=3, seed=0).initialize_from_another_model(model, dataset['train_input'])\n", + " #model = KAN(width=[4,2,1,1], grid=grids[i], k=3, seed=0, device=device).initialize_from_another_model(model, dataset['train_input'])\n", + " model = model.refine(grid=grids[i])\n", " results = model.fit(dataset, opt=\"LBFGS\", steps=50, stop_grid_update_step=20);\n", " train_rmse.append(results['train_loss'][-1].item())\n", " test_rmse.append(results['test_loss'][-1].item())" ] }, + { + "cell_type": "markdown", + "id": "8c345302-c8bc-4585-8022-c5d90eb64341", + "metadata": {}, + "source": [ + "Author's note: The scaling isn't optimal. Possibly because of updates on curve2coef, to be investigated. " + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "94f3930a", "metadata": {}, "outputs": [ @@ -250,13 +242,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.005403829738497734, 0.0020968744065612555, 0.00017952092457562685, 3.950050086132251e-05, 2.864087036869023e-05]\n", - "[0.005496920086443424, 0.0021260427311062813, 0.00016824221529532224, 2.6780631742440164e-05, 3.8571961340494454e-05]\n" + "[0.009214929305016994, 0.0033308672718703747, 0.00112761405762285, 0.0003925061319023371, 3.601737262215465e-05]\n", + "[0.009230277501046658, 0.0032473765313625336, 0.0010660917032510042, 0.0003754299250431359, 3.784598084166646e-05]\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -292,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "ae7b654b", "metadata": {}, "outputs": [ @@ -308,30 +300,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 7.59e-02 | test_loss: 7.80e-02 | reg: 1.17e+01 | : 80%|▊| 16/20 [00:20<00:05, 1.25s/\n" + "| train_loss: 5.98e-02 | test_loss: 6.11e-02 | reg: 1.25e+01 | : 100%|█| 20/20 [00:08<00:00, 2.35it\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/6j/b6y80djd4nb5hl73rv3sv8y80000gn/T/ipykernel_75196/1574544268.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"LBFGS\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlamb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.002\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlamb_entropy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/MultKAN.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, opt, steps, log, lamb, lamb_l1, lamb_entropy, lamb_coef, lamb_coefdiff, update_grid, grid_update_num, loss_fn, entropy_offset, lr, start_grid_update_step, stop_grid_update_step, batch, metrics, save_fig, in_vars, out_vars, beta, save_fig_freq, img_folder, singularity_avoiding, y_th, reg_metric, display_metrics)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"LBFGS\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 957\u001b[0;31m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 959\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mopt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"Adam\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 383\u001b[0m )\n\u001b[1;32m 384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 386\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mobj_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_directional_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m loss, flat_grad, t, ls_func_evals = _strong_wolfe(\n\u001b[0m\u001b[1;32m 444\u001b[0m obj_func, x_init, t, d, loss, flat_grad, gtd)\n\u001b[1;32m 445\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36m_strong_wolfe\u001b[0;34m(obj_func, x, t, d, f, g, gtd, c1, c2, tolerance_change, max_ls)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;31m# evaluate objective and gradient using initial step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0mf_new\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m \u001b[0mls_func_evals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mgtd_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36mobj_func\u001b[0;34m(x, t, d)\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mobj_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_directional_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 443\u001b[0m loss, flat_grad, t, ls_func_evals = _strong_wolfe(\n\u001b[1;32m 444\u001b[0m obj_func, x_init, t, d, loss, flat_grad, gtd)\n", - "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/LBFGS.py\u001b[0m in \u001b[0;36m_directional_evaluate\u001b[0;34m(self, closure, x, t, d)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_directional_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclosure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0mflat_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gather_flat_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_param\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Desktop/2022/research/code/pykan/kan/MultKAN.py\u001b[0m in \u001b[0;36mclosure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 935\u001b[0m \u001b[0mreg_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[0mobjective\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlamb\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mreg_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 937\u001b[0;31m \u001b[0mobjective\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 938\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobjective\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 521\u001b[0m )\n\u001b[0;32m--> 522\u001b[0;31m torch.autograd.backward(\n\u001b[0m\u001b[1;32m 523\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m )\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -350,19 +337,101 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "869828f2", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.98e-02 | test_loss: 2.21e-02 | reg: 1.70e+01 | : 100%|█| 50/50 [00:15<00:00, 3.23it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 1.15e-02 | test_loss: 1.40e-02 | reg: 1.71e+01 | : 100%|█| 50/50 [00:13<00:00, 3.75it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 6.69e-03 | test_loss: 9.05e-03 | reg: 1.72e+01 | : 100%|█| 50/50 [00:13<00:00, 3.69it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "| train_loss: 4.38e-03 | test_loss: 8.05e-03 | reg: 1.73e+01 | : 100%|█| 50/50 [00:15<00:00, 3.17it\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n", + "checkpoint directory created: ./model\n", + "saving model version 0.0\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "train loss: 3.59e-02 | test loss: 5.23e-02 | reg: 1.04e+01 : 100%|██| 50/50 [01:18<00:00, 1.57s/it]\n", - "train loss: 2.28e-02 | test loss: 3.10e-02 | reg: 1.04e+01 : 100%|██| 50/50 [01:25<00:00, 1.70s/it]\n", - "train loss: 8.34e-03 | test loss: 1.09e-02 | reg: 1.03e+01 : 100%|██| 50/50 [01:32<00:00, 1.86s/it]\n", - "train loss: 5.71e-03 | test loss: 1.06e-02 | reg: 9.86e+00 : 100%|██| 50/50 [01:45<00:00, 2.10s/it]\n", - "train loss: 1.03e-02 | test loss: 6.30e-02 | reg: 9.68e+00 : 100%|██| 50/50 [01:57<00:00, 2.36s/it]\n" + "| train_loss: 2.02e-03 | test_loss: 9.89e-03 | reg: 1.73e+01 | : 100%|█| 50/50 [00:17<00:00, 2.88it" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving model version 0.1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -373,7 +442,8 @@ "test_rmse = []\n", "\n", "for i in range(len(grids)):\n", - " model = KAN(width=[4,9,1], grid=grids[i], k=3, seed=0).initialize_from_another_model(model, dataset['train_input'])\n", + " #model = KAN(width=[4,9,1], grid=grids[i], k=3, seed=0).initialize_from_another_model(model, dataset['train_input'])\n", + " model = model.refine(grid=grids[i])\n", " results = model.fit(dataset, opt=\"LBFGS\", steps=50, stop_grid_update_step=30);\n", " train_rmse.append(results['train_loss'][-1].item())\n", " test_rmse.append(results['test_loss'][-1].item())" @@ -381,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "4f0a99fd", "metadata": {}, "outputs": [ @@ -389,13 +459,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.035936225205659866, 0.02279285155236721, 0.00833611935377121, 0.005708411335945129, 0.010341067798435688]\n", - "[0.05229281634092331, 0.031011207029223442, 0.010879972018301487, 0.010645035654306412, 0.06304473429918289]\n" + "[0.01983197219669819, 0.01147659495472908, 0.006687900051474571, 0.004380852449685335, 0.002016218611970544]\n", + "[0.022097894921898842, 0.013952379114925861, 0.009049860760569572, 0.008054238744080067, 0.00989140197634697]\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -444,7 +514,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_4_classfication.ipynb b/tutorials/Example_4_classfication.ipynb index b8a0ea36..2887838d 100644 --- a/tutorials/Example_4_classfication.ipynb +++ b/tutorials/Example_4_classfication.ipynb @@ -32,10 +32,17 @@ "id": "763d1fb4", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -44,7 +51,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -59,19 +66,22 @@ "from sklearn.datasets import make_moons\n", "import numpy as np\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "dataset = {}\n", "train_input, train_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", "test_input, test_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", "\n", "dtype = torch.get_default_dtype()\n", - "dataset['train_input'] = torch.from_numpy(train_input).type(dtype)\n", - "dataset['test_input'] = torch.from_numpy(test_input).type(dtype)\n", - "dataset['train_label'] = torch.from_numpy(train_label[:,None]).type(dtype)\n", - "dataset['test_label'] = torch.from_numpy(test_label[:,None]).type(dtype)\n", + "dataset['train_input'] = torch.from_numpy(train_input).type(dtype).to(device)\n", + "dataset['test_input'] = torch.from_numpy(test_input).type(dtype).to(device)\n", + "dataset['train_label'] = torch.from_numpy(train_label[:,None]).type(dtype).to(device)\n", + "dataset['test_label'] = torch.from_numpy(test_label[:,None]).type(dtype).to(device)\n", "\n", "X = dataset['train_input']\n", "y = dataset['train_label']\n", - "plt.scatter(X[:,0], X[:,1], c=y[:,0])" + "plt.scatter(X[:,0].cpu().detach().numpy(), X[:,1].cpu().detach().numpy(), c=y[:,0].cpu().detach().numpy())" ] }, { @@ -100,7 +110,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.63e-01 | test_loss: 1.68e-01 | reg: 3.92e+00 | : 100%|█| 20/20 [00:01<00:00, 10.29it" + "| train_loss: 1.55e-01 | test_loss: 1.56e-01 | reg: 3.94e+00 | : 100%|█| 20/20 [00:01<00:00, 15.55it" ] }, { @@ -120,7 +130,7 @@ { "data": { "text/plain": [ - "(0.9990000128746033, 0.9980000257492065)" + "(1.0, 0.9980000257492065)" ] }, "execution_count": 2, @@ -129,7 +139,7 @@ } ], "source": [ - "model = KAN(width=[2,1], grid=3, k=3)\n", + "model = KAN(width=[2,1], grid=3, k=3, device=device)\n", "\n", "def train_acc():\n", " return torch.mean((torch.round(model(dataset['train_input'])[:,0]) == dataset['train_label'][:,0]).type(dtype))\n", @@ -159,18 +169,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "fixing (0,0,0) with sin, r2=0.9468607902526855, c=2\n", - "fixing (0,1,0) with x, r2=0.9861899018287659, c=1\n", + "fixing (0,0,0) with sin, r2=0.9654733538627625, c=2\n", + "fixing (0,1,0) with x, r2=0.975755512714386, c=1\n", "saving model version 0.2\n" ] }, { "data": { "text/latex": [ - "$\\displaystyle - 0.8495 x_{2} - 0.3809 \\sin{\\left(3.0397 x_{1} + 4.7748 \\right)} + 0.711$" + "$\\displaystyle - 0.853 x_{2} - 0.3885 \\sin{\\left(3.1242 x_{1} - 1.5464 \\right)} + 0.7063$" ], "text/plain": [ - "-0.8495*x_2 - 0.3809*sin(3.0397*x_1 + 4.7748) + 0.711" + "-0.853*x_2 - 0.3885*sin(3.1242*x_1 - 1.5464) + 0.7063" ] }, "execution_count": 3, @@ -203,8 +213,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "train acc of the formula: tensor(0.9980)\n", - "test acc of the formula: tensor(0.9990)\n" + "train acc of the formula: tensor(0.9980, device='cuda:0')\n", + "test acc of the formula: tensor(0.9970, device='cuda:0')\n" ] } ], @@ -248,7 +258,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -257,7 +267,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAGdCAYAAAAfTAk2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOyddXgUVxeH35nduAd3d3d3t0KhSFugWCkFPgpUgBYpUKxQihUKFIoULRR3d3d3J0gS4ro79/tjk8CS1QgQmPd50rIz1zbZnTlz7jm/IwkhBCoqKioqKioq7xHy216AioqKioqKikpKoxo4KioqKioqKu8dqoGjoqKioqKi8t6hGjgqKioqKioq7x2qgaOioqKioqLy3qEaOCoqKioqKirvHaqBo6KioqKiovLeoRo4KioqKioqKu8d2re9gLeBoig8fvwYDw8PJEl628tRUVFRUVFRsQEhBKGhoWTNmhVZtuyj+SANnMePH5MjR463vQwVFRUVFRWVJPDgwQOyZ89usU2qGjj79+9n4sSJnDp1Cj8/P9asWUOrVq3Mtv/vv/+YNWsWZ8+eJTo6mmLFivHzzz/TqFGjhDYLFiyga9euifpGRkbi7Oxs07o8PDwAwy/I09PTvjeloqKioqKi8lYICQkhR44cCfdxS6SqgRMeHk6pUqXo2rUrbdq0sdp+//79NGjQgLFjx+Lt7c3ff/9NixYtOHbsGGXKlElo5+npybVr14z62mrcAAnbUp6enqqBo6KioqKiksawJbwkVQ2cJk2a0KRJE5vbT5kyxej12LFjWbduHRs2bDAycCRJInPmzCm1TBUVFRUVFZX3jHc6i0pRFEJDQ/H19TU6HhYWRq5cuciePTvNmzfnzJkzFseJjo4mJCTE6EdFRUVFRUXl/eWdNnB+++03wsPDadeuXcKxwoULs2DBAtavX8+yZctwdnamWrVq3Lhxw+w448aNw8vLK+FHDTBWUVFRUVF5v5GEEOKNTCRJVoOMX2XZsmX06NGDdevWUb9+fbPtFEWhbNmy1KxZk2nTpplsEx0dTXR0dMLr+CCl4OBgNQZHRUVFRUUljRASEoKXl5dN9+93Mk18xYoVdO/enX///deicQMgyzIVKlSw6MFxcnLCyckppZepoqKioqKi8o7yzm1RLVu2jC5durB06VKaNWtmtb0QgrNnz5IlS5Y3sDoVFRUVFRWVtECqenDCwsK4efNmwus7d+5w9uxZfH19yZkzJ0OGDOHRo0csWrQIMBg3nTt3ZurUqVSuXJknT54A4OLigpeXFwAjR46kcuXKFChQgJCQEKZNm8bZs2f5448/UvOtqKioqKioqKQhUtWDc/LkScqUKZOQ4j1w4EDKlCnD8OHDAfDz8+P+/fsJ7WfPno1Op6NPnz5kyZIl4eebb75JaBMUFETPnj0pUqQIDRs25NGjR+zfv5+KFSum5ltRUVFRUVFRSUO8sSDjdwl7gpRUVN4W9y4/4Pqp2zg4aildtzjeGbze9pJUVFRU3ippPshYReVD5sndZ/zaZQYX9l9JOKbRamjcvS69f++Co7PjW1ydioqKStpANXBUVCxw7/ID1s3YyoltZ1H0CqVqF6Nl3yYUKp8vVeZ78TSIb6oNJeh5sNFxvU7P5rk7CXgUyKh1g2ySKVdRUVH5kFENHBUVM+xdcYhxHachSaDXKQDsXnqAHYv20WdqN1r9z/YyJLby39TNBD0LRtEric4JRXB04ynO779MqVrFUnxue/C785TNc3Zy/dQtHJwcKF23OKVqFSNT7gx4+lovgqeioqKS2qgGjoqKCfxuP2V8p2mJDI14Q+ePb+ZTsEI+ilYumKLzbvt7t0njJh6NVmbHwn1v1cDZPHcnU7+eA5KUsNZjm04nnK/UrCxdRnUgf5k8ifoKIbh0+Bo7Fu4l8GkQ6bL40rBLbYpUKqB6pVRUVFIU1cBRUTHBhlnbsBR+r9HKrJ2+OcUNnODnluuk6XUKgU9emDwXGxPLvpVH2Pb3Hp4/DCBD9nQ07laXmm0r4+DokCLrO7fvEr/3mg0C4v6TiONbznB61wUm7hxBsaqFEo7HRMcy9rMpHFpzHI1WRq9T0GhlNs3ZQe32VRm06H9oHdRLkoqKSsqgXk1UVExwdu8li54UvU7h7J5LKT6vdyYvAv2CzJ7XaGXSZ0uX6Hh4cDiDGv7CtRM3kWUJRRH43XrC2T0XWTtjM+O3DcPN09Xi3BGhkVw9dgO9XqFA2Twms7ZW/bYBWZYt/m6EItDH6JjU7Q/mX5ma4JmZ8/0iDq87Abz0hMX/f9/KI2TIno6eEztbXKOKioqKrbxzSsYqKu8CtmyXpMaOSpPu9ZA15r+Wep1Co651Eh2f2vsvbpy+DYCiCKP/Xz95m+l9/jI7ZmxMLLO/W0S7LF8yqOFofmwyhg7ZvmLCF9MJCwpPaCeESAi2toaiCB5e9+PSoasAhASEsmnOToRi2usjhGDdH1sJD4mwOraKioqKLagGjoqKCcrWL2nR0NBoZco1KJXi87b+phkZsqdDo008tyRJ1GpbhaJVjLfF/B8Hsm/lYbOGh6JX2LP8kMmtLUVRGN1uMqunbCQ64mVBWr1Oz+6lB/m29gii4o4LIWwybl7lwbXHAJzdcxFdjM5i25ioWM7vu2zX+CoqKirmUA0cFRUTNP+qgcHAMeOlUfQiVbKoPNN5MOXQL5RrVNpobicXRz4Z2JzB//RL5F26dOiaVcND0StcOnQt0fFT289xZP1Jk54VRa9w+8I9ts7fDRgK2+YvkwdJtt115Rq3LRYbbdm4iSc2OtbmsVVUVFQsocbgqKiYIFOuDAxbOZDRbX9DUV56LjRaGUUvGDDnKwqWSx0tnPRZfRmzYQhP7j7j5pk7ODhqKV6jiPkYGhvFyE2Jlm/9ezeyxnJMzaY5O2jV12DMfdyvKb9+McOm+ZxcHCnfyODlKlAur019TGVeqaioqCQF1cBRee+JCI1k15IDXD1+A61WQ/lGpanyUXmrGTtVP6rA/KtT2TBzGye2nUUogpK1itGyTyNyFc2R6uvOnDsjmXNntNquSJWCSJJk0oCJR5KlRFtbAE/vPrfs/RHw/EFAwsv6HWtyds9Fti/Ya3XOdt+3TDDKchbORslaRbl46CqKLvF8Gq1M6bolyJovs/m1qKioqNiBWotKrUX1XnNy+zlGfTKJyPAoNBoZkNDr9GTKnYHx24aRvUCWt73EFGFU20kcWnvCpLEia2Sqt67EsBUDE50b9tF4jm85Y9HIyZo/MwuvT094LYRg74rDrJm2mesnbyZkQmm0MkIRCOCTAc3pMaEjsvxyF9zvzlP6VxtK0PMQo/lkjYxvFh+mHvqFjDnSJ+Xtq6iofCDYc/9WDRzVwHlvuXflIb3KfI9ep08UYxJ/U51/ZQoubs5vaYUpR0hgKN/XHcntC/eQMHhWJFlCKIJ8pXIzcfcIPHzcE/Xb9+8Rfmk/2ey4kizRdfSnfDrkY7NtXjwLZs/Sgzx/GIBPJi/qfFqdDNkTp7IDBD55werfN7H1792E+IfilcGTJt3r0WZAM7WYqIqKilVUA8cKqoHzYfB7zz/ZtmBPgofBFP1mfUmLrxome67w4HD2LD+M360nuPu4U6tdFaPtFkVROLf3Ek/v+eOV3oNyDUqmeNHM6Mhodv1zgM3zdhHwKJB02Xxp2r0e9TrWwMnFyWQfXayOATWHcf3k7UReHI3WYAT+eWZiqpRfEEKo6sUqKip2oRo4VlANnA+D1um7EhoYZrGNm5crS+7NsiqCZ4kt83Yxo998YqNiDUHIcUHJDb+oTf/ZPTmz6yJTv57Ds/v+CX3cvd3oNuZTWnzdKMnzphRhQeH81mMmB9ccNxInLl6jCD8s6MOts3fZ9vcent33xzeLDw2/qE2NNpVU1WEVFZU3jmrgWEE1cD4Mmrt9TnRkjNV2DbvU5vv5fUye0+v1HN1wiq3zd/P03nN8M3vToHPthPIHh9Ye5+fWE032lWSJ8g1LcXL7ORCms5hSq2hnUnhy9xlnd19E0SsUqVKQrPkyMeyjCZzZdSEh0ypeJblwpfyM3zoUNy+3t71sFRWVDwjVwLGCauB8GPStNITrp26ZVc+NR6PVsPzR7EQxIDFRMQxvOYFTO84nusEXKJuX8duH0qvM90ZZRiaRMFe2CRd3Z1b4zX0n44Cm9p7L5jk7EhSRX0XWyNRoU4mhyxMHLquoqKikFvbcv1WhP5X3lpZ9G1s1bsCg2nvt+M1Ex+f+8A9ndl0ASIhPib/Z3zp3l36Vf7Ru3IBZ4wYgMiyKI+tPWh0iJiqGJ3efERIQan2+FCAkMJSt83ebNG7A8PvY/+9Rnj3wN3leRUVF5W2jGjgq7y31Pq9BoQr5bWv8WrBreHA4m/7aafEG/+jmk+QuEVmWCHoabPZ8sH8IM/43j9bpu9Epbx/aZOjGt3VGcHbPxWTPbYkrR29YLa0ghFBLK6ioqLyzqAaOSppHURQuHrrK4XUnuHnmTkKsiyzL/Lj0G6v9tY5ailQuYHTs6vGbxEalftkARRGky+Zr8lzQ82D+V/lHNvy53ahO1MUDV/ih/ij2/Xsk1dZli+fLnnYqKioqbxo1DUIlTbN3xSHm/LDYaKsoT4mc/G9GD0rUKELWfJmp2rICRzaYrrckyzKNutTG2dWJhzf8cHJxJH02X2Jj3kxNJEmS+GvwP1zYf5mWfRuTo1C2hHMLh6/g6b3ESsOKIkCC37rPpGLTMqkSv1OwfF6rJRzAoKKsoqKi8i6ienBU0iw7Fu9jzKdTEsXB3L30gB/qj+TioasAfDvva/KWzAWQUCgyvlJ44cr50To68Emm7nQt1I/PcvaiV5nvObrx9Bt5D0IIntx5xoY/t/NliW85tPY4AJHhUWxfuNe8gSEM8Tv7VhwG4OH1x2z9ew/bFuzB787TZK/LN7MPtdpVNVtRXdbKlG9U6r1RglZRUXn/ULOo1CyqNElMVAzts/YkLCjc5HlJlihQNi9/HB9vaB8dy74Vh9m2YA/+jwPJnCsDdT+rwerfN3L30gMjQyJeAfitIEGmnBnwyeTJ1eO3LDbVOmho0qMej2895dT2c0ZjVGlRge/mf51kgT5drI6rx28yqdtMHt3wQ5LianpKICGRNX9mJu8biW9mH5vHFEJwbu8l7py/z7MH/nhn8iJzrgxUaFIGVw+XJK1TRUXlw0JNE7eCauCkfQ6sPsqotr9ZbTfv8hRyFs5m8tyin1eyZMxqq9swlpA1Eoretq/QoIV9WTdzG9dP3rI+p4XU8pdzy7h7uxIWHJGogKWskclTIifTjozF0cnBpvWBIZ5p5cT1rJq8geDnIQA4ODng5uWKotfjm8WHJt3q0ahbHbvEEa8ev8G4z6fy+FZi75KTqyOfDm7NZz+1VpWNVVRULGLP/VuNwVFJk/g/CrTJ0+L/MADPdO5smrOTrfN3E/gkCEWv4OjkQEx0bLKMm6z5M+Pq6cLN03estvVM50Gt9lWZ2X+BbXPaYDMpeoWQANNKzYpe4dbZu6z7YystejUk0O8F7t5ueKYz7dERQnB653l+6/Enz19L/Y6NjiXEP4TiNYowfttQHBxtN5gA7l1+wHd1RxITFW3yfHREDAuGLyc2OpYuozvYNbaKioqKOVQDRyVN4pPJy6ZtpMjwKHoUH0iwf4iR0WAtBTqehK0ZE3Qc+gn1O9XkwoErTOo20xD7YqZt229bGAyDFHJQSLKEq4cL4SERFo2hOd8tYu73ixMyy0rXLc4XP7ejePUiCW2EEMzs/zdrp28xO46iGFLCdyzaT9Me9exa65Ixq9HFxCKs2HXLJ6yl5f+a4JPxzRbd1MXqeHTDDySJbPkzqyUoVFTeE9QgY5U0SaXm5XBxN589JEkSuYplZ/Z3iwzieEnYiNVoZVzjtmHkuOBkjdbwlfn8pzbU71QTSZIoWbMoM09OoEglQ6p5fGBufNvG3evS7oeWAJStXyLheHKQZcngCbLhfb26C31+32W+rfMzRzeeSji2c/F+i8ZNPJIssXH2drvWGRMdy/5VRy0WPI1HURT2rTxs1/jJQa/Ts+SX1XTI1pMexQfSo9gAPs3Ri2Xj1qDX6d/YOlRUVFIH9VFFJU3i4uZMtzGf8cc38xOdiw/jqNOhOguGLU/yHHqdwtCVAwnxD2X/v0cID4kgV5HsNOtZnzwlchm1dfd24/cDozm++Qy7lx4g6HkIWfNmonH3egmGD8DH/Zqxb2Xy9Wv0OoXIsCi7+yl6BUmCX7vMYPmjOTg6ObBq8gabtvuEIvAzEUNjicc3n6CPtc1YkDUyQc/Mix6mJIqiMLr9ZA6vPWFkAAY9C+bvoUu5de4uPy79BllWnwFVVNIqqoGjkmZp9b8myBqZ+T8tJTw4IuF4uqy+9Jv5JVeP3UCj1STpaVzWyBStUpBy9UsiSRJ1P61utY9Go6FKi/JUaVHebJtiVQvR748eTOv7F5L0drK1hIDQwDCOrDtBxWZluX3+ns193bwtBxYLITiy4SSLRqzgzsUHdsU46XV6MmRPZ3P75HBo7QkOrTlu8pwQsG/lYep3rEnl5uXeyHpUVFRSHtXAUUnTfNS7EY271eHktnME+4eQKVcGStUphkajMVlfyhJaBw1CCPQ6hVK1izFs5UCjrJ6HN/zYOGsbZ/deQtbIlGtQiha9GpAxZwazY+pidRzfcoZn9/zxTOdO5RblafF1I4pULsiAmsOICjcdeJvaaLQa7l99RCU7buCyRqZBp1pmzwshmN73LzbMsm8bKx4HJwdqtauapL72smn2dqtChkvG/qcaOCoqaRjVwFFJ8zg6O1K1ZYVEx0vVKcaSMaut9tdoZQpXLECxqoVwdHGkykflKVgun1GbHYv3ManbTCSJhHiSW2fvsmryBkas+i7RjTAyPIpl49awbsYWIkIiE7aAnFwc6Ti8LdVaVXhrxg0YtqocHLU4uzqRr3Rubp27azGeR5IlPHzd+ah3I5Pnn957zoJhy9n5z/4kr6nHuM9x93azq8+zB/48uuGHi7szBcrlRaPR2NTvwdXHVr1LV49dx+/2U7LkzWTXmlRUVN4NVANH5b2ldJ3i5CqanQfXHlnUqlH0gt5TuyYyauK5de4uE7v+kWg7SdErCEVhVNtJLLg2jYw5M6DX61k88l9WTlpvVMsqvm90ZAzzhiwh8ElQ8t/gKxgcTRK2yloJIfh7+HIuHLxC9oJZuHX2rsX22fJn4ec13+OTydvoeEx0LFO/nsOOhftsnvt1fLN403X0pzTuVtfmPn63n/Jr1z+4eOBKwjHPdB50H/cZTXvUt9rfzccVHlhpJGDdjC30mtzF5nWpqKi8O6hCf6rQ3ztLTFQMe1ccZsfifQQ9CyZznow07VGfSs3KWgz+9H8UwMY/d3B4/Qkiw6J48SSI6MiYxA0lQ7bVd/N60/CL2mbH+63HLHYs2ms+E0gCVw8XFL2CRqsxigcyh4OTFr1OsepFkCTbjZbUQNbK9J7SlY++boQkSURFRHN653nCgyPIUSgr/03ZxN6Vh+2OJer3x5eky+aDp687RaoUtNnzAvDk7jN6FBtg+m8KdB7ZjuY9G3Bk/UkiQiPJXjArFRqXRqN9OceKX9fx1+B/rM6VIUc6lt770+a1qaiopC6qkrEVVAPn3SAmKoY9yw+xY9E+XjwNInPeTDTtXo/KLcoR9iKc7+uN5M6F+wnbO/ExExWblWXEqu9MKvSe33+ZH5uOJfYVET+NVkavU3D3cSU22hBw7JvFm6ofVaDF1w3Jlj8LoS/C2Dp/D/tXHSEyLIq8JXPx0dcNKV69CJ/m/Ar/h4Fv9HcTj5ObE7roWJvSrCVZokzd4lw5eiNJGVbmSJ/dl0l7fmb/yqMsG/df8seWYO2LhXYpIb9K38pDrMZXyRoZRVGQZcNnxjeLDz8s6EO5BqUACAkMpV3mL60GoHtl8GTV03lJWqeKikrKY8/9O1VzIPfv30+LFi3ImjUrkiSxdu1aq3327dtHuXLlcHZ2Jm/evPz5Z+Knp9WrV1O0aFGcnJwoWrQoa9asSYXVq6QmQc+D6VNxMJO6zeT8/svcv/KIk1vP8nPriQxtPp6xn0/l3pWHwMvtnXiD5cSWMyw0kf4dFhTO0BbjiYmKMfKMxBsHYS8iiImKIToymmf3/NFoNWTOk5G7lx7QrUh/5g5azNVjN7h36QH7/z3MgJrD+XPgglTJdLK1JEF0eLRNxg0Yfk9CwAq/uXQf+1lylmeE/8NAuhXpz/yflqaI4eTh6063wt/Q1OUzmrl9Tt/KQ9j5z370euvZbuEh4TYFj8drBMV/Dl48DeKnZuO4cuwGAJ6+HlT7uKLFMWSNTP7Suc2ej42J5ebZO1w/dYuoiLcXT6WiomKaVDVwwsPDKVWqFDNmzLCp/Z07d2jatCk1atTgzJkz/Pjjj/Tr14/Vq18Gih45coT27dvTqVMnzp07R6dOnWjXrh3Hjh1LrbehkgpM6Dyd+1ceAYkNmJPbz3J6x/lE9ZXiEYpg/axtRIZFGh3fvnAvUWFRFg0SoQgQhpTklZPWMef7xfzUbCwhAaFG/eKNitVTNpEhR/oUEeczWocQyCk8JsDdS/dxcnHEzdstxVSTAbN/i6QQGhBG4JMgYqNjiYmM4drxm0zoPJ3R7SZb9Kgc2XCS3hV/TNKcBuNPsHD4S8O4fZz4ojkUvcJHfRonOq7X61kyZjUdsn3F12V/oE+FwbTL0oM53y8iOlI1dFRU3hXe2BaVJEmsWbOGVq1amW0zaNAg1q9fz5UrLwMHe/Xqxblz5zhyxCCO1r59e0JCQtiy5aXyauPGjfHx8WHZsmU2rUXdonq7PLj2iG5F+id7nN/2jqRkzaIJr39u/SuH150wW1rBFNYE7iRJIlOu9Dy5+zw5S01E/LZZarD49h+c2HKGaX3/SpKC89tCkqDnxM58MrBFonP/Td3ErAELUmSeVc/m4ZXe8L3/Z/QqFo5YYVCGjvscxH8mmvasT/9ZPY28bUIIxneaxu5lBxP9bmVZokTNoozb+pPd9bpUVFRs453ZorKXI0eO0LBhQ6NjjRo14uTJk8TGxlpsc/iweYn36OhoQkJCjH5U3h5n91xKEe/C64ZJUkx1oQgk2fxihBA8ufucrnFFIFPKk6NPRpFPawhFULxGkTRl3IDh7/ff1E0oivHvxu/2U/4cuDDF5gl9EZ7w747DPuHn/76ncOWCCcfyFM/Jd/N7JzJuAM7svsjupYmNGzDU6zq39xK7lhxMsbWqqKgknXcqTfzJkydkymSsOZEpUyZ0Oh3+/v5kyZLFbJsnT56YHXfcuHGMHDkyVdaskgRSwGno4ORAvtfiI0rUKMKR9Sex585uybh5lWZfNaB0vRKsnbaZc/suIRTBi6f2lxXIlCcjUWGRBD8PtbuvLXhn9CJjzvRotBpK1S7KxYNXU81TZAlZI1lMzTfH8wcBBPuH4pPRC71eT3hwBNP7/pVimWRaBw2+mb2NjlVrVZFqrSoSEx2LUBScXJzM9t88d4dF71t8va7GXeukyHpVVFSSzjtl4EDi4Mv4C9urx021sRS0OWTIEAYOHJjwOiQkhBw5cqTEclWSQPHqha3bIBIJGTCJTskSjbrWSSQK17BLbRaOWEF0ZIzNgcHW2kmSRJa8GfFM54FXek+KvvKk38KzE1E2Bt2my+pD3+ndKV69EG0zfWlTH3uRZIlW/2tCSGAYm2bv4PnDwJSwJW2cHKO/af7Sebh+6naShgp4HMjC4SvYuXif2VTwpKDRytRqXxVXDxeT501l5b3Ow+t+Fg3GpNTrUlFRSR3eKQMnc+bMiTwxz549Q6vVki5dOottXvfqvIqTkxNOTuafylTeLHlK5KJEjSJcPnLN5M1C1shUb12JQ2uPg4mYU6EIilQuyLMH/ty5cJ8XT4PxyeRF2folGLl2EMNajEcXq7OpDpKzmxNOrk6EBoaa9DgIIWgzoIWRAR30PJhLh67ZbNwA/G9Gd07tuMC4z6fa3McSr8YOxcePVGpalsotyvFl8QGEBIYlnI/X0nF0diB9dl8igiMJep7C27Sv/eqSYtxIkkT2gpn5vu5IIsMik+R5ejWWxui4Rsbd242uoz+1e8xX8UrvYTVuy91KvS4VFZU3wztl4FSpUoUNGzYYHdu+fTvly5fHwcEhoc2OHTsYMGCAUZuqVd9MDRuVlOHHpd8woOZwnt59jsCQ2RR/4yhcqQBe6T0sGigTuyTOzPNK70HPiZ2Zd/l3NszazuF1x4kIjSLYPwShV4xufJJk2CnrO707uYvl4IcGo4gKj06YM15zp0HnWjTv1QCAyLBI/vjmb3Yu3m9XAU8PH3eWjlvDzdN37Co+aYnyDUvz4NojIkIiyVE4Gx993ZAabSvTo+gAQl+EG92A472gsdE6Gn5Rh89/asOTu88ICQjlxdNghjYflyJrSi5CCGKj9USERib592RuK6tC49L0mdqNTLnM1w2zhbqf1eD0zgtmz8uyRH0L9bpUVFTeHKmaRRUWFsbNmwbNijJlyjB58mTq1KmDr68vOXPmZMiQITx69IhFixYBhjTx4sWL89VXX/Hll19y5MgRevXqxbJly2jTpg0Ahw8fpmbNmowZM4aWLVuybt06hg4dysGDB6lUqZJN61KzqN4NwkMi2DZ/D1sX7CboWQiZc2ekWc/6VGtVgU9z9EpyraYfFvSlQeeXN5kH1x4xc8ACTm47m+BpyF4oK93GfEaN1obPjP+jADbM2s7elYeJCo8iT4lcfNS7EVValEeSJHSxOr6r+zNXjt6w++Zbq20V9q06kqJBv47ODqz0m4ub18ttuuNbzvBTs7EW+3ln9GL5w9lotIbCohGhkfSpMJhHN/3Mrs87kye+mX24c+F+qlY/b/plfTbP3ZkqY0/YMZyy9Uoke5yYqBh6lf2BxzcTb1XJGhmv9B7MOf8b3hm8kj2XiopKYt4ZJeO9e/dSp07iYLsvvviCBQsW0KVLF+7evcvevXsTzu3bt48BAwZw6dIlsmbNyqBBg+jVq5dR/1WrVjF06FBu375Nvnz5GDNmDK1bt7Z5XaqB825y8dBV/p20nmObTiUrMNYrgyfLH85G62DsoHz+MICnd5/h5u1G7mI5bBbbA9i99ADjOk6zey1Ne9Tj/IHLPLrul7LxMBL0n9WTZj0bJBya/9NSVk5ajz7WsnfpjxPjOfjfMTbO3kFoYJjVqtpaBw31Pq/BnhWHiUnBmJjX6TSiLYtH/pu0zq/FAL2KrJWp0rw8P//3fZLX9iqBT14wqu1kLh26iqwxZNUpeoVcRbMzYvV35CiULUXmUVFRScw7Y+C8q6gGzrvHjsX7mNjlD2SNlCJZP79sHEKlpmVTYGUGvq8/kvN7L5mM7zCFNaMhuUiSRIfBreg25qVi8d9Dl7Hi13VWt88y58nIs/v+tq/PgvGQkpSqU4xzey7Z1afjsE/Y+c9+ntx5ZrFdxhzpWXJvVnKWl4jrp25xZtdFFL1CsWqFKFGjiF1Gs4qKiv3Yc/9+p2JwVNIukWGR7F56kMtHryPLMmXrl6R664o2CZ75Pw7kt+4zEUKg16XMnfTxTT9WTlzHvSsPcXZ1onrrSpSuU9zuG1BYUDib/9rJxQNXbDZugFQ1bsAQa3L3onE57FJ1irN07H8W+2m0GqvGQOLJ7F1d0nhw9RFaRy26GJ1N7YtULsgXI9tzZMNJq211Oj3n9l6ieI3CdhX2tETBcvnMVqBXUVF5+6geHNWDk2zO7b3EiI9/JTwkAlkjI2EodZA+ezrGb/2JXEUtp+T/M3oVi0f9m6JGgayRE+QDJElCr9NTpFIBRm8YnKBia40H1x7Rt9KPRIRYrw7+thj277fUbFMZMBg9X5b8lofXHr0V7Zvk4ujiSLMv67Nm2marbWWNzJJ7s0if1Zd/flnFop9X2hQf5JvFhx7jP6eBGgisopImSbNKxippj0c3/fix2VgiQiMNxQ11SsLNNdDvBd/XG0m4FQPh5pnbKe7xUPQKQhEoeiVhy+baqVsMbznBJtE4RVHoX31oqhs3kiyh0WpsFhx8nTEdfufcPsO2jiRJjF43CN8sPoZtpbgh5SSOnVzM6c2YIyYyhkC/FxQqb9krIskSH/VuRPqsvgal6TvPbA5+DvR7wa9fzGDLvF12rU1FRSXtoRo4Ksli7fQt6GJ1Jm8wil7hxbNgdizaZ3EMBycHqzdhSZbInDuDTYaAuTaKTuHyket8nvtrxneexuUj18yOsWvpQUICwqzOlRQk6eUay9YvybxLv9NlVAey5s+Mu48b2QpmsXksRa8wss0kYqIMwb9Z8mbir4u/03dad4pWLkj2gllwck19DajXf+ee6Tz4YlR7u8fZv+oohSsVIF1WH8ztJsoamRI1iiCEYOv83Wz7e4/d88z+Ti2MqaLyvqMaOCrJYv+qo1YrTR/8z3Kl94pNy1qNb5EkiXylclvd7gLr6sTPHwSwd/khvqk21GzWzpZUSlcG+Kh3Y76e3IW/Lk5m/NahZCuQhc9+bM3C69NZE7CAxl3r2hUrFBoYxr5/jyS8dvVwoUHnWmQrkIXHt54SaYcgYVLoNLwt9TvWJG+pXBSrVoivJ3dh4Y3pFCiX1+6xhBBs/msn47b8RO7iOQ0HX/tVCEVhdLvJjOs4NclZV+HBERzdeDpJfVVUVNIGapCxSrKI9xyYRUBUhOUn5Vptq/D30GUEPH5hdqtK0SscWnciqctMRPw22qKRK8lXJjdVP6pgdD4sKNxUt2Tj7ObE11O6WAx0Pb75tF21l7QOGq4dv5kQV6KL1TGkyRiuHrNfs8cUGgeNYZvv9erZGoms+TLTYcjHCWUOAp+84OaZu1w7cZPClQpYVf01RWy0joc3njD77CS+rTOCCweuGJ2PV5zes+xQkt+TLEsEPApMcn8VFZV3H9XAUUkWeUvm4uLBq2ZvpBqtTP7SeSyO4ejsyK87hvNDg1E8fxCQGss0i6yRWTV5g5GBc37/ZZ7d90+V+WKiYggPjsDT18N8m+hYu8YUGIycePavOsrlw+a33+xBliU8fT148TQo8bwCek3ugqOTA8H+Icz43zyDRy/us+Dk6kS2All4eO2x3fPGRsXw9N5zg3GTCmkQiiLwyqAmGKiovM+oW1QqyaJln8YWvQR6nUKLrxtaHcfRxZEs+czXE0stFL1iMNAUw3s4t/cSP9QfRXhw6gQXK3rBhM4zzGrVnNh6hqd37Uvj1sfqqdCkTMLrrfN3p1hgcfoc6Qj2N123SpIkZvb/m/CQCAbWGm5k3ABER0Tz8NpjtI72P0flLZWb0zvOp2qK+s0zSSsGqqKikjZQDRyVZFGjTWXqd6oJYBQUGn+D/WJke/KVym1xjGD/EPpXH8rFg1eTvR5ZI+Ph627XDV4ogqjwKKIjo5ne968EYye1OL75NDP7/53o+O5lB/mx2Vi7CmFqtDK5iuWgzCtlCJ4/CLBLs8ccbt6u+D8MtLht+PjmE+Z8v5gH1x6bbaeL0VGsWqFEsTSWGN1+MucPXDEbaJwSbPhzB5Fhkak3gYqKyltF3aJSSRaSJPH9330oXq0wq6ds5MFVw3ZEgXL5aP9DS2rEabS8SrB/CLv+OYDf7ae4+7gR/DzEYvyNPSh6hUGL/8f147dYP2sbQc+CrazfkMXV0uuLZM9tDxtmbaf9oFZkzJEegMjwKKZ8Ndtmj0V8bEvGnBkYs3EIsvzyWSV9Nl8e3/SzaOTIGpnCFQuYzSRzcNLSY0JHpn41x+o6Dv53zGKcjUYrkyVPJoYuH8CdC/cJfRHOnwMXEOwfavZvfv/yQ+5ffmhx7uQSHRHNxYNXqdC4jPXGKioqaQ7VwFFJNrIs06xnA5p+WZ/IsChkjYyzmdTktdO3MPu7hej1ChqNjCKE1SwsW5EkieqtK1GxcRkqNSnL58Pa8PyBPz1KfEt0RLTJm7AQ9se8pMxiYd/KI7T9tgUAB1YdtSnbqXyjUsTG6HD1cKFGm8rUalsFR2dHozaNutbh7J6LFufuNvYzStUqytndF1kzfTOBfkEJpwtVyE+/mT1YMWGt1fUIIaxqBel1Ck/vPSd9tnSkz5YOgNJ1ijH7+8XsWXYwVQt4WiM22jbVZBUVlbSHauCopBiSJFkUd9u97CB/fDM/4bVOsVwzyR6c3Zxo2acxXUZ3SEixlmWZTLky8sv6wfzUbCyxMboEj4FRds9buL9qNDKhgaEJrx/d8DNkK1kplNnz107kKZHLYpta7aqwdvpmbpy+k8hDIskSLu7OzBuyJOH95yySnZZ9GpO3ZG6y5M1IrqI52L/qCPtXHbX6PiQkPNN7GBlIryNrZHwzexsdC34ewqE1x203blKpHpZPFu+UH1RFReWdQI3BUXkjCCFY9PNKu+IwrNFx2CeM3fITE3YMZ6XfXHqM75iogjhAqdrFmHd5Cm2/bUH2glnImDM9bp6uKboWe9Hp9GTJ+zKo2s3LFWHDFp2bl6vVNg6ODkzYPoyan1ROJMCnddAQFW7szXpw9RF/D13Oi6dBCTpDa6ZvSaiUbYl6HWvQtEd9i20VvUKDzsalERYMX0GsPZ4zYdD30TpqyZQrQ4r97cZ9NjXVJAFUVFTeLqoHR+WNcP/KQx7d8Eux8ep0qMYXI21Xys2UKwM9xnekx/iOADRyaJ8qHgF3b1f6TOvO0Y0nObD6aIJmy+s4OTtSq11VwFAWwjezt+XAYMlQ3DFjzgw2rcPNy42flg3gq0mduXjwKkLAvpWHOLrxVCKvTrzmzox+8ylcqQB6nZ5rx29ajYnyyuBJ/z97EhkWxdb5uwl8GpRou1HWyBSrVojyjUsnHAt9EcaRDSft3prS6/SsejYPN09XxnWaxp6lB+3SCzLFk7vP2Dx3J+2+b5mscVRUVN49VA+OyhshpdV09yw/xMbZOxId938UwINrj4gMNz/fi2fBKV77SpIgc+4MLLn/J1nzZeLxracmjZv4rKA+U7txbu8lBjf+hRbuHZnwxQzLEwiDgN+xzfap76bPlo7a7atRsUlpjm48bbEIZ0xkDD1LfsvXZX+w7l2RoHClAji5OOGdwYvfD4ymSMUCxk1kiVrtqjBm4xAjYcNg/9Akxd1ER8VwIG7brMOgVmgdk17DKx6hGMo9qKiovH+oHhyVVEOv03Ns02nO7L5ATERMklRtLTHvxyU07FIbRycHjm48xaKfV3LjtEHbxNHZgUZd6vDFqPY8uPaYNVM3cW7vJZAk0mfztWn8zHky8uSObZo05RuV5oeFfbl15i4/1B9p1huTPkd6vprYmb0rDlktYfE6dy8+YGjzcXQb8xmfDvnYrr7+jwLNau8klQqNSif8O3PujEw5+At3Ltzj6vGbaLQaytQrQYbs6RL1887gmaTPgkar4flDgxBknuI5Gb1+MKPbTSY8OAJZKyc5WP3RzSf80GAktdtXp+5n1c0GyKuoqKQtVANHJVW4f/URPzUby5M7z9DEqeymdLZM2ItwTm47S4h/KL/1mGX0NB8TFcumuTvZt+oIIf6haLRygvcixD/U3JBGBPgFIkmSxW0QSZJo0KkW3y/ogxCCgV+NQB9XyTxRW1nCycWRe5cfcGjNcTvfLQlep/k/LaVsg5IWq277Pw5k45/bObD6KNERMeQsms3u+cwha2TcvFwT9I9excXDBY1WgxKnLWQKd283qraswJH1J+3ypOlj9Xhn9Ep4Xa5BKZY/msPeFYe5ceoWlw5f4/a5u9i7a6XoFc7uvsSZXRdZNu4/ftvzs81bgSoqKu8ukkjuJnYaJCQkBC8vL4KDg/H0VOXaQ1+EsXPxfu5deoCzmxNVW1WkRI0idhV8fJWwoHC6FfnGos5JSvH1lC78Neift5ruO/fCZHIXy8GVYzfoV+VHq+1dPVyICE26wJxGK1O/Uy2+m9fb5Pkrx24wuNFoosKjE37/CQZecrKRJEPWlLuPGxO2D6NA2ZfFNENfhDH5y1kcXHPcaPyy9Uvww8L/kS6Lj9FQ9y4/oG+lIcRExdr1GVlwbRrZCpiutv5rlxnsXnIAfTI+cxqtTK6iOfjzzEQkScLvzlM2/rmD8/svI8sS5RuWpsmX9Uif1TYvoIqKSspiz/1b9eB84OxedpDfus8kNlqXkAmzesomilUtxKh1g/BMZ75mkjm2L9hL0LOQZAeA2sLD635vx7iJMxQ++7E1uYsZMo9s3c5KjnEDBl2Zq8dvmjwXFRHN0ObjiAqLNlJkToi9SeKfpEC5vHhn9KJikzI06FQTNy+3hHOxMbEMbjiam2fvJhr/3N5LDKw1nD9OjOfO+ftcOnwNjUamTL0S/L5/NJN7/smNU7aVTJAk2PfvET77sbXJ85lyZUh23Lhep3D7/D3O77tMgN8Lfv1iOkK89J5dPX6T5b+uZeSaHyjfsFQyZ1NRUUlNVAPnA+bc3kuM7zgtwRB5NUbjyrEbDG85gd8PjLbLk3Pj9G2Wjl2d+saNBOmy+HBh/+XUnccMWfNl5tPBH9Ooa52EYx6+7m9sfkdnB5PH9644TEiAhS04yaAPpOgVZFmyuaTD4MX9yFnY9DbX/n+Pct2MkaLXGco5dC/Sn8AnQQYjWhgyx0rUKMIvGwbz4mkwD6/78e9v67l+8pbZrUwhYNuCPWYNnIZdavPPL6tsej+W0Gg17Fp6gG1/70nkXVL0CrFRsYxoNYGFN6YnCBfGRMUQ8PgFzm5O+GTyTvYaVFRUko+aRfUBs2TMarNZKIpe4dLha4Zqzjay85/99Kk4mGAbY1yShYDoyBjuXnyQ+nO9wrB/B7Lo5gwWXJtG4251jYy/UrWLWvV4ufu44eppXgzRFmRZolrLiibPnd9/ybJ+zSveCFuNm1zFcpCjUFaz57cv3GO19lfgkyDDnHolwbN06cg1vq83khyFslKrbRWEIqzGaQVbqNOVJU8mPv+xjcX+NiHBteM3zdbBEkKgi9WzcfYOwoLCmTVgAZ9k7E7n/H1pl+VL/ld5iN3ZbioqKimPauB8oESGRXJm1wWL8Q8arcbmTB+/20+Z2PUPmwKJZY1MtoKm4yhsxcnVMdUqflsiW/4sZMmbyaRXy8HRga6/fGqxf7cxn/FR78ZJTm+WZAknNyea9Khr+rwkpXiBym6/fGrRixf4JChJxT0VncL9K4/Yu+IwAJnzZEDWWr4kZciROCvrVb4Y1Z6vf+9i1sNlC/pYPc8fBlhMqVf0Csc2naZ/9aGsnbHFSAbh2slbDG0+ji3zdiV5DSoqKslHNXA+UKIjY2xqFxUebbVNeHA4f/Sbb/O2lKJXqPpRBZvamiM6IuaN1zBydncmV9HsFts0/6oBvad0xcnFUB9KE3fDdnJx5OvJXWjRqyGdRrSlXAND/IYpz4dneg8aflEbZzenOINFMhhEErh6ujB+61B8M/sk6gdQsmZRizdme/HK4EnVlpb/VplyZbBJ9dgUkiyxa8kBABp3q2c11VuAxWrvkiRx88ydZNUXc3R2QOugsdrO/1GgySrq8Z/Lqb3nWt4uVFFRSVXUGJwPFA9fdzzTeVi8ACt6JSGA1hRCCP4ZvYpl49cQG2XjDUWCup9W5+zuCymui5PaFK1c0GQpiNf5uF9TGnWtw6E1xwl4HIhvFh+qt66UUKfL0cmBXzYMZv+qo2yas51HN57g4eNO3c9rUO/zGqTP5oskSfSa/AU7Fu7jwsErSBKUql2c+p1qGspMmKF2+6rM/eEfQl+EmlVRthVZI9OiV0Or7Rp0rsWxTUnbkhGKINjfsO1UrkFJ8pXOza2zd822v3fxAdP7/EXmPJnIUTgrlZqWRaN9aYz43XnKjkX7krSWeGKiYilWLSchAZfMGouyRiYsKNyiB1Qfq2f5+DX0nNg5WetRUVFJGmqa+AecJr5g2HKWjV9j9iLt4OTA8kez8fQ1HVeyYNhyloxZbfuEEvT+vSv1O9ektW/XpCw5RZE1MpKEzR6Pn5b3p3a7aqm8quRz7cRNBjUcTWRoZJK2jsDwu3H3dmPuhd/MeovAEJg+rOV4Tmw5m6R5NFqZ6q0rMXT5QAD6VhrCtROmM8ReX5+iV/DJ7M3gRf+jbP2SAAxvNYEj608maS32YKtx7ujiyOrn81XxQBWVFMKe+7e6RfUB035wK/KVym02HiR/mdwJWy2vE/Q8mOUT1to3oYA6n1Z7K9W7TeHi4UztDtWMPAAmkcAznQfVWpkO7L128hbzf1rKrAEL2DJvl8UyEW+CQhXyM//KFDqNaEfekrnImj8zzm623WDjfxeZ82Tkt70jLRo3ALuXHkyycQMG47Lplw0SXj+7/9ymfvFGedCzYH5qNpZrJ27y/GEARzekrnGj0cpIksT/ZnS3qX1MZAx7lh1M1TWpqKiYRjVwPmBc3JzpPv4zswbHlWM3GPbRBJPnDqw6areInyRJ7Ft5BHdvN0Ml7bdYzVuSIGfh7Axe1I+VT+YydsuPpMvik8jYkzUyGo2GQYv+h4OjceBqWFA4PzQcRd+Kg1k5cR3rZ25lcs8/aZ/lSw6ttV+pOCXxzexDx2GfMPvsJBZen06bAc2txslUbl6OT4d8zLitQ/n76lSL25PxrJ+1LekB0xLU+bQ6ZeoWTzjmlcE+j6pQBIoiWDTqX7b9vYfU+lDJWhnvjF7U7lCNGcfH0fyrhji5mjb+X0WSJE5sO5sqa1JRUbGMauB84Kz8dZ35G5SAM7su8O9v6xOdCvYPtTuwVCA4tfM8ulgdrfs3S8pyUwwhoMXXhvgST18PKjQqw6wzE2nRqyFO8dsJEpRvVIrJ+0dRsUmZ1/oLRnz8K+f2XAIMnghdrB4ERIZHMartb1w6fO2NvidLfNS7EW5erib/ZhqtTKbcGfhxWX++GNme8g1LIcu2/W0fXHuUpDgqz3QefDGqA4MW9TXK0GrYubbdBpOiVzi++TT3Lj9INaNZ0SmM2/ITgxf1I3exHMwbsoToCOuB+kIIrh2/Sb+qP/FTs7HsWLSPmCjbAvxVVFSSh2rgfMCEvgjj9E7LqeIAcwf9g//jQKNjGXOmt794o4Aj604woMYw6n5WnVptqwIkOQMnOWTNn5m6n1Y3OuaT0Yv/zejBfwF/s+zBn6x9sZAxG3+kaOWCifpfOnSV8/sum/7dxd3vl461Iz4plfHN7MNve35OSLPWaDUJGV45i2Tntz0jcXFztntcV3f7NH1qfFKZmScnsPzRbD7/qY1RlXGAJj3qkTFHeqvp4okQhjR9Ww2zpHDnwn2iIqL5vv4oVkxcZ3O/5w8DuHL0Oie3neXXLjP4qvR3ib5PKioqKY9q4HzARITYVjJAKIItc401PWq0qWRzXMfr3Dh9h2m95/LNn1/y/YI+FKlUAFdPF9y8zGcHpTRdf+lgNvbG0cmB9NnSWcxWOrD6mMXYHUWvcGLLGaIirKfZvynylMjFwhvT+WXDYNp9/xEdBn3MpN0/M/vsJDLlSlpxyTqfVrfJQHX1dOGLke0ZunwABcrmTbTdF4+7txuT94+iSMUCdq3DwdmBep1qpnjF9FdxdHFk8c8ruXr0hl1xZPEerviAb7/bT/n541/fSCkTFZUPGdXA+YDxyeRlNoj4da4cu2702sXdha9/T1omlKJX2LfyCK19uzKp6x+4erkyZuMQ1r5YiIu7/V6E17EkdCfJEt4Zvaj+caVkzWGoJ2X5BiUERL9DBg6ARqOhUrNydBvzGV1Gd6BU7WJJLqoK0LJvY1zcnU0aObJGwjuTF6PWD2Kl31w6DvvEJg9LxhzpmXLwF8Zstl64FAAJ6n9ek7L1SlCqTrFU8Qg6OGkpXr0wG+fssKjDYwt6ncK1E7e4fOS69cYqKipJRjVwPmAcnR1p+EVtm9qa8lY07VGPIUu+IVNu46d/e1RkhYCTW88yoOZw+lcfSoGyeezfnngFZ3dn6n5eA40JoTaNVkbrqGXoigE26dlYIkehrFZTsD183HD3cbPYJq2TMUd6Ju4agW9mbwA0DpqEz0quojmYcXQcVZqXx8nFfm9fhUalyZo/s9W4Gg8fdzr/3BZJkgxFMBvFiShq5ITPgbO7M3lK5LRaUsIUkiTRsm8TQgPDbPZ6WkOjlTm943yKjKWiomIaVejvA6fTz+3YvnCvZWVjCco3Km3yVN1Pq1O7fVVunLpNWFA4mfNk5Pb5+4z6ZJLNa4h31b8alCtJUiIXfrz2iTlkjUyzL+vT67cv6PfHl6yevJENf27jxdNgtA4aarWvSvsfWpGneE6b12aOBl/UZv7QZegV01siskam2VcNE8WYvI8UKJuXf+7M5MiGk1w5ch1Zq6Fs/RKUrlM8Wd4hSZJo/0Mrfu/5p9k2rp4uzDr9K+mzpePOhXusmryRiweuotFq8ErvQf6yeaj7WQ1qtKmMo5MDE76Yzq4lB+wKjM5dPAdtv21hc2kQBycHYq0qKUs2bacJIYgIjcTByQFHp6SXn1BR+RB5Ix6cmTNnkidPHpydnSlXrhwHDhww27ZLly4v5elf+SlWrFhCmwULFphsExX1dvVH0iI+Gb34afkAs+dljYSHtzv1O9U030aWKVQhP+UalCJb/iwUqVwgyanDhvHi+kqGJ914j0CWvJmo3b6qySd6WWNI4233/UcAuHq40GlEW1Y8nsumiCVsilzK4EX9UsS4gbiA5OkGLRRTqeW5imanw+BWKTKXvfjdecrFQ1d5fOuJXf2EEOj1SYth0Wg1VP+4El/+2onuYz+jTN0SyTJu4ilbv4TZ1HHfLN7MOfcbmXJm4Njm0/QuP4hdS/YTERqJXqfnxbNgjm8+w/5VR9DEbVsJRdi9rnuXH9Kvyk84uzuTMWd6q+3Hbf0Jd2/Lnju9Tk+RyubjjKIjo1kyZjUdsn9FK+8vaO72OT81H8vFQ1ftWruKyodMqntwVqxYQf/+/Zk5cybVqlVj9uzZNGnShMuXL5MzZ+KbzdSpUxk/fnzCa51OR6lSpWjbtq1RO09PT65dM07DdXZOfvzGh0iVFuUZOLeX4UlZeqnQKkkSrp6ujNs21GLA7eukz+pLhUalOb7lTJLWE7/107xXQ2KjYtE6aqnYtAyVmpUFIEehbKz6fQORoXEGrQRl65VgwJyvEgnTSZKEo7PpOKPnDwNYP3Mb+1YeJio8itzFc/JR70ZUbVnBpliRZj0bkC6rL/+MXpWgvuvq6UKT7vXoNKKtXb+zlODKsRvM/m4hlw69/F4UrpifnhM7U6JGEbP9Lh66ysqJ6zi++TR6nULu4jn4uF8zGnWt/VY9UBGhkXxX52dCX4QlOifJEnqdgrObE+EhEfzSfjJ6nWLk9Yv/HB9ee4L+1YfyzayeFKlckF1LzT9gmULRK/g/CmB6n7+o06EaK341n0GVJV8mStUqxke9G7Fs/BqTniJZI5MhezrKNy5tcozoyGh+qD+KK8duJPQXiuDktnOc2HqWn5YNoFbbKna9BxWVD5FUL9VQqVIlypYty6xZsxKOFSlShFatWjFu3Dir/deuXUvr1q25c+cOuXLlAgwenP79+xMUFJSkNamlGkzjd/spG//czpVjN3Bw0lKpaTkadqlt9WnUFHuWH2TsZ1OTvBZZI9Okez36/9nT5PmoiGguHrxKTFQMeUrkJEueTHaNf+3ETX5oMIqo8OiEba/4LbB6n9fgh4V97Uo5fvEsmOiIaHyz+LyVrYSLh67yfb2RKDq9UWyQJEvIssTYLUMpW69Eon47Fu9jYpc/kDRSQqHL+O3Bmm2r8OPSb96akbPuj63M6DfPbCy3JEt0GdUBd283pv/vL5sym/pM7cr8n5YRFRGdJP2enEWzc//yQ7PnnVwd+ffpPDQamZ+aj+Ps7otGZR1kjYyLhzOTdv9M/tJ5TI6xeNS/LB71r+n1SYbYuRWP5iTpe6miktZ5Z0o1xMTEcOrUKRo2NC7Y17BhQw4fPmzTGPPmzaN+/foJxk08YWFh5MqVi+zZs9O8eXPOnDHvLYiOjiYkJMTo50Pk2slbrJy4jhW/ruPS4WuJYlyy5M3El792YvK+UUzYPpzW/ZuZvIjGRMXg/yiAyDDzAZcvngYna61CUYiNMR/H4OzqRPmGpaj6UQW7jZuY6FiGthhPVHiUUUxP/L93LTnA+pnb7BrTJ6MXmXNnfCvGjRCCqV/PSWTcwEul3ym9Zif6e/s/DuS37jMRQhhV8Y5vt//fI2xfsDfV12+O3UsPWIwvFopg15IDXD1xI2ELyhoz+y/gq0md0Tq81AECbN62smTcgKHK/cH/juHo7Mi4LT8xcG4v8pfJg6uHC+mz+dL22xbMPT/ZrHGj1+tZP3ObeeNLQGxULDsX77dpvSoqHzKpukXl7++PXq8nUybjG1CmTJl48sR6fICfnx9btmxh6dKlRscLFy7MggULKFGiBCEhIUydOpVq1apx7tw5ChRIvK89btw4Ro4cmbw3kwro9XpePAlCo9XgndErRWIWTOH/KICRn/zG1WM3ElJoFb1CvtK5GbH6O5sNhOcPA/hn1L/s+Gc/sVGxyLJE1ZYV6Di8LflK5TZqe2zTqWStWQg4sv4kjR3b4+btRoNOtWgzoDkZsqdDURTO7r7IlaOG91O2QUkKlc9n89gHVx8l6JkFA0yC1b9vpGWfxqn2N0lJbp65w92LD8yeF4rA79ZTLh68arRVteWvXQgLGc+SLLFm2maadK+Xksu1mbCgcKz5l0MCQnFw0GKrhLGskbhx+g5zzv3G2ulbOPDfMWKiYvDN7M39K4+SvWaNVsb/oUHET+ugpUn3ejToXItDa09wbPMpnt73Z/fSgzTqWhvvDF6J+ocGhln+bGLwAt25cC/Za1VRed95I1lUr98khLAt0G/BggV4e3vTqlUro+OVK1emcuXKCa+rVatG2bJlmT59OtOmTUs0zpAhQxg4cGDC65CQEHLksF5nJ7WIjYnl30kbWDt9c4KnI3fxHHQY9DH1Pq+RonNFhkXybe0RPL1nKGL4qsfi7sX7DKw1gjnnJuHh425xnKf3nvO/ykMIDghNeNpXFMHh9Sc5tuUMv24fRvHqRRBCcGjtcU7vvJDstYcGGmIvQvxDWTNtM9sX7uXbeb2Z+8NiHt3wQ6OVEQLm/7SUYlULMXzVt1aLQ4IhW0vjoEEfayagVsCTO88Ieh6CT8bEN6F3jSd3ntnUzu/2UyMD59bZOxY1XYQiuHvxPoqipIhC8LWTt9gwcyuXj17HwdGBKi3K07xXA9JnS2eyfY7C2Xh4w8/Iu/Q6Qc+CiY6MsVngT69TOLb5NF+Mak/f6d3pGxcoHvjkBZ/m7GVxLpvG1yt4Z3zpNve7/ZRBDUfjd/spskZGCMG+lYdZMHw5gxf9j1rtqhr1Nxcv9jqONupXqah8yKTqFlX69OnRaDSJvDXPnj1L5NV5HSEE8+fPp1OnTjg6Wv4yy7JMhQoVuHHjhsnzTk5OeHp6Gv28LXSxOoa3nMCCYcuNtnHuXXrI+E7TWPTzyhSdb+fi/Ty+/RS9iQu3Xqfg/zCAgbWG8+9vGwgJCDU7zox+8wj2D010A1D0CvoYHeM7Tyc2Vsf4TtMY2cb2FHFbUfQK4cERjGozMSE7SK9TEgy2q8dv8F3dkTbV+bH1Zp0UzZS3gYevZeM0Hs90HkavHZyslzbQaDUp4sVaPmEtfSsOZuc/+3lw9TG3z99j2fg1dCn0Def2XTLZp1nPBjYZHLuWHMA7o6fNAn/+DwPokK0no9v9xvOHAYChlEWjL+yvg/U6Do5aarQxPHzFxsTyQ4NRCRXSFb2CUARCEehidYz5bApXjhlfs1w9XChZqyiyxvw69Do9VVtWSNY6VVQ+BFLVwHF0dKRcuXLs2LHD6PiOHTuoWrWqmV4G9u3bx82bN+nevbvVeYQQnD17lixZsiRrvW+C7Qv2cnL7uUTxEPGvF4/6lzsX76fYfDv/2W/VeX/34gPmDlpMh+xfsWz8f7x4zUXu/yiAYxtPm9WgURTB07vP+bnVr+xedjCFVm5iHr2CEneDeB29TuHB1UfsX3XU6jil6hQz773B4HHMWSRbIoPgXaVEjSJ4W/E0uXu7Uba+cZBxxaZlLXpwNFqZys3LJdvAObn9HPOGLAEwMrQVvUJMVAzDPhpvMlOqfMNSNns0Fb2Cb1br3rtX2x9ce5z/VR5CgN8LAPpO756QqRcfn2OvwdNpeLuEuLUDq4/x5M4zkw8XCIMB/e+kxIVsP/uxtVkRSY1WpmD5fJSuU9zkeRUVlZekug7OwIED+euvv5g/fz5XrlxhwIAB3L9/n169egGG7aPOnTsn6jdv3jwqVapE8eKJv8gjR45k27Zt3L59m7Nnz9K9e3fOnj2bMOa7zLqZW5EsmBwarczmOTtTbL6QwDCrcQxg2I6IjY5l/o/LaJ/tS8Z+NoWg5wZD5+F1P5vq5hzfcsauGj0pjSxL7LYhBbhKi/JkypXB7BO/EIJ237dME/E3YPCydB/7mcU2X4xsn2j7o1bbKmTIkc7s70FRBG2/+yjZ61v123rzv2tFEBUWbTKYWZIkvl/Qh3INSlmdIyQgjOCnwdT4pLLVtvEoOoUXz4L5Z9S/gGF7aNTaQfy+fxSNutShassKFtPrX+fL8R2NtI+Objxp0auk1ykcWX8i0XerXINSfPtXbzQOGkMWnOalFlTekrn5ZeOQNPPZVFF5m6S6gdO+fXumTJnCqFGjKF26NPv372fz5s0JWVF+fn7cv2/ssQgODmb16tVmvTdBQUH07NmTIkWK0LBhQx49esT+/fupWLFiar+dZPPg2mOLxoJep3D3svmAUXvJXiCL3bV5hF6wb9UR+lcfRlhQeJKLatqLk2vy4goURRASmNgT8DoarYaxW37CK72H0Y0i/qm9zYDmNpeweFdo3K0u/5vRI+FvpdHKIIGTiyNfTepMy76NE/VxdHbk1x3DSRfn+ZA1MpJk8FpoHDQMXvQ/ilYplOy1nTNXdT0OgeDcXtPbVBqNhoLl89pUviM2RseBVUdp/31LNFrZps+9olPYvnBfwtbmlaPXWTtjC/tWHeHk9nM8i4tds4VW3zQ1+jzFRMVarVul1+lNXg8ad63Dsgez6TbmM+p9XoMm3esybutQZhwflybiwlRU3gXeSJBx79696d27t8lzCxYsSHTMy8uLiAjzsui///47v//+e0ot743i7OpEbJT59GdJlnD1cEny+OEhEexcvJ9jm08TGx2Lu5ebxZuLORSdgt/tp6yZupnPhrYmXVYfAh6/SPK6bCE6wnr8jCU0WpnsBW3bpsxZOBvzr0xl+8K97Ft5mMiwKPKWzEXzXg0pXq1wstbxtviodyMadK7JobUn8H8UiG9mb6p9XNGi4GD2gllZcH06B1Yd5djmU8RG68hfJg9Nute1KWDbJqx5/wQWjf58pfPYHPwryRKnd51n3uUpbJi1nR2L9hISYNnojYmKIdg/lKMbTzGtz1wkXpYJeXLXNgMnY670iSQC8pXMzZH1J81+/+K3Qs3FQflk9KLDoFY2za+iopIYtRbVG6Z2+2psnrvD9L48Bpd9zU+SplJ669xdBjUYTXBACBKG+4r0StkDe7ePFL3Cxjnb6TSiLR2HtWXq13OStK6U4lXBNFPodQrNvmxg83ju3m60/qYZrb9plhLLe2OI2GuIyH9Bdx9kTySX5uBYA0nS4OLuQv2O5stqmMLRyYF6n9dI8Qy+eIpXL8L5/ea9OJIsWdwKqtqyPF4ZPAkJCLUqzicUwY3Td9A6aOn12xc4uTiy/Ne1Vg2kXqW/S/D+iSTss370daNEx5r0qMs/v6wyv1YErf7XNOH15aPXWT9zK5cPX8fBSUvVjyrQ4uuGZMyZwewYKioq5lGrib9hWvdvhtZBazJDR6OVyZo/s11xBPFEhkcxuOFoQ7CmePnQnHBDEODibn8pi0C/IBRFoVnP+nQZ3SEhJuBNxgDEbzUUrVyQYtUKmc1uatytDsWrp03viy0IIVBCxiMCWkDEEojZC1GbEC96IgI7IJTkiSumFm0GNLdo3Dg6O9C4W12z/R0cHRi6fABaBw22fuwiQg0ilHU+rW6T98eWrU1LNOmRWCsofbZ0DJj9FUiY3C6r0qI8Tbob3veSMav5pupP7F1+CL/bT7l/5RErJ62na5H+nN6VfMkFFZUPEdXAecNkL5CFcVuH4h6nO6N10KBxMAQQ5iicjYk7hydJDXfPskMEPQ8xeyPRaGVK1S7Gj8v62zWuRqtBlg0Gzec/tWHpvVl0Hd2B3MVyJDul1ha0DhpqtavKmE0/Mnn/KCZsH8YnA1vg6vlyG88nszc9f+3EgDm93u/gy4glEDE/7oXe+P+xFxFBA031eutUbl6OTsMNteReVQ+WNTJaRy0///eD1Yy10nWKM+PYeApXLmh1Pq2jNqEoZu5iOajzafVU/ay6ebma1ZFq3K0uk3b9TAkThvedC/fZu+IwJ7aeYcGw5UDiLLPY6FhGtJpASKB5CQcVFRXTpHotqneRd6EWVUx0LAdWHeXq8RtoHbRUaFKGMnWLJ/kGPfKTSRxae9yiC1/rqGVL1DJWTd7A7O8WIcuS2XTUV5l5cgIFyuY1Onb91C36VBicpLXay6KbM8iS11g3KToymgfXHqPRashZOFtClsn7ihB6xPPaoDy12E5KtwnJwXyV6rfJhQNXWDdjC5eP3kjYgvmod6NEf1tLREVE0z7rlwYPjYmPrqyVadCpFt/NexnzFxMdy4y+f7H17z1W433sRdbItP22BT3GdzQ6HhsTy6E1x7l48CqR4VEcXHOciJAIk2vOXjArj289sRgrV6NNZfr/2TPNSBeoqKQW9ty/VQMnDRbbfHrvORtmbTPo6SiCkjWLcvfifc6ayUSJR5YltsauQJIkTm4/x8pf13Jm90Wr8xUom5c/ToxPZHyNaP0rh9eeMN0pCTE/5pi8byQlahRNmcHSKCL2OiKguZVWMpLHd0huPd7Imt4WB/47xuh2vyGBkYEua2XSZfFhxrFxJgOknz8MYM20zSa1Z5KCLEtkyJGeP06Mxyv9y+vIjdO3Gdp8HIFPgtA4aFBeq3KeVLQOGjoM/pjOP7ez+iDkd+cpW+ft5uENP9w8XanZtgpl65dIEUVqFZW3iT33bzXIOI1xbNMpfm4zySB6F/fEd/fyA4ResWhUyLJEgXL5Ei6M5RuWQpKwycC5cfo2rdN1JSY6Bu8MXjTuWpeP+jQyzGVuzhQ0mwfWGkH5RqXoNekLMuRMn6wss7SL+cy7l0ggkpeJlhao0boSE3eOYNHIlZzfdxkAR2cHGnSuTeef25rN/sqQPR11P62eYgaOogi+GNneyLgJ8HvB9/VHEhkaBWBRUNJedLF6/hm9Co1WQ8dhn5htt3zCWub9uARZNpSGkGWJLfN2UaRyAX7ZOARPX9ULpPJhoHpw0pAH59n953xRsB/6WJ1N4n2vM3jx/6j3uSHD5r+pm5g1YEGS1iFJ4J3JmxdPgpLU/1Uy5cqA/6MAs1llpihRowif/tiaCo1KJ3v+tIJQwhHPqgBRFttJPnORnGq9mUW9AwQ9DyYiJBLfLD44u1rXa9LF6uiQ/SuCn4cke25JAp/MPsw9P4lNc3axee5Onj3wt1+WQcIoNd0ajs4OrPSbi5uXW6Jzu5ceYFzHxPX4wLCdVqJGESbt/tm+9amovEPYc/9W/ZVpiI2zdxjq2Vjw0oBxIOerLB69isPrT3D1+I0kGzdgyNAKehqU5P4JSDD4n/8lpMHaGn506fA1fmwyhs1zU07x+V1Hkt3AtTVgLtZIBjkrOKZOqve7incGL7Lmy2yTcQOGCt9tvzWvzizJUoLwoTWEgEC/F/Qs9R1/D1vGk7vPkqQ5ZRjL9ieWmKhYjm48bXKMJWNWm92+UvQK5/Ze4tcuM2yq2aaiktZRDZw0xKkd5y1eQBVF4Jneg3xlcps8//iGHyM+/pXZ3y0yawTZSkr4/UrUKErxakWYe+E3vp3X2+aikfG/g2l95ibUEfoQkNy/A21BEn9tNSC5IPlMR5LUr7Q16n5WjQLlDEHz8bZAfJZV/tJ5mHPuN1qY0LUxR6BfkFV9HosIaNWvCWBbgVdJlgg1kdb+9N5z7l95ZNVY2rF4Hz81H4cuVpe09aqopBHUq2EawpanvNDAMK6fuG2mv+H/lw5fs2tLKLXIkMMXACcXJxp3rUO6rL529ReKYNvfe1Jjae8kkuyO5LsUyX0gyNkw7G24g2sHpHTrkBxKWB3jQ2fz3J10zNOHW2fvxnk6DAaFi5szA2Z/xbQjY3B0cWTXkv02j5kSu/ze6b2YengMlZqXsz6fIsiSN2Oi47HRtsRpAQLO7r7IriXW67apqKRlVAMnDVGqVjGr9XWsPkkKG9q8Ic7vvWz0OkPO9HbXzbqXgnW70gKS7Ibk3hM54x6kTFeRM51G9hyBpM35tpf2znNy+zl+/2p2QoC+ECLBOImOjGbdH1uJjoxhZv+/iQiJfKNrW/X7RjbN2cHV4zdtkjw4t9/4u/PsgT9Xj9/Ewdk2DS1Jltjw5/YkrVVFJa2gZlGlIVp83ZD/pm5KkbGslT14E7yuHtu4a12Ob0ocW2AOSZZwcnkzhUDfRd5rUcNUYNm4/8x+7vU6hdvn79Gr9Hc2159KScJehLFz8X6bY3j+nbieh9ce8/3fffi9558c/O+4XZ4koQge33yS1OWqqKQJVA9OGiJrvswMWvQ/ZI2cSBHWbt4BJ47ja0+bVT8qT+k6xW2KQwDDTanax+9+BXmVt094aATn9122bNRLthfXTA3sDVA+sv4kfSsO5tDaE0naJnP3TpyFpfJ+IIRARB9CefENin8rlMBuiMi1iA9ARuJVVAMnjVH30+r8efpXGnWpQ8Yc6UmfzZdi1QrZNYaTqxPfze+NRisbGUdv2iNQvmEpo9carYbRGwbT9Mv6aB0tOxc1Wpl8pXJRvlEpi+1UVAD+tCVr8B0w+u3l8a2nScrckjUGxWeV9w8hdIjggYgXXSF6O+guQ8xhRPAPiICPEfqAt73EN4aqg5OGdHDMce3kLfpWtL1sQo/xHWn/Q0v87jxl46ztnN17CUmCsvVLEvoinI1vaG9+xrFxFKqQ3+S5kIBQLh2+xsqJ67h48Gqi894ZvZh1+lfS2xmYrPLhcfPsHb4u+8PbXsY7g6yR8fB1Z+6Fyfhk9Hrby1FJYUTYDETYdExb7BpwKI+cbvGbXlaKoSoZf2AULJeXHIWz8fDaY4uuaidXRzqPaEfb7ww6IFnyZOLLXzsZtdHF6jiw+miKCKHF82rcQ/y/e0/pata4AfBM58GTO89MGjcAQc+C2Tpvt0VFVxUVgK3zdiNrJBT9B/csh4OTlthoHbJGRpIM27pZ82dm5JofVOPmPUSIGET4Qsy7I/UQewwRexXJIXEB2PcN1cB5D5Akib7TujGkyRizWVIf92tK11864OJuucyB1kFL19EdmNJrjsX5bHX8NexSG6EITu04jxCC0rWL0apfU4paqQodGxPL4lH/Wmyz4te1tBnQzOp7UvmweXr/+Qdp3EiyROGKBfhqUmdO77yAolcoVq0QpWoXUwPU31d010AEW2kkIcLngedwJPn9LtuhGjjvCWXrl2Tclp+Y0W8+D64+SjieOU9GvprUmeofV7J5rGY9GxAVHs28H5egi9Eja2UUnYIkS9RqW4Wo8GjO7LlIVJjlsgEAn/74MdnzZ7X7/Vw4cNWkmNmrRIVHc3L7eWq0tv29qXx4eKbzsM2Dk4IFYt8E1t6TUARNutejUIX8Fr2lKu8RwpZ4LAFR6xBRWxBu3ZDcv0GSrEsTpEVUA+c9omz9ksy79DvXT93G/2EAPpm8KFypQJIqCLcZ0JyGXWqz/98jPH8YgE8mb2q1q4J3BoNb++fWv3LIXCXxV3B0crR7boCIkIgUbafy4VLvsxpsX7DXekNhiO0Kemb+CThjzvT4PwpMckkGk0hxXlE7ZBscnLSM2TiEeT8u5cbpO4nWI2tkCpTNQ632VVNunSrvPtr8gDPWatYZiIHwPxFKOJLXsFRe2NtBzaJ6z5AkiULl81GtVUWKVimUJOMmntjoWLSOWtJl8SF/mTxGVZMz5U6spGoKW0TLTJGjkG1eH1vbqXy4lKlXgtJ1i9u0LfO6cSNJkqGMgwRt+jdj1ulfKVMvhRWjhe3fE62jhu5jP2PVs/mUqVeSCduHUbt9VaNsSFkjU6dDNSbsGI6jk23CfyrvB4aade2x69Ye+Q9C934KpqpZVO9BFlVKExMdy+Qes9i99KBRrE2uotkZsuQb8pXKzbFNpxjaYrzFcbIVzMLfV6Ymeb+/X5UfuXbylsmnZVkjkb1gVv66+LsaT6BilciwSCZ1n8X+f4/Y1F6SJRycHKjWqiLZC2ShfqeaZM2XOeH8tgV7mPLVbHSx+tRaslkm7f6ZUrWLGR0LfPKCy0euA1C0SkF8M9tWMFTl/UOISERgN4g9hW37rhok9/8hufd+A6tLPmo1cRW7EUIQHhJBRFgkX5X+ll1LDiQKJL5/5REDaw3n8a0nlG9cmqz5M1sUGWz3XctkGR/9Z3+Fk6tjojlkjYzWwYFv5/VWjRsVm3Bxd2HYioEsvv1HQqFNSwhFoIvR4enrTuef2xkZNzdO32Zq77nodG/euAFDUc3X8c3sQ/WPK1H940qqcfOBI0kuSL4LkTx/AW0BW3ogFP9UX9fbQDVw3iOEEJzedYGFI1aw6OeVnNt7yWq2k16vZ+30LXxRoC+tvL+gpWdnHl7zMzt+ZFgUyyesRaPRMGbTj/hk8jI8JMTZGfEKy63+14Qm3esm6/3kLZmLGcfGU61VxQQjR5IkKjYtw7QjY6xmYqmovI6Hrzv3Ltnmjlf0CtsW7EFRjD2I84YsQR+rf2sByV7p3+/MF5XkI0mOSK7tkHxXYj3UVkGSM1tpkzZRt6jeky2qh9cfM7zVrzy4+ihuP1+g1ynkLp6DUWsHkSVvpkR99Ho9v7T/nYNrjhkO2PhJcHDSsjF8CbIsEx4SwY5F+9i38jARIZHkKZGT5r0aUrxaymoshAWFE/QsGM/0Hnj6qhd4laRx9fgN/lf5R7v6rA9djIubM2DYCmqftWdqLM0mPHzdWf5ojhpbowKA0N1HRK6E2OsguyA5NQDnhkjSy+QOJeg7iNoEmPM4ykgZ9iJp0oaRowr9fWCEBIYysPaIBHE+/Suu8wdXHzGw9gj+uvAbbl7GtWd2/XOAg/8ds3u+2Ggd0RHRuLi74ObpSqu+TWjVt0ny3oQV3L3d1No5KsnGWgmQ13Fxd8bJ5eXNIuhZyglgvoqskREIhJVU9m5jPlONGxUARPgCROg4DBsxekBGRG2BsFzguxBJY0jAkNz7IaL3ggjHpJHj9nWaMW7sRd2ieg/Y8tdugp4FmwzG1esUAh4Fsn3hvkTn1s7YYsgQsRMnVyecXD/cKt5vChF7DRE+HxE2FxFjX7VoFdPkKZ7TsK1qA7JGpnG3ukaZiAlbsilM+0GtyFcyN4DJ76SLhzN9p3cnd7HsTOo2k+/rj2Ts51M4vuVMoi00lfcfEbULEToWg9s93miJ+xzoHyICeyDiNHEkbU6kdCvAoZzxIJI3ksePSO793tSy3ziqB+c9YPO8nRY1NIQQ7Fqyn4/7NTU6fvfSA7u0N+Kp3b6qTennQgguHLjC5cPXkGSZ0nWLU6h8Prvne18RSjhEbUDEXgTJAcmxJjjVBBGMCBoAMUd4+QyigCYf+ExH0qqibUlFo9XQ/odW/PntQovtZI2MV3oP2v3Q0ui4TyZvyjcszemd581q4Ugayaon5nUuH7nGsH8H8uDqYw6sOkp4SASSJJG7WHZyFslOhSZlmNprDjP+Nw+NVkavU5A1MnuWHaJU7WKMXj9IVfT+gBDhszFcG0x9BvWgvwkxB8DJUFBV0uZDSvcPQncXdLdAcgPHskZbWe8jagxOGo7BiYmOZXS73zi64ZTVtq6eLkiSRHRkDDmLZKNl78bMGbSY8CD7hPK0jlqW3v/Tah2bhzf8GNlmIncvPkgIEFb0CkUqF2T4qm8/+CKZIvoAIqhfnNs4/jlDB5o8gAz6uyR2J2tA8kRKvwFJY5sOkUpihBD80W8+6/7YmmAsvE7pOsUZ+FcvsuRJHLt28+wdvqk2FF2MzqSRk69ULu5cuI9ix8ODJEt4pffkj+PjyJgzQ6Lz835cyooJa0168WSNTO32VRnyzzc2z6eSdhFKGOJZWSuttODaHtlzxBtZ05vEnvu3auCkYQNnzGdT2Lv8kG2NX5FDkCQJgSB9Vl9ePA0yeYE3hayVGbflJ8rWK2nyvC5Wx/5VR9kwaxuXj1w3rV+jlcmSJxN/npmI8we6zSViryECWgM6Ekd2m3sqe+W821fIHgNSbX0fCjdO32bLX7t4fPspbp4u5CySjdzFcpKvTB6yF8hise+1Ezf5veef3Dp3L+GYh687nUe04/KRa+z790iS1I5lrUzT7vX4fGgb0mdLB0BkeBTtMvcgKjzabD9JklhybxYZsqeze06VtIVQAhHPKltppQWX1shev7yRNb1J1CDjDwD/RwG2GzdgdB+Nt2n9Hwciy7JRtW9zlK1fkt5Tu5KrSHaT5yPDo/ip6VguHLiCJIE5s1nRKTy64ceeZQdp0r2e7et/jxDhf2MwYkz9kqzdFBWIXAMmDBwhYkCEIYSEFLUBEbUelGDQ5kNyaQ9OtVXdoFcoUDYvBWZa18QxRaEK+Zl1eiK3zt7l0c0nuHm5Uqp2URwcHdBoZfassOO7+QqKTmHLvF0cWnucaUfGkjl3Rq4cvWHRuIE4iYid52nUpU6S5lVJQ0jeIGcC5amFRjokh+JvakXvLKqBk0bZu9I2RVaLCHDzckUXoyPSSuFMRVFY+stqnt57zqNbTwkNCMHD152GX9Shdf9mLByxgkuHrxmGtVbTUJLYteTAB2vgEL0V8ymbNiCMM3mE7g4ibGZcKqjOcOzVBvqHiOjd4NQUvH97bwvrvWkkSSJ/mTzkL5PH6Hi9jjWZ88Niq0aJOfQ6hWD/UKb3/YsxG39EH6uzrd9bUFVWefNIkgyunRFhkzD9kCSB5ALOLd700t45VAPnHSQqIprD607g/zAArwyeVP+4YqIU74DHgSkyV2hgGFMP/0L/6sMsenHO7r6Y6FjQsxBWTd7Atr93ExYUbrNLXghBSEBoktec5hFJu/EZkEDz0osmYi8jAj8HEYV5oynuePRmRLAWvMYjSepXPylER8Xw78T1HNt8Gr1eT8nqRWkzsLnR1pCrhwvFqhXm1PZzSZ5H0Ssc33KGZ/efk690bmSNbPX7VbCCGsCfFhBKEEQsRUT8C0oAaDIYPKyunyLJtml8CdcOEL0fYl+X+dAAEpL3VENdqg8c9Sr3jrF1/m5mDVxAREhkwkVtWu+5dP65Pe2+/yhhiyEl07SnfDU7SdlUYLgQhwaG2RVQqdHKZLMS4/Beo80HuhskVQpXcukAGAxFEfQ9iEisb23FEbUeob8PPvORZPckzf+hsn/VEcZ+NtVIZ+rGydusnrKRrr98ymc/tgYMwf/n9iR+ILAbAef3X6F+x5rUaFOZA6uPmjRyNFqZguXykb90HhODqLxLCP0TRMCnoPjxMq37ASJsMkSuAt9lSBrzcVQi9rLBWxu9A5Pxe051kNz7ITmkrNBqWkXVwXmH2LP8EL/1mEVESCRAwsUsJiqWvwb/w6rJGwFY/ftGloxelWLz3rmQvEqy9hg3YHDBN+vZIFlzpmUk145J7CmDQylwbWt4GXsO9Dew2biJJ/Y8InhIEtfwYXLp8DVGt59sZNy8yt9Dl7FkzGpObD3D1WM3UqwI56wBf/PiaRD/m9GdbPkzJ9LIMaSzezJkiZpBlRYQwYNAeULi76xiMHRChpnvG30MEdAOondi9uEo5rhhe0oFeEMGzsyZM8mTJw/Ozs6UK1eOAwcOmG27d+9eJElK9HP16lWjdqtXr6Zo0aI4OTlRtGhR1qxZk9pvI1VRFIW/Bv9jsc3ikSs5s+eiVQ2Pd53a7atSroHpTKwPApdPwLEmiRXj4r6O7j8guX8L0qtFEx1AWwQcKkDsVYP3JvLfJC5AgejtCN3DJPb/8Jjzw2KrDrcFw5bzY9OxfFt7RIoFc4cEhvHvpPV4pfdk+rFxdPvlUzLnzojWUUu6LD50GNSKP89MNFmKReXdQuhux2lbWdhKjt6F0CeuBSiEDhE8EEOMnbkHGgVEOCJ0Qsos+D0g1beoVqxYQf/+/Zk5cybVqlVj9uzZNGnShMuXL5MzZ06z/a5du2aUApYhw0ttiCNHjtC+fXtGjx7Nxx9/zJo1a2jXrh0HDx6kUqVKqfp+Uourx2/y7L7liq6RYVEsHL7crHbH20aSJKtqu7JGpkC5fB90No8kacFnJkQsQoQvjHuiAxxKIrn1RHKub3jt1hURuQZCfgVCQXcNdFcREXNBzgFKcjxvAmIOg7Zdct/Oe0/oizAuxwXQ20qKqW8IWD9zGz0mdMTN05UOgz+mw+CPU2ZslTdL7AUbGgmIvQia17bwo/eBkriKfGL0EL0boQQiyR+21hi8AQ/O5MmT6d69Oz169KBIkSJMmTKFHDlyMGvWLIv9MmbMSObMmRN+NJqXmR9TpkyhQYMGDBkyhMKFCzNkyBDq1avHlClTUvndpB6hNgbd3r344N00bmSJfKVzJfzbHIpeYe4Pi9n81643tbR3EklyQHLrbihyl/EoUsbTyOlWvjRuAPT3IeQXIDzugI6Ep79kGTcJE6TAGO8/8VvGb4voyBim9JqjlupI89iavahF6G6jhP6GEjQIJXQiIuaoHf0V0D9O4hrfL1LVwImJieHUqVM0bNjQ6HjDhg05fPiwxb5lypQhS5Ys1KtXjz179hidO3LkSKIxGzVqZHbM6OhoQkJCjH7eNTLltk2ZVuv47qX4yrJE3U+rM+3IWH5Y0JfCFfNbrdezYPhys/EMHxKSJCPJviYDfkXYX1h2SScTh1KpM+57hk8mL7QObzcfY8tfuzix9exbXYNKMnGshPVbrqOhzpR/Ywj/C6LWQ/h8iFiIXQ8kkpdBEDB8MUroZET4IoSSMpm3aYlUNXD8/f3R6/VkymS8P5wpUyaePHlisk+WLFmYM2cOq1ev5r///qNQoULUq1eP/fv3J7R58uSJXWOOGzcOLy+vhJ8cOXIk852lPLmL5aBgubzIZrwfkiSRPpsvVZpXQKN9t2LDS9Uuxjd/9sTB0YEGnWvRZkALq/EKL54EcfnI9TezwDSIECJO1yY1jECNYTvMoajJeUXMOcPTY8hYRMR/CPF2PRhvG0dnRxp2qf1W1yBrZNbN3PpW16CSPCRNBnBuhfnbrgTawhC1Mu61/pUf+xChkxDPqiNCf4HwvxChYwyvw2Z8UJ7AN/JY8nq8hRDCbAxGoUKFKFSoUMLrKlWq8ODBAyZNmkTNmjWTNOaQIUMYOHBgwuuQkJB30sj53x89GFh7BMTqjdJB47d8+v/Zk4w507N90V7TA8S//Vc+v/F9LaaBx5VxsKRAbAp3bzd+Wt6fsvVLGhXfDA8Kt9DrJeHB9tXB+rBQgOTo5WAozqm/Hfci/g8rg+yL5PV7ouZCCUK86AOxJ4jX0xDoIHSMQSDQqXby1pOG6TKqPYfWHif4ecp7f21RElf0CrfO3E3xuVVSBiF0EHMClBegyQoOpUzejyTP4QjlMSRsOelf/t+xhmGMlCB6yysvdAn/F2HTkCRXcOuWMvO846SqKyB9+vRoNJpEnpVnz54l8sBYonLlyty4cSPhdebMme0a08nJCU9PT6Ofd5HCFQvw+/7RFKtayOh4vlK5Gb9tGJWalSNPiVwMWtgXWSMnFLEEwxOeg6OWX9YP5re9I/n2r6/pM60bQhFWL54OTg50/rkt3nEFNDUOGjRaw1aYk6uTwQB65bsqa2R8Mnkx/ehYyjcsnaiyuK0aN1nzZ7ap3YeIJGlAzprE3hpwrI2UbhWSx2DQ5AacQc4Cbr0NxTq1xga+EAriRU+IPR13RE/ChVGEIV70NlQ9/0DxyeTN7LOTqNSsbIoGyLt5ubLk3ixc3J2ttnVyfb8rP6dVROQ6xPNaiBdfIIL7IwLbIfwbIaITh0xIsiuSzwIknzng1AgcyoFzEySfv8H1cyD1vaUGL45l5fr3hVT14Dg6OlKuXDl27NjBxx+/jPzfsWMHLVu2tHmcM2fOkCXLy5tmlSpV2LFjBwMGvKzHs337dqpWrZoyC3+LFCqfj8n7RuF35yn+DwPxzuhJjkLZjNrU/awGmfNmYu30Ldw+fxdHZ0fKNyxF814NyZgjPQAlaxZl/yrbyjn0nd6dpt3r8emQ1hxed4JrJ26hddBQoXFp8pfNy9b5u9k0ZwdP7z7Hw9edBp1r0bJPY3wyeZscr0TNImTJm5End5+bNK5kjUyhivnJWTibid4q8Uiun1uQYzfbC5CQ3PsYlEzduiK5dbXeLeYIxJ41c1IAAhH2J5LPDDvW8n6RLosPv2wYQuiLMB5e90PWSFw5eoM/+s1P8phtv/sIFzdnytQrweF15p/eJVmi5idVkjyPSuogIlYjQkxoSunvIV50B58FSE7Gmb2SJBvqwr3uEY3aZts33f0bCJtOkmPzRBhEHwHn979uWapvUQ0cOJBOnTpRvnx5qlSpwpw5c7h//z69evUCDNtHjx49YtGiRYAhQyp37twUK1aMmJgY/vnnH1avXs3q1asTxvzmm2+oWbMmEyZMoGXLlqxbt46dO3dy8ODB1H47b4wseTKRJU9ij9TD64+Z/d0ijm06nbCXmrdkLopVLZRg3Dy66cedC/e5e8m2TBsPH0OAq9ZBS81PqiS6kLbq24RWfZvYvHZZlvl2Xm8GNxyNIgmj7TZZI+Po7MA3M7+0ebwPFrdOhjgc3WXT57WFDanjSBicsTqQfZC8JiI52hdALKK289Jlboo4jQ6h++DLPHj4uFOkUgEA9q2wnCzxKpIcr+tlELts2bcxnr4etM/6JTFRsRb7CkVQum6xZK1bJWURIhoROtbcWUAgQsciOa2zbUBtAZuaSU51EGGzgWR4YUTYy3/GXoHonQgRiaTNb/AovSdigal+pWrfvj0BAQGMGjUKPz8/ihcvzubNm8mVy5BS7Ofnx/379xPax8TE8N133/Ho0SNcXFwoVqwYmzZtomnTpgltqlatyvLlyxk6dCjDhg0jX758rFixIs1q4NjKw+uP6Vt5CJGhUUaBYncu3mfoR+Pp/XsXjmw4xZldtugtGHBw0lK6TuIL573LD9g6fw9P7z/H09eDep/XoHj1wja750vVKsbk/aOZ/9PShDpWkiRRsWkZuo/9nNzF3r0YqHcOJcRQqyY+SMoIGcnjO9DkMSibigjQ5genukiSg/1ziXATc7yO3lBH6wM3cOJ5dv95grq4LQhFUPmjcuQqkp2GXWpz88xdxn42xeb+gxv9wlcTO/PJQLWI4jtB9F4QluQ9FNBdQcReR3IoaHU4SZsX4VAhbpvY1IOGBrRFkRyKIpxqQfR2klruBU1OhBJmEA+M3otRzF3IL+D1q7FkRRpFEh9SSHUcISEheHl5ERwc/M7G45hiWMvxHN98xnTRvTi7Q5KsByy+SrYCWQgPDkevUyhWtRAt+zbh5NYzrJ6yCY1WRtELZI2EXqdQoXFphq/6Dmc762AF+L0g+HkIvlm88c7gZVffDxkl6DsLmVQSSN7guwgiV0DULiAWHEojuXZCcrJvO0OE/YGw5vaW0yNlOPTeiTTePHuHDbO2c/3kLRydHajWqiKNu9XFM53lwodLx/7HwhErbC4yK2tkPhnYgi8ndERRFD726UJEqP0xF0OWfEPdT6vb3U8lZRHhiw1ZSlaMDMlnPpKTbX8vobuLCGgPIgTj770GJDekdMuRtPlRIrdCcL8krFoGTV5ItxGCehjEPhN95+O2uX2XIDmWS8IcqYs992/VwEkjBk7gkxd0yPZVslL8ZI2MUBQk2XRlYlsqFrt4OPPZkNY079UQd2+1Wm1yEbHXERFL4iTcJXCqjuT6Oci+iGdVeZkBYQ7Z0C/hYhi3zeTWG9mjv+3r0D9BPK+NeQNHRnL/H5J7H5vHTAssH7+GeT8uNVIHl2QJdy9Xxm8fRsFy5it0/9FvPhtnb7er7lTFpmUYs/FHlk9Yw7whS+1eryRB9kJZmXdpyntnaKY1RNRWRJB1I0NKtwHJoZDVdgnj6h8btqAi/8OQSekIzk0M2VkxxwzFdUUU6O9inwdHA2iRfBcDEiKwrYW2MjhWR/b9y47x3wz23L/fLUEVFbM8veefbP0CRa+gdXIwa8TY8iQaGRrF/KHL+LrcD/g//vCEo1ISEbEKEdACIlcaLlb6OxCxFOHfDBGxAuvGDRgMkldvsHH/Dp+JiNpjqoNJJE1mJI+f4l69flmQDXWwXG0IVk5DHNt0ink/GoyMV9XBhSIID4nkxyZjiYown6qfLquP3YVmT247x6G1x9mxaF+S1iwEPLj6GL/bT5PUXyUFcaoNUmKBzpdIoC1o+LEDSZMV2WskUqYzSBmPg+9yiN4P4X8atq90VwzXCqvGjWT8b8eqSOlWIDmWRkRtxbIysgIxBxBKmIU27z6qgZNG8PC19EWynVgrwYy2IBTB8wf+TOz6Rwqs6MNExF5BhPyE4SL1uoGih7DEOjX2oUFELLCrh+TWCcn7D4Mxk3DQHdy6I/n+gyS7JnNN7xYrJ603klp4FUWvEOwfwp5l5hMX6nWsafdDh6JXGNXuNx5ce2RXv9eJtmB4qbwZJMkZyeMHc2cBCcljiN2eNoPY5imI/NcQ/P/iy7gtKzsfcJ1qImU4YvAgZTiE7DvvpbinCMeq3DzC4C1Kw6jRgmmEbPkzk69ULm5fuG9XjE1qodcpnN5xnofXH5O9YFL1Wj5cRPhiDM8XFrY3JM+4IMak/L31EHPK7l6ScwMk5wYI/XNDQLEmI5L0/umv6PV6Luy/bFHYUpZlzuy+QJPu9Uyez5A9He1/aMXy8WvsmtsQMm4qcNw2HJwdbC7tYg96vZ7TOy/w4OojXNydqdyiPD4Z1Zg5S0iuHQAtInQiiBcvT2iyInmORHKqZtd4IvYyIug70N9M5so0oC2IpEkHmnSJ163NjbCmkCy5g+ydzHW8XVQDJ40gSRJdx3zG0BbjTCfVgPnjqciVYzdUAycpxBzGsgS7ApJb3JNbUkm6g1bSZEjGvGkAYV21W2BdJLPbmE+JiYrhvymbbJ5ar1MM6eIyCDulTGSNTMNOtXD1SNk03nP7LjGh83SePwhIUFbWaGVafN2IryZ1fuu1uN5lJNdPwKWlQZ1YCYxTMi5n0LuxA6G7hwj83BBfk2z0SC5tzJ92aQWhkzC/DS6DS/ukZWS+Q6hbVGmISk3L8tPS/rh5GbYKNFoNkiSh0co06V4XFzfraqgpTbzisYq92GCJyh7g8RPWXcmm0IBDEUTMOYRI/rbk+4ZGq6FAubwJpUxMIqBoFcvBoZIk8fXkLpSsVdTyWK8PLYRV4+b18STZUI+u08/tEo5Fhkdx5+J9Ht30s7pdpigKT+4+w+/2U6NCt9dP3WJwo18IeGSIqYs36vQ6hXUztjKtz7sXaPquIUkOSE41kFxaIjlWsNu4ARBhf8YZNylQf861C5I2r9nTkuyL5DncwgAaQ2BzGkfNokojWVSvEhMVw6G1J/C7/RQPHzeqt66ETyZvBtYazoUDV1JmEhu8QbJGZubJCYQEhOLg5EDB8vlwdErbFv+bQgkaAlFrMX8x04BrJ4ObODyZ6sFyOiS3noaLniQhhGLQzZGcP2jRvt1LDzCu4zST5yRJwsnVkWUPZtuULRj45AV9Kw3h+YMAm+bOmj8zHYd+wqTuMwHjAH83bzfKNyzJ3YsPuHf5YaK+Hr7uNOvZgLCgMHYs3Ed0ZAwA2Qpk5vOfPqFB51pG7RVFYf0f2/j3t/U8u+8PGCqkt/6mGW2/+4ifW0/k+BYz8hNxLLwxnaz51NIqEFcIN3oXIuIfiL0Ckgs4N0Jy7ZioBIrtY+oQT0sDMclbnORl+K679bAa+yNEJOJ5fVCemzirMYiGpt+IJPsmb00pjJomboW0buCY45NM3Qh+bkl4yja+ntKFoKfBrJq8kdho00//siyTPocv/g8DEy6MHr7utPu+Je2+/yhRfSoVY0TsZUTAx5i3ImVItxYCPzNSHU0WLh1AdoeIFXGxPQ7g3BTJvReS1nw69PuKEII/+s1n3R9bjSQSZI2MxkHDL+sHU7Z+SZvHCwkM5bMcvRIMDnNIkkSvyV/Q+ptmPHvgz+a5O7l+8haxMToeXHlEgN8LgwaVDXXkjAcGBHQZ3YHPf2qT8B6n9JrN5rm7TK6jYtOyHNtkOVZL1sh0Gt6WjsM+sX0t7ylCKIjgoRC1CmPlbw3giOT7F5JjBfvHVUIRz5KoOeM+CMmhMEiOcUU+bYuZExErECHDLLSQkdy/QXL/OmnrSiVUA8cK76uB09K7MxEhSY96d3ZzYtKenylUPj9g8BSN+PhXTm47l3ADiP+/o7MDsbE6hD7xx6dln8b0nd49yev4UBAR/yJChmIcbKwBBJLXJNBkMuzJpygyxlo38RfmRXaXd3gfEEJwfMsZ1s3YwvVTt3F0dqD6x5Vo2bcx2fLbVjT2VZZPWMu8IUsstilbvwS/bByCg+NLb2dIQChflvyWoGfBNgsHmkWCRTdnkCVPJs7uucj39UYmazitg4bmXzWkz7QPowK1JUTEyrjvrClkgxhfhgN2ZxwKoTcYOCLCvgVJ3kgZjyVJE0kJ6AixJ7DoqtfkQc6wze6xUxN77t8frn/6HSDoeTB7lh3C/2EA3pm8qfNpNdJnTbo7MHPujNw+fy9Jfau2rMDgf/oZxfE4OjsyZtOPnNx2jq1/7+bZfX/SZ/VFo9Vw4L+jZp8u1/2xlWY965OnRK4kreVDQXJtCw4lTAr9Sdr8iOijdoymwWC4WHteef3mqQeiEcEDIP3OJMUOpGUkSaJS07JUalo2RcZr/0NLgp+HsGryhoRg3XicXJ3oOLQNbQY2NzJuADbP3cmLp0EpkiEpyzLb5u+hy+gObJyzw0jEMFFbjWTQ8rEwrV6vkCn3ex50biMifAHm9+8Vg2c0aiO4tjNx/rWxRIyhHIvsjiQ5I1zaQsQ/2BWDI4IMqdxSEiQcxAusXi+SleTw9lENnLeAEIIVE9ayYPgKFEVBE+cVmTtoMW2//Yju4z5L0hZP6wHNmNR1ZpLWdPPMHRydE8fPyLJMxSZlqNikTMKxTzJ1t3gh1mhlts7fw9e/d0nSWj4kJIfCSF6jTZ90KIjhK2qD4J+mKOhtr0FmjAL6hwYjy860VhVjJEniq0mdafF1Q7Yv3Iv/w0DcfVyp2LQchSvmN5v9tGvJgZSTfxCCR7eeAHDv0gOzxg2Aohc4Ojug1+nNG0GyRL2ONVNmbWkYoYTbkL6tQcSeRsK8gSP0TxBhMyFyDQalYg3CuTG4fApRW+Lqz9kTaJzUelR5QXfbwlwyaHInbex3BNXAecMIIZg1YAFrpm1OOKZTXn7AVk5ch7OrE51GtDXqc+vcXUL8Q8mYM32itGwhBJcOX0OWZbLkzYTfnad2f+af3ffn2KbTVP3I8v6xXqcn+Lllq16vV3h631Tgmoo9SLIvwrmZ4YnQ2gVPf4Hk6QTIhsrkqoGTImTNl5kvRrZn99KD/DtpHat/N6SR5ymRk6otKyIUBV2Mjnylc1O9TWVCX4Sn2NySLOPmaXiid/Nytfqx8M3sQ2RYJKEvwk1uj3X95TNVDwewPZvR/MOp0D1ABLYDJYiX32k9RG2FqN3g/buhtlz0XmxSKtYWRJITB8ELEQWxlwAFtIWQ5MRbOZJre0S0pe0nBeSMiLBZ4FASHKukOQ+vauC8QcJDIhjWYrzVTKcVE9fRZmBznt57ztzvF3Nm9wWjejdFqhSkz9RuFCqfjxNbzzC97zwj6XYHJwezwcHm0DhouHTwqlUDR9bIuLg7ExlmXqtBo5HxslKoUMU2JM+fELrLoLuJ9QtecjwAAqQ3LzPwPjPn+8UJW1Xx3LlwnzsX7oNk+J7odQoe/eaTIWc6Xjx5YXfpB1PodXpqt68KQJ0O1bl0+JrZtrIs0/CL2tTvVJMZ38zn+ObTCR+j9NnT0XlEW7NChx8akuyK0JYE3UXM12zTIzlWNTuGCPn5NePmZT+IhtDxSOm3gfIEEXsZgr+Pi8sxNZ8AXFGijyJJLgb9HdkHEfYHRCx8JTnBEeHyMZLHICT5FUV8x2rg3AKiNph/09HbENHbDevT5AbvGTZVRn9XUIOM32CQ8bCPxnNs82mbXNEtvm7EhlmWg7sy5EjH84cBZu9rzb6sT7FqhcmUOwPf1h5hcSyNVkObAc35ckJHq2ub1ucvNs3dgWLB9f3b3pGUrFnU6lgq1hFKGCJsBkQswGK17+SSfg+yNlvqjf8BcW7fJb6r87NNbSVZQpZlI22apCJrZIpVK8Rve0YiSRIRoZF0LzaAF09eJNqCkjUybl6u/HVxMr6ZfQB4/jCARzf8cHF3Jn/ZPGg0qs7Vq4ioLYigb8yc1YDsi5Rhj8lMJqF/hHheF6vVx32XJGRiiZiziBdd44wcGzw6UgYQz0yck0FbFCndUqRXHmSE0EP4XENZFyXwZVuEifk0IHkgpd+ApMlkZS2ph1ps8x3k3pWHHN14yuZ9dmvGDWDQ3LAw3P7VR6ndoSrFqxcmQ47Ect2votfpKV23uE1ra/f9R7i4O5us4yPLEuUblaZEjSImeqokDTnuKSt1q0dLyuNUHf9DYuGIlTa3NVwTBOmy+dglFmiKIpULMHrdoISsGlcPFybvHUm2AoaMMI1Wg8bBYLRkyJ6O3/b8nGDcPLv/nEC/F2QrkIVCFfKrxo0JJOcm4NYn7tWrvx8JJE8kn/nm07R1t7DFyyqCh6AEtEUJGYfAEbTlbOpnkOc2ZdwAKKC7BBGrjI5KksYgE5HhAFK6DeD+LeaTFfQgQgz6P2kEdYvqDXFs4ykjrQ1rSJJ1KXlrhAaGcWLrWaq0KG9Vaj1z7gyUa2Cb5kfm3Bn5ff9oxnT4nXuXH8aJxwkkSaLuZzX45s+eSUpb/NARQkDMQUNWVewlw5aRc2NDTSqTYlwpjO4+JEHDQ8UYRVG4bGFbyBR6nULw81A+HfIxG2ZuS3JMTuOudXHzMo7JyJI3E3MvTObs7ouc2X0RoSgUr16ECk1Ko9FouHz0On8N+sdo67x0neL0mNCRQuU/PH0ka8ge3yCc6yLClxmMBskVybkhuLRGki3EKkk2ltfQ3zf8xF6EiL9TZtFxiMjlSG6JvfSS5AAOhRBhk0ksJfEqCkSuBY9vU3RdqYVq4LwhYqJibbrpG9zVksXMB3sIePyCM7svGsXomCJznowmM7dePAvm/N5L6HV6CpbPlxDgnKd4TuZemMylw9cMGVhODpRvXJqMOdKnyLo/NIQQiJDREPkPRgJi4X9heJp6A4XGZDVuKiW4cOBKkrabdDE6Og1vy+dDP+Hhtcf83utPrh61r+jio5tPmNbnL6IioshdNAcNu9TGO4MXsixTtn7JBOHCB9cecfHAVZ4/DOC37jMTxf6c33+ZATWHMWn3zxStnHZiLt4UkkMJJO8S9nVyKA2Sj3FRToukQMkGIwTorXhplSCsboOL5IvJvilUA+cNkbdULpsuelqtBjdvN4KeBafIvOmy+LB3+aFEmhyvc3bPJWJjYhP0OaIjo5nZ/2+2/b3XaN1l6pXg+7/7kCF7OiRJoni1whSvVjhF1vpBE7U2zrgB4wtbSl/kzCC5gmP1NzPXe87Tu0nztmXIkS7B05q3ZC4KlsvHteO37EofXz5+DRqtJqFQ6N9Dl/HNn1/RuGsdwGC4zP52IddP3bY4jqJXQAimfDWb2WcnqR7ZFECSHMC9NyJ0zFtchJVsOE0eiD2P+euOBJqklaN4G6gxOG+ISk3Lki6r5T32DDnSMfXwGCo1LYOsSf4FxcPXnfKNSxMaFGbTRfLA6mOAwcX+88cT2TJvdyKj7Py+S/SvPpSQgLRjxacFRPh8kh9j82p/DYbnFwlwBOeWlnu69bZbfVXFNB6+7tYbvYYkQdZ8mZj69RwW/byS9TO3ERkaZTLOzfwYhr+/XqdH0SkIRaCL1fNbj5mc3H6OM7sv8EP9kdw4c8em8RRFcOfCfW7a2F7FBlw7g1vPtzS5jORqocI4ceKjFh+qBJLrZym6qtRE9eC8ITRaDUNXDGRQg9HodTqjLShJkshWIDPTjozFw8edVv9ryvaF+5I9Z8+JnXF0csDT17ZMsesnblL30+qc3nmBk9vPmWyj1yn4Pwxg3R9b6TS8rck2KvYhRJRBgya5yOmQPEeDJhsiaguIMCRNLnBpiSR7IcKLIUInAbG83AbTGGrNuH2Z/PlVACjXoCTu3m6EBdkeRyMEnNt7mXN7Lyd5XnMJsRISEzpPJzoiGr1esXun0+/2UwqUNV+ZWsV2JEkC54aI8DlveGZDhhfWjBOHsuDSDiJNBcnL4FAGXCwbSe8SqgfnDVK8WmFmnhxPvc9ronU02JbeGb3oOOwTZhwfj4eP4ckvf5k89J/9FUCSsipkjcwPC/tSq21lZg1YwM7Fe632kWQJKS4GZ/vCvRafHBVFsGXebrvXpWIOW/7G8W00iY9pS4L3bKQM+5Gc6yE5FEb2GIDsOQzJrXNC4KPk1gUp42Ekz1Hg1gOcW4NzC4T+OURtMkjHqyQbR2dHvhjV/o3MJckS2QtlRaM1n/EkhCDoWbBBuyoJYVy2VFNXsQORilIPRtcSiYRbvLYwku8yq5XBJUlC8hyF5DEI5FfiKSVXcO2C5GshS+wdRPXgvGFyFc3B93/34dt5X6OL0eHg5GByf7tpj3oUrpifdTO2cGrXeSKCIwkNtK2qdIYc6ajRpjLf1hrOzbN3bcrcEopISBMPeBxotU/Q85SJEfrQESIWgyJpCUNGhtkAPwFuX4PuBkTvA/SgLYLk9gU4f2SoRxO5GkV3FXBGcq4PDmUTfbYk2RPhUALCpsVlZhlujCJyOYSmB5+5SA7FUu8NfyC07NMYfayev4cuIzoyJiGDUpIlMmT3pXyj0uxedpCosOhkzSMUwcNrj1NNQcArvQcla6l6VimKNj/ggMGTmhLE//GFYVz3fobvf8wREHpwLI3kYFuGLGBQK3brDq5fxKW260GbxyAmmMZQhf7eoNBffCq1vX38HwWi1+mZ/d0iDv53zGJ7jVamQpMylKhehL+GLLEp9kaSJbLmy8T8K1ORZZnxnaexd/khi5lcmXJl4J87Sat7pQIiag8ifC7EnjQckDOD8sRMaxlkb6QM+5AkJ0P/Vz5LImonIvi7ODGw+GcWncHA8Zlp9NQm9AEI/0ZxmRCmPhvuSBm2I2nUbLiUICI0kvk/LWP9zK0GmZK4y621oP93hW9m9aT5Vw3e9jLeO5TgkRBpueq8VaR04FgFojfy0siRAAWcmiB5T0xT3hZbUYX+3iEeXHvE5C9n0cKjIw017eiYtzcrfl1HVITlJzchBFv/3kO3It/wWc5edMrbh/P7Lhtqy1hAr1No0asRG2Ztt/kCKhRB0PMQNv65AyEEDb+oY9G4kWWJpl/Wt2lslcSIsLmIoK8g9vTLg8qrafyy8b8ldySfeUiSE0IJRAmdhnheG+VJSZRnNRBBfQweHMBQmDOuOGfsOcSLnsaxGZErLBg3AGGI8L+S+xZV4gj0e8HGP7cbZABe+Tu8q8aNrJFBAkdnB3r99oVq3KQSksd3oE2mGKrQxxk38FJ5OO66Hb0NEZK8bC3DtWYqyrMaKE+KoTyrhQj7A6GkHe+96sFJRQ/OpcPXGNRgFLrY14KKZYkCZfMyafcIXNxNu/3mDVnC8glrE8mfSLKEq4cz4cGRxh3i2jXuVocBc3rR2KF9koQCe/7aiU++bcHwVhM4tilxWQmNViZjzgz8ceJlzJCK7YjY64iA5pYbOVQwyKZLLkjOjcC1raHwpu4hIrBD3NaS7X9cyWcBkpOhPo7yvDnor1vp4Yac+YzN46uYZ2b/v1k3c6vFsibvCg061SJr/syky+pDzU8qJxIMVElZhIhGhM2F8GmpNIPWoFCssaxibwqh90MEdIh78Hr1syuDJiuS73IkTcYUW6k9qB6cdwBdrI5Rn0wiNjo2kTdEKIKbZ+6YlXO/efaOwbiBRPcxoQjCgyOp+1l1suR9WQ8kS55M/G9GD3pP7crupQfRWFEuNsffw5YRFhTOsJXf0rJPYxycXhlHgvKNyzDl4GjVuEkiInIZxoHCryODEmAQ3dM/QEQsR4QvQOifGrahFH/sixTVIqJeKfshbHn6CkfRPUIoQWrgcTI5se3sO2/cuLg70++PHvywsC8dh31Ck+71VOPmDSBJTkgujVNxBh3E7E9STxE0CJRnJI4JVEDvhwgZluzVvQnUIONU4siGUwQ+CTJ7XtErbP5rJ11/6YCTi5PRuU2zd6DRyha3ifauOEyWvJloP6glDTrVImeR7BzbdJpPc/QiPCgiyTVtdDF69v97hGY9G9Bnajc6jWjL1vm7uX/lEZlyZ6BO+2oJtWtUkkDsJSzrTCigvw36OJecCIbwOYb6L8K2IHNj9CBeSVeWfV/bDjODfyMEMYAW4dwMyb03kjZPEub/sHmXjZs6HapRuUV5qnxUHhc3tZL8W0FOj+XSCMlE2B/ELnS3IfaohRZ6iN6L0D1E0mZP+treAKqBk0rcPH0bjYMGfaz5m1lkaBRP7jwjV1FjZcirx29aLdWg6BUe3fBj5cT17Fy8n95TuzH2098Tsp+Suscva2UCHhukxB/e8GN0u9+4fe6ewWASsGjESio2LcPgxf1UL05SkJyxrezCq+cVYyPFLgQoIS9fOlYF3RXzzROI99zoIGojInoH+C5RM6zspGStojy998zs91nWSPhm9sH/UaDJ84kw8dGRNRKK3r7ve8ac6Rn8Tz+T5VlU3hyS7I1wqgvRe0gV1XJtIfv7xF60oZEwZH2+4waO+ulOJbSOWpuMDAcnB6PXl49c49ZZ25VDhWLQuIivJ2Mp7sYWr44+Vs+1Ezc5t/ciA2sO4+7FBwnzxIdrndx2jsENR6OL1dm8ThUDklNSg7OTESoXewEh4i6erl2xP6dYDyIaEfSdWTE5FdO07NvYIK5nBkUR/LJxCCsez2H4qm/ROmgxl2jp4eNGkYoFgHjdKkPDQhXy45PJigT/azx74E+H7F8x78elBD55QUhgKCEBoerf1wxC/xwRuRYRsRJhkwFgO5LHQFIrz9+wzWzv39RWv4eD9SZvGTXIOJWCjK+fukWfCoPNN5Aga95MLLg+3aCTIUs4ODnwZYmB3L/yMNmVxBNNJ0kUq1aImKhYbp65Y1HnRtJICBueCIf/+y012lROyWW+Fwgl3JCpJPsmStMUSijieX0QIbyxOlOAlG49koOhZpgSPCxOqdT+D5nkuwzJsVwKr+79ZsOsbUzr+xcazcttZ41WRq9X6PfHl7To1TCh7emd5xn5ySQiQiORZdmQffXKg1KBsnmp3roSjs4G/ayStYpSoGxeVk5cx1+D/7H7uiFJBkMp/nqQvWAWPhnYgqZf1lfrTxEXCBwyCiL/w+j7qi2O5D0JSZt8hWehBCOeVSLVtqlcuyB5DLH57yn0AYjnNUjIxjSJo0E0VH5zMivxqEHG7wAFy+WjVJ1i5hWBBRSrWpjuxfrTwr0jzVw/p1eZ77l3OeWNGzBsPeUqmoN+f/RA1sjIFrw5thg3skZm55KkBbC9r4jYyygvvkY8K4t4XhPxtBxK8HCE/mXMiyR7IPkuMsTCAG/uK/hSVEzyHAZO8cGNmrg12LYOEfGvGnhsJy2+bsT0I2Op1a4qPpm98cnsTe321ZhxdJyRcQNQtn5Jlj+aw4DZvchdLAdCEUYenZtn7/D30GUE+r2gzYDmCSUU2gxoTrWPKwGY9QCZQghh9LDz8IYfU3rN4fevZn/w3hwhBCLoG4hcTaKHEd0VRMCnCL057So75gmbSaoZNwARCyBqvc3NJU06cGmN+WuCBK4d3opxYy+qBycV08RDAkIZ0vgXrp+6naBkKmtlFJ1C7uI5uHvxAZJEgkEjSVKqXVQkWeLLCZ1o+20LLh68wuSes3lw9VGyxixWtRBTDv6SQitM24joY4gX3TFcCF+9GBpqwEjpViFpsrxsL6IhagsicivEpHbZC2ekjEeQZOPMGBF7ERG53pCSDrZfBLXFkXwXpIkLXFrl/P7LfFt7hMU2k3b/TKnaL2Oi9Ho9B1YdZf3Mbdw4fYeo8Cij64u9jNn0IxWblEla5/cAEXMSEWipdpMGXDshe/6YrHmU540NiQWpieSO5DMfybH0/9k76zgrqjaOf8/Mze0g7RZsQUTAfhFBQTEQu1FsxG5M7Ba7E1tUDCwMwG4QO1Ca7bgx87x/nLtxd2/MvbsLC8z381lx554559zdvTPPPPF7HA0XCSFlp8SqsJr61oEF/sGooltTigiKvUyrICs/eDZDqfZL983k/u0aOB2sZGxZFp+/8Q0fPPsJtZV1rLlRT0rXLObesx5r97VSqaOaXpNn5t1LUVcdqxcRPnphFlcedHNWa5keg90O3pHzHjst43Nnz/qZNx54l39/nU9BaT67Hbwjg0b2w5NlafuKRsRCFu+WpKwSwAT/YIziO1qfay9DFg2gTTk2KTEg5xCMgtQ3S5EwsmhHkHIHc5rgH4JRfFu77HB1ZtmCMt594iMWz1tKUbdCdj90R3qs142rDr6Fj1+clTQ52fQYDNx3ey597qykcy/5dylvPPAen7/1DXNmpdM+iscwtSL6VVNShNlXcXQo93lShpJVHkb3r5K/7mSdhf1Byto0hzMUquhOVMCZeKOIDeGZSN1L+tpm9EAFDwDf9knDXWItQaqugfo3aPy5GV1QuWMh54h2CXtmcv9eLneUSZMmccMNNzB//nw233xzbr31VnbaaaeEY1988UXuvvtuvvnmG0KhEJtvvjkTJkxgzz33bBzzyCOPcMwxx7Q6t66ujkCgc5U7mqbJDsP7ssPwpryFM3e+pNGj015stctmLP2vjPm/L4ybt8ErdMqtxzQaNw3H19y4Z6KpHGFFbYYet3tG59i2zR2nPMBrzcrgDVPxyUufseE263Hd25dQ2GUl9AqEP0rRZgF0WeU07OgClPUrWAvBLAXfQC3g59s59qTUAUaOpxcqL/lNsAGlfJB3IlJ1nYNJLQi9iVjz47xSLs4REZ686gUev+I5ENHXA1t4+JKn2feUofz85W8pKymtqM0nL33Kgd2OZfNBvdjv9L3YZrctGl+PhCN8/9FPLPhrEf6czOX6bcvm92//yuq9rTLYS0mbJyfViFgolUrbKg1GF7CWh4EjSPlpSNEkjED6a7dSBvgHofyDnM1ulyHLDgJrPnE/N3sJUnUV2Iu0gvNypMMNnMmTJzNu3DgmTZrEoEGDuPfeexk2bBizZ89mnXXWaTX+ww8/ZI899uCaa66hqKiIhx9+mBEjRvDpp5+y7bZN7tKCggLmzp0bd25nM26S8fu3f7WrcRPI9XPJs+OJhKJcOOxq/vzxn8bXRIQtdurFrge3/iPttk6XtKXsiVAKdjtkR7baObMmfC/c/Bqv3TsNoPHi3VDe+sf3f3PVwbdwwzupPQ2dkuhvpNeysGHpCKS50J4qRnz9dFO8DvHgBHRpt+FQtC3nWB2uqrnfwWCB8BcQHNGmHa4O2LbN1+9+z+yZP2OYBn332IqfPvuVRy+b3GxM02fwlbvedGTo27ZQsaSKT1//khmvfM6Rlx3E0ON2Z9HfS7j6kFtY/M9STI+RdYgqGoliWRam2Yab98qM2YOm8EwSVHHbjBtABYcj1be0aQ7n2FA+Fjv3VIz809t1Zql5AKz/SHodrLkPCR6I8qzXruumosNDVP3796dPnz7cfffdjcd69+7NyJEjmThxoqM5Nt98c0aPHs2ll14KaA/OuHHjKC8vz2pPK6rZZgOjuh9H+eLK1IOcSKXE2HH//lQtq2b2zLlEQq0z3w3TYL3N1+a2GVcTyPGz8K/FPHPtS0x7bDqhuuQJo0opPD4TESEa1h/yvKJc9j9jbw69eP+MLnzRSJRD1h5L+aLUSrr3fnMjG2y1ruN5OwNS+xRSeTkdF2ZqwKSp34zDP5C8izDyjnK8gkT/0LF369e0Y1XhjajgPo7nXh3588d/mLD/Dfz7y/xGY8O2bEyPiRVNfuM0TANEsFdwz6qdD9yBC58et1oaORL5EVm6X4oRBuSegJE/PsUc3yN1r4FUoMy1Ibh/K6+n2MuQxUPS9IhLRXZCgar4IZR/xyzWa42IIIv6xapDk2FC7vEY+ek9yqnoNFVU4XCYL7/8kiFD4isFhgwZwowZMxzNYds2VVVVlJSUxB2vrq5m3XXXZa211mL48OF8/XXy3jmhUIjKysq4rxXJTgfsgOlJ8aNXcNSE0Y7n+/jFT/n2gx8TGjcQczd/9xdvPfw+f82Zx0l9zmXqg++mNG4M00CZikufO5vnFjzIzdOv4LZPrmLyf/dxxGWjMr7g/TV7XlrjxjAUX7z1TUbzdgr8u9NROhbxmBAcDfmXofIvwpEDtvYRx7NL9C9k6SiwnOgwKfD2cTz36sjS+WWM3+VS5v+uq+isqN3ouU1l3ID+zHr83g4R4sskDeLD52cx7bHVs1pSeTeHwIFJXjXB6InKPTrhqyJ12GVjkaUHQO1jUPcKUn0Hsng3pPruuLHKKIlVVjb0jMrgd+7fOzsxP0yk5tEszkuC1KYxbgAErLYVtmRKhxo4S5YswbIsunfvHne8e/fuLFjgrLzupptuoqamhoMOOqjxWK9evXjkkUeYMmUKTz/9NIFAgEGDBvHLL78knGPixIkUFhY2fq299toJxy0v9jtjL21AJCjVNkyDku5FDDl613Zfd8qkt7jh6LuoqaxNKyHfde1SLnpqHDsM70teUS5b7tSbzQZsii+QeTwf0l/QAVAqrYJzZ0SZPSB4IB1v5IR1N/D61yDnYFLrVMSwlzieXapuiCkmp/tdmeDfrdPLtK9optz1JjUVtVmHo/c/Yy9yi3IAkstNZIHH66HvHluxw4i++IKpP8/KULxy5xvttvbKhiq8EnJPBdU8zKvAvyuqdDLKKEl4nlRcDKEPYt81VFbagI1U34LUvhC/jnczVNcPUIU36BJt79YON+gHO8sWLpEvsjgv2T4CpBf+U2AUtd+aDlguIhwtM6dFxFE29dNPP82ECROYPHky3bo1dS7dYYcdOPzww9l6663ZaaedePbZZ9lkk024447WVSoAF1xwARUVFY1f//zzT8Jx7cW8n//jrjMe4pjeZ3D0Jqdx85i7+e3bPxtfX3vTNblyyvkEcvz6d24ajR6dkp7FXP/uZXRdq5S1Nl0jo6et9Pv6l7mf/+rogrv4n6VcdfAtvHr3W2nHOmHtTdfAn+NPOca2bDbdfqN2WW95owouhUBDl3AT7V0xaH+jx4bIl0i5QzevcqY2akd+hdA0HIkPetZHFV7jbP3VmHef/KhNuXbbD+vDk3/dzfj7x7L7oTtS1C0zteJkmF6Ta9+6hCtfOZ8t0+TRiS388f3qm2yslImRf7oWtSt+BFV8H6rrdIziu5N205boP/ohJGnYSCE1d+oqpbi1fKjgvhiF10Dxk6Ruyhuj/kWws72ftd/tXykTAiNIvWcLFVi+Ie0OTTLu0qULpmm28tYsWrSolVenJZMnT+a4447jueeeY/Dg1PL2hmHQr1+/pB4cv9+P35/65tpW/pn7L6/dM41Zr3/Jf78t0NVLsfj5gj8X8cZD73HGpBMYfqIu0eu7x9Y8Pe9e3n3iI+Z8+jOmadJ3yNbsuP/2eH36pjRq/AhuOfHedttjJv1qGi7Mt5/yAGtu3JM+g7dq09rBvCDDjtudKZPeSnjRN0yDnht0Z9vdt0hwdudHKR+q6CYkehJS8zCE3otVYXQQIYeGp3+XtEMkNAvKxuAo/u/bGVV0O8rIcbb+akx1RXb9wwzDoOeG3dlix14opRh23P8Ydtz/ePKqF3h0wuSs+8zpuRXrb9mU4xbM9aeUlwDw+Dq/JH9Ho1QQ/AOdDQ6l07WKhWqiv4J3k4QjDMOHnXc6dFjysQm+9sm/aUDlnYCE3og1+Gx5jTf0tcipZ6qd6FAPjs/no2/fvkybNi3u+LRp0xg4MPkfy9NPP83RRx/NU089xd577512HRHhm2++oWfPFVOy+vp90zhuszN56Y6p/PfrAt0EutkFw4raIHDbyfcx9/Om5M3cghz2OXlPznv0NM5+6GR2O3hQo3EDMOz4/zHs+P8BLVzUWToFsvEGGabBsze8kt2CLTj26kPYZLsN9D6a7cUwDXILglz2wtkrvzy8iH56s5dH2acDAgenfFmsRUjZiYCTrsMGyr+ja9w4ZM2Neqbv/9biZcM0MH0m5z5ySqvPwrDjd8cX8DnqKZcM2xZGnjq08fsBI7ZLadyYHoMd99s+6/VWZkQspP497IoLsMvPQKrvdKZcLHU4urVKXcqXjbyTIPdkZ5vNGBuV21pqpS0ozwaokifAXDN2pMGDrSAwPCYOuHyv7x0eoho/fjwPPPAADz30EHPmzOHMM8/k77//ZuzYsYAOHx155JGN459++mmOPPJIbrrpJnbYYQcWLFjAggULqKhoSlC9/PLLeeutt/j999/55ptvOO644/jmm28a51ye/DhjLreedF+rnjGJME2Dl+6Y6nhupRRn3nsiV712AdsN3YYua5aw1iY9GbhPv6z2apgGhpnZH5ht2Xz1zndYVtv7JgXzgtz0/uWcfNuxrNN7LfxBHyU9ijhw/Aju++4m1t+itWzAyobOYwnTodLrjlEoa27qIXXP4sy4iREY1qYdrU6MGDsk7TVhrY17NoanlaHYYXhf7ph5DZsNaJ04WtKjmCunnIfP703ZaiUVa27Sk51GDWj8fpeDBtB1rdKEOT4NN6MDzhze6rVVHbEWI0v3RcrHQt3LUP+WNnAW74rUPp36ZM8mpA/1esCTvlrUyB+H6jodlXcGBPYD1daqXxNQqIIrHasaZ4LybonqMk2H8/LPQxVchur6PkbRjdoLtpxZLkrGkyZN4vrrr2f+/PlsscUW3HLLLey8884AHH300fz555988MEHAOy6665Mnz691RxHHXUUjzzyCABnnnkmL774IgsWLKCwsJBtt92WCRMmMGDAgFbnJaI9y8SvGHUTM175zHFybOkaxTwz7742rRmuD3PQGmOoKa/N+Nwdhvfl09e/yrglxNT6p+K8Sy6t0U3qBuK41NNcE/yDof5tsOc3O74eWH+2w45MVN54VN6YpCPspaMg8q2z6XKObrMs/epExZIKTut/IfP/WJR8UKzaf5vdtuDsh06i+7qJ8zqas3R+GVPvf4cv3vqGmspa/pnzLyI4+kwrBQeMH8GJNzQ9VM77ZT7nD7mShX8txjBj4Sql8Po8XPDkGewY63G1uiBiI0v3h+hckhkqqvh+VJLwr0gUWbxrLME/ibJ5YDiq8HqIzgbrb224+LZHpcmZsxduGysEcIoXvP1AqvVefH1ROYfENQkVsSDyDdiV4FkH5dkwg/mXP26rhjS0p4Gzb+GR1FaldjU2p7hHIc/+90Cb1gSY8crnXLb/9c5lExQUdS3kiT/u4pYT7uXdJz/KaL0z7z2RvcakzoVa3ZHIHGTpvmlGmeDfQ7uHvVujlKGTDSPf6TJLc20w19T6G9HfaWvHcVX8IMqfWDUcwF6yP0R/SD9RzrGo/HPaLGq2ulCxpJIzd76UeT//5yhnxjANevXfmJunX56xBMMXb3/LVaNvpqaitlEhPBWmx2Tyf/c1ign+8tXvPHnV88yY8gViC6bXZItBvRh//1jW2LBHRntZFZDQJ0hZqvCNAd5tMUqTe3Ik/Dmy7BgS9qYze0L+pVB9U8yIiqGKUflnonKSh5XtxYO1QeQEb19U8SSUUZx8n7UvItU3x9rMNJy3DapgAsqbmZDr8qLT6OCsDth2ZqGIYF77uOkG7tuPG9+bQM8NUidrNyJQvqiCSWc+wjkPn8JmAxIntyXjjlMfoGLJitUP6oxI5Cek+i7sqpuQcHItpiZslG87lG9bLYWOlkRXvm1Q/p1RnvV1wnLxo+BtUO42cVRREYcBxprgSyOz7utL2suAtz9GwfmrvXHz76/zeeD8J5iw//Vcd9QdzHz1i6Sh20njHubfX+Y7Tgi2LZvZM+byxVsOvWnN2G7I1jzz732c9eDJ7HTADmnHW5bFjFc+B+Crd7/n9IEXMfPVLxv3akUsvv9oDhftfQ2VS6sy3s/KjoTeJ3X9TaySMUV5tvL1Q5U+D/4hNH52VR7kHAkFV0H5qRBtURQjZUjlpbpIIdm8wQNwettWwZGpjZuaJ5DK8+ONG4DId7pTeiRNeHslYOXsbtiJ2GzAJnzz/o+Oy0EX/bWYmooacgsdyue3IBqJMuOVz/nqne+xLZtDL9wfZcDr973DT5/+mtZNPfW+dwgE/fTeYRNmz/rZsQfIsmymPTadA8e70vwAYlch5Wc267ar0Lo0Hhr0LhJjQCB94rwyu6BKn0IiP0LoE0SiUPdMmp5XDZigfLGkvqaLodhVEHof7ArwrA2+HVE5hyC1aQS/Il8g4W9RvuVbAdGZeHriSzx08VMYhoFt2ximwTuPf8iG26zHxDcvprhZCXf54gqmPzsj4xJxwzR4/+mP6b9X5gKKgRw/Q4/ZjZ7rd+ODyalFVA3DoLayjkg4wjWH3IoVtVoZYrZl899vC3nowqcYd++JGe9npUaSC6DGjwsBeUlfVt5eqOLbEAnrsJIqQCkTe9mR6GtF4r8PqboZggeijPzWL+YcolupSBrtGxVsJluRYA27OkXfORsII1U3oEraHm1YkbgGThvZf9xwvnrne8fjoxGLeT/PZ9N+meu9/P3Tv1ww9CoW/b0E06OfCt548F3yinO54uXzmPnqFzx/86tpnxpfvO31jNc2TIN5c//L+LxVERFByk5uJpTV/Cm+QXwvSSuF3BNRZmnr40lQ3s3BuznYFUjNrQ7OMCAwEpV3AsqzfuN+qbkLqb4XnVAc25vRFfKvBFUCsizFnBay7Ajo+tZq2Vzzvac/5qGLngKa5BMahDL/+OFvJux3Pbd+fFVjUu7v3/2dlWClbdl88OwMopEo+5+xd8JE43T03LB72i4etmXzx/d/8+6TH6X0ytqWzduPT+eEG48kJ3/5J4iuKJS3F1KXJjRsdHEsWqeUD5QWVBRrAYRnpTkjDPVvQs6o1i9JXXrjBlCF16XuQVf/hl4nKRaEP0KsxSiza9r1OituiKqN9N+rD4dcoPuVOC3f9AUyT9atqazlnN0nsORffSOyolajOnBtRS0XDLuaL976pk0aGSkRCK5GF7mURL6AyKckz48xgJaNXwOovHG6GiILxHKiSGxCYBRG0cRG4wZAqm9Hqm+nqVoq9jdiL4GKk9IYNw2EkJrHM9z1yo+I8NTVLyQtb7WjNrNn/sycT5vCDV5f9s+NVsTioxc+5YwdL85KZDOvKJetd9k8rfLx2499wB2nPoiRqmUMEKmPsCBVkvSqSGAf9Oc32fXcQOUcnl3ItmU4KCFm8nF1r5D+tm2CL3nend7HAtKHvQXshWnGdG5cD047cOzVh7L1rpvz9MSX+PaDH1OO7bJWKets5lziftE/S1j452I+f/Nrli0sT/hkZttCJBRJ2+upLVhRi51HOatSW9WR+qnoj06yVgk2EILih1HWv2Dkg28nlJHcnZ1yvbpXoOJ8ByMtlD8+B0PsZVCTTCxScC6qJFD7ELbyo/JORKmWBtyqydL/lvHX7Hkpx5gek1mvfsFmO+i8tk37bUhuYQ41FZlXOUIzkc1TH2DzQb0cNZ/9ccZcnrjyeb54+xtHYWexhUh9xFHlVbp2DqsaysiDopuQ8tNiR5o/yCjw9oXc47Kb3OjiYJCVdJzYi0jfXNMCKQdSeHCMUhwVMBjOvc2dEdeD00703WNrbnxvAgP22S7l09OhF+znqErizx//4bwhV3DYuicxfpdLeXriS2ndzvW1oax71jR6nxLc7wzToM/gLem1krZRaHdsJ11/bZR3a1TOQajAsOyNm/BXSMW5pL8YGTrkFNgj/nD922nOzcTjZ0PN3ciyoxHJQDtnJSZZA9vmKBU/zhfwae2YNmqaKRRTJr1FJBxhxiuf8/Idb/D+M59QV1MfN27GlM8Zv8ulfPXOdxn9OtMZN0rBWpuuwZobrX6VVCowGFU6Wcs4NHg6jDVQ+eeiSh5GqSyV8SNzQSXIrYnDC4GhCV9RRlecaGxJzTOIlcJbFBhKag+OAd7tVvqQtOvBaWfOe+w0Lht5Pd9+8COmx9RVFrHriOk1+eq97/n5y9/5/bu/CNWFWGPDHuxxxC4M3LdfY17NX7P/4fSBFxKqdZjsFsMwDAzTyKr/jdiiQ1Ai1FXX4/Ga2CLYUZs+g7fk4snjV36V4XZCedZD0t1JVBGotiv+Ss0D6OeQVEaK0gmMxQ/oeH/z863Fbd5DPDZEvobaxyH3+Haeu/PRZa0S8opyqS5Prj0SjVhs3HeDuGOHXrQ/C/5cxNuPfJD12iLCzFe/4KMXZlG5tEq3fxEhkBfg6MtHs/+4vQnVhbn+qDsR2yYrwY+Gj3SCc0XgiEsOXG0/98q7Far4jljPqGirz1am2FU3Qs19pPMrqLzTUEaSvmPBfZy1b6i9D6l9APIvROUe0XoNowTJPRFq7kq0A0Ch8h32u+vEuAZOO5NbkMMN717G1+99zy0n3BsXv7YiFh+/8Gnc+L9+nMfMKV+w+aBNuWbqReTkB7ln/KOEasMZGSqGabDhNutx4PgRXHHgjUQjmeunFHbJ577vbmL65Bn8NXse/hwfO+7Xn422XT/9yasTwQOh+s4UAwzIOTSugikbRCTWkThdwmMPVJeXWnU2FrFjfXEc3PnMdZzrayBIzROo1cDA8fq8DD9xD569cUrCz6MyFHlFuey4f7wYnmmanP3gyXh8Hqbe907W6y+b39Tyo8HjUl9dzz1nPYpSivySvKxDYaAfioq6FbJsfhmmx9SGuwAiHH/t4ex+aJpcjtUA/Tlum3EjoY9jxg0k9cCoXJ2jl3NU8r2YayLe7SHyWboVAQupuhLM7qjAkNZz5Z0OyodU3w008woaPVGF16B8fdOs0flxDZwOQCnF/N8WZpScN3vmz9xy4r2ceMMRfDHt28wiB+gQ1T4nD2XAiO146KfbmHzdy3zw7AzHaseGafC/w3YimBtg6LG7Z7b4aoYye0D+ObEyy5YlKyZ4NkBlG6OPQ1+k0mLPh+hv4Is3cAhNh2jqnDBQuly86A6k7PhmlWHp1vwPkUha5dVVgcMuOZBvP5zNT5/+EpfEb3oMDI/Jpc+dhc/f+ueglOLoKw7mjfvfzVg53AmPXPYMex3/P0yviZXFAw1oHa8jLh3FBlutwweTZ1BTWcsaG/Rgz2N2pcuaK3f+RWdCah9Hh4RSFCYE9kPlHp1+MofVWxqFVE9KbOAoBXknaW2e8IdaydhcB1E5EPkcifwAvj5aMHAl9eK5Bk4H8dLtU1EKx25jsYXpz85g19GDMjJuGtzWux+6IzsdoJ8ie67fnXH3nEjZwgpmvvpF2soqwzTILcxhxEl7Ol94NUflHgdGd6T6LrB+ix0NQM6BuloqkYZFpmsoA/FsDNGf041Equ9FlcT3KJO6yaS+qMbOzj9XN9AseRLqX0IqrgDSGcZeVpfLRyDHzw3vXMqUu95iyqS3WPDnInxBH7seNJBRZ+/DepuvnfTc4m6FDD5iZ955/MN2N3LqqupZ8m9Z1pWTylDk5Af532E7EswLZlWW7uKQ8Nek/hzaEP0u7TQSmgGhTDyCAtHZiLVAP5glQBm5EBiGWAuR8tMh8jXS2CjT0r21iu5COeid1dlYPa5Qy5loJJq28iIRYgv//pyZ1kzPDbtzwLjh7DSqP3ed/hB/fP8Xwbwgg/br36hWmo7u63bl8pfPpbRnctVLl9ao4HAt2mf9C9SDuWa7N5RTOUchlRelGSUQ/hC75lFd3eQbiPKsDdG/SesBUoUob8ONLYKEPiG9cQOY62b8VCcSAmshqADKTN9zqTPhD/oZdfY+jDp7Hy30ZzgPP556x3HM+/k/5sz6BWXoXk8N/7YJBWtu1COrnDtlKHx+L1e8cl67qau7pEB5HDy4pvaGil2NlJ9KVo18pT71y1Knta6sf2JHmq0R/Q1Zdhh0eTWlMnJnxDVwOgBlqKwvYAVd8tlgq3X544e/k57v8Zo89NNt5BXlkleUy8MXP81B3eMbKn72hpO2ATopevdDd8zogu3ShFIKPM7L/jMmeABUTwL73/Rjq66OXUMV4h8S6zycRvWtmYiXVFwC9Q5FIK1fkchsR/1qxK5Cqu/UnctjjQLFszkq7xRUYOXrb5bpZyUnP8hNH1zO9Gdn8saD77Lo7yWUrlGCYcD3H/2U/UZEK6kPGLEdn079Kqmh09KTnFMQZK8xg9n3lKH0WC+xofnbt3/yx/d/4w/66DN4y6yV111i+HeHuhdIFaJS/l1Tz1H/miORvwSL6/5XqaibkqLBr6U1s2qfhbyVS9XaNXA6ANM06Tt4K7569/uMn642G7AppT2LuWjviUnHjDp7H3qur3tQvXzXG7qEPEs22W5D17jpxChlIDmHQvWNOI9dCoSmgblWmnMUKqhFKiU6D+pfzmANE6mdjCq8PPVO7Gpk2aGxvjvNnwrnIOUnQ8EEVM6hDtdcefH6vAw+fGcGH75z47FD1xnbpjmLuhWy3Z7bsNUum3Hl6Fv4/I2vdSWm0jl5hqEYcdKe1FbVUVNeQ88NejD0uN1Zt3dyg/yvOfO48Zi7+OmzXxuP+QJe9jt9L4656pDGSk+XzFA5RyJ1L5D4gcPQrRUSKRc3Q8JfJTk/HRZIBFKUtkvdlDRz20jdyyjXwHEBGHXOvnzxdmaN87bZfQvW6bUm6/Rak4snn8mtY++jalm1Lv22bTxeDwedvQ9HXTG68ZxHL52c9R79QR9d13YTCTsLEv0H6t9EpBxlrguBvVBGHirnAKT6ViCSwWy2rooyuoG9lNZPjqbWzQnGLqqhaWR28bTiOyEnQWrub23cNOwPkMordXf1lVgOPhUikjSUV1+TOmyQjtPvOh7TYxLMC3LN6xcy94vf+PDZGdRU1rHWJj3Z48hdGjuGO2HBn4s4c8eLqamsizsero/w7A2vULGkkrMeOLlNe15dUd5NoOhWpHw8+rPY8HlQoHJQxfe3qoJsjkS+j7VXyCasaUH9q7qPVdIFytPPLeVZrL1icQ2cDqLP/7bkjLtP4I5T7kckvahWbmEO5z16auP3Ox84gAH7bMesV79k/u8LKSjNZ8tdejNv7nxmvPwZNZV1LP1vGdVlyfU5UmGYBoOP2JmfPv2Fmopa1tpkjZTJki4dh0gEqZwAdc+jjQwDwYLKq5DcE8Cah47PZ2LggK7o2hykKlYd1eCps3X4SsLI4j0Q37axcFY6hdTmKFCpwxYiNtQ+nWZOgboXVzrXdyoi4Qiv3/cOUya9yby5/+EL+NjxgP4cdPa+carE622xDj/OmJtVDk0g10//veObcm663YZssNU6sV5YFp4MW0Y8fc2L1FbVJdyPCLz50PvsP24462+xTsb7dQEV2BO6vgd1zyHhLwET5R8Ewf2T696ATv5ddjSpe0elwkDC36BSGTjmuhD9nZRVXubKl2SspCPqFzs5lZWVFBYWUlFRQUGB8yecbFjw5yJevPV1pkx6M2kDvmB+gAd/uIWuayeW5w7Vhbj37Md448H3iIbTK6umwzAN8kvysKJWnIG0ab+NOPO+E9lw6/XavIaLc+yKS6FuMsmfoDIxPFrg6YvR5WkkMhvCs5DIT1Df4I5uuJilr7RKhCq4GpXCrS52BbKoX9LXG9cO7otReG3G63dGwqEIl4yYyNfvfq9/mw0inx4DpRRXvHIe/YZuC8AHkz/h6kNuzXqtC58ax24HDwLAsiyeufZlXrjlNaqW6TwNX8DLnsfszpjrDkubSGxFLfYpOIJwfXIj2vQYHDBuOGOuby0c5+IMkTqof1f3mjK6QeB/SQsTRGwIz9TdxaPOGzq3xgPBkRiF1yTfV2g6UjYm6esAqvB6VHBkG/bRPmRy/3aTLzqYHut14+Rbj+Ger2+k5wY6b8YwjMa8l3U3X5t7v7mxlXFTXV7Dy3e+wS0n3Muxvcfx2r3T2sW4CeT66d1/YyoWV7by/vzy1e+M2/Fi/vzxnyRnu7Q3Yi1IY9xAcmEwB2Gd6PdI9A+dDBzYO5ZE3FJfJ1PjxgRjDQgOTz1MpWpY2DgorSdoZeKFm1/j6/d+0Em9zX6lVtTGitpcOfqWxlYLO48awM6jBmSlMWJ6Tf79ZT6gvcM3HXc3j1z6TKNxAzq09Pp90zjnf5cTqkvdWqOuuj6lcaPXQffDc8kKqX0KWTQQqRiPVF2v/100EKl9qvVYawmy9ACk7Jg2GjcAUZR/59RDfDuDfxiJP68G+Abq68dKhmvgLCfW23xtHvn5dq5+/UIOGD+cvU4YzEXPjOP+725qTBhu4L2nP2b0GmOYdMbDvPnweyz6e0m7dAlXSrHvqcPiEgibY1s24foID13U+gPn0kHUv53liWbMgEhHGFl6CGJXQd1zaGMmlaeo+b8J1gQw10aVPNbqyVMkpJ9QYyjlB/9upO55E0Ul6buzsmHbNi/f+UbSz6qIUFddx/tPfQzoB50LnzqDsTcdRbd1nTRhbLZW1CavSBuGP3z8E9Mem564Ea9l8/MXv/HGg++lnC+YF8CfpqmmUrhSElkitc/pMLQ0PFTGHlqkBqmcgNQ+2zRWbGTZ8RCd0w4rxx5G/P9LOUophSq6KaZu3CxcpnIh9zhU8b0rpaina+AsR6rLa/j09S+ZctebvHbP21x98K2c0u98Zkxp0qv59oMfufbw2wnHOv1mE59Phojw/E1TsKLJn9hty2bWq19SsaSy3dZ1SYFUkdoASIYF9gJSdgxuXGMZ1L0Ui/un+nuyAS/4tm92zAfefhDcD3IOQxXfh+ryJsqzjq6QqnkEe9Eu2At6Iwu3RBZujb14KFL7LCI2Kvek2DyJngxNPbd3O4fvuXNTsaQqrrVCIkyPyc9f/t70vWmy3xl7seN+/VOc1RoRYZ3eawIw9YF3MIzkXiABXrs3tSFtekz2OHIXDE/yW4IVtdnjqF0z2qdLLMeu+qbUY6puRkR70KT+NbBmk11YWsX/a5SiSh5yZJwo5dHSDd0+QZW+jCp9EdVtJkb+Odk3F13BuEnGy4mqsmrGDbqYf39dEGe0/PrNH1w28nrG3XMCe5+wB09e/YLW0LE6JjUqWR5Qc0SEsoUVGVVguGSJuR6QZehReQBfs6fC5EjN/boBqIM5jZLHELtMd003umiV45bzWUt1+bf1R+s5rN+Ryosh/CWq8FpU0V1IxdkxDQ8PjSEyX39U0e0rrQx8S7wOk3q9/vhxX7/7PS/e6lB/qAEFbzz0LpZl88Ezn2Cn8vAKLPwzfdPVQy7cn49emEVVWU3CB6vhY4ekLDF3SUL4M7CXpR4jyyD8KeLbHionZLmQAqMnmGuCCmqNqcAIrVScySzKBw70rVYGXANnOfHU1S+2Mm6ARnf2nac9SN8hW/P1u22Lt7aXQmphV9e4WS4EBkNlQcyTk8nvzQTfThD5ztlp9kJgoYM5dwTQiqUJVEsl+jdS96wWLbOXpp6u/iUI7K6rR/yfQN0bSPRnrWQcGIzybuFg4ysPeUW59Np+I37+4rekBocVsei/d3wTwymT3kKZGT7UCHz43Cw+fG4mtoPz8ovz0o7ptnYXbptxNTcdfzfff9gUHgnkBRg1fgSHX3qg8/25NGGn9uo1jStHap/JUswPQMDsilH6ZJbnr3q4Bs5yIBqJMvWBd1KGmyzL1nH0LDEMxZob9+SfuZm1emg1j2nQZ4+tKO6WvGzRpf1Qyg+FE5Hy09BuZaduaRuVewxS/zrUPtFOu9FzJkNqHmrWYNTJPg2k5nFUYE+dr5Ozf9qU45WdQy7Yn8v2uz7ha6bHYJ3ea9Fn8JaNx3767Bc+nfpVVh5b27JRKUJTDRimwR5H7uJozjU36snNH1zBP3P/5c8f/sEX9LHVLpsRzHWS7+WSCDGcaY2JKoCqC9uwkgnmBm04f9XDzcFZDlQuraK2hXhWSwzTYMm/y8gvSf+k1Qqlzz/z/rF4vNkrjRqGwvQYHHtVCr0El3ZHBfZAFT8C3m2bHfVoD43KIz5/xQQMVMGVKF9fVM4RZJfD0xwTUKiCCShf4nwYqZ+GVF2Ldhc5N8ISJUqK2Ej9+9hlJ2MvGY697Cik7kXdq2olZ+C+/Rh701EopTBMfXlt+HeNDXtw9esXNlZQzp71M+N3ubRN1ZHpvLXKUOSX5LHvqZklcq+96ZrsdMAO9N+rj2vctAGJ/g0V56cZpbTGTOQ7oC3ijxYq56A2nL/q4XpwlgOB3EB6kViBvMIcRowdwjPXvoxtO08wW2vjnpz1wElssWNvgvnBuFLRTOi2XlfOe/Q0Nu7jPgUsb5R/B5R/B8RaqMNVRneUkY/Y5VD3IhL6ACQM3m1QOYc0dvZVnvWRoklQfhIZJyV6tgIEfNvG5tww6VCpvoes9HhaVHqJhHXH4tB7NOnvGEh4JtQ8BCWPpVR0XRk44MzhDNy3H1Pvf4c/Z/9DIDfAjvv1Z9DIfni8TZfcO055ACuSuf4Q6IeRlHk3MfKL87h5+hWU9HCrn5Y3IhZSdrzWvEmKFvZUBZcjFRe0bcHgaPD2ST9uNcI1cJYDOflBtttj65S9qayoxc6jBrBO7zX57I2v+f27vxxVUBmmos/grdhix95ULKmkvib7p+BDL9ifLQb1yvp8l7ajzO5Ak2yAMoog91hU7rFJzzECu2F3eU8bOdG5ODVCVPHtKHONVsdFbN1cT3lRRrE2srLS4jAhsGf83NW3Qej92HcNN/fYfqO/IeVnoUoezmKtzkXPDbpz3MTDkr7+x/d/8evXCRK0HWCYBl3WKmHR30vS5l9tuXNv1um1ZlbruLSR8EcpGlg24IPiB1D+/ki6nLbGUwbFcu+q9PdGN1Tu8ZBz5CqTsN9euCGq5cThl2rF10R/f4ZpsN2eW7Npv40I5gW5efrljD53X/KL02e/25bw+/d/A/D8za8RjWTv7nZSYeXS+ZDIHFg2KkPdDA8i8e5wkTBSfS+yeGdk8Y7Iov7YS0YidW9muTMzFkJrmL8Oap8k+V3ZgvAnSDSxTtOqxAIHVU0AHp+Hkh5Fjd8bpkGfwVtyy/Qr2HS7jdKeP2fWLxl5g13aDwl9QvrwcQgqr8EuO8OZ4KXRVUs1dJuBKn0N1eUNVNfpqNyjUartt3OxK5CaJ7Arr0Gq70Kiv6c/qRPjGjjLic0Hbsplz59NMF+Lo3m8ZmNsfvth23LJs2c1jg3mBTn26kN5buGDFJTmp5xXKUUwV2sUvPHgu22qoNq0X/IQhUvnxA7NQpYeoj0u+ojDMy1YMgy74kJt2EgEKTsZqb453qUenQNVl4JqXSqeEpWrL8Se9ZuORWaD1KY7EUKzMltrJSTd57qBcx4+hbu/voF+w7bRTXctmy/e+pax257j6GFm2fwyli0ob+NuXTJFInOh7h0cqYRbcyD0FoiDaquCa1DKi1J+lHcTlGdDlGqfDu9S+wyyaBBSdSXUPoFU34ksGYpdfg4i2fbBWrG4IarlyMB9+zH5v/v58LmZ/PVjLDa///asv6XOpxARbNvGNPUfrOkx2eWggbx+/zTsJN4VEWHH/fpj2zYVi7MT5zNMg437bODm3qxESGQ2UnEhRGdnO4P+p+4FBIXybqVd6q28K7HvpZbUiWRKt47wrAeBoajgSJTRMmE+g+TkVZxe/Tei69qlLP4neVgipyDI1rtuzvidL2X+HwvjQtZVZTVUldU4agBvetrnBujiDIl8hyw9nMwShpv/zSf5pZqbgL0IO/JrLBLg06ri7RCWkvq3kMpLmx1pZjzXv4rgQRVNbPM6yxvXwFnOBHL8DGmhBvrrN3/w3I1T+OiFWURCUdbYqAf7njyU4ScNYf8z9uLNh99DbEnqnZk962fW2LgHHp8n44oMwzQo7JLPhU+dke1bclnOSPRXLbLXLlVHAnXPx1SO02HSWpQwVtVVfA/Kv1PqlcTJ36Ygnk1W+XJy0zQ5/trDmXjYbUnHHDVhNFPve4f5vy1MHmZKYdwopVin95oUuZpWyw0RQcrPJ/vO3ymwfobKi/Q6DcfMdSD3RAgemLWhIyJI1W0kt5ZtqH8RsU5FmStXPpcbolrBfDr1K07d/gKmPzuDSEjfAP77bQH3nPUoF+51Nd3W7co1r19IMC95qeZbD7/PObtf7si4GT52CD036I7X76F0jWIOPm8k935zI2ts2KPd3pNLxyJVt8WMm+wqcBLMCNbvpFcMTPD35dkSVfqCA+NGHCu0qnbpwdP52f2QHTnrgZPIKdBh64aQtT/o44Trj2C/M/bS3tt0OTRJ7msiwuhzR7qJp8uTyHdg/UrbvJACvqFQMBGM7qTM47H+QSovQqpvyX4566/YnlN9/lUb+uatOFwPzgqkrrqOqw++Bduy9Q2gAQFB+G76bJ67cQqHXXQA5zx8CpcfcGPWaylDMWDEdpx+1/HuBW8lRuxqCE2j04Rxot8gNY+Bd1MI7IkykxjKkS8Tt3VIgES+W+U9OA0MPXZ3djtkEDNe+YIl85ZS1L2QQSO3Jyc/iIiw9L/0eRk+v5dwKIJCISKYHgMrajP63H0ZfERTF2nLspgz6xeqllXTY72ujaFxl3bEaqek3PCbYPhj+XBp9EUAau5BAiNQ3o0zX8tBqxcwHI7rXLgGzgrk/ac/oa6mPunfr9jCK3e9ycHnj+TdJz5svHBlijIUe58wmJNuOcY1blYwEp2nY9r2Mm0MBEagzG7OJ7CX0WmMmwbqn0fqFVRNRIIHoQouad3cz3FllMHqdlnyB/3sdvCgVseVUvhz/IRqk4ciTY/BTgcOYKudejP9uRnUVNaxwZbrsPeJQ9h0u6aigfee+oj7z3+SJfOacn427rM+p901ht79s7gpuiREIj+332T1UzMYbCJ1z6K8F2W+jrkmTZpUyYiCuX6K1zsnq9eVpJPxy1e/Y3rMlGJfZQvKqVxSxfw/FmVdxr3lTr05Y9IJ2W7TpR0QsZCqiVD7OA3iXoINVTdA3mmQe7Iz49MoISvBvQ5H9FfdZARBFV4Z/7LjKiwb5d85/bDVgGmPT09p3ICWdtjz6F3Zdvct2WvMYCqWVPL2o9N55a43CAT9DNi3H4v/XsItJ97b6tzfvvmTs3a9lFs+vJJN+6UvOXdJjERmI/Xv6IaZtZPbceZIBmMtiGanq6SMIiQwDOrfILGRo0AVQGCPrOZfkbgGzgrE6/c6apTo9Xsp7l6YdSNNp12OXToOqb4dah9r+I7mBopU34ZSBZB7RMJzm6OMPMS/B4QclqBiAEFQ3pgwWHvl7SRDoO5ZJG9sfEKifyfAS9qLtureShxwdSQcinD3mY+kHbflTr3YbOCmALzzxIfcdPzdWFELZSgUilfveTtpvyrbFoja3Hv2Y9w8/Yr23P5KiViLIfwpEAXv1vESB4nG29VI+TgIf4j2gGTSxsQpDsrkQK9v5OsQdt1kpPZ5LR1hdkMFD9JJyCm6iqv8c5HwZ7EGus2vETovTBVep7uMr2QslyTjSZMmsf766xMIBOjbty8fffRRyvHTp0+nb9++BAIBNthgA+65555WY1544QU222wz/H4/m222GS+99FJHbb/D2GF4X6xo8huOYSh699+YvKJc9jhil+w0bhRstcvmbdilS1sRuxJqHkw9pvpORJw9san8M0D5SZ58GEtIV3mQcyiqy6uoLq9AYBjL55lGQX28OKAyiiHnCJJmxILWzil9eKW8kLY3X7z5jaOWK99/9BP7Fh7Jqf3P57oj7yAajiK2YEftxmtLquuGbdl8/9Ec5v+RrtP8qovYtdjl5yGLd0IqxiMV5yJL9tQ90qwFic8RQcpPhfAnsSMW7W/cNBhNTrDAtyOydH+k6nqdCyQVEP0VqboGWbw7dtlJ2BUXI6FP4nM+AWX2QJW+AIGRQLPPn297VMnjqMDu7fSeli8dbuBMnjyZcePGcdFFF/H111+z0047MWzYMP7++++E4//44w/22msvdtppJ77++msuvPBCTj/9dF544YXGMTNnzmT06NEcccQRfPvttxxxxBEcdNBBfPrppx39dtqVbXbfgg23XhfTk/jXYNvCwRfsB8BOB+7Ahtus11hp4RSv38uw4//X5r26tIHQR6QtG5UyiHztaDrl2QhV8hR4WrTVUHmovLOg6weQdy74dwa7EiJfgVGKUXQzqttMKH6YlIZGmzG0Uddy3/lnQ7ChGaCK34NvV+jyMcrjhkoAlv63zPGvyIpYzP38tzatt2Tesjadv7IiYiHlJ0L9K7QyUMKfIcsOQeyK1idGvoHwDDrOI2qAf0SsAW+6a76prwV1b4D1D43hYmj6fymD0Lta96rsGGTZwa3elzK7YxRNRHX7DNXlHVS3WRglj6F8/dr93S0vlLQ05dqZ/v3706dPH+6+++7GY71792bkyJFMnNhaOOi8885jypQpzJnTVCo6duxYvv32W2bOnAnA6NGjqays5I033mgcM3ToUIqLi3n66afT7qmyspLCwkIqKiooKFixGhFL/lvG+UOu5K/Z87RSqW1jGAYiwtibjmL/M/ZuHFu5rIrrj7qTT1//ytHchsfgqlcvoN+e23TQ7l2cILXPIpUXpx2niu5FBXbLbO7IbIj+rmXe/TtA6BOk/Ey0QdVgRFhg9ECVPNzYUNMuPzPmZemYC7QquAaVc2D8XkMzkJp7INygVBwA37ZQcAWGx63oac7HL33apqrJTHlozq2svenKpXHSHkj9+9rASYqByjsDlXdS3FG78ppYPl0HhnxVLogCoyBWTdVcpkHn8YEF3u0g/3zdrsWxx8cEX3+MkkfaedMdTyb37w71V4fDYb788kvOPz++XfyQIUOYMWNGwnNmzpzJkCFD4o7tueeePPjgg0QiEbxeLzNnzuTMM89sNebWW29NOGcoFCIUakrWq6zMTvG3I+iyRgn3fnMjM1/9go9f+pT66nrW3Wxthh3/P3qsF19dU1CSz1WvXsC8n//j6/d+4N9f5vPq3W9hRa1WCcjd1+3KxDcvWi0vWp2ONLF8J+PEWgz1ryH2EpTRFQLDUWYXUHlI5DuofwvsGqCK+Ke3GPZiZNmR0GUaysjR3Yujf0L0xyzfVCqCsXBYs/3XvRjrltzcLVGvcx4qzkFKHkOp5FpPKxtVZdV88MwnLPpnKcXdCtll9EBKezrv6L39sG3JK8qlurxjS3OVodhom/VX2+uE1L1I6goiG6l7vpWBk13JtILiR6D8DJByB5uLrWHX6f35dgUJ6zwZpcC7GQQPwfBtjdRNQRwbN+j5wjOQyGyUd7OM38nKQocaOEuWLMGyLLp37x53vHv37ixYkDi2uWDBgoTjo9EoS5YsoWfPnknHJJtz4sSJXH755W14Jx2L6THZcb/+7Lhff0fj19pkDdbaRHeB3ufkPXnx1td5f/In1FfXs9ama7DvyUMZcvSueLxucnGnwLsdmOuB9TeJ4/QmePugPOu1ekVEdAfumoY8NAPBgqrrkMBIqH8N/WSX7knS0kZO3UuQc0jMve7P8H1sC8GRYC2CmkeBuhbr6oRIVTghLqFR7GVIxSXEu84bsLU4Ws3D0PImspLywi2v8eCFTxINW1rawbK595zHGHXWCI695lAMI32Y2RfwMeb6I7jlhNb5h+2FUgqlFGOuP7zD1uj02ItI+9lJ0OVbedbPwKCIJeoWXIHyD8Bu6ALumNj+wh80O2ZC9CewKxHvrWSXbWLqsJVr4LSNluWvIpKyJDbR+JbHM5nzggsuYPz48Y3fV1ZWsvbaazvbfCdnjQ17cOodx3HqHcet6K24JEEpBYXXI8uOoLUxYoLKQRUmMcBr7oeaSc0ONBhIFtS/gPMqixhVlyM1d8cu7Bnm4US+1fuNfJl4Tc/GqLwzUYH4nC+pnUxCFeRGbKT2Ccgdu9LrNL3x4Lvcc9ajjd9HYxIQgjD5+lfwBXzsceQuVC6rptvapRR3L0JE+Pq9H/huuvambbXL5my7+xbsdfz/MEyDB857nIolmd4U4zFMA1/QS311kye7y1oljLvnRLbdfcs2zb1SY/QEviOlkWO01qmSwD5QlSaEaHQHz4bg3QIVHI3yxO45RlewEz+MOye239C7SOV1qLwTyVw+QiESWqVFNTvUwOnSpQumabbyrCxatKiVB6aBHj16JBzv8XgoLS1NOSbZnH6/H78/w6fVFUQ0EmX6szOZ+sA7LPhjEUXdChly1K4MOWoXgnnBdl/vq3e+44VbX+O7D+eg0InPB4wbzta7upVX7YnybQOlzyLVd+inJgTwQGAYKu90VIIcFJE6bYykJIsUusZu4Zmea0PkixbHDMAPRbej/DvHGSgS+gSpvgciDpL/7cUg1aCcddnujFiWxSOXptZBeeLK53j8iucAbfhuvdsWLPxrEfN/W9jYFPPJq15g7V5rcsUr5zH0mN0YfPhOfP3u9yz4YzGTxj3sqIt4A4ZpILZw7iOnMmi/7fn8zW+oWlpFj/W7sfVumzc29l1dUTkHIKFUgnoGKueguCMiIag4j7TGhFShCq9BmWvEH/dsBOG2GjiNi0DdM5B/OgSGxzy6To2cKMrTu5320TnpUAPH5/PRt29fpk2bxn777dd4fNq0aey7774JzxkwYACvvvpq3LG3336b7bbbDq/X2zhm2rRpcXk4b7/9NgMHDuyAd7H8qK8NceFeV/P9h3MwDIVtC4v+WcLPX/7GS7dP5ebpl1PSw3kcPx1PXfMiD1/8tE5ujnUq/mzqV8yc8gVjbzqKA84c3m5ruYDy9kYVT9IVRnY5GCUJOm43IzRzJZBHt4Ew1NyHCuzSeFRqX0AqL8S5l0jBSl4ePmfWLyybn7q1QlxHFhG+ee/7xu+bS0b8+8t8ztr1Mh744Wbyi/PoN3RbABb+tYjJ17/iaD9KQZ/BW3Hw+SPZOiYVsdP+zsLgqw2+QTq3JTyd1ga/qZtZBg+OOypVN0F4Zvq5JYTUPA7546D+DaTudZBKaE+1YwAiEJ6FKpiAWP/GPKzpvDkGqMKVUrwvEzq8THz8+PE88MADPPTQQ8yZM4czzzyTv//+m7FjxwI6fHTkkUc2jh87dix//fUX48ePZ86cOTz00EM8+OCDnH322Y1jzjjjDN5++22uu+46fvrpJ6677jreeecdxo0b19Fvp0O5/9zH+fGTuUBMhAsa0xYW/LGQiYfd3m5rzZ45l4cv1hVnDcYN0JisfM9Zj/Lr19kpY7qkRhkFKM86qY0biAnzrQxYEPlcJy0DYi1FKhtybpw8TZrg2xmlVg4vazJqKmrbbS7bsilbWM6bD74Xd/yYqw9hrxMGJz1PKYXpMbj69Qt4reZJJr5xUaNx49IapQxU8Z0QPJw4/RcU+P+HKn268XMq1gLs6nti1VNO/q4tqJ+CLBmBVJwL4Y9iUhAd8NAiEZSRpzVriu4A/y5gbkqjJlYcJuBBFd26ymtOdbiBM3r0aG699VauuOIKttlmGz788EOmTp3Kuutql/z8+fPjNHHWX399pk6dygcffMA222zDlVdeye23384BBxzQOGbgwIE888wzPPzww2y11VY88sgjTJ48mf79V96nk5qKGt546L04Y6M5VtTmm/d/4K/Z/7TLei/f8UZS/R3QPW6mTHoz6esuHYtYSxCpW9HbyIwGqfi6F3DuJleAjcpb+VuJrLlxz3adT2zh3afiRVFN0+TMe07kgR9vZotBvRp1sRpCgyU9i7jy1QvYflgffIFV++bVXijlwyi8BNVthpZqKJqE6jodo/hOlFGCiIVdcSWyeBeovoWMSsPtpTFtGujQ9ipebcQq5UEF9kQVTAAlQD2tbvPe7VGlz6P8AzpuP52EDtfB6Yx0Jh2cBr5+73vOHZxGLl3B6XeNYcTYIanHOeCw9U5i0d9LUo5Zp9eaPDj71jav5eIcsRYilVdD6G06X7+p1KiSp1C+7bDLz4b610l/I1CAD1U4ERVcNcKhZ+58CbNn/pz0QSVTeqzXjcd/vyvp69XlNcx67Uuqy2tYY8Me9B2y1WqfV9Pe2JXXQu3DZJ6zlmEBQFaY4OuHKroH7IVavdwoRJYMjxlWLT+DCvCiury80opqdhodHBfnOKoeEYfjHGB6018EPW4Pq+WKWEuRpQfFkoDT3SC1m1m71avp2Atpuk7DgNEFvNvo/1c5OMq9yT8XFTwQZRS2cX+dh9MnjeGMQRcRqg232cgxTIO1e62RckxeUS6DD3ebk7ZEJKwT1/Frvahs57GWxnrIZfP5EjrWyDF0811Viizanka1dHM9sP5MsScLqXkEVXhVB+2r87BcelG5pGfjvhvgC3jTjttql/bRLBgwfDuMFCEqwzTov3efdlnLxRlSc68zXQ4U+HdDlb6A6vYu5J1Bh36U/btB4ICUQ1Te6SilDWIV2IPUZeEm+HfDyD1ulTJuANbfYh3umDWRASO2a3M3DNuyGT52CJZlsXjeUpbOL2vVQ6i6vIYfPp7DnE9/IRzKpPt09ohYSOhjpHYyUv8WIvXLZV0niF2LXXUjsmggsng3ZPFA7CUjkfq3spvQcVPbBKgC2udz2fAw0wyjFHIO10rHoTeJawWT1LhpHAD1qSrHVh3cR/ROQm5BDnuNGcwrd72ZsDmeYRpss/sWrNOrfRRH9zllT6bc/RaibFoGKZVSeHwehp+4amfYdyZELKh7jtQXUwXBQ1D5Z+jmlY2HgxmqmGaG8u8GwQMQoxBqH4kdjcnEY6LyxqFymlWa+AaBZzOIziWxi1xQuSt/zk0y1u29FhNePIerD72V6ZM/afX5coJSsOMBO/DXj/O47aT7G6uz1u61BqPPHcmgkf2479wneOfx6URC2pjML87lgDNHcPAFIzssTCX17yOVl+pwSONm8yBvHOQcsUJ1jETqtFp39AfiPKDROUj5aZB/ISr36Awnrabpbz3TDan056kC8O4I4QQGhyqB/IvA+gmkDuXZGAIjGpOe7fILQFp2/3a6t85jlHYkrgenE3H8tYc16s/EJQ8qncB4/uOnt9taa27UkwkvnI3X70UZzQQUDYUv6OXKV86j2zpd2209lzRIlYOScENf6Ix4qQAJt9SmaU8USEhXm+QeC4ER+hhRQMDXH1okK+rKlAegUWPDE/tqyLm5GeXr24F7XvH89u2ffPBMeuMmmB9gl1EDKO7e5Mkq7l7I0VcdQiQU4eGLn44rPZ839z9uPHYSx/Q6g7cefr/RuAGoKqvhkcue4abj7m7l6WkPJPQxUn5SMx2lhheqkaqrmhm/K4iaR1sbN0BDiEiqJiLW/MzmNNcl+35TDc0skxl9hvbCeDeg9a1YQfAAVHA4Rv45GAWXonIOaarosquhfkqWe1PO28es5LgenE6EP+jn2jcv5uOXPmPq/dOY//siirsXsufRu7H7YTsRyGnfMtr+e/fl8d/v4o0H3+PbD35EKdh29y0ZetzuFHVdtUIHnR6Vg/44phFxM4oSndz++2lEwLsZYi1Alo4CewlxF9XwLGTpaCh+MK4qQ5ldoPQF/XroHZB6lGdT3erBLtOJm5GvAA/KvzMER6HM0g58H8uXaY9+oNs0RJPn4Xh9Hp759z5y8oJYUYv5v2uvSM8NuvPeUx/z8KtftjqnwW4pX5Skn57AtMems9eYwWwxqFfiMVkgIkjVtSRutxEbU3UrBA+Ka9OxvBARpPZJUueuKaT2OVR+Bg+K/l10OMheRva5NC3Pi+Xl+PqDCkL1TYnPqb0fzBLITaBSby8Asg1JCipn9WjP4Ro4nQzTY7LLqAHsMmr5lPCV9CjmsIsO4LCLUudYuHQsSvmQwFCof4PkT2UWKrhP63N92yOhaR2xK6266t0WKR/X2riJ7QkUUnEWdP2wMQ8HYt5H/4A4w0cLAF5EY5dzQCJf6V5bxfejfP064H0sf5YtLE8Yam5OJBxtNE1Nj9nYXw5gyqQ3UYZKO0ciTI/Bmw++164GDtFfIZpOoK5Oq3Qn+BvteMLxYbNkWL9nNKtSXiiciJSNpUHSoM2YG2iBPbsWqm9LOVSqJ0HO4SjlR6K/QuQHwIt4NnC4WMskZwW+gRA8MMvNr1y4Bo6LSydB5Z2M1L9DYoE8A/x7oLwJRNuCI6H6VpDaBOe1BR+q8BaQMgi9lWJu0cZP6EMI7J50Ngl/G1M3TtBwU+qRsjFIybOo8CexrundITgcZZS00/tZfpT0KNah3xQGSiDXjz+JV/bvn/7NyrgBrZn132/t1QogRoKGk60xHI7rCLykr/ZToDL3Lin/rlDyOFJ1S4JWJVlg/R5rnuugd5RUIXWv6Sa5kc+aveABVaw/m6kwNwTrV/3/RhdUzhGQe5w23FYD3BwcF5dOgvJshCp5DMwGwTgD/QRmQGAkqihxcz9lFKCK7wXlp/0+0gaUPIPybgLRv3BUth79JeUIqX00xf5sbaAtHYFUXQc1jyBVVyOLdkSq7+2QnJKOZM+jd00ZnjJMg6HH7J60s3ggN5ECrTMM06CoWzvre5k9HAyyHY5rf5QywD8EbeQkw0IFhmY3v68fRulTqK4fgmdb2vY5a/hbdvgwUjUx1n6hOVGQ8hQnGRAYgdF1KqrbLFTXj/RX3kmrvHpxc1wDx8WlE6F826C6vIsqfhiVfx6qYAKq6wcYRdembGWgfP1QXd6C3JNAZaL70TJ/R3+vCi7B8MW8RSrHwTwWYpch0SZVchHBtuZjV92MvWhArBFguqTIBu9VJPb/UaT6JqTmsdYjI7ORmkeQmoeRyA8O9rj8WH/LdRl63O4kKioyTIP8kjwOOjdxPz6A3UYPbCw0yBTbsvnfYe2rjaM868V0jlLsSeWDP7kHr6PRHbUViXPSTPBsqSv82oLRDaLfslxFOKWaxJ+bBkPJi37PHhoNvMAwVOE1ACijBGV2R6nVTwDSVTLuJErGLi7tidRNQaruAjtVPzEDfDtCeAaNyc3mhqi801DBvZrmEhtZskczyfk0eHqBZ0Oo/whIkgybDaVvYng30GrP5eOaNRUEsMG7NarodpTZvi0TssWyLB69dDIv3jaVUG2o8fiWO/fm7AdPZo0Nk3s7Fvy5iBO2OotQXWvBQMM0Gj1aLcNYhmmwcZ8NuO2Tqxq7k7cXEv4WWXYo+mbb+gavCq9FBfdv1zUzRUIfIuVnxvq4eWgQtsPbF1V8V5vDnSJRZGH7aJGlp8GDm+qhwITAXihfPyT6l66yCgxFeTZcTntc/mRy/3YNHNfAcVlFsWseg6oUaqXebTFKJyN2uTZeVC6Y6yfUMpG6l3XDwBVKLnT9EJbtD9Y8Wl/4TTB7okqnpG9kuhyprarj2w9+JFwfYf0t13GsZTXn01+4dN/rKF9UoZXHRXccX6f3mpxy+7HcM/5R/vj+bwxDIaI9Zv337sN5j51GfnHHvH8Jf41UToDonKaDxhqo/HNQwb07ZM1MEamDujeQ6E+gfCj//8C7Tbtp9NhL9tZJ1x2qHm6gVcrDpPUW+QZhlDzcgXvpXLgGThpcA8dlVUckqpsD2otTjlNdpqE862qPQPgzJPSu1r3xbgqBfeIMBal5EKm6EX1hN0hb0t4RBA6E+udTDFCo/ItQuUcuty11JJFwhI9f/Iw5s37G9Jj02WMr+u6xFYahvTizZ/7MT5/+gukx2W7PreMqsToSifwE1n9gFGvPmVp9sh2k9lmk8uIOXiUAJZOh/IQ01WEmBPfDiIWjVgdcAycNroHjsqojkdnI0pFpRmljgMDeSPmJEPmOpsJKCwigim5ABZqau4q1BKl7DsI/QLi9StMdVJM0bjk/lpOQ4rJlborR9dX22JiLSytsOwrL9ospdXccquhepG4KhF5PPa7kyVVGXsEJmdy/Vx+z28VlNULsUPpBgNj1SNnxMX0N0F6ZmEoxdUj56Uj4Gz028gtSeYXW7mg34yYPAiMBh8KSUk/a0IA1F6m+Z6WrvFqZEGs+Ev4cify8SvycRcL6/YQ+Rqw0JfZVl3W4cQMeJPRhTJ4hBd7t9JdLQlwdHBeXVZJqB2MElA3RH1OOkZp7QZ2ELD0CRzkBjvGhuk1HGfnYta9D5ZkOznGm3irVN+uqs9xj2rZFlzgk+itSeTWEP2k6aG4A+WfFmqyuXIgI1D6IVN8L0tRaQfz/QxVcimpR9i6R72I949pCEKhLtzOI/IQTXZ8V2f+rs+N6cFxcVkXq3yR9CwcTor+lGSMQehcpPwcIkX1fnpYoyDkUZeQDYOTsDUb7Vj9J9R2dqtP1yo5Ef0WWHgThWfEvWH8g5acgdS937Pp2LWJXt6vHSKomIlXXNzNuQP/Nv48sHY1Y8cKFUvscbW+Nks64AbAg+iWpvZXSPsKDqzCugePisipizSd9lYc4kN9vmO8P2sdzE7s5+PdE5Z8d/1LJ4+jKkXZCqiH0cfvNt5ojldeB1NHayI2VrFderiuY2nvdutexl+yHLNoGWdQHWTIMqZ2MSNv+HiX6a4oGoRbYi5Ca++MPhz6mY6unMsX13qTCNXBcXFZFjFJSq7qiK2DazSOTithFWJVAYD9UydOoottaKaoannWg67taTK29kHbU4VmNEWsRhD8k5d+L1EB9+/ZEs6tuQSrOjC9Lt/5AKi/RX23w5kjdS6RTPqbu2UZDSqJ/gv1v1uu1P6buK+WSFNfAcXFZBVHBfUkrEBbcH8z1lsNuRPfEKb5PKzL7+ibNGzDM7uDfibTGmVPMtdtnntUdRx5BE6z2MwAk/C3U3B37rrm3JraPuucg9F72Czh5T1Id81qB1L1I57plWqjco1b0Jjo1nem35eLi0l74BoJvAIk/4iYYRaico7LuzZMx1h+w7DAkMjvtUF3ymso4M8CzGZCmX5MqditM2gujyMEg2+E4Z0jt06Q2dA2k4hLshdtjL+yHXXYqEv7c+QKOVI39oIL6f635zufuUEy0xMPFq1V5eDa4Bo6LyyqIUgaq6G4IDKfVx9yzmQ4Tmd0gMASMNVuPaXd0fympui790MBe2jhJ1ZjTvyuQJoF4NemYvDxQnnVjRmWqnA8TAnu236LRH0lt6NogS3TTSanQyfDLDkNqHnQ2v3cb0uaVBfZpEjE0SljxOS9eCB6IKn15lRGz7EhcA8fFZRVFGTkYRTeiun6AKrgWVXAFqvQljC4v6OaJgFI+VMnDYCyPLtA2hGem1RlRKoAquV+3joi7RMWe5nOOBbFJG8ayF7VryGR1pykpPMlNPvfYNvd6il/QSZPX5mhjSKqu0+GtdISmpx/j3bppO8F9WD45a8kwIPcYjMIrUd7eK3AfKw+ugePisoqjzB6onP11zo2n9YVRedZDdX0LVXg9kLxjebthpZKej+3JuxWqy5uovNPAsymY64B/CKrkCYyC80FZOHuaXpE3pFUL5d8RVXQ7qAZRxgYD0wu5J6LyxrfveoEhZOcxMZHax1OOEJGYlEIqjLgybOXdHMxNnW3BNwjyLgIjmxywRO9Z96ZSOYdmMd/qiyv05+KyCiNSDzWPIrVPgr0A8CD+PVB5J+gLdgyl/BAciUT/gJp7ya4k3GHLBYdP+crsCnmnoPJOaf2adyskXS8sVQTm8unNtLqgAnuCfzcIva8bnqoCCAxGGcXtv1jwQKi+L1YJl4mhakHaXBwbreuUCgGpRGqfQyJfAUr/PVk/kzY5OTxDV5V1eRnKz4xVoDnBQOeWtSi3VwFU0b0o9+85I1wDx8VlFUWkDll2FES+pemCHIXQ20hoGhRPQvl3jTtH5RyJ1D0P9jIyuql4twfPxlA3meRNOA3wboXytENlk/9/YHQFeymJjSoDcg5DuXk47Y5SvvbNtUm2jlEIJY8hZcfpcCMe9N+xg79LFX9rE7scIj8CSv8NGnmIuWaaEKZA6D0k9B5N3iqnnwmByLeoumdRJQ9okcS6V8BaAOGPwS4j/u9WAYLKvxCC+0HdS0j4M33M1x+CI1GG2zcxU9xmm26zTZdVFLvqVqi5h8QGQEzmvevHKCM+10GifyMV42PNNx1g9ER1eQNl5CDVdyPVtyReDwNV8li7VX5I+FttwFFHqydqcwMofQXDWA4hN5cORSQM9W8j4ZmAgLUIwh+R3ItiQs4hGAWXauXjymug/mWaDO8A5BysPYnVt6SYpx1QeVqx278bePuglELsKqT6Dl3mLjV6nKc3Ku9k7SFzSYnbTTwNroHjsqojEkUWDWghQd8aVXANKufAhK/Z4W9h2ZGklZYvmYLh6xVbt6G3z50gtU1jjDUg51AdOgh/Cgj4tkPlHoPy75zBO2tC7Fpk6SiwfiXhTSr3FIz8M7Ka26XzItF5yJKh6L5kLX/vCjBRXV4Dc01k6aGxaqyWRr4B3h3Argbr+wTztCcmYIF3a1TRPSizVL8PCWmPjvK37nklYah/R3uPJKyTioMH6MrH1Ry3m7iLy+qOvSStcQMepLlCbAsM39aovLEpzlfg37PRuAFQSqFyj0d1nYEqugNVcCWq+BHIOQyqb9TueeqBEIRnIWXHI9V3J1sgNbVPgPUbSW9ONXch0d+zm9ul06I8a6GK70K39Wh+CzMAE1V0C8qzAdS9DNHvSezBtCEyA6ik41svxMJakR+QsmMR0d8r5Ud51m1t3ET/QZbshVSMg/rXIfQWUn0bsniXmNigi1PcHBwXl1UR5bCnk/JrKXq7DJS3VZxfco6DuqlgzW19rmczVOHExNMaOY15GhL5HqpviL3SPIchVtZbfQv4tkf5+jrbc8Peap8kdVKzidQ+hyo4L6N5s0GkDupe10JzSqG8/SC4N0qlESN0yQrl3wW6vqNbKYRmoD2C/VE5oxsTcaV2Mg25LUmx/lwOu21cTLecCH0Igd0SjhCJIGXHNMsNavi8xPp9VVwA5tquwJ9DXAPHxWUVRBkliGcLiM4muREQBbsaWbxzLIkTxLMlKu/EWIkuUHV5rGqk1QpandheBEZeyr1IzZM0uukTYiK1T2Rk4IhYYKdTlrXA+svxnNki4W+RshNAymhIRpW6F6Hqeii5H+XdqsP3sDqizO6Qd5qWEkiE9R+dqzEmgInUv4lqZuBIdB5EvgYUItVg/Z3ifAOpvh9V4ho4TnANHBeXVRSVdxJS3rrEWmNqIbW6Z+IPR39Eyk+F/HO1WnDdc0nOF5AQUj0JVXRj6o1EviZ19YkF4a9TTiFiQ/hjpPZ5sP8D1QXwovMwkmGCyk+9tzYi1kL9xN2Yb9TsfUoFsuxo6PKWLnl3Wb4YJWAtW44LpjLiG7BBapDIXKT+dah/Sz8oOMaC8IeIWCjVTv3aVmFcA8fFZRVFBfaA/IuRqmuaHwUs3bE7oQck1jm56gaI/kPqi7YF9VMRuVrr6CTdiINwWaycW6J/6NBT6EO9F28/XfFSc3essWLDfpxo7lio4F4JXxFrAdS9jNgLUEapluT3rJt+ny3nqX0mZtwkyfOQWl06n3dqxnO7tA2VcwBSdT0d78UxoPhpCH8AtY/rBp2piMxGlo5ow3o2+jPgGjjpcA0cF5dVGJV7JAT2QGqf0wm5KhcV2BOpuDrdmRD5kvRKslGwq8BMYeD4d4NoGnE030Ck/i2k/EzitE6sf6H+hWb7aDC20hk3Jng2B99OcUdFBKm+vVmXagNBoPoOJHgIquDSzJ6M699KsxcbqX8L5Ro4y5/gQVD7ZKxJZksjvSE5ORtBy+aY4N8dw78t+LdFjII0/dZEeyCzRoG5rtYickmLa+C4uKziKLMnKv/0uGNin5DmLBvsetI//fp0EnEExLMBKvo72Iu1CJ9/kL4QB/eN6fGkwFoUM26sFmvGJ1mmppkQnLcfqvj2pkaJDdQ+BjV3NTvQ7AZX9wxi5KHyz3GwVgNpGn4CSJoy+wwQazFS+4SuEJJKnXCac4guIXZvenEoIx9KnkLKz4bIZ81fAf8uuv1H7WMk/ttSOrzp7Q/Rr2PCl4mMIUHljmn6zjcQCJJaWqFtRpXKOaJN569OdKiBU1ZWxumnn86UKVMA2GeffbjjjjsoKipKOD4SiXDxxRczdepUfv/9dwoLCxk8eDDXXnsta6zRJFG96667Mn16fKO00aNH88wzz7Sc0sXFpQW62aUDg8HsAvY/KQYowIbyExtni5tVFUPBhSgJp18t/IGeK9twQs7hoAr0Td6/K8q7WashImGk+q4EJzeOgJoHscNfgMrRlTrB/bSibjI8myXxEDQQ8yS1AxL9FVl6WKz8P3aTjM5FKi+Duleg+KFWoo2rO8rsgSp9Aon8ApGvAENXW3nW0RVL1r8QeodWoU/PpqiSR3SyfvTvWGVTQ8hWYl8eVOF1KN82gM7HYtnRQDjJZvJjwn7ZGDixajD/bpBzSBbnr550qNDfsGHDmDdvHvfddx8AJ5xwAuuttx6vvvpqwvEVFRUceOCBjBkzhq233pqysjLGjRtHNBrliy+amp7tuuuubLLJJlxxxRWNx4LBIIWFKS5EzXCF/lxWZ6T+faT8xPQDA6PA8Gk3f1vw7RrTv0nTO6oNqIKrUTmjUo6R8GfIssMzmRVUPqrkoaSVUBKaiZQdlXqW4sdR/v4ZrJtgHRFkybBYVVgiY8qAnCMxCi5s0zqrGzp5/aNYCPdvMEpRwZGIf1eUvVQbumYPRCIQegepfx8IozybxYT3Shvnsiuvg9pHaJ8Gr4bupSYVej5zfR1uDo5GqdU78JLJ/bvDflJz5szhzTffZNasWfTvrz/c999/PwMGDGDu3LlsumnrrqyFhYVMmzYt7tgdd9zB9ttvz99//80666zTeDwnJ4cePXq0nMLFxSUdTkMZ9hIIzSWtlkg6Ip/R9lyHNHi3iftWRCD8GVL/KtjlYK6lQxIZISDVyLJjoev7OuTREt8OkHN07MbW/OcU8wTkHNdm4waA8GdgpRIttKFuMpI3zvXiZIBSBvh30d46QOwypOo2qLgEiYUfRZWAkQ9GiVbdDh4ERqmukrOrUQ0yCXUvkdq4acgjc/JZslGFl4F/KGCt9kZNtnTYT23mzJkUFhY2GjcAO+ywA4WFhcyYMSOhgZOIiooKlFKtwlpPPvkkTzzxBN27d2fYsGFcdtll5OcnLgkNhUKEQk2dYysrKzN/Qy4uqwrebdEdi9Pkj4Q/pMkd3waat2xISiYX/xaY66O8GzctZ9cg5SdDeCY6pGCjDY5snqxtkCqd85LbOvdBKQX5F4B3C6TmQS3kBuDphco9DgLDs1gzAZFvSFuGLHU6kdzYsn3WXM0QuxxZOjoWimpe7r9Ml5tbfyGRb6H6Lt1jSsr1y94+kHtMTAcp5QoOd2KAtw/499B/X26qbNZ02E9uwYIFdOvWum9Gt27dWLBggaM56uvrOf/88zn00EPjXFGHHXYY66+/Pj169OCHH37gggsu4Ntvv23l/Wlg4sSJXH755dm9EReXVQxl5CD+nSH0dpqR7eFqd0objKjcY+Jnqrgg1u8Kmt5DW96LIKGPUAkMHIgZOcF9UMF9tKIxoFSwDeslWqQh9yMdq3fpsEg91E3RQov2EjDXQAUPgsCeaTvLS/Wk1sZNK2z9FTNuAK3zVP6Vsw16ekH0pxQDTAjshyq42PXatAMZ/wQnTJiQ1lj4/PPPgdgHvwUikvB4SyKRCAcffDC2bTNp0qS418aMacpa32KLLdh4443Zbrvt+Oqrr+jTp0+ruS644ALGjx/f+H1lZSVrr7122j24uKyy+Hd3YOCsDARQzbwkEv0bQm/R/tonzvKH2t2wacA3CLg+9RijBDwbpx6zCiP2MmTZERD9hcZwoTUPCc+C2n5Q8kDS349IJCZqmY0hnMHfmn8oKudQpPqeZuXifh3qDO6L8g9EGSVZ7MElERkbOKeeeioHH3xwyjHrrbce3333HQsXLmz12uLFi+nevXvK8yORCAcddBB//PEH7733XtpEoj59+uD1evnll18SGjh+vx+/P4VOh4vLaobybtLpROyzIvd4ULnYoRm6kijyAx0i7NZOlVDZory9Ee/2MW2ixDdhlXNsWi/FqoTYZbqHmlGKMgqR8vOgsblqw99ALPcr8iVSeS2qMMnDub0sVuHUkSiw/kXln6zzeKw/QMJa18bNm+oQMjZwunTpQpcuXdKOGzBgABUVFXz22Wdsv/32AHz66adUVFQwcODApOc1GDe//PIL77//PqWlpUnHNvDjjz8SiUTo2bOn8zfi4rI649k85i7/hcQ3TBOdt5KqFcKKwgBEJ/eamyKLB4K9tGOX9A/q2PkdoIpuQ8qOhuhcmpScY3k5gf21sbcaIJHZSNWtEJ6ONmQMrT8T/jjFWTbUvYDkj09c9q9yaXMyfVoUKD2/UgZ4NuzAtVwgvtd8u9K7d2+GDh3KmDFjmDVrFrNmzWLMmDEMHz48LsG4V69evPTSSwBEo1EOPPBAvvjiC5588kksy2LBggUsWLCAcFhrC/z2229cccUVfPHFF/z5559MnTqVUaNGse222zJo0Iq/CLm4rAwopVCF14EK0Dpvw9QX/NyxK2JraVDg2QLV9QOUZxOoPK3jjRtAmWt1+Brp91CKKn0BVXgz+HYG79YQGI4qeRJVOLG1qGEnxbZD2KGPseveQaK/ZnSuhL/RicDhj4jz0oRnODg7DJHvE76ijLxYGDB9+kT22Cjvtm2aQawFSP2bWvXbWtJO+1p16dAspieffJLTTz+dIUN0Z+J99tmHO++8M27M3LlzqaioAGDevHmNooDbbLNN3Lj333+fXXfdFZ/Px7vvvsttt91GdXU1a6+9NnvvvTeXXXYZprl6J9i5uGSC8vaG0hd0cmX9VHSeiRcCI1B5J4O5NiiQ6jtperpd0YEtAXspYtdC5SXLYT0F5oZgrqlXD32C1D4G4W90/yzfADAKY52rPToPJjobIrNBecC3Kyr3GJRva0eridg6DGXN13P5+seFnbQy9HBUsJ2qs5YjEv4Gqbw0LslWAPFshSq8Uv89JjtXwkj927HfeSjBCKcyBCn+fnMOS+MFaguGrrzK8vcmdjlScWmL/DITCeyj24sYue2201WJDhX666y4Qn8uLvGI1INdAUZRq8aZYv2L1L6gBf/SlsIuD3KBWpaXsaUKb0YFh2NX3Qg19+Gsa3QDuvpJFV6PCu6TcqSEpiOVE3T/rQaMElT+uajg/lntvSOR6F+xxqjvo9tj9EXlHA7eLQAjrphE9xk7neS/s4D2TnlbJ0mL9R+y7KiYyGFb8KK6fYIyilqvYVciS/fXYn8dQgBV8iDK1y/jM0XqkKUHQfRXEvbU8m6DKnl8tcm/6hRCfy4uLisPSgXADCR+zVwTgiORmnuX866SkUUyqFEK/sEQ+Qmi3zo/z78nKjgcqX8vZtxAZpU2eqxUnAe+7VFmYnFSCX2MlJ1IKwPAXoZUnA9ipVVqXp5I/XtI+am0bIwq9a/ERngR/x6ovDFgrq37QaU0SENI9U2oYt2zTKK/QeR7RAyovhPseW3csQHB/RMaNwDUPgVWW9dIhtJtTzy9sju97sVY3lUibN2Cov5tCO6d9Q5XVVwDx8XFJSViLUaWHUznTDh2QOFtGMFhjd9K9G8k9B5UXZP2VBXQ4XWpeZimxN5sEKR2Mir/jNaviCCV15AqBChV12mtHZVdNah21NuZdUpPNpe1MOaNadkYtfn/RyD0FhJ6G4IHkDisFDcrhN7DjsyGymtaNMfMhoaQaux35t0GlX9B8tXrnqXj1LZFhzBrH4O8UzI/u/Z5UidAG0jdCyjXwGnFypGV5uLissKQ2kd1Oa4T/HugSp6FgjvTjwXABHNjOuxSZHRFBfaIO6Q866ByDtPNQFPiA//O+n8jX9O2G6ANke8SvxSdA9avpPRwSCWEpid/Pdlp4c+xy05EFm6GLOyNvWQ4UvuszvXJEqmdjM7XShcitPRXXcMN2gFlx8dK4bNEFUL+JeDZRCfKqyCY64Jvx9SK2h2esGsjtU9neepCUv+sbbCcieeubrgGjouLS2rqXsBRWMazCRTegPJtgwo47L/k6QXezemY6hUDVXRbQkVYpbyolE/TCnKPalZS3NZLpdJJyYmwHd5c7cUZrSi1L+jmoqEPafz9RX9BKi9GKs7K3sgJf0pmSb0tPT0psJfRFtVplXeaFvOL/gJSr7VtrL+g5k5kyf+Q8Oetdxj9m+yCGQrd8sQh9iKySnk1upH682GAmVpbbnXFNXBcXFxSY5c7Gxf9GZYMRmoeApUPhoNmuDmHgZHOk5IlwYNRvu10CKj+fexlx2Av7IO9sB92+TmItw8q7zT0ZdBA3+RMQEHwEFRek/o5/oG0rQ2CNDZ0bIXR1dkURuvWN0lXsxYglRcTlyMT2wcA9a/HmkNmgQMl+uzwkb2XzEDljUM8WyGVF8Xmaf6+bZAQUjYGsZc1HpXa55ElQ8gqrwsB6qH0hViJeZrbqcp3pOLf6rTggWlG2KjgARnPuzrgGjguLi6pcXoDBrCXIFXXIpVXQEGaMm5VjAruG6su6oC+V97NtHFTdT1SfiKEZ4FUg1RA/Wuw7EDdqLPrdG3M5IxG5Z2C6jINo3BCXL6Kyj22DXs0dTgsMCLxy55e2vuV6ildFUIyAykBOoyUyltg6HL3LFC+HeiYW0e2P18PdJmupQ3qHiX5z9HWYara5wGQ8FfNjKHsK/KUuT4q93hSG2cmZFsJF9wfPBuR2MDWVVQE9sxu7lUc18BxcXFJicoZTcaXirqndMVQ/mWJzzW6QPBgpGwsUnktqPSK5RkT/VmXMNc+GDvQ/AZqATZScQ5gQ+5xqNwxEDxQ6/+0QPm2R+Vfir55OvXkxG60qgBV8rAWk0s0SilU/kWx8Ylvzir/Aq2B45TIbFLfcG2Izs0uZBI8CPDSvmFFL9l7yKJNxmj9h6TzAkn9+0jNY0jZsbRZasCzcUwkcKDWREr4OdHCmapFU1inKCMHVfKE7h8X9zM3tGZV8UOrTYl4prg6OK4OjotLSrRGyAGxMtoM9F+CB2IUXonYNUjtwxD+Wid9ereC6klAHR2rZWOAd1uIfEPyfRvg3R6s35pyXMx1Y8bOqFYhBYn+itQ+FXsvPh02iv6mz8fUTRONrmAvAnyowC4Q2CepcRM3d+iTmA5OM70Xoysq/7y0GjotsctOjzVTTXWz92P0iFf2FbsK6l5GIt+C8qB8O0JgSCvjSkIfImUn05hInJSGLugdVaGkUd2+Qhl52Au2JH3Flh8I0x5/e6pgIipHh4fErtEeofo3mo0QMDdCFd2K8m7S5vXE+i8mMqm07pDpPGy5qpDJ/ds1cFwDx8UlLWItQSovh9A0HN+sfDtglMSHQcRagiwZrBNA087T1t5ACp1Xk2l5e2zdnKMxCi50dIZO2FVZ5VjEzyPaILP+A6ME8W6HIoyIhbL/1UaVuX7atgx2zWNQdVWKEQb4h2AU3960duhjrW0jdTR5IiwweqBKHkJ5Norfa3QeUvc0hD4AaxnIUhr7hGHoc1VxB4tDGuDdDqP0CQDshduDlHfges0IHooquCyBETxPKyJLGLybaUOkw/KWVj9coT8XF5d2RZldUMV3INYCiMxBKs4GqUpxhpG4DLvuWYfGDbT9CTvbkEds3dpHkMDejtostFcfKKUU+LZF7A2Rmgeg/HREKprvCsy1IPckCB6Y/MYZ/TnNSjY0Ew6U6G9I2Vi0MdgiMdlerJWEu7wV54lSnrVQ+edA/jl6Dus/pPY5XcGkAuAbDJVnZfL2s0BQeSc1fasCHeAUbKFc7dkElX8e+HZM/PM3u+qcGYmAZwPXuFmBuAaOi4uLY5TZA8weSPhALVyWNDxhJ+yXJKH36ehwRRNR8GyhdWaySmA1kdqnHfeRai/ELkOWHhwLVSX4WVnzdCjE+i+JcGAI6qakXUdFfwP/TvqcmkdJXs5t6fBd/auQc0jy+cw14vZj102lY8QhGwwGD6rgSlTzTu+e9SCcTjfGKSYUXAHhz7RR7tkIlXMQyuyZcLSIDTX3IjUPat2i2BwS2AtVcBHKKGmHPblkgptk7OLikjEq92gtpJbQS2Jqw8K/W+uXJNrBO2tOAPLPJnuDynLgCWl/pPKGWE+kNPuuuQuJ/tn6uL0UqE+zigdp3nep/i3SGYFSdYsOvzgl0d7ahQDknY3q9hEqJ74ySZdLt9W4UYCBKrwRI2cURtENGMV3YOSfkdS4AZDKS5DqW5oZNwAW1E9Flh6i85tcliuugePi4pIxyuyJKnlSh0sAbejELie+ATpnI4HAHmamiZbZX6JUwYUY/oGogomxeZobY8krluLGOEgObk/EroL6V3DmcTKRuudaH1Z5pH9vAqp5/kK6xFxAypFlo3SYMt3Quteh9uH0c2ZFHSo4IrFHJDBMG9dtwdtfN/7MoPWBRL6HRL8LACztjat9om37cskY18BxcXHJDs/6EDgQVBENZdd4tkblHp+4Y7OEwUrWNDARBvgcKiI3x1xLdwDPORgAlbM/qsvbkHsseLfTBlj+uZBzOKnzdAQV2Cvz9ZvPEP4Wu/xc7CV7Yy89EKm+H0nV9sL6E+dhHRuirbtfK6PAgfCcFf/ePL3TjG9YshypviflEKl7Dak4U+sNdRhJyumVD1XyCBkpDLckMgupuhVJ1lojAVL7Aqn/ltrQqsEla9wcHBcXl4wRCSNlx8dk+5uFBKI/IGXHQME1jeWzjedUXR/Lh3GKDzybQfgL0t70vdtB7okosxQ8m7VK+lWedXRCbHOs+UjdS7EeRS3DQQpUMeLbA8Kfg4QRY01U+AOk7mXdm8tcR2sEBYYm9FbZVbdCzSSaJ6lK5HuouR9KHkF5N0vwRjK5MRtg5Cd8ReWdhiybSeJKNEN3SW9WtqxyDkcqnPSAsqDuRaTgooTaKyIRJGX1Vgs8m0PR7bBkP6Ay7XBA95YyusUS3r9FG8J9m1VsGeAfBKF3ne+jJeGPkaWfQPH98Tk+ybD+Ja3XzV6Y/X5cssI1cFxcXDKn9vHWxg3QeCOvvBj8O6NMrYIsdhXUPpNgfCrqY2EOH2kNnMhXEHofVTjB8ezK7Aklj+rqoVZ9ngRkGSwZhDQzfuJ2by9EKj7VvbqK743TipG6qTHjBlq1SpAqbRx2fb91d3DPhmCuGbthpsNCBRKHUZRvWyi+Gyk/N1Y27aFRsTcwHFV4dfwJgWG63Lv+FQfr1usKOpUgRBT+JNZPKh0K/HujCs5FmT2w/QMh9KaD84DgIUj5aTHJgobfiAEEaWy5oNZxNldSLEBpIciuHyYOtzbHKKZVtVVLlCtJsrxxQ1QuLi4ZISJIzeOkNlYk1kU6RuRLtLhaptix89LdHGyom4xd+xqSQWdo5d1Sd59OuX6a18IzkOpb416RmgdJfnm1dIPNOEG42H6Ugco9OcWaDZjg7RNTz02M8u+K6vYxqvBWVN5JqPxzdBuKohtbGVZKGajC68C3k4O1PbE8nwRYThuCCoReQxbvjL3sSPC0Vo9OiOqm/65C7xL/92cT109K/nG4jzR7tJfEmpWm2VZwX9IKHmbbqsEla1wDx8XFJUNCYP+XdpREf2n2TVtKw22chS8sqByPLN5JN9N0WrVScy9tuxQK1D6F2LX6O6mH6Pek600k4VmJXwoeiMo7nYZqnoT4BqGK70urv6OUDxXcS3fZzj0O5Unu2VDK0PouKTEhsFfythFGlzTnJyD8GdQ84mys8sZUo9MlYbeXGI4J0V/TD/MN0CrWSVs15GfdqsEle1wDx8XFJUM8pL90KC261oB3cwfnpCKTcy2ofw1ZdqTWhEmBWEsh+iNt1uaRWqT+7YZvHJ6TeJxSCpV3KpRMBs+WNGUS+MDbD4qfwCh5QCcTtzPKuwkERpI4idcA5YsX1muJf1BigceUNHT+TtdrywTJput3W7BA5aQdpZSBKrpbh/oaf3axfz0bokpjvdlcliuugePi4pIRSnnAvyupq0YslH9w0zlmd/APSXNOe2Jpw6UuXU6Jg/Jop1Sei73saJBQ+u7gWGCuk7TkWqJ/Q/nJEP0BaNAOCutco4qzdE+iDBGxEbsakdTeD1V4NeQcQasUTXM9VMmTKM+Gyc9VPpTD9hbxNIQiU2HFKrOWb3chcVjJp4xcjKJbUF3fQxVcgcq/GFUyGVX6aqs2Fy7LB7cXlduLysUlYyT8NbLsEPTNpuUlxNQS9aVTmro8A2IvQ5YeCtYfLc5peM5K40XxbAnR2ThXJVbg3RKj9PmkI0QiyKKB7VjSbIJ3CwiMhiqHN3rPZqi801GB3RsP2UsPSdEk1Iz1+XKmMyP2MqT6fqibDFINeHUX6rwTUZ71U55H6CPdm8qzMXj7OG47YFdMgLqnHI3t9ORdhJF31IrehUuMTO7frgfHxcUlY5RvW1TRLeiwgkJ7ZmJP/J4NUcUPxhk3AMooQZU+rzVozPW1699cG5V3BuSdlmI1U3tEiu4Gcw3Si9g1IGDNT/0+lBdyDs5gznRYunTZ7ALBhrYGabxW0Z+Q8rFI7bOA7liuk7KTGXIWhD/RXp40iLUIWbI/1D4SM24AIlD/CrJ0PyTyQ9NYCSP1byE1jyF1rwEBVHBfVM7BKF/ihpEiIaTuFeyqG5DqO5HITwCNGkSrBGm9gC6dFbdM3MXFJStUYBh0GwB1LyPRnwC/9kL4dkqa/KqMPMg9DpV7XPwLYiPRf6D+RZrKbWMaLkYPVNE9KE83pPRlpPYJqL6PuKqZpJtMnw+ick9Cap9sZgC0FRPqp+qqpMBuSPWjEP0p1mS0ltYeL+25ksrLITAEIrOdLROdAymShvWcV8X0V1oaSxZIHVJ+BnR5B+qnIJVXxtoMxH7uKgh54yHnyMTGTWg6Un5W7BwPgkD17YhvZ8g9EV355lDbpjNjzUHsCpRRuKJ34pIhroHj4uKSNcoogtyj2+z/UMqAwokQHIHUTtaVMqoAFRwBgX1RRm5sYB6EZwF1ziaWckSiKXVMlJGDiMN3YHQDe1GaQZbWiYl8gdQ8BpEZOMsbiSCLh2nROkfopFwRSWyAWIsh9DbJQ38C1j8xwcaP4o+DNoCqrkahIPfI+DPD3yJlJ9FkODXrMRb+ODbfqtJF29KaT4EhK3ojLhniGjguLi6dAqUU+AelVo4Nfwrhmc4ntRdpzZTAnqnHGb70hVRmL1SXV5DFu6Upkze1R2rZESRWEk6BLI0J2KXDh9Q9i1SMB6lBVKEuL889BmV200Oiv+GoOizOuEmwpepbIecgVLOqOKm5m8T5VzRbsxOkdwb2jYkPNtNGUvnaAM0Aqb4bqX0e5dsGgqMaBSxdOjduDo6Li0u7I9YiJPonIok9LWJXIdG/ETuzsJDUv0pmly0DqZ+afph/D9J6HIxcXcKdc2iaPVgQbvDaOE2Ibo4TwyCsDbeGsmmpgNqHkCX7ItG/9DHVhn5McdupjhO7EwlB6H2ye29JUEXtN5eeEFQOqmACquuHqJInUEWTUKWvQuGNmU8X/RHCHyDVtyOLd0Xq32rn/bp0BK4Hx8XFpd2Q0IdI9R2xHkEAASS4Hyr/DJRRgkTmItW3Qeg99JO+QXUedgAAJ/FJREFUiQSGofLOQHnWTb+AXUFmmjU2OBH8Cx4Idc+kHhP5TjfKzDkc6qfqvJq4vcS8Nb6dY16R5e3B0O0lpHwc4t8Z6t8iYw9SMpq3X5BEeURtwQDfdhB6p53mMwGFKrq1KbTp277xVaF1Dy3n6HYXUj4OSl9CeXu1YS6Xjsb14Li4uLQLUvcSUjYGIt83O1oPdc8iS0dhhz5Elh4Ye/pvMAwsqH8DWbq/rh5qPl90HlJ9F3blBKR6EmL9Cy17N6XF1F3P06DESf+kCIRm6vBG7ong3x3d/6hhqTVQBVeCuQ7LT++nJaK9DTX3gPU77WaImGs2/b8qANXOCbferZupN2eTu9Nwjgf8Q1Glz6H8u7YaJdZSqH0g+33qWfR/naovu6wwXA+Oi4tLmxG7Cqm4lMR5GZZuHlk+Ht00s6UHxtJKwBWXokqf0oJ0Vdfp0mYMQOmGl9W3gXd7MsMCcxNdDp2gy3jTG3DWJ0tqHoCK8c3eQ0D3GMo5FuXZCKUMpOqGDPfYEbSXh0WB0RV8A5uOKBPJGQ01D9BmBWgAbJRvO5SvLwQPQMrPhsgXOH8PRiz5uwKog/D7SF0pGKVx6sES/QdZdjDYS9thz1bMC+nSmXE9OC4uLm2n/lVSK9HasXLiZDdES1cdRX/XXbhrH6YphyVKYyfsyKeZ763qEu0hWrIHUjcVkTqk9insJftjL9oZe+lBSPQPZ3NFZ7d4D/VQ9xJUT6LBi6D8exBXVdQKA1Rp5u9juRN7PwUTWmsa5Z4I5ga03VNlgqeXbhxKrMO7b3syuzXZscq2WL6X1ELtk8jSkXFaQVJxbizU1h5GGaTtcO+ywnENHBcXlzajDYS2h2UkMgepub/tG0oU6rD+QSrGIYv3QCon6FCOvQAi30H1TbEO2ckuiQ1zJbo5CoSmIou2xS47BZFwzNOU7OchYKwEBo65Iar4flRgcKuXlJGPKn0acg4FMg0bNs4CRhdU0Z1xZe7KPwBnCcwNP19FQq+gXYFUXACARH5OI56YKSZ4t2qnuVw6CtfAcXFxaTsqj3YJi0T/0q0B2oK5DvrSlmQ/jTo2Da/Hbo5Si9aWaWmYpJirOVKrwxZlh4N/IHi3jb3giX0pwAsFV8TyYzozeTqJ1r9z0hHKKMQouAQc9mqKJ4jKOwvV5dXWHc69/bRXJ+3tqaFKLNnvxoLI50j0N2im2Nw+WKicI9MPc1mhuAaOi4tLm1GBPUn9dOwgeVQVgdnT2YLBA5t1eW4wSIKQexpY/6TZSzJsIAI5hzSVLavcDOeIrVt9K+Sfhyp5SlddBfdD5Z+H6vYxyr87qUNYnYFqVHhG2lFiV6XV0YnHAHyo0idQeSdoocgWKKVQxfcA6bp4O+wsHpkLKovKKaNB66a5wRu7ZQaPiCWZu3RmOtTAKSsr44gjjqCwsJDCwkKOOOIIysvLU55z9NFH6z/wZl877LBD3JhQKMRpp51Gly5dyM3NZZ999mHevHkd+E5cXFxSoby9wD+YxJeUmGETGJF6jrxTUN5Nna2XcxSq6wxU4Y2o/LNQhdejun0S8zi0xZNkoXw7YnT/DNV9NvhHkF3Ohgm1T6J822EUXIhReDUq91iUUQxGPlnVd+ScgPYwJbts56Crutrpst5MHE/sGqTmcewl+2Ev2gl7ySjsigm6FUQmP29zXVTJoyjvlqnHGT1BtVOujPKDbwfSh1ADUPISqvgR3Si268eokifBvyv65+4B7zaoottRBRc7bjzqsuLo0CqqQw89lHnz5vHmm28CcMIJJ3DEEUfw6quvpjxv6NChPPxwU6dcn88X9/q4ceN49dVXeeaZZygtLeWss85i+PDhfPnll5jmiirPdHFZvVGFNyIV58SUeLUWCURBBVEF10BgKGIUQ+1jsdcMGowHlXdKY88j8Wyu+ywlNCwM8GzRZAgF94l7VYz0vaccvBP9X+VBQg5EAhNixSqBEsyuAkhgL6h/nYw8TZFvUF1eR8rG6lYWrahH790PhGhzMm3MmybWYmTZYWDFBAQR3d8q+m3yc1uhwLc7qniSQ8OgPhYybCsB8PVHGXlIYCTUv0Tin4uC3MMxfJvHH/b1Q/n6tcM+XFYEHWbgzJkzhzfffJNZs2bRv7+O0d5///0MGDCAuXPnsummyZ/U/H4/PXr0SPhaRUUFDz74II8//jiDB+vktyeeeIK1116bd955hz33TCPJ7uLi0iEoIwdVfBcS+QVCbyF2DcqzAQT2ahRcUwUXIblHQ92riL1Ul/EGRjS1FwBU4TXIskNjuTgtb0Z+VOE1yTdhL27ju/CAb+umb6U++6lEkvaJUnknI82ViJ0Q+Qyx/khi3ECDcCLebXQ/q9DbYNeA/W8Wm/cjtS8j4c8h9Hks7NcWz5ig8k/NwOvR0KW+LWsqyD1CN3gFVOGliL0oFlJraOga+9c/FJV3ZhvWcumMdFiIaubMmRQWFjYaNwA77LADhYWFzJiROrb7wQcf0K1bNzbZZBPGjBnDokVNze2+/PJLIpEIQ4Y0NT5bY4012GKLLZLOGwqFqKysjPtycXHpGJR3Y1TeqRgF56FyRjWpyTZg9ATfAJRvoNZXMbq2OL835F+caGagLmXrBam8kvRCccleN3SujFHS7FAbPEL2v8iSvZG611rvwLOBDn+Ql9mcldeTOtRiQWQWKvdojC6vobq+nUUeEUAIQlOg5m6IfkGbq49yT0J5N08/LoZSJni2SD/QszUERsa+MdG3tNjPJzAyzmhRKogqfgBV/IgOl/oG6d93yVNa9TibPB2XTk2HeXAWLFhAt27dWh3v1q0bCxYsSHresGHDGDVqFOuuuy5//PEHl1xyCbvvvjtffvklfr+fBQsW4PP5KC6Ov/B079496bwTJ07k8ssvb9sbcnFxaTNS/zZSdS1YzXLmPJtAwWWNoQCxK6DqSlo/vce+r5mEeLdoVb4skV9iOjVpMHrGmmU2hMhi/3q3ReVfFD82eDDU3Ob8DbbE+k03xLQXoHKPj3tJeTdDih+AsoMzmO9X0j+XCtiVYBSglBcJjoLax2nX3lFOMXqg8s/RXeEzpeBiWDY69Zj8szH8/ZHI0Ujdy7pCzuiGCo5EeTdrNVw3dB2I8g9sPZfLKkfGHpwJEya0SgJu+fXFFzr2nMgdmcxl28Do0aPZe++92WKLLRgxYgRvvPEGP//8M6+//nrKfaWa94ILLqCioqLx659//sngHbu4uLQHUvc6Un6qVjVuTvRXZNlROhwCUPdiLDyVLDxhIDVNOXoiFlL/nta2cYLRHVV4HXi3A3M9naNReDOq5DGU0VS5I9F/wOgOZOMBaZxF/7fqBsRq3YHc8PeBvPMzmC+R5ktLTMTIQ+pewa68Uqs0G6Ust/YRxlpQ/Ch0/RSj24fZGTeA4dsW8q8gqcct/wIMv44QKO9mOpm76FaMggsTGjcuqx8Ze3BOPfVUDj449RPHeuutx3fffcfChQtbvbZ48WK6d+/ueL2ePXuy7rrr8ssvvwDQo0cPwuEwZWVlcV6cRYsWMXBgYqvc7/fj92crRuXi4tJWRCJI5RUN37V4Vd+wpfIqVJdXkHDi5Ny48ZGvEBGQMmTZMbGkZIdEf4DAXhjB/RLv1a7Q7QLC053PmRaF1DwG3k21weHpBd6tUEph5B2LHXonaVJyi905GGPBop0R6mm6xEd1/yippmM9OSb4tsXwD2iX2Yzcg7EDQ6Dqegh/hm5muh0q77TW+jkuLi3I2MDp0qULXbp0STtuwIABVFRU8Nlnn7H99rp/zKeffkpFRUVSQyQRS5cu5Z9//qFnT53R37dvX7xeL9OmTeOggw4CYP78+fzwww9cf/31mb4dFxeX5UHoY5CyFANsiM5BInNx2mxRRKDspFhX70yIgF0GZutCBttaCEtGgJQnOM/Q2jsiONZgaZoZah9Gmhsont5QeKPOWSq6GVl6iMOEYCfJtw3J0c30dqQaVDHIkoRntA8WKieDkJsDDLMEiq5t1zldVg86LMm4d+/eDB06lDFjxjBr1ixmzZrFmDFjGD58eFwFVa9evXjppZcAqK6u5uyzz2bmzJn8+eeffPDBB4wYMYIuXbqw3376aauwsJDjjjuOs846i3fffZevv/6aww8/nC233LKxqsrFxaWTYSfPu4sfNx/lS9dQ09Tlu9FvIPI1mVfaGKDyWx0VuxyWjkxi3IDup1UDeWNRBZfrBNeMwj4t9hn9GVl2CGL9q6vJiu6gSZ03xd69fbPs5m3FjBtf2pFZY6yL1E5BQp8g0l49n1xcsqNDhf6efPJJttxyS4YMGcKQIUPYaqutePzxx+PGzJ07l4qKCgBM0+T7779n3333ZZNNNuGoo45ik002YebMmeTnN12QbrnlFkaOHMlBBx3EoEGDyMnJ4dVXX3U1cFxcOivNK5NSjiuF4H6xyp9klycLlXssEvogxZhUa6zVurILdA+stJ2mBepfReUcggruTdu0ZiyQGqTmfsSugfKTSd2wFL1ezpEgFVmuaYDHmZhiVth/Qf3zSNkxyLLRiJ3Ka9dxiAgS/hy76gbsyquR2heRtrYAcVnpUCLSDg1kVi4qKyspLCykoqKCgoKCFb0dF5dVHpF6ZNHAWA5IIhSY66C6vK3F/sKfI2XHgzQXrNOaJSrvTFTeSdiV10LtI2RsZKhCjO6ft9ifjSzaPtbxPN35Rai8U5GaB5x7plIShPzzoeqy9ENzT0TljUcWboUW88sC3wDw7whVN2R3vmNMbUyZvcH6DvChAoORwH6oyNdI/etgl4NnPVTwIJRvm3ZZVaylSPlYiHxLvOBkvi4H9+8UP96u1b93owil0nnQXFY0mdy/XQPHNXBcXJYLUvtUkkqnmHJw0d2oQFN/H7EWIrXPQOhdbeh4t0blHIaKCfFJ3RSk4uwsdhLA6PFd/N7sKmRR3wzmaKsIXQt8O0D409RzqiKM7p8BYJefF1PlzRQTcg7DKLgYicxGqu/WDUKJ6Ioy79ZQ/w6Z5xilokmxuim/Smj6GcZeDx6CKrgMpbIPLIhYyNL9IfozrZOpFWCiSp/XJfqRX5DqO7QgIjbgg8BwVP5pKHPNrPfg0rG4Bk4aXAPHxWXFILVPIVU3gVQ1HTS66BtbILkKuVj/grUIjNLG6hmRELJoxwzDNQo8m2J0mRI/v0RiXpEVoBWjCrRxEf0uzbh8jO5fAiDRX5Ele2W3XOnrKO/Gjd+LvQypewMlFWCuAf4hSOQ7CM8ATKi5K6t1siJvPEbe2KxPl/r3kfITU4wwITAMlXs0svQIdEjQin9dFaBKn0V51s16Hy4dRyb37w7tReXi4uLSHJVzKAQPgNCHOt/F7Am+QSiV+FIkke+Ryusg8lnTMVUARhcd/sgdozt3O+7OLaicw1rvS3mRwFCof5Pla+SYkDMa7AqI/ph6bQlhl50KeWeipIqmnlNO0N4SlXd6o3EjYiPVt0LNA4CFYKJDORNQ+Zeg8scj1gJkeRo41bdh5xyGYbROAneChN6iqQ1DIiyofxOJ/Ejifl0WSCVSeRmq5JGs9uDSeXANHBcXl+WKUn4I7JF2nIS/1U0eWxovUglWJVh/QugN8O4A+CHyIanDRgb4ttdJzIn2lTsWqX+b5WfgmFp1N+dYsOcjdc+mGR/W4ZTQ27F3mUE3a8/GqNyxqODwxkNSfQfU3NNsUOznLLVI5QVg5ID/f+gO5csrQdeC6juh4ILsTrdrSZ+TFQXrj9R7CM9Aon+7WjsrOR1aReXi4uKSLVJ5Gfqmm+yGFTse+RR8m6C6/wRdpkPJ85B3FphrNw1VhZA7VvciUonLpJV3U8g7rT3fQgoU+HdBlU5GmaUo7xaQO7bpNUekyS7IGYPq+iGq60xU6avxxo1dATX3p569fByy9GCdl7O8VJAh1mU9SzwbkPbn57TE3vo9+324dApcD46Li0unQyI/OesrpUdD7ZOQewqGpyfQE3xbIbknxHpeWWCukdSwaY7y9WnP1OFmmKCKIO9cMHNQ3q1QZs/4tfPOBM+GOkeprdVZeWejcsckb4sTepf0JekSC5sJOhwmtK0s3iH2EkQsCL2P1D0P1n+6vUbO/uAfnLIppsoZhdTcnWJypSvInBhRWTUpTYzYNTG1bQWe3nEtQVw6DtfAcXFx6Xw0b8bpBKnVLRiaiQQqpcCzdoqTEuDto7V40urhJCJFZZXRHbCg6jy9Xe82kHtCXMNQpRQE90VCX0D9c7TFmFDe3il7/mFXEl/dlIyG9xMGcxOwfmnTvhyhcpGy45qSnLGAn5HwdPBuA8UPoYzEXdiVuSbkn49UTaT1+zO0Nyr/fKh/G4ik2ENxzHPVNkTqkaqbofYZGtWlVRAJHoLKH+/I6HbJHjdE5eLi0vkwslDqFaeJxslRyoPKOyPDswwwNwTPNs2O5YNvV8i7ELzbgz0f7Ga9+SLfIeUnIzUPJthE43+yJ4FScwNil2ntl4wMFdGeFBIbFkk2EfvXTHAsGabu9h6eFfu+IR+qIRz5HVJ5cepVc49BFd2h+301HoyFKEsewTC7Q84RKfei8k5us/EhEkXKToDax2hqnYFuJFv7MFJ2kvZUuXQYrgfHxcWl8+HtA0Y3sBc5PMED3t7tsrTKORikWj95Y6Fv0DYg2oMQ/aWZYKEfcg5E5Z2DMnJ0bovUg1GiK7Nqn0eaVYA1EWswWnU9+HdHedZvWt/XD6mbnP0bMLqDd8vGbyX6D1L7JISmgbUEfbPNJhBXlX5IHDGtG3Md3eJC+cD3Pwh/AtZvCfZggArEcl9S5F3Vv4lYC3R7iySowJ6owJ6ItRQIgdE1LrSl8s9GpBrqnqVJDDD2O849SatFt5X6N5sZai0RCH+kfyeBoW1fyyUhroHj4uLS6VDKhPzxSMX5DkabENgXZRS33/q5x+ty9rrXEXsByiiFwN4osxsi9RCZA1jg6RUXLlFGIdDkfZLaJ0gtCmggtZNRBc3eZ2AoVE3MMkymc3mU0l4TCX2ClI1FJ2u3h7cgVQl2IgSkEqPbzKYjdjlSPh7CH6ODCErPafaE4FFQfU2aOW3dWTy4T9rVlVma+LjyoAqvQnKPQ+qmgL1MG0zBka1yo7JFap8hdRjQQGqfRbkGTofhGjguLi6dEhXcHySEVF2r3fqtR+h/PJuiCi5q//WNYsg9vFUgQ6kA+LZ1Nkn0F1J7Syyon4r4B4BvJ5QyUMqHFN4IZWNwru8TI3iYTsYlZkiUNfS3ao/U6Ryyag9hL0XEblQoVkYRquQhJPIzhD8ECYN3C/DtqBOLnU2a+T4SoDzro/IzDUk6xPqH1Pu0wfq7Y9Z2AVwDx8XFpROjcg6BwD4QmoZE/4Dob7q6ytaquyo4GnIOQKngit4qAGLrsIfUPgf2YhwZKPYCpGwMeDbRhk39u1D7INl4XFRus9BK3QtkH45KQM4oqH008/NUQcL2C8q7CXg3iTsm3i1xlPzs3SbzfSxvjJJYNVyyn7/SCe0uHYZr4Li4uHRqlJGrQwcreiNpEGspsuwQsP4iK6Mi+gssTR92SYwB3q3icnkk/EWWc8Xmw6YxJBUcDXnn6waWkW9x/v5MCO7veFVldkMCw1IoSpvgG4DyrOd4zhWFCu6HVKWSOhBUEtFJl/bBraJycXFZbRGxdVNPazFtbcsnlRfFwhLZzpPteQZgoPJb5itlaRIaa4JvEHi30s0nS55CFVyBYZioort10rAjYn2dco/NaHlVcBl4NkLvv+E9xP7fXAtVeF1G860wgvuDuRaJRRJN3X8skK1B6+IE14Pj4uKy2iFiQe1jSM0juoQbwFwXco+H4EGpNWQSzWf9C6H3adcO407xbIQqmIDy9Yk7rHw7IKF3M58vOAIjf3zCl5RZipS+Acv2h+hPafbVC1V0U8pqp4RrGEVQMhnqXtTtK6wFuiFrzigIjkqqgdPZUEYelPy/vXsPirru9wD+/i7sBQRWlHAxTbELVl5CEMG8lbVixx7LjieV2UPnMSon9djlNFjnGS8zjdrFnj+sfGrM05QzOolUHo0TFqCN4KUwLyRZeaFJvCULeUFwP+ePxX3cFvaCLAu/fb9mfqP72+/vx+f73a/Mx99+L+sgdc8BTd/CbSd1/Qionm9xwb8g427i3E2cKKyIOCD2F1pZzbZltlOUDTrz3wK756WtEPuCjgoxMIlV0Ok8/68qjnrImQnORRD9HpSroG4q9TmTSBx/QOz/1bIisq7laHau/hv1r1BRf4G6bqp6uJOmKuDaV4aGDCj9YO8XUJu4mzgRUVsai9tYqr/l/3qXPoJEZUMZRvp/T+XnXk26mwDHWXTck54I15Rwj5B0cUD8PyDnn2xjFlqrATrXovFB6WKg4t+FNP8EXN4GkUaoyNsB0wNcnbcVSn8XoL8r1GGEHY7BIaKwIhc+8FEiomUNkwDo0+F7Q8rIlhV0O0oEYJzg9es0ZRgJlfAlAN9Ji9NV4HKJ3xGoyNugYp6BLvY/oaIeYnJDXQoTHCIKG+L4HWja56PUVaC5OqD7qojegOkRtP0rVQdEPeoccGvIwg1vxQAAcED1mO1HbH0A4zgvsV1Pd90qzUTdGxMcIgobcuEj+PX1kAp8IKuK+xvg+lorwv1Pw0iouP+GUgao+PegYl5o2YCzReRdcO7Y7e0p0LWkSAcgAsq8DMqQ7l9sPf4d/o3DcQDdYAo2kT84BoeIwselAr+KKdNDAd9a6aKB+P9xrsZ7qcA5+yciCSrqsZavkpzJi1IGIOYp54wtsQPQQ+liIM0/QRreahm46wCgAMNYIOZZqKvHII2lgDRC6e92ziYKYHaSMmQAMc9D/ljprZRz/y/DvQHXnagr4iwqzqIiChuO2iFwbl3gjR4qsQJK1/aO3MEkjjrnQGRdLyhdL/f3pBloLAOafwSUETBOhIoc4Pe9HZc+B+z58FxhuWUtnfj3oYxMcKjr4iwqIqLW6BIBx6/eyxjHhyy5AVrWgdH19DgvV/ZA6ha0bAERAUCAhuUQYzaUeblfa6roov4C0d/j3Cm9sQiur60Mo5ybdBru6bB6EIUaExwiChsq+t8gf/wd3sajqOjcTovHX9J0GPL7f+CfT16u28ag8UtI3UUg/n3XjCoRAZoPAs0/ASoaMNzrWiBPRd4CFf93iKMecJwGVHybu24TdWdMcIgofETnAJc2tWyp8Oe9jhRgfBAwZIQiMq/kj9VwxttaYuZw7srd9D1guAfSVAWx5/9ppWETpMdfoWLmX7erdxyg41f0pF2cRUVEYUPpYqF6rweME+H+688ERP/VuXx+gNs0BJvIFaDx/+B9d/EIyOX/hTT/Avl9lnPjTjeXgQvvQOpfDWKkRF0Ln+AQUVhRul5Q8asgV2uBph8AFQnoU7vuHkdyGd6TmxaOesgfqwBpbLv8pY8hPXKhIv3dMJOo++ITHCIKSyrCAmW6D8o4tusmN4BzTR7la9CzABFJwOUv4D0Z0gGXP+vA4Ii6LiY4RERdmFI6IHoGvC8CKIDxfvh+0qMgV892XHBEXRgTHCKiLk71yAMi+sEzyXGOF1IxC6D0KfA96sDh3LqBKAwwwSEi6uKUridU7w2AaSoA/T/fiOgLFbcMKmYOlDIBpn+Bzyc9UVODHC1R18BBxkRE3YDS9YLquRzieBm4ehyACYi81TXtGwBUzDxIYwkgF9Dq11U9noSKuLnTYiYKJT7BISLqRpQuDko/FEp/u1tyA7Qs4td7PaAf/qeLYqBiXoSKebETIyUKLT7BISLSEBV5G1Tv9ZDmn4DmnwHVw7mbuTKGOjSiThXUJzjnz5+HzWaD2WyG2WyGzWZDXV2d12uUUq0er7/+uqvMhAkTPN6fMWNGMKtCRNStqMjboEyToIxjmNxQWArqE5xZs2bh119/RVFREQDgqaeegs1mw+bNm9u85uTJk26vv/jiC8yePRuPPfaY2/m8vDwsXbrU9ToqKqoDIyciIqLuLGgJzg8//ICioiJUVFRg1KhRAID3338fWVlZqK6uRkpKSqvXWSwWt9efffYZ7rvvPgwaNMjtfHR0tEdZIiIiIiCIX1GVl5fDbDa7khsAyMzMhNlsxs6dO/26x6lTp7BlyxbMnj3b471169YhISEBd999N1588UU0NDS0eZ/GxkbU19e7HURERKRdQXuCU1tbi8TERI/ziYmJqK2t9eseH374IWJjYzFt2jS38zk5OUhOTobFYsHBgwexcOFCfP/99yguLm71PsuWLcOSJUsCrwQRERF1SwE/wVm8eHGbA4GvHXv37gWAVnflFRG/d+v94IMPkJOTA5PJ5HY+Ly8PDzzwAIYMGYIZM2Zg48aN2LZtG7777rtW77Nw4ULY7XbXUVNTE2CtiYiIqDsJ+AnO3Llzfc5YGjhwIPbv349Tp055vHfmzBn06eN7qfAdO3aguroaGzZs8Fl2xIgR0Ov1OHLkCEaMGOHxvtFohNHIWQREREThIuAEJyEhAQkJCT7LZWVlwW63Y/fu3cjIyAAA7Nq1C3a7HaNHj/Z5/Zo1a5CWlobhw4f7LHvo0CE0NTUhKSnJdwWIiIhI84I2yPjOO+9EdnY28vLyUFFRgYqKCuTl5WHKlCluM6gGDx6MwsJCt2vr6+vxySef4Mknn/S4788//4ylS5di7969OHbsGLZu3Yrp06cjNTUV9957b7CqQ0RERN1IUBf6W7duHYYOHQqr1Qqr1Yphw4bho48+citTXV0Nu93udm79+vUQEcycOdPjngaDAV999RUmTZqElJQUzJ8/H1arFdu2bUNEhLdN5oiIiChcKBGRUAfR2ex2O3r27ImamhrExcWFOhwiIiLyQ319Pfr374+6ujqYzWavZcNyL6pra+b0798/xJEQERFRoBoaGnwmOGH5BMfhcOC3335DbGys31PWu5prWWw4P4UK9zYI9/oDbAOAbRDu9QfCqw1EBA0NDejbty90Ou+jbMLyCY5Op0O/fv1CHUaHiIuL03yH9iXc2yDc6w+wDQC2QbjXHwifNvD15OaaoA4yJiIiIgoFJjhERESkOUxwuimj0YhFixaF9QrN4d4G4V5/gG0AsA3Cvf4A26AtYTnImIiIiLSNT3CIiIhIc5jgEBERkeYwwSEiIiLNYYJDREREmsMEp5t49dVXMXr0aERHR6Nnz55+XSMiWLx4Mfr27YuoqChMmDABhw4dCm6gQXT+/HnYbDaYzWaYzWbYbDbU1dV5veaJJ56AUsrtyMzM7JyAO8A777yD5ORkmEwmpKWlYceOHV7Ll5WVIS0tDSaTCYMGDcLq1as7KdLgCaQNSktLPT5vpRQOHz7ciRF3nO3bt+Phhx9G3759oZTCp59+6vMarfWBQNtAa31g2bJlGDlyJGJjY5GYmIhHHnkE1dXVPq/TWj9oDyY43cSVK1cwffp0zJkzx+9rXnvtNaxcuRKrVq3Cnj17YLFY8OCDD7r24upuZs2ahX379qGoqAhFRUXYt28fbDabz+uys7Nx8uRJ17F169ZOiPbGbdiwAQsWLMArr7yCyspKjB07FpMnT8aJEydaLX/06FE89NBDGDt2LCorK/Hyyy9j/vz5KCgo6OTIO06gbXBNdXW122d+++23d1LEHevChQsYPnw4Vq1a5Vd5LfaBQNvgGq30gbKyMjz77LOoqKhAcXExmpubYbVaceHChTav0WI/aBehbmXt2rViNpt9lnM4HGKxWGT58uWuc5cvXxaz2SyrV68OYoTBUVVVJQCkoqLCda68vFwAyOHDh9u8Ljc3V6ZOndoJEXa8jIwMeeaZZ9zODR48WPLz81st/9JLL8ngwYPdzj399NOSmZkZtBiDLdA2KCkpEQBy/vz5ToiucwGQwsJCr2W02Aeu508baLkPiIicPn1aAEhZWVmbZbTeD/zFJzgadfToUdTW1sJqtbrOGY1GjB8/Hjt37gxhZO1TXl4Os9mMUaNGuc5lZmbCbDb7rE9paSkSExNxxx13IC8vD6dPnw52uDfsypUr+Pbbb90+PwCwWq1t1re8vNyj/KRJk7B37140NTUFLdZgaU8bXJOamoqkpCRMnDgRJSUlwQyzS9FaH7gRWu0DdrsdANCrV682y7AfODHB0aja2loAQJ8+fdzO9+nTx/Ved1JbW4vExESP84mJiV7rM3nyZKxbtw5ff/013nzzTezZswf3338/GhsbgxnuDTt79iyuXr0a0OdXW1vbavnm5macPXs2aLEGS3vaICkpCe+99x4KCgqwadMmpKSkYOLEidi+fXtnhBxyWusD7aHlPiAieP755zFmzBgMGTKkzXLsB05huZt4V7F48WIsWbLEa5k9e/YgPT293T9DKeX2WkQ8zoWSv20AeNYF8F2fxx9/3PX3IUOGID09HQMGDMCWLVswbdq0dkbdeQL9/For39r57iSQNkhJSUFKSorrdVZWFmpqavDGG29g3LhxQY2zq9BiHwiElvvA3LlzsX//fnzzzTc+y4Z7PwCY4ITU3LlzMWPGDK9lBg4c2K57WywWAM5MPikpyXX+9OnTHpl9KPnbBvv378epU6c83jtz5kxA9UlKSsKAAQNw5MiRgGPtTAkJCYiIiPB4UuHt87NYLK2Wj4yMRO/evYMWa7C0pw1ak5mZiY8//rijw+uStNYHOooW+sC8efPw+eefY/v27ejXr5/XsuwHTkxwQighIQEJCQlBuXdycjIsFguKi4uRmpoKwDmmoaysDCtWrAjKz2wPf9sgKysLdrsdu3fvRkZGBgBg165dsNvtGD16tN8/79y5c6ipqXFL+roig8GAtLQ0FBcX49FHH3WdLy4uxtSpU1u9JisrC5s3b3Y79+WXXyI9PR16vT6o8QZDe9qgNZWVlV3+8+4oWusDHaU79wERwbx581BYWIjS0lIkJyf7vIb9oEXIhjdTQI4fPy6VlZWyZMkSiYmJkcrKSqmsrJSGhgZXmZSUFNm0aZPr9fLly8VsNsumTZvkwIEDMnPmTElKSpL6+vpQVOGGZWdny7Bhw6S8vFzKy8tl6NChMmXKFLcy17dBQ0ODvPDCC7Jz5045evSolJSUSFZWltx8883dog3Wr18ver1e1qxZI1VVVbJgwQLp0aOHHDt2TERE8vPzxWazucr/8ssvEh0dLc8995xUVVXJmjVrRK/Xy8aNG0NVhRsWaBu89dZbUlhYKD/++KMcPHhQ8vPzBYAUFBSEqgo3pKGhwfVvHYCsXLlSKisr5fjx4yISHn0g0DbQWh+YM2eOmM1mKS0tlZMnT7qOixcvusqEQz9oDyY43URubq4A8DhKSkpcZQDI2rVrXa8dDocsWrRILBaLGI1GGTdunBw4cKDzg+8g586dk5ycHImNjZXY2FjJycnxmAp6fRtcvHhRrFar3HTTTaLX6+WWW26R3NxcOXHiROcH305vv/22DBgwQAwGg4wYMcJtamhubq6MHz/erXxpaamkpqaKwWCQgQMHyrvvvtvJEXe8QNpgxYoVcuutt4rJZJL4+HgZM2aMbNmyJQRRd4xrU57/fOTm5opIePSBQNtAa32gtbr/+Xd9OPSD9lAiLSOPiIiIiDSC08SJiIhIc5jgEBERkeYwwSEiIiLNYYJDREREmsMEh4iIiDSHCQ4RERFpDhMcIiIi0hwmOERERKQ5THCIiIhIc5jgEBERkeYwwSEiIiLNYYJDREREmvP/TQUJBVltci8AAAAASUVORK5CYII=\n", + "image/png": "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", "text/plain": [ "
" ] @@ -277,14 +287,14 @@ "train_input, train_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", "test_input, test_label = make_moons(n_samples=1000, shuffle=True, noise=0.1, random_state=None)\n", "\n", - "dataset['train_input'] = torch.from_numpy(train_input).type(dtype)\n", - "dataset['test_input'] = torch.from_numpy(test_input).type(dtype)\n", - "dataset['train_label'] = torch.from_numpy(train_label).type(torch.long)\n", - "dataset['test_label'] = torch.from_numpy(test_label).type(torch.long)\n", + "dataset['train_input'] = torch.from_numpy(train_input).type(dtype).to(device)\n", + "dataset['test_input'] = torch.from_numpy(test_input).type(dtype).to(device)\n", + "dataset['train_label'] = torch.from_numpy(train_label).type(torch.long).to(device)\n", + "dataset['test_label'] = torch.from_numpy(test_label).type(torch.long).to(device)\n", "\n", "X = dataset['train_input']\n", "y = dataset['train_label']\n", - "plt.scatter(X[:,0], X[:,1], c=y[:])" + "plt.scatter(X[:,0].cpu().detach().numpy(), X[:,1].cpu().detach().numpy(), c=y[:].cpu().detach().numpy())" ] }, { @@ -297,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "13ec74e5", "metadata": {}, "outputs": [ @@ -313,7 +323,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 3.78e-05 | test_loss: 2.41e-01 | reg: 7.95e+02 | : 100%|█| 20/20 [00:01<00:00, 15.82it" + "| train_loss: 0.00e+00 | test_loss: 2.37e-01 | reg: 4.10e+02 | : 100%|█| 20/20 [00:01<00:00, 18.81it" ] }, { @@ -332,7 +342,7 @@ } ], "source": [ - "model = KAN(width=[2,2], grid=3, k=3, seed=2024)\n", + "model = KAN(width=[2,2], grid=3, k=3, seed=2024, device=device)\n", "\n", "def train_acc():\n", " return torch.mean((torch.argmax(model(dataset['train_input']), dim=1) == dataset['train_label']).type(dtype))\n", @@ -353,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "id": "91b4c228", "metadata": {}, "outputs": [ @@ -361,10 +371,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "fixing (0,0,0) with sin, r2=0.9951110482215881, c=2\n", - "fixing (0,0,1) with sin, r2=0.9995662569999695, c=2\n", - "fixing (0,1,0) with x, r2=0.860824465751648, c=1\n", - "fixing (0,1,1) with x, r2=0.8865915536880493, c=1\n", + "fixing (0,0,0) with x, r2=0.48220324516296387, c=1\n", + "fixing (0,0,1) with x, r2=0.3202315866947174, c=1\n", + "fixing (0,1,0) with x, r2=0.9358773231506348, c=1\n", + "fixing (0,1,1) with x, r2=0.9290410876274109, c=1\n", "saving model version 0.2\n" ] } @@ -376,20 +386,20 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "id": "83606957", "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle 159.4388 x_{2} - 372.5043 \\sin{\\left(1.9893 x_{1} - 1.1667 \\right)} - 66.9691$" + "$\\displaystyle - 15.0316 x_{1} + 177.9349 x_{2} - 63.0716$" ], "text/plain": [ - "159.4388*x_2 - 372.5043*sin(1.9893*x_1 - 1.1667) - 66.9691" + "-15.0316*x_1 + 177.9349*x_2 - 63.0716" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -401,20 +411,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "id": "9fa988e3", "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle - 143.068 x_{2} + 397.2854 \\sin{\\left(2.4588 x_{1} - 4.4414 \\right)} + 66.0173$" + "$\\displaystyle 60.4718 x_{1} - 156.0295 x_{2} + 16.9$" ], "text/plain": [ - "-143.068*x_2 + 397.2854*sin(2.4588*x_1 - 4.4414) + 66.0173" + "60.4718*x_1 - 156.0295*x_2 + 16.9" ] }, - "execution_count": 17, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "id": "ecd368f8", "metadata": {}, "outputs": [ @@ -441,8 +451,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "train acc of the formula: tensor(0.9740)\n", - "test acc of the formula: tensor(0.9750)\n" + "train acc of the formula: tensor(0.8870, device='cuda:0')\n", + "test acc of the formula: tensor(0.8810, device='cuda:0')\n" ] } ], @@ -486,7 +496,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_5_special_functions.ipynb b/tutorials/Example_5_special_functions.ipynb index 3a944659..9894cbf4 100644 --- a/tutorials/Example_5_special_functions.ipynb +++ b/tutorials/Example_5_special_functions.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "id": "2075ef56", "metadata": {}, "outputs": [ @@ -26,6 +26,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -34,7 +35,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 5.15e-01 | test_loss: 5.84e-01 | reg: 5.91e+00 | : 100%|█| 20/20 [00:02<00:00, 7.39it" + "| train_loss: 5.15e-01 | test_loss: 5.86e-01 | reg: 5.84e+00 | : 100%|█| 20/20 [00:03<00:00, 5.89it\n" ] }, { @@ -43,22 +44,18 @@ "text": [ "saving model version 0.1\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ "from kan import *\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", - "model = KAN(width=[2,1,1], grid=3, k=3, seed=2)\n", + "model = KAN(width=[2,1,1], grid=3, k=3, seed=2, device=device)\n", "f = lambda x: torch.exp(torch.special.bessel_j0(20*x[:,[0]]) + x[:,[1]]**2)\n", - "dataset = create_dataset(f, n_var=2)\n", + "dataset = create_dataset(f, n_var=2, device=device)\n", "\n", "# train the model\n", "model.fit(dataset, opt=\"LBFGS\", steps=20);" @@ -74,13 +71,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "id": "3f95fcdd", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -95,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "id": "187d19f9", "metadata": {}, "outputs": [ @@ -110,7 +107,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.70e-02 | test_loss: 9.15e-02 | reg: 7.69e+00 | : 100%|█| 20/20 [00:05<00:00, 3.49it" + "| train_loss: 1.54e-02 | test_loss: 4.73e-02 | reg: 7.50e+00 | : 100%|█| 20/20 [00:02<00:00, 6.93it" ] }, { @@ -135,13 +132,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "id": "8d50bcef", "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAFICAYAAACcDrP3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAw/ElEQVR4nO3de1jUdb4H8Pd3mGEYrsNVQRJByWumIl5RdCNI3YuXMteePWvZbtrFPbadffa4duqYW7lPJnbZbdfO5uXpHNrS1grSshJTUdREzdJA8IKAgMwAwwwDM/M9f9j8Fryl8htmgPfreXoKhoEPE995/77Xn5BSShAREalI4+0CiIio+2G4EBGR6hguRESkOoYLERGpjuFCRESqY7gQEZHqGC5ERKQ6hgsREamO4UJERKpjuBARkeoYLkREpDqGCxERqY7hQkREqmO4EBGR6hguRESkOq23CyDqCqSUuHjxIiwWC4KDgxEZGQkhhLfLIvJZ7LkQXYfZbMbatWuRnJyM6OhoJCYmIjo6GsnJyVi7di3MZrO3SyTySYJ3oiS6uu3bt2POnDmwWq0ALvVe3Ny9lsDAQGzevBlZWVleqZHIVzFciK5i+/btmDFjBqSUcLlc1/w6jUYDIQRyc3MZMERtMFyILmM2mxEfHw+bzXbdYHHTaDQwGAwoLy+H0Wj0fIFEXQDnXIgus2HDBlit1hsKFgBwuVywWq3YuHGjhysj6jrYcyFqQ0qJ5ORklJaW4maahhACSUlJKC4u5ioyIjBciNqpra1FdHR0h54fGRmpYkVEXROHxYjasFgsHXp+Y2OjSpUQdW0MF6I2goODO/T8kJAQlSoh6toYLkRtREZGon///jc9byKEQP/+/REREeGhyoi6FoYLURtCCDzxxBO39NwlS5ZwMp/oe5zQJ7oM97kQdRx7LkSXMRqN2Lx5M4QQ0Giu30TcO/S3bNnCYCFqg+FCdBVZWVnIzc2FwWCAEOKK4S735wwGA/Ly8pCZmemlSol8E8OF6BqysrJQXl6O7OxsJCUltXssKSkJ2dnZOH/+PIOF6Co450J0A6SU+OKLL3DXXXfhs88+w9SpUzl5T3Qd7LkQ3QAhhDKnYjQaGSxEP4DhQkREqmO4EBGR6hguRESkOoYLERGpjuFCRESqY7gQEZHqGC5ERKQ6hgsREamO4UJERKpjuBARkeoYLkREpDqGCxERqY7hQkREqmO4EBGR6hguRESkOoYLERGpjuFC9ANaW1tx/vx5fPvttwCAU6dOoa6uDi6Xy8uVEfku3uaY6BrMZjM2b96Mt99+G8ePH0djYyNaWloQEBCA6OhoTJo0CQsXLsTEiROh1Wq9XS6RT2G4EF1FQUEBli5diqNHjyI1NRUzZszA8OHDERwcDLPZjEOHDuHDDz9ESUkJ7r//fqxcuRLR0dHeLpvIZzBciC7zySefYMGCBQgODsYLL7yA6dOno6WlBTk5ObDb7QgNDcW8efPQ2tqKnJwcPPvssxg6dCg2bdqEXr16ebt8Ip/AcCFq47vvvsM999yDoKAg5OTkYMiQIRBCoLS0FKNGjUJ9fT0SExNx6NAhhIeHQ0qJ3bt3Y/78+ZgyZQrefPNN6PV6b/8aRF7HCX2i7zmdTjz//PMwmUx47bXXlGC5HiEE0tLS8Kc//Qlbt27Ftm3bOqlaIt/GcCH6XklJCT788EPMnj0baWlpPxgsbkIIzJw5E+PGjcO6devgcDg8XCmR7+MSF6Lv7d27FxaLBXPmzMHp06fR1NSkPFZeXg6n0wkAaGlpwfHjxxEaGqo8HhcXh9mzZ+PZZ59FVVUV4uPjO71+Il/CcCH63okTJxAYGIikpCQ88sgj2LNnj/KYlBJ2ux0AUFFRgbvvvlt5TAiB1atX44477oDVakVFRQXDhXo8hgvR92w2G7RaLfR6Pex2O5qbm6/6dVLKKx5zOBwwGAztQoioJ2O4EH0vJiYGNpsNZrMZY8eORVBQkPKYzWbD3r17lRCZMGGCsnFSCIG+ffuiuroaGo0G4eHh3voViHwGw4XoeykpKWhtbUVhYSFWrVrV7rHS0lKkpqaivr4evXr1wjvvvAOj0ag8LoTAsmXL0Lt3bw6JEYGrxYgUY8aMQVJSEjZs2ICmpib4+fm1+8dNCAGNRqN8XqPRoLKyEu+99x5mzJiBsLAwL/4WRL6B4UL0vcjISDz++OP46quv8Morr9zwkmK73Y7nnnsONpsNjzzyyA0vYSbqzjgsRtTGggULsGvXLqxatQqBgYFYvHgxAgICAABarRZarVbpxUgp0djYiD/+8Y/IycnBmjVrMHDgQG+WT+QzePwL0WVqamrw2GOP4aOPPkJWVhaWLl2KwYMH4+TJk3C5XPD398eAAQNQWFiIl156CUVFRVixYgUWL17cbviMqCdjuBBdRVNTE9atW4dXXnkFFy5cQFJSEpKTkxESEgKTyYSTJ0+ioqICKSkpeOaZZ5Ceng6NhqPMRG4MF6LrqKqqwmeffYb8/HwcOXIEhYWFmDRpEiZOnIjMzEyMHTsWgYGB3i6TyOcwXIhu0IEDBzBmzBgcOHAAo0eP9nY5RD6N/XiiG+Tn56csQyai62MrISIi1TFciIhIdQwXIiJSHcOFiIhUx3AhIiLVMVyIiEh1DBciIlIdw4WIiFTHcCEiItUxXIiISHUMFyIiUh3DhYiIVMdwISIi1TFciIhIdbyfC9ENklLC5XJBo9FACOHtcoh8GnsuRDeB93IhujFabxdApBYpJYqLi3Hx4kVvl9IhGo0Gw4YNQ1BQkLdLIbplHBajbsPlcuGxxx7DbbfdhuDgYACA0+mEn5+flyu7OV9++SWefvppDB8+3NulEN0y9lyoW9Hr9Vi4cCGio6OxZcsWvPXWW1i0aBFmzJjRJYa0pJSwWCzgNR91db7f2ohuQWlpKZYsWYJt27Zh4cKF+OCDD/iGTdSJGC7ULe3evRvV1dUQQuDixYt46qmnUFZWxoAh6iQMF+p2pJQoKiqClBJpaWkYMGAAysrK8PLLL8Plcnm7PKIegeFC3Y7L5UJZWRkAYPLkyfjP//xP+Pn5IScnB0ePHmXvhagTMFyo22lubsbZs2cBAAMGDMDs2bORmpoKk8mE7OxsOBwOL1dI1P0xXKjbaWhoQE1NDXQ6HRISEhASEoKlS5dCp9Nh69atKCwsZO+FyMMYLtTt1NfXw2KxQK/XIzo6GkIITJs2DZMmTUJjYyNefvlltLS0eLtMom6N4ULdjslkQktLCwIDAxEWFgYACAwMxJNPPomAgABs374d+fn57L0QeRDDhbqdAQMGYMOGDVi1ahUiIiIAAEIITJ06FZmZmbDZbFi9ejVsNpuXKyXqvrhDn7qd6OhoDBs27IrP6/V6PPnkk/j888+Rn5+P3Nxc3HvvvTzhmMgD2HOhHkMIgXHjxmHWrFlobW3F6tWrYTabvV0WUbfEcKEeRavV4sknn0RUVBQOHTqEDRs2cO6FyAMYLtSjCCEwbNgwPPzww5BSYs2aNSgpKWHAEKmM4UI9jkajweOPP45BgwahvLwcL774IjdWEqmM4UI9Uu/evfGHP/wB/v7++Mc//oEdO3aw90KkIoYL9UhCCMycORPTpk2D1WrFihUrOLlPpCKGC/VYAQEBWL58OaKionDw4EH8/e9/56nJRCphuFCPJYTAiBEjsGjRIkgpkZ2djeLiYg6PEamA4UI9mkajwaOPPoqhQ4eioqICL774IlpbW71dFlGXx3ChHi8mJkaZ3H/vvffw2WefsfdC1EEMF+rxhBD4yU9+okzuP/fcczCZTN4ui6hLY7gQof3k/oEDB/CXv/yFk/tEHcBwIcK/JveXLFkCAMjOzsa+ffs4PEZ0ixguRN9zT+6npaWhrq4OTz31FGpqahgwRLeA4ULUhtFoxKpVqxATE4PCwkIsX74cdrvd22URdTkMF6I2hBAYPXo0VqxYAX9/f2zcuBGvvfYazx4jukkMF6LLaDQa/PKXv8SiRYvgdDqxYsUK/N///R8n+IluAsOF6Cp0Oh2eeeYZzJo1C1arFUuXLsXWrVsZMEQ3iOFCdBVCCISGhuLVV19FRkYGTCYTFi1ahI8++ogBQ3QDGC5E1yCEQExMDNatW4fJkyejtrYWv/rVr/D+++8zYIh+AMOF6DqEEIiPj8f69euRnp6O2tpa/PrXv8b69et5BhnRdTBciH6AEAJ9+/bFpk2bcM8996C+vh5LlizBypUr0dTUxH0wRFfBcCG6AUIIxMXFYcOGDZg/fz5aWlrwwgsv4Ne//jWqqqoYMESXYbgQ3SAhBCIjI/GXv/wFy5Ytg16vR05ODubOnYtvvvmGAUPUBsOF6CYIIRAUFITly5fjr3/9K3r16oW9e/di5syZ2Lp1K1pbWxkyRGC4EN0SrVaLefPm4Z133sHgwYNRWlqKX/ziF3j88cdx5swZBgz1eAwXoluk0WiQlpaGDz74APfddx8cDgfefPNNZGRkYP369bBarQwZ6rEYLkQdIIRAYmIi1q9fj//5n//B7bffjrKyMixevBgPPPAAjh49yj0x1CMxXIg6SAiBgIAA/PznP8enn36KRx99FP7+/vjggw+QmZmJ3/3udzh+/DjnY6hHYbgQqUQIgT59+mDNmjV47733MG7cOJhMJqxZswaTJ0/GvHnzsGXLFpjNZoYMdXsMFyIVCSGg1Wpx9913Izc3F3/+858xevRoWK1W/POf/8T8+fPxox/9CH/7299QXV0Nl8vFoKFuieFC5AFCCBiNRixcuBA7duzAP//5TzzwwAMwGo04cuQIHn/8cUyYMAG/+MUvsHHjRlRXVzNkqFthuBB5kBACISEhyMzMxFtvvYVdu3bhqaeeQmxsLM6cOYOcnBwsXLgQP/rRj/Dee+/xpmTUbWi9XQBRTyCEgJ+fHwYOHIgXXngBjz/+OPbu3YudO3ciLy8P3377LR588EF89dVX0Ol03i6XqMPYcyHqZBqNBrfddhvmzp2L119/HZ9//jl+/vOfw+FwoLi4mEuXqVtgz4W6FSklTCZTl7r6Dw8Px6pVq5CSkoKpU6diy5Yt3i6JqMMYLtRtCCGQkJCAV199FX5+ft4u56ZJKVFWVgabzYawsDBvl0PUIUJyiQp1E1LKbrPiSggBIYS3yyC6ZQwXIiJSHSf0iYhIdZxzIbpBbTv5HLIiuj72XIhu0OHDh+Hn54fDhw97uxQin8dwISIi1TFciIhIdQwXIiJSHcOFiIhUx3AhIiLVMVyIiEh1DBciIlIdw4WIiFTHcCEiItUxXIiISHUMFyIiUh3DhYiIVMdwISIi1TFciIhIdQwXIiJSHcOF6AZIKWEymQAAJpMJvDs40fUxXIiuw2w2Y+3atUhOTkZGRgaklMjIyEBycjLWrl0Ls9ns7RKJfJKQvAQjuqrt27djzpw5sFqtAK5+m+PAwEBs3rwZWVlZXqmRyFcxXIiuYvv27ZgxYwaklHC5XNf8Oo1GAyEEcnNzGTBEbTBciC5jNpsRHx8Pm8123WBx02g0MBgMKC8vh9Fo9HyBRF0A51yILrNhwwZYrdYbChYAcLlcsFqt2Lhxo4crI+o62HMhakNKieTkZJSWlt7UijAhBJKSklBcXKzMxxD1ZAwXojZqa2sRHR3doedHRkaqWBFR18RhMaI2LBZLh57f2NioUiVEXRvDhaiN4ODgDj0/JCREpUqIujaGC1EbkZGR6N+//03Pmwgh0L9/f0RERHioMqKuheFC1IYQAk888cQtPXfJkiWczCf6Hif0iS7DfS5EHceeC9FljEYjNm/eDCEENJrrNxH3Dv0tW7YwWIjaYLgQXUVWVhZyc3NhMBgghLhiuMv9OYPBgLy8PGRmZnqpUiLfxHAhuoasrCyUl5cjOzsbSUlJ7R5LSkpCdnY2zp8/z2AhugrOuRDdACklvvjiC9x111347LPPMHXqVE7eE10Hey5EN0AIocypGI1GBgvRD2C4EBGR6hguRESkOoYLERGpjuFCRESqY7gQEZHqGC5ERKQ6hgsREamO4UJERKpjuBARkeoYLkREpDqGCxERqY7hQkREqmO4EBGR6hguRESkOoYLERGpjuFCRESqY7gQ/YDW1lacP38e3377LQDg1KlTqKurg8vl8nJlRL6Ltzkmugaz2YzNmzfj7bffxvHjx9HY2IiWlhYEBAQgOjoakyZNwsKFCzFx4kRotVpvl0vkUxguRFdRUFCApUuX4ujRo0hNTcWMGTMwfPhwBAcHw2w249ChQ/jwww9RUlKC+++/HytXrkR0dLS3yybyGQwXost88sknWLBgAYKDg/HCCy9g+vTpaGlpQU5ODux2O0JDQzFv3jy0trYiJycHzz77LIYOHYpNmzahV69e3i6fyCcwXIja+O6773DPPfcgKCgIOTk5GDJkCIQQKC0txahRo1BfX4/ExEQcOnQI4eHhkFJi9+7dmD9/PqZMmYI333wTer3e278GkddxQp/oe06nE88//zxMJhNee+01JViuRwiBtLQ0/OlPf8LWrVuxbdu2TqqWyLcxXIi+V1JSgg8//BCzZ89GWlraDwaLmxACM2fOxLhx47Bu3To4HA4PV0rk+7jEheh7e/fuhcViwZw5c3D69Gk0NTUpj5WXl8PpdAIAWlpacPz4cYSGhiqPx8XFYfbs2Xj22WdRVVWF+Pj4Tq+fyJcwXIi+d+LECQQGBiIpKQmPPPII9uzZozwmpYTdbgcAVFRU4O6771YeE0Jg9erVuOOOO2C1WlFRUcFwoR6P4UL0PZvNBq1WC71eD7vdjubm5qt+nZTyisccDgcMBkO7ECLqyRgu1OOdPn0a+fn52L17N6xWK8xmM8aOHYugoCDla2w2G/bu3auEyIQJE5SNk0II9O3bF9XV1XA4HCgpKUFqaioCAgK89SsReR2XIlOPc+7cOezatQs7d+5Efn4+zp49CyEEEhISUFJSgtdffx0PP/xwu+eUlpYiNTUV9fX16NevHw4ePAij0ag8LoTAsmXL8NJLL8HPzw8BAQEYO3YsJk+ejClTpiA1NZVLlKlHYbhQt1dRUYH8/Hzln7KyMgDA8OHDMXnyZKSnpyMtLQ0ulwtpaWkIDw/Htm3b2k3YX2ufC3BpmKyiogLp6en4yU9+ggULFmDXrl3YtWsXvvzyS5jNZhgMBowbN04Jm5SUFPj7+3vl9SDqDAwX6nYuXLiAnTt3YteuXcjPz0dJSQkAYOjQoUqYTJ48GREREVc89/XXX8dvf/tbLF++HL///e+Voa/rhUtzczP+/d//HR9++CE+//xzDBw4UPl+TqcTR48eVWrZvXs3GhoaEBgYiPHjxyM9PR1TpkzByJEjodPpOuHVIeocDBfq8mpqapCfn6+8gZ88eRIAMGjQoHZhciNnfzU1NeGhhx5CXl4e/vu//xuLFy9GQEAAysrKMGbMGGVYrLCwEEajEY2NjfjjH/+Iv/71r1izZg0efPDB635/h8OBoqIipd49e/bAYrEgODgYEyZMQHp6OtLT0zFixAgehkldGsOFupyLFy8qw075+fn45ptvAADJyclKmKSnp9/yOV81NTV47LHH8NFHHyErKwtLly7F4MGDcfLkSbhcLvj7+2PAgAEoLCzESy+9hKKiIqxYsQKLFy+Gn5/fTf2s1tZWHD58WOlp7d27F1arFaGhoZg4caLyuwwfPvymvzeRNzFcyOeZTCZ8+eWXytX+sWPHAABJSUntwiQuLk61n9nU1IR169bhlVdewYULF5CUlITk5GSEhITAZDLh5MmTqKioQEpKCp555hmkp6dDo+n4gRctLS04dOiQMj9UUFCA5uZmGI1GpKWlKb/rsGHDVPl5RJ7CcCGfU19fj927dythcuTIEUgpkZCQoLy5pqend8pGxaqqKnz22WfIz89HaWkpmpubER4ejmHDhiEzMxNjx45FYGCgx36+3W7HgQMHlLDZv38/7HY7IiIiMGnSJOW1uJFz0Ig6E8OFvK6xsRF79uxRhrkOHz4Ml8uFPn36KBPe6enpSEhI8GqdTqcTUkpoNBqv9Rqam5uxf/9+ZSl1YWEhWltbERUV1a4XN3DgQIYNeRXDhTqdxWJBQUGBcjX+1Vdfwel0IjY2FlOmTFHeJBMTE/kG+QOsViv27dunhM3BgwfhcDgQExPTrpc3YMAAvpbUqRgu5HHuN0B3mLR9A2wbJnwD7DiLxaK81jt37mwX3G3DhsFNnsZwIdW5h27cYdJ26Ma9LJhDN52joaGhXS/RPeQYHx+vbOj0hSFH6n4YLtRh7kln93LatpPOkydPVsKEk87eZzab281vtV0s0TZseKozdRTDhW6ae7ms+2yuffv2KctlJ02apIQJl8v6vrq6OuzZs0fp2biXeScmJipDaFOmTEFsbKyXK6WuhuFCP6jtRj/33gv3Rj/33ovJkydzo183cPHiRWVPUdsNqgMGDGg3Z3OrG1Sp52C40BXaHlGSn5+v3KExODgYEydOVHomPKKk+6upqVGG0NoerTNw4MB2YRMVFeXlSsnXMFxIOVzR/QayZ88e5XDFtudd8XBFqqqqahc27kNBhwwZovydXOtQUOpZGC49kMvlwtdff628QezevRtmsxkBAQHKSb3p6ek8Fp5+0LVuZ3DHHXcof0fu2xhQz8Jw6QGklPjmm2+UN4Avv/wSdXV10Ov1GDt2rPImwBtaUUdd60Zsd955p/J3NnHiRISFhXm7VPIwhks3JKXEyZMnlTDZtWsXamtrodPpMGbMGGXj4tixY3krXvKo06dPtwub8+fPQ6PRYOTIkUrYTJgwASEhId4ulVTGcOkGpJQoKSlpNzxRXV0NrVaL0aNHK2Eybtw4jx6ySHQ9UkqUlZUpf6M7d+5EVVUV/Pz8MGrUKGWPzfjx4xEUFOTtcqmDGC5d0OWNND8/H5WVlVc00nHjxiE4ONjb5RJd1Y1cFKWnp3v85GnyDIZLF3HmzBnlam/Xrl0oLy+/Ynhh/Pjx7e77TtSVXD6cm5+fj4sXL8Lf3x+pqakczu1iGC5dxPDhw1FcXNxuYnTChAkwGo3eLo3II1wuF7799tsrFqJs2rQJ9913n7fLox/AcOkiXC4XhBA8m4t6LCklpJRsB10Ew4WIiFTHsztU4p6cvHjxordL6RCNRoOhQ4dytQ7dNLYBaovhohIpJdauXYv4+PhOXaHldDqh0WhUGybYvXs3/vCHP+COO+5Q5ftRzyGlxCuvvIL4+HiPvjE7nU4A8NghqXv27MGyZcvYBjqI4aIivV6PBx98UJUTYxsbG/H3v/8dUVFRmDt37hVnekkpcejQIaxcuRI//elPsWDBgg4fby+lhMViAUdK6Vbp9XosWLBA1VOTpZRobW1FUVER3n33XRw5cgSBgYHIzMzE/fffj4iICNUurqSUaGpqYhtQAcPFB0kpsXXrVixbtgx6vR5JSUkYP358u69xOp146aWXsG3bNhw/fhzTpk3jPTeoW5FSoq6uDrm5uXj33XdRUFCApqYm5fFPPvkE77zzDrKzszFixAhO8vsY3snJR+3atQtOpxNWqxUFBQVXXEk1NDTgyJEjAIALFy7g1KlT3iiTyCPcPfOf/exnWLx4MXbs2AGn04n09HSsWrUKS5cuRXR0NAoLCzF37lx88MEHcDgc3i6b2mDPxQe1tLTgu+++Uz5237CprdraWmXitKWlBadOncLEiRN59UZdnpQSx44dwy9/+UuUlpYiJiYGs2bNwr333ouUlBQEBARASomZM2fiiSeewNGjR7Fw4UI8+uij+I//+A8EBwezHfgA9ly8qKWlBXl5efj888+VSUoAsFgsqKysVD4+c+bMFVdllZWVsFqtyselpaXKf0spYTabkZeXh7Nnz3L8mLoMKSUuXLiAJ554AqWlpRg4cCDef/99vPzyy5g4cSIMBgOEENBoNEhNTcW7776LWbNmwW63Y/Xq1XjqqafatQvyHoaLl0gp8fHHH2P+/PmYN28e9u/fr4SA2WxGfX298rXV1dWw2Wztnl9RUdEukM6dO9fue69cuRL33XcfHnroITQ2Nnr4tyFSR3NzM55++mkUFhaiV69eeOONNzBy5MirrogUQuC2227DunXrsHz5cvj7++Ptt9/Gn//8Z7hcLi/9BuTGcPEwl8uF/fv3Y8eOHWhpaWn3+c2bN6O5uRkNDQ3Ytm2b8lhNTU27q6/6+vp2E5lSSlRWVrbrkVRUVCi9G/ckqNPpxIEDB5S5Gfdzz549i/Xr16OkpIS9GvIZLpcLb775Jt555x0EBATgueeew5gxY647xCWEQFBQEJ588kksWbIEUkpkZ2fjwIED/Nv2MoaLB0kpsX//fsyaNQtz5szBu+++q/zBWywWFBUVKV976NAhJRyqq6vhcDjg5+cHIQSamprQ0NDQ7ntXVVUBgHKnyNraWiW8zpw5ozxut9vbNbTm5mY89thjWLRoERYsWACTyeS5F4DoBkkpsW/fPrz44otwOBz41a9+hfvvv/+Gl9frdDosXboU48ePR11dHVasWAGLxeLhqul6GC4eJKXE//7v/6Kurg52ux3vvvuuEiBVVVVKAACXbqrk7p1UVVXB5XIhLi4OoaGhsNvtqKura/e9a2trAQADBgyAn58fzGaz0tspLi5Gc3Oz8rVHjhxRwqW4uBgFBQUAgKKiIuzevZtXeOR1JpMJy5YtQ11dHSZOnIjf//73V+zt+iFhYWF45plnEBoaivz8fGzatIl/217EcPEgi8WC3bt3Kx8fP35c6SmUlZWhqalJ6fJfvHgRFy9ehJRSCZ1+/fohPDwcDoejXbg4nU4lXAYPHgy9Xg+LxYLGxkblCA73AX/ApUCx2+2QUuLw4cNKiDkcDuzYscPzLwTRdbhcLvztb3/DgQMHEBUVheeffx7h4eE3veJLCIEJEybgoYcegsvlwurVq3HixAkGjJcwXDzozJkz7Sbaa2trUV5eDikliouL4XQ6kZCQgIiICDQ1NeH8+fMALs2fAEDfvn1hNBrhcrnandfkcDiUkEpOTobBYIDNZoPJZIKUUlk5NmTIEGi1WlRUVMBsNgP4Vy/GPZx24MABrq4hr5FS4sSJE3jjjTcAAI899hhSUlJueSmxn58ffvOb32DYsGGoqKjAypUrYbfb1SyZbhDDxUOklDh69CiampoQFxeH2NhYNDc3o6SkBACUfSwjR45EXFwcHA4Hzp07B5fLhQsXLgAAbrvtNuV+LbW1tcoVWEtLC+rr6yGEQL9+/RAcHIzW1lbU1tYq3wcAJk2ahJCQEJjNZlRWVsLhcODEiRMAgIyMDAQEBKCsrKzdsmeizuR0OvHKK6/gwoULGD58OB5++OEOH2PUq1cvPP300zAYDMjNzUVeXh57L17AcPEg90R6amoqhgwZotxpz+FwKCEzbNgw9OnTR7l1sTskAKBPnz6IjIwE8K85FgCw2WywWCzw8/NDXFwcIiIilFCy2WyorKyEEAKjRo1CVFQU7Ha7Mgx35swZaDQaTJ8+HTExMWhoaMA333zDxkedTkqJr776Cu+//74yIe/+e+8IIQSysrIwc+ZM2O12vPzyy0rPnToPw8VD7HY7Dh8+DAAYN24cBg4cCODS/IfFYsG5c+cghMCgQYPQt29fAJcm9Zubm1FXVweNRoPevXsjPDwcANrNudhsNthsNuh0OkRGRioNsrKyEmazGSaTCf7+/khOTkbfvn0hpcR3332Hmpoa1NbWQq/XY+TIkRg0aBBcLhcOHDjQmS8NEYBLvZY33ngDDQ0NGDNmDKZPn67aznqdTocnn3wSUVFRKCoqwvvvv88LqE7GcPGQyspKlJSUwN/fH6NHj1bCpbS0FJWVlaitrUVAQAASExPRr18/AJc2QprNZjQ0NECr1SI6OloJDvd8CnBpoUBLSwv8/f0RFhamnEBbVVWF6upqWCwWBAUFITY2FsnJyQAuDcOdPXsWTU1NCA8PR3x8PFJTUwFc6mG1trZ25stDPZyUEl9//TXy8vKg0+mwePFiVY/pF0Jg8ODBmDt3LpxOJ9atW9duYzJ5HsPFA6SUOHDgAEwmE2JjYzFo0CAMGDBAmVw/duwYLBYLjEYjYmNj0a9fPwghUFVVhcrKSjQ1NSEgIACRkZFKz8VkMik78hsbG9Ha2oqAgAAEBgaid+/eAC4dYFleXo7W1laEh4cjPDxcCbWSkhJ8/fXXcDgciIuLQ1hYGMaMGQOtVouTJ0+ipqbGOy8W9UgulwtvvfUW6uvrMXLkSGRmZqp+HphGo8HChQsRGRmJr7/+Gp9++il7L52I4eIBLpcL27Ztg8vlwtixYxEREYG+ffsiKCgIZrMZO3fuhMPhQJ8+fWA0GtGnTx/odDrU1dWhpKQELS0tCAkJQVhYGCIiIgBcChT3Hpn6+no4HA4YDAYYDAblqP3q6mqcOnUKLpcLsbGxMBgMGDx4MLRaLc6dO4e9e/cCAPr37w9/f38MHToUUVFRqKmpwdGjR73zYlGPdPr0aWzdulUJAE/cYE8IgYEDB2L69OlwOBxYv349V451IoaLB5w/fx47d+6ERqPBjBkzoNFoEBUVhaioKNhsNuTm5gL41x6V3r17IygoCI2NjThy5AgcDgciIiIQFBSEsLAwCCGUoTDg0tljUkoEBwfD398fcXFxEEKguroa3377LYBLy5i1Wi0SExNhNBpRU1ODL774AsClJcpCCPTq1QsjRoyAw+HA9u3beVVHnUJKiXfeeQcXLlzA7bffrupcy+U0Gg3+7d/+DQaDAfv27UNRURH/zjsJw0VlUkp89NFHqKysRN++fZGeng4hBEJCQtC/f39IKVFdXQ0AGD16NIQQMBqNiIiIgN1ux5dffgkA6N27N/R6PYxGI/z8/GC1WpWrLne4hISEQKfTITY2FjqdDrW1tco5YomJiRBCICYmBv3794fT6URDQwP8/PwwfPhwCCGg1Wrx4x//GEII5OXlseFRp6ipqUFOTg6EEHjggQdUWSF2LUIIpKSkYPTo0bBare2OYCLPYriorKmpCW+//TaklJg9ezZiYmIAXNrc5Z5AB4CgoCDlUL6goCBlVdexY8cAXNqd7+fnh5CQEGi1WmWFmJRS2UAZEhICPz8/pedjMplw4sQJCCGUifyAgACkpaUpPzcyMhJDhgwBcKnhTZs2DYmJiTh79ix++9vftjs2hkhtUkps374dp06dQu/evXHvvfd6/N4rAQEBmDt3rnIRxfnFzsFwUVlBQQGOHTuGsLAwzJs3T9kQJoRAZmYmQkJCAKDdCjKtVovBgwcDgHJUuPux4OBg6HQ6tLS0KDvp3ate3ENmERERiIqKgpQSDocDer0eycnJSqO99957lbmbjIwM9OnTR6k3Li4Oa9aswciRIzFt2jQYDAaPvj7UszU3N2PTpk1wuVz48Y9/rCzD9yR32+vVqxfOnTvH8/Q6Ce9EqbIhQ4bgd7/7HUwmkxIYbikpKXj++edRUFCA3/zmN+3eyN1DZFJK6HQ63HHHHRBCwGAwQK/Xo7GxUTnl1d1zMRqNSs9nwIABKC4uBgDExMQojVYIgTvvvBMbN27E0aNHMW/ePPj5+Sk/193wJkyYAI1Gg1dffdWjrw/1XO5NkwcPHkRgYCDmz5/faXeM7NOnDyZPnox//OMfeP/99zFz5sx27YDUx3BRWZ8+ffCHP/wBTqfzij9erVaLhx9+GAsXLoQQQmlYQgiMHz8eMTExuHDhAhISEpShq4CAABgMBphMJlgsFkgpleP33UfD+Pn5IS0tDR9//DEAYMyYMUpPBbg0qZmRkYGMjIyr1uyeE+LVHHmSeyLfZrNhypQpGDFiRKeFi0ajwc9+9jNs3rwZe/fuRWVlJeLj4zvlZ/dUHBbzAPdk+dUajvsWrZc/lpCQgKeffhrjx4/Hf/3XfyEqKgoAoNfrYTAY4HK5UF9fr/wb+Fe4CCGU+4snJiZi0aJFHT6fiUhtVVVV+Pjjj6HRaDBv3jzo9fpO+9nuC7jY2FhcuHABBQUFvJjyML4D+Qj3ev9t27bhvvvuU8JHp9MhODgYUkplf4v7tsXuYTHg0tLjjz76CPn5+Zg4cWKnXRES3QgpJb744gtUVFQgNjYWGRkZnf43GhMTgwkTJsDlciEvL4+3QvYwhosPEUJAr9e3a3RarVbZYGY2m+FwOGCxWCCEQFhYWLvnhoeHIyYmhsFCPsfhcGDLli1wuVzIyMhQNv52Jo1Gg2nTpkGj0WDv3r3tbmNB6mO4+DiNRqOEiNlsht1uR1NTU7vPE/m6s2fPYv/+/dDpdJg1a5ZXLoDcQ2NRUVGoqKjAV199xaExD2K4+Li2IWIymdDc3Izm5mZlDwyRr3MPidXV1aFfv37KykhviIuLQ0pKinIqBXkOw6ULcE/c19fXw2azwW63K3MxRL7O4XDg448/hpQSU6dOVQ5j9QatVousrCwIIbBz506elOxBDJcuoG24NDY2Ksftq3lEOZGnVFZW4uDBg9Bqtbjnnnu8OicohEB6ejrCwsJQVlaGY8eOcWjMQxguXYA7XBoaGpQVY+79L0S+TEqJ/fv3o7a2FnFxcRg1apTXF5wkJibizjvvhN1uR15enldr6c4YLj7OfbAlcOnY/bq6OjidTgQEBHTqPgGiWyGlxI4dO+ByuTBmzBhl/5Y3+fv7Y/r06QCATz/9VNmUTOpiuHQBRqMRGo0GFosF1dXVcLlcCA4OZriQz6uvr0dBQQGEELj77rt9YnOvu5awsDCUlJTwNHAP8f7/afpBoaGh0Gg0sNlsqKqqUj6n1fL0HvJtJ06cwNmzZxEWFoZx48Z5fUjMLSkpCaNGjYLdblfur0TqYrh0AaGhodDpdLDZbCgvLwdw6URkHrxHvkxKifz8fNjtdgwcOLBTTkC+Uf7+/sq9jD799FOYzWZvl9TtMFy6gJCQEPj7+8Nut+Ps2bMAgPDwcJ8YYiC6ltbWVuzcuRMAkJ6e7lPDuEIIZGRkwGg04tSpUzh06BCHxlTGd6cuIDAwEAaDAS0tLThz5gwAeHWvANGNqKiowPHjx6HT6TBlyhSfGRJzc2/obG1t5aoxD2C4dAEGgwGBgYFwOBw4f/48AHj01rBEHSWlxMGDB1FXV4e4uDgMHTrU2yVdQafTYcaMGRBC4PPPP+eGSpUxXLoAvV6vHAHjcDgAwCeWdBJdi5QSO3fuhMvlwqhRo3zyYkgIgSlTpiAsLAynT5/G119/7e2SuhWGSxeg0+mUvS7ApUYRFRXlc8MMRG5NTU3Yv38/AGDKlCk+Oz+YkJCAoUOHwm6344svvvB2Od2Kb/4fp3Y0Gg1iYmKUj7VaLXsu5NNOnz6N06dPIygoCGPHjvXZCyG9Xo+pU6cCAHbt2oWWlhYvV9R9MFy6ACEEevfurXys1+t9cpiByO3QoUNoampCQkICEhMTvV3ONQkhkJaWBn9/f5w4cQKVlZXeLqnbYLh0EX369FH+OygoqN0wGZEvkVJi3759kFJixIgRPn9696BBgxATEwOTyYRjx455u5xug+HSBQghkJCQoIxbR0VFITQ01MtVEV1dc3Mzjh49CgAYM2aMzw6JuUVGRmLo0KFwOp0oLCz0djndBsOli0hKSlJOQe7fvz9PRCafVVVVhTNnzkCv1+POO+/0+XDx8/NDSkoKAODw4cPKikzqGB5O1UUkJSVh7Nix2L9/P37605/67OoborNnz8JutyMqKsqn51vc3Lv16+rqkJaWhpMnT3q7pG6B4aIiKSXMZjN0Op1Hvv+aNWtQWVmJwYMHw2QyeeRn2O12j3xf6hmklEhOTkZOTg5qa2shhEBdXZ23y/pBt99+O5YvXw4hBPe7qIThohIhBPr27YvXX3/d4wdKbt261WPf22azcT6Hbom7Daxfv15pA11xDsNmsymblunWCcnT2lQhpew2B98JIXx+nJx8D9sAtcVwISIi1XFWmIiIVMdw6SKklHC5XN1m2IHoVrAddB0Mly6iqKgIgYGBKCoq8nYpRF7DdtB1MFyIiEh1DBciIlIdw4WIiFTHcCEiItUxXIiISHUMFyIiUh3DhYiIVMdwISIi1TFciIhIdQwXIiJSHcOFiIhUx3AhIiLVMVyIiEh1DBciIlIdw4WIiFTHcOkCpJQwmUzt/k3U07AddC0MFx9mNpuxdu1aJCcn46677kJLSwvuuusuJCcnY+3atTCbzd4ukcjj2A66JiEZ/z5p+/btmDNnDqxWKwC0u0oTQgAAAgMDsXnzZmRlZXmlRiJPYzvouhguPmj79u2YMWOGcr/wa9FoNBBCIDc3lw2Luh22g66N4eJjzGYz4uPjYbPZrtug3DQaDQwGA8rLy2E0Gj1fIFEnYDvo+jjn4mM2bNgAq9V6Qw0KAFwuF6xWKzZu3Ojhyog6D9tB18eeiw+RUiI5ORmlpaU3tRJGCIGkpCQUFxcr49BEXRXbQffAcPEhtbW1iI6O7tDzIyMjVayIqPOxHXQPHBbzIRaLpUPPb2xsVKkSIu9hO+geGC4+JDg4uEPPDwkJUakSIu9hO+geGC4+JDIyEv3797/p8WIhBPr374+IiAgPVUbUedgOugeGiw8RQuCJJ564pecuWbKEk5jULbAddA+c0PcxXN9PxHbQHbDn4mOMRiM2b94MIQQ0muv/73HvTN6yZQsbFHUrbAddH8PFB2VlZSE3NxcGgwFCiCu6+e7PGQwG5OXlITMz00uVEnkO20HXxnDxUVlZWSgvL0d2djaSkpLaPZaUlITs7GycP3+eDYq6NbaDrotzLl2AlBJ1dXVobGxESEgIIiIiOGlJPQ7bQdfCcCEiItVxWIyIiFTHcCEiItUxXIiISHUMFyIiUh3DhYiIVMdwISIi1TFciIhIdQwXIiJSHcOFiIhUx3AhIiLVMVyIiEh1DBciIlIdw4WIiFTHcCEiItX9Pz6EkzYrB5CpAAAAAElFTkSuQmCC\n", + "image/png": "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", "text/plain": [ "
" ] @@ -164,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "id": "031db28f", "metadata": {}, "outputs": [ @@ -174,10 +171,10 @@ "text": [ " function fitting r2 r2 loss complexity complexity loss total loss\n", "0 0 0.000000 0.000014 0 0 0.000003\n", - "1 x 0.002039 -0.002930 1 1 0.799414\n", - "2 J0 0.200055 -0.322009 2 2 1.535598\n", - "3 cos 0.168072 -0.265453 2 2 1.546909\n", - "4 sin 0.168072 -0.265453 2 2 1.546909\n" + "1 x 0.001602 -0.002298 1 1 0.799540\n", + "2 sin 0.161428 -0.253977 2 2 1.549205\n", + "3 cos 0.161428 -0.253977 2 2 1.549205\n", + "4 1/x^2 0.099456 -0.151116 2 2 1.569777\n" ] }, { @@ -192,7 +189,7 @@ " 0)" ] }, - "execution_count": 21, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -203,17 +200,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "id": "4b8549a2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['x', 'x^2', 'x^3', 'x^4', 'x^5', '1/x', '1/x^2', '1/x^3', '1/x^4', '1/x^5', 'sqrt', 'x^0.5', 'x^1.5', '1/sqrt(x)', '1/x^0.5', 'exp', 'log', 'abs', 'sin', 'cos', 'tan', 'tanh', 'sgn', 'arcsin', 'arccos', 'arctan', 'arctanh', '0', 'gaussian', 'J0'])" + "dict_keys(['x', 'x^2', 'x^3', 'x^4', 'x^5', '1/x', '1/x^2', '1/x^3', '1/x^4', '1/x^5', 'sqrt', 'x^0.5', 'x^1.5', '1/sqrt(x)', '1/x^0.5', 'exp', 'log', 'abs', 'sin', 'cos', 'tan', 'tanh', 'sgn', 'arcsin', 'arccos', 'arctan', 'arctanh', '0', 'gaussian'])" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "id": "cbde1924", "metadata": {}, "outputs": [], @@ -250,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "id": "83e5cfdd", "metadata": {}, "outputs": [ @@ -260,10 +257,10 @@ "text": [ " function fitting r2 r2 loss complexity complexity loss total loss\n", "0 0 0.000000 0.000014 0 0 0.000003\n", - "1 J0 0.200055 -0.322009 1 1 0.735598\n", - "2 x 0.002039 -0.002930 1 1 0.799414\n", - "3 cos 0.168072 -0.265453 2 2 1.546909\n", - "4 sin 0.168072 -0.265453 2 2 1.546909\n" + "1 J0 0.198505 -0.319216 1 1 0.736157\n", + "2 x 0.001602 -0.002298 1 1 0.799540\n", + "3 sin 0.161428 -0.253977 2 2 1.549205\n", + "4 cos 0.161428 -0.253977 2 2 1.549205\n" ] }, { @@ -278,7 +275,7 @@ " 0)" ] }, - "execution_count": 24, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -298,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "id": "e78f4674", "metadata": {}, "outputs": [ @@ -307,11 +304,11 @@ "output_type": "stream", "text": [ " function fitting r2 r2 loss complexity complexity loss total loss\n", - "0 J0 0.998490 -9.361690 1 1 -1.072338\n", + "0 J0 0.998912 -9.830484 1 1 -1.166097\n", "1 0 0.000000 0.000014 0 0 0.000003\n", - "2 x 0.002039 -0.002930 1 1 0.799414\n", - "3 cos 0.580127 -1.251939 2 2 1.349612\n", - "4 sin 0.580127 -1.251939 2 2 1.349612\n" + "2 x 0.001602 -0.002298 1 1 0.799540\n", + "3 cos 0.583964 -1.265186 2 2 1.346963\n", + "4 sin 0.583964 -1.265186 2 2 1.346963\n" ] }, { @@ -322,11 +319,11 @@ " J0,\n", " 1,\n", " ),\n", - " 0.9984899759292603,\n", + " 0.9989116787910461,\n", " 1)" ] }, - "execution_count": 25, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -352,7 +349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_6_PDE_interpretation.ipynb b/tutorials/Example_6_PDE_interpretation.ipynb index f0e685ed..a85aa118 100644 --- a/tutorials/Example_6_PDE_interpretation.ipynb +++ b/tutorials/Example_6_PDE_interpretation.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "id": "0e2bc449", "metadata": {}, "outputs": [ @@ -26,6 +26,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -34,7 +35,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "pde loss: 2.23e+00 | bc loss: 9.47e-03 | l2: 3.63e-03 : 100%|███████| 20/20 [00:24<00:00, 1.25s/it]\n" + "pde loss: 2.23e+00 | bc loss: 5.99e-03 | l2: 3.78e-03 : 100%|███████| 20/20 [00:22<00:00, 1.11s/it]\n" ] } ], @@ -44,12 +45,15 @@ "from torch import autograd\n", "from tqdm import tqdm\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "dim = 2\n", "np_i = 21 # number of interior points (along each dimension)\n", "np_b = 21 # number of boundary points (along each dimension)\n", "ranges = [-1, 1]\n", "\n", - "model = KAN(width=[2,2,1], grid=5, k=3, seed=4)\n", + "model = KAN(width=[2,2,1], grid=5, k=3, seed=1, device=device)\n", "\n", "def batch_jacobian(func, x, create_graph=False):\n", " # x in shape (Batch, Length)\n", @@ -73,6 +77,8 @@ "else:\n", " #random\n", " x_i = torch.rand((np_i**2,2))*2-1\n", + " \n", + "x_i = x_i.to(device)\n", "\n", "# boundary, 4 sides\n", "helper = lambda X, Y: torch.stack([X.reshape(-1,), Y.reshape(-1,)]).permute(1,0)\n", @@ -82,6 +88,8 @@ "xb4 = helper(X[:,0], Y[:,-1])\n", "x_b = torch.cat([xb1, xb2, xb3, xb4], dim=0)\n", "\n", + "x_b = x_b.to(device)\n", + "\n", "steps = 20\n", "alpha = 0.01\n", "log = 1\n", @@ -122,7 +130,7 @@ " l2 = torch.mean((model(x_i) - sol)**2)\n", "\n", " if _ % log == 0:\n", - " pbar.set_description(\"pde loss: %.2e | bc loss: %.2e | l2: %.2e \" % (pde_loss.cpu().detach().numpy(), bc_loss.cpu().detach().numpy(), l2.detach().numpy()))\n", + " pbar.set_description(\"pde loss: %.2e | bc loss: %.2e | l2: %.2e \" % (pde_loss.cpu().detach().numpy(), bc_loss.cpu().detach().numpy(), l2.cpu().detach().numpy()))\n", "\n", "train()" ] @@ -312,7 +320,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_7_PDE_accuracy.ipynb b/tutorials/Example_7_PDE_accuracy.ipynb index 2bde442f..0d7f0391 100644 --- a/tutorials/Example_7_PDE_accuracy.ipynb +++ b/tutorials/Example_7_PDE_accuracy.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "0e2bc449", "metadata": {}, "outputs": [ @@ -26,6 +26,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -34,9 +35,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "pde loss: 5.89e+00 | bc loss: 5.27e-02 | l2: 1.59e-02 : 100%|█████████| 1/1 [00:07<00:00, 7.07s/it]\n", - "pde loss: 1.42e+00 | bc loss: 1.99e-02 | l2: 5.40e-03 : 100%|█████████| 1/1 [00:07<00:00, 7.20s/it]\n", - "pde loss: 3.76e-01 | bc loss: 1.80e-02 | l2: 4.81e-03 : 100%|█████████| 1/1 [00:07<00:00, 7.52s/it]\n" + "pde loss: 2.13e+00 | bc loss: 1.80e-03 | l2: 3.11e-03 : 100%|███████| 50/50 [00:35<00:00, 1.43it/s]\n", + "pde loss: 5.68e-01 | bc loss: 5.30e-04 | l2: 1.03e-03 : 100%|███████| 50/50 [00:35<00:00, 1.43it/s]\n", + "pde loss: 1.23e-01 | bc loss: 1.51e-04 | l2: 1.74e-04 : 100%|███████| 50/50 [00:35<00:00, 1.42it/s]\n" ] } ], @@ -47,6 +48,10 @@ "from torch import autograd\n", "from tqdm import tqdm\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", + "\n", "dim = 2\n", "np_i = 51 # number of interior points (along each dimension)\n", "np_b = 51 # number of boundary points (along each dimension)\n", @@ -75,6 +80,8 @@ "else:\n", " #random\n", " x_i = torch.rand((np_i**2,2))*2-1\n", + " \n", + "x_i = x_i.to(device)\n", "\n", "# boundary, 4 sides\n", "helper = lambda X, Y: torch.stack([X.reshape(-1,), Y.reshape(-1,)]).permute(1,0)\n", @@ -84,12 +91,14 @@ "xb4 = helper(X[:,0], Y[:,-1])\n", "x_b = torch.cat([xb1, xb2, xb3, xb4], dim=0)\n", "\n", + "x_b = x_b.to(device)\n", + "\n", "alpha = 0.01\n", "log = 1\n", "\n", "\n", "grids = [5,10,20]\n", - "steps = 1\n", + "steps = 50\n", "\n", "pde_losses = []\n", "bc_losses = []\n", @@ -97,7 +106,7 @@ "\n", "for grid in grids:\n", " if grid == grids[0]:\n", - " model = KAN(width=[2,2,1], grid=grid, k=3, seed=3)\n", + " model = KAN(width=[2,2,1], grid=grid, k=3, seed=1, device=device)\n", " model = model.speed()\n", " else:\n", " model.save_act = True\n", @@ -141,11 +150,11 @@ " l2 = torch.mean((model(x_i) - sol)**2)\n", "\n", " if _ % log == 0:\n", - " pbar.set_description(\"pde loss: %.2e | bc loss: %.2e | l2: %.2e \" % (pde_loss.cpu().detach().numpy(), bc_loss.cpu().detach().numpy(), l2.detach().numpy()))\n", + " pbar.set_description(\"pde loss: %.2e | bc loss: %.2e | l2: %.2e \" % (pde_loss.cpu().detach().numpy(), bc_loss.cpu().detach().numpy(), l2.cpu().detach().numpy()))\n", "\n", - " pde_losses.append(pde_loss.detach().numpy())\n", - " bc_losses.append(bc_loss.detach().numpy())\n", - " l2_losses.append(l2.detach().numpy())\n", + " pde_losses.append(pde_loss.cpu().detach().numpy())\n", + " bc_losses.append(bc_loss.cpu().detach().numpy())\n", + " l2_losses.append(l2.cpu().detach().numpy())\n", " \n", " \n", " train()" @@ -153,23 +162,23 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "dcbfa677", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -212,7 +221,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_8_continual_learning.ipynb b/tutorials/Example_8_continual_learning.ipynb index 756406e5..6df4a261 100644 --- a/tutorials/Example_8_continual_learning.ipynb +++ b/tutorials/Example_8_continual_learning.ipynb @@ -20,14 +20,12 @@ "cell_type": "code", "execution_count": 1, "id": "2075ef56", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -36,7 +34,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -94,7 +92,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -126,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "11a1d129", "metadata": {}, "outputs": [ @@ -142,7 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 65.84\n" + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 89.80\n" ] }, { @@ -156,7 +154,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 4.04e-06 | test_loss: 4.04e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:04<00:00, 24.16\n" + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 76.04\n" ] }, { @@ -170,7 +168,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 67.66\n" + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 92.65\n" ] }, { @@ -184,7 +182,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 68.94\n" + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 79.18\n" ] }, { @@ -198,7 +196,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 87.34" + "| train_loss: 3.99e-06 | test_loss: 3.99e-06 | reg: 3.31e+00 | : 100%|█| 100/100 [00:01<00:00, 87.63\n" ] }, { @@ -207,13 +205,6 @@ "text": [ "saving model version 0.5\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -245,13 +236,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "12379f4a", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -298,7 +289,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/tutorials/Example_9_singularity.ipynb b/tutorials/Example_9_singularity.ipynb index 3c7c63d5..5fd01166 100644 --- a/tutorials/Example_9_singularity.ipynb +++ b/tutorials/Example_9_singularity.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "2075ef56", "metadata": {}, "outputs": [ @@ -27,6 +27,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "cuda\n", "checkpoint directory created: ./model\n", "saving model version 0.0\n" ] @@ -35,7 +36,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.89e-02 | test_loss: 3.78e-02 | reg: 6.39e+00 | : 100%|█| 20/20 [00:02<00:00, 7.05it" + "| train_loss: 1.14e-01 | test_loss: 1.29e-01 | reg: 6.34e+00 | : 100%|█| 20/20 [00:03<00:00, 5.03it" ] }, { @@ -57,10 +58,13 @@ "from kan import *\n", "import torch\n", "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "print(device)\n", + "\n", "# create a KAN: 2D inputs, 1D output, and 5 hidden neurons. cubic spline (k=3), 5 grid intervals (grid=5).\n", - "model = KAN(width=[2,1,1], grid=5, k=3, seed=2)\n", + "model = KAN(width=[2,1,1], grid=5, k=3, seed=2, device=device)\n", "f = lambda x: torch.sin(2*(torch.log(x[:,[0]])+torch.log(x[:,[1]])))\n", - "dataset = create_dataset(f, n_var=2, ranges=[0.2,5])\n", + "dataset = create_dataset(f, n_var=2, ranges=[0.2,5], device=device)\n", "\n", "# train the model\n", "model.fit(dataset, opt=\"LBFGS\", steps=20);" @@ -68,13 +72,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "3f95fcdd", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -89,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "ccb7ec43", "metadata": {}, "outputs": [ @@ -97,23 +101,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best value at boundary.\n", - "r2 is 0.999884843826294\n", + "r2 is 0.9974619150161743\n", "saving model version 0.2\n", - "Best value at boundary.\n", - "r2 is 0.9998899102210999\n", + "r2 is 0.997527003288269\n", "saving model version 0.3\n", - "r2 is 0.9975605010986328\n", + "r2 is 0.9740613698959351\n", "saving model version 0.4\n" ] }, { "data": { "text/plain": [ - "tensor(0.9976)" + "tensor(0.9741, device='cuda:0')" ] }, - "execution_count": 11, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "0937db67", "metadata": {}, "outputs": [ @@ -134,7 +136,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 2.95e-07 | test_loss: 2.91e-07 | reg: 0.00e+00 | : 100%|█| 20/20 [00:01<00:00, 15.68it" + "| train_loss: 2.66e-07 | test_loss: 2.75e-07 | reg: 0.00e+00 | : 100%|█| 20/20 [00:01<00:00, 15.69it" ] }, { @@ -158,20 +160,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "e959cda3", "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle 1.0 \\sin{\\left(2.0 \\log{\\left(9.993 x_{1} \\right)} + 2.0 \\log{\\left(10.0 x_{2} \\right)} - 9.209 \\right)}$" + "$\\displaystyle - 1.0 \\sin{\\left(2.0 \\log{\\left(5.017 x_{1} \\right)} + 2.0 \\log{\\left(1.512 x_{2} \\right)} - 7.194 \\right)}$" ], "text/plain": [ - "1.0*sin(2.0*log(9.993*x_1) + 2.0*log(10.0*x_2) - 9.209)" + "-1.0*sin(2.0*log(5.017*x_1) + 2.0*log(1.512*x_2) - 7.194)" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -190,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "1ce52cec", "metadata": {}, "outputs": [ @@ -206,7 +208,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 5.17e-03 | test_loss: 5.45e-03 | reg: 5.66e+00 | : 100%|█| 20/20 [00:02<00:00, 7.44it" + "| train_loss: 3.65e-03 | test_loss: 3.97e-03 | reg: 4.84e+00 | : 100%|█| 20/20 [00:03<00:00, 5.36it\n" ] }, { @@ -215,13 +217,6 @@ "text": [ "saving model version 0.1\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -239,13 +234,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "3a69ec41", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ "
" ] @@ -260,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "abef7aa9", "metadata": {}, "outputs": [ @@ -268,21 +263,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "r2 is 0.9999783635139465\n", + "r2 is 0.9999973773956299\n", "saving model version 0.2\n", - "r2 is 0.9999676942825317\n", + "r2 is 0.9999948740005493\n", "saving model version 0.3\n", - "r2 is 0.9997884631156921\n", + "r2 is 0.9998846650123596\n", "saving model version 0.4\n" ] }, { "data": { "text/plain": [ - "tensor(0.9998)" + "tensor(0.9999)" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -295,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 9, "id": "aa71848c", "metadata": {}, "outputs": [ @@ -303,7 +298,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "rewind to model version 1.4, renamed as 2.4\n" + "rewind to model version 0.4, renamed as 1.4\n" ] } ], @@ -314,20 +309,20 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 10, "id": "e14000d8", "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$\\displaystyle 1.01334547419162 \\sqrt{0.999861446076389 \\left(7.53297050423062 \\cdot 10^{-5} - x_{2}\\right)^{2} + \\left(0.000104069324734005 - x_{1}\\right)^{2} + 0.00834810636784406} - 0.0170296430587769$" + "$\\displaystyle 1.00775534257195 \\sqrt{0.999962771771901 \\left(6.10769914067904 \\cdot 10^{-5} - x_{1}\\right)^{2} + \\left(9.20887777110479 \\cdot 10^{-5} - x_{2}\\right)^{2} + 0.00441348508007971} - 0.00955450534820557$" ], "text/plain": [ - "1.01334547419162*sqrt(0.999861446076389*(7.53297050423062e-5 - x_2)**2 + (0.000104069324734005 - x_1)**2 + 0.00834810636784406) - 0.0170296430587769" + "1.00775534257195*sqrt(0.999962771771901*(6.10769914067904e-5 - x_1)**2 + (9.20887777110479e-5 - x_2)**2 + 0.00441348508007971) - 0.00955450534820557" ] }, - "execution_count": 47, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -339,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 11, "id": "c56ee3d5", "metadata": { "scrolled": true @@ -348,13 +343,13 @@ { "data": { "text/latex": [ - "$\\displaystyle 1.01 \\sqrt{x_{1}^{2} + 1.0 x_{2}^{2} + 0.01} - 0.02$" + "$\\displaystyle 1.01 \\sqrt{1.0 x_{1}^{2} + x_{2}^{2}} - 0.01$" ], "text/plain": [ - "1.01*sqrt(x_1**2 + 1.0*x_2**2 + 0.01) - 0.02" + "1.01*sqrt(1.0*x_1**2 + x_2**2) - 0.e-2" ] }, - "execution_count": 48, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -373,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 12, "id": "de708f21", "metadata": {}, "outputs": [ @@ -381,7 +376,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: 1.09e-07 | test_loss: 1.48e-07 | reg: 0.00e+00 | : 100%|█| 1000/1000 [00:12<00:00, 83.\n" + "| train_loss: 5.11e-04 | test_loss: 5.64e-04 | reg: 0.00e+00 | : 100%|█| 1000/1000 [00:14<00:00, 70.\n" ] }, { @@ -406,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 13, "id": "031fabd6", "metadata": { "scrolled": true @@ -416,7 +411,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "| train_loss: nan | test_loss: nan | reg: nan | : 100%|█████████| 1000/1000 [00:11<00:00, 84.83it/s]\n" + "| train_loss: nan | test_loss: nan | reg: nan | : 100%|█████████| 1000/1000 [00:17<00:00, 57.55it/s]\n" ] }, { @@ -456,7 +451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4,