def entropy_cc(X, Y): #ENTROPY CORRELATION COEFFICIENT
"""
Entropy correlation coefficient p(H) = sqrt(I(X,Y)/0.5*(H(X)+H(Y)))
"""
import numpy as np
from CPAC.series_mod import entropy
from CPAC.series_mod import mutual_information
Hx = entropy(X)
Hy = entropy(Y)
Ixy = mutual_information(Y, X)
ECC = np.sqrt(Ixy / (0.5 * (Hx + Hy)))
return ECC
def entropy_cc(X,Y): #ENTROPY CORRELATION COEFFICIENT
"""
Entropy correlation coefficient p(H) = sqrt(I(X,Y)/0.5*(H(X)+H(Y)))
"""
import numpy as np
from CPAC.series_mod import entropy
from CPAC.series_mod import mutual_information
Hx = entropy(X)
Hy = entropy(Y)
Ixy = mutual_information(Y,X)
ECC = np.sqrt(Ixy/(0.5*(Hx+Hy)))
return ECC
def compute_MI(in_file):
"""
Computes Mutual Information from a 1D datafile and returns a np.array.
Parameters
----------
in_file : 1D file
Returns
-------
data_array = voxel(x,y,z) * voxel(x,y,z)
"""
from CPAC.series_mod import transform
from CPAC.series_mod import mutual_information
import numpy as np
import math
data = np.genfromtxt(in_file, skip_header=1)[:, 2:].T
n_var = data.shape[0]
points = data.shape[1]
bins = math.pow(points,
1 / 3.) #to the 3rd due to Equiquantization formula
# Proposed by Milan Palus. n+1 where n is the number of vars in the computation
# as it is pairwise, n+1 is 3
bins = np.round(bins)
data = transform(data, bins).astype(int)
MI_mat = np.zeros((n_var, n_var))
for i_ in range(n_var):
for j_ in range(n_var):
MI_mat[i_, j_] = mutual_information(data[i_, :], data[j_, :])
np.save(in_file[:-7] + '_MI.npy', MI_mat)
return MI_mat
def compute_MI(in_file):
"""
Computes Mutual Information from a 1D datafile and returns a np.array.
Parameters
----------
in_file : 1D file
Returns
-------
data_array = voxel(x,y,z) * voxel(x,y,z)
"""
from CPAC.series_mod import transform
from CPAC.series_mod import mutual_information
import numpy as np
import math
data = np.genfromtxt(in_file, skip_header = 1)[:,2:].T
n_var = data.shape[0]
points = data.shape[1]
bins = math.pow(points, 1/3.) #to the 3rd due to Equiquantization formula
# Proposed by Milan Palus. n+1 where n is the number of vars in the computation
# as it is pairwise, n+1 is 3
bins = np.round(bins)
data = transform(data,bins).astype(int)
MI_mat = np.zeros((n_var,n_var))
for i_ in range(n_var):
for j_ in range(n_var):
MI_mat[i_,j_] = mutual_information(data[i_,:],data[j_,:])
np.save(in_file[:-7]+'_MI.npy', MI_mat)
return MI_mat
