更新辣

支持向量机/主程序代码.py

@@ -2,34 +2,34 @@
 
 
 class SVM:
-    def __init__(self, max_iter=100, kernel='linear'): #设置最大迭代量，若超过max_iter仍不满足KKT条件则跳出； kernel即为核函数。
+    def __init__(self, max_iter, kernel='linear'): #设置最大迭代量，若超过max_iter仍不满足KKT条件则跳出； kernel即为核函数。
         self.max_iter = max_iter
         self._kernel = kernel
 
-    def init_args(self, features, labels):
-        self.m, self.n = features.shape
+    def init_args(self, features, labels): #features特征空间 labels变迁
+        self.m, self.n = features.shape #m行  n列;n为特征数 m为样本量
         self.X = features
         self.Y = labels
         self.b = 0.0
 
         # 将Ei保存在一个列表里
-        self.alpha = np.ones(self.m)
+        self.alpha = np.ones(self.m) #alpha_i 阿尔法初始化
         self.E = [self._E(i) for i in range(self.m)]
         # 松弛变量
         self.C = 1.0
 
-    def _KKT(self, i):
+    def _KKT(self, i): #KKT条件的检验,这里没有在精度范围内进行检验呐
         y_g = self._g(i) * self.Y[i]
         if self.alpha[i] == 0:
             return y_g >= 1
         elif 0 < self.alpha[i] < self.C:
-            return y_g == 1
+            return y_g == 1  #满足式子则return True 反之False
         else:
             return y_g <= 1
 
     # g(x)预测值，输入xi（X[i]）
     def _g(self, i):
-        r = self.b
+        r = self.b  #g(x)=sum(a_y*y_j*K(xi,xj))  + b
         for j in range(self.m):
             r += self.alpha[j] * self.Y[j] * self.kernel(self.X[i], self.X[j])
         return r
@@ -38,7 +38,7 @@ def _g(self, i):
     def kernel(self, x1, x2):
         if self._kernel == 'linear':
             return sum([x1[k] * x2[k] for k in range(self.n)])
-        elif self._kernel == 'poly':
+        elif self._kernel == 'poly': #多项式和核函数 取p=2
             return (sum([x1[k] * x2[k] for k in range(self.n)]) + 1)**2
 
         return 0
@@ -47,41 +47,41 @@ def kernel(self, x1, x2):
     def _E(self, i):
         return self._g(i) - self.Y[i]
 
-    def _init_alpha(self):
+    def _init_alpha(self):#alpha的选择
         # 外层循环首先遍历所有满足0<a<C的样本点，检验是否满足KKT
         index_list = [i for i in range(self.m) if 0 < self.alpha[i] < self.C]
         # 否则遍历整个训练集
-        non_satisfy_list = [i for i in range(self.m) if i not in index_list]
+        non_satisfy_list = [i for i in range(self.m) if i not in index_list] #alpha=0 or C
         index_list.extend(non_satisfy_list)
 
         for i in index_list:
             if self._KKT(i):
-                continue
+                continue#满足KKT条件的a  则终止下列运行返回for循环重新判断
 
-            E1 = self.E[i]
+            E1 = self.E[i] #E1确定
             # 如果E2是+，选择最小的；如果E2是负的，选择最大的
             if E1 >= 0:
                 j = min(range(self.m), key=lambda x: self.E[x])
             else:
                 j = max(range(self.m), key=lambda x: self.E[x])
             return i, j
 
-    def _compare(self, _alpha, L, H):
+    def _compare(self, _alpha, L, H): #
         if _alpha > H:
             return H
         elif _alpha < L:
             return L
         else:
             return _alpha
 
-    def fit(self, features, labels):
+    def fit(self, features, labels): #Train
         self.init_args(features, labels)
 
         for t in range(self.max_iter):
             # train
-            i1, i2 = self._init_alpha()
+            i1, i2 = self._init_alpha() #得到选择的两个alpha
 
-            # 边界
+            # 边界 L,H的确认：
             if self.Y[i1] == self.Y[i2]:
                 L = max(0, self.alpha[i1] + self.alpha[i2] - self.C)
                 H = min(self.C, self.alpha[i1] + self.alpha[i2])
@@ -91,12 +91,13 @@ def fit(self, features, labels):
 
             E1 = self.E[i1]
             E2 = self.E[i2]
-            # eta=K11+K22-2K12
+            # eta=K11+K22-2K12 （书上的n）
             eta = self.kernel(self.X[i1], self.X[i1]) + self.kernel(
                 self.X[i2],
                 self.X[i2]) - 2 * self.kernel(self.X[i1], self.X[i2])
-            if eta <= 0:
-                # print('eta <= 0')
+            print(eta)
+            if eta <= 0: #为什么这里会小于0？？？？为什么小于0就不计算了？
+                #print(eta)
                 continue
 
             alpha2_new_unc = self.alpha[i2] + self.Y[i2] * (
@@ -150,5 +151,15 @@ def score(self, X_test, y_test):
     def _weight(self):
         # linear model
         yx = self.Y.reshape(-1, 1) * self.X
-        self.w = np.dot(yx.T, self.alpha)
-        return self.w
+        self.w = np.dot(yx.T, self.alpha) #np.dot矩阵乘法
+        return self.w
+
+
+
+svm=SVM(max_iter=30)
+
+X_train=np.array([[1,2],[0,2],[1,1],[3,4],[2,3],[1,4],[-1,-1]])
+y_train=np.array([-1,-1,-1,1,1,1,-1])
+svm.fit(X_train, y_train)
+
+print(svm.score(X_train, y_train))

草稿.py

@@ -0,0 +1,4 @@
+
+
+
+print(1>2)