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cov.py
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cov.py
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import DataModel as dm
import numpy as np
from sklearn.covariance import empirical_covariance
import matplotlib.pyplot as plt
import math
import seaborn as sns
def get_cov(data):
dat = data.training_data_all_ways + data.testing_data_all_ways
num_ways = len(data.get_list_of_ways())
m = {}
i = 0
for way in data.get_list_of_ways():
m[way] = i
i += 1
mat = np.zeros((num_ways,num_ways))
for elem in dat:
ways = elem[1]
for way in ways:
mat[m[way],m[way]] = mat[m[way],m[way]] + 1
for w1 in ways:
for w2 in ways:
if w1 == w2: continue
mat[m[w1],m[w2]] = mat[m[w1],m[w2]] + 1
print mat
emp_cov = empirical_covariance(mat)
print emp_cov
corr = np.zeros((num_ways,num_ways))
for i in range(num_ways):
for j in range(num_ways):
corr[i,j] = emp_cov[i,j]/(math.sqrt(emp_cov[i,i])*math.sqrt(emp_cov[j,j]))
print corr
sns.heatmap(corr,vmin = -1, vmax = 1,square=True,xticklabels=m.keys(),yticklabels=m.keys())
sns.plt.title("Covariance of WAYS frequencies")
sns.plt.show()
def main():
data = dm.DataModel()
get_cov(data)
if __name__ == '__main__':
main()