samplematrix.py

import Erdos_Renyi as er 
import json 
import pandas as pd 
import numpy as np
import pdb 
import networkx as nx 
import matplotlib.pyplot as plt

from Erdos_Renyi import get_W 

# Parameters
p = 0.3
subset = 15
K = subset
#const = 60*60*24*7 # 1 week

# Load settings file
settings = json.load(open("settings.json","r"))
# Path to file
events_embd = settings["out_path"]+"concepts_date_embd.csv"
# Load events
print("Reading in data")
events = pd.read_csv(events_embd,header=None)
L = events.head(subset)

print("Calculating W from data")
W_real = er.get_W(L)

# Set to minimum of limit and shape of data (due to starting period with less events)
S_real = np.eye(K,dtype=np.int)

def resample_graph(N,data,K):
    ld_network = er.LatentDistanceAdjacencyModel(K=K, L = None, dim=2, v=None, alpha=1.0, beta=1.0,kappa=1.0,p = p)

    A,W = np.ones((K, K)), np.zeros((K, K))

    for i in range(N):
        if i%10 == 0: print(i)
        ld_network.resample(data=data)
        # Define weight model
        weight_model = er.SpikeAndSlabGammaWeights(model = ld_network, parallel_resampling=False)
        weight_model.A = A 
        weight_model.W = W 

        weight_model.resample_new(data=data)

        A,W = [weight_model.A,weight_model.W]

    return weight_model
    
weight_model = resample_graph(N=20,data=[S_real,W_real],K=K)

# Print adjecancy matrix and it's properties 
print(weight_model.A)
print(sum(weight_model.A))
print(max(sum(weight_model.A)))

def W_sample(A,W):
    return A*W

print(W_sample(W_real,weight_model.A))