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simulate_Data_LR_HR.py
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simulate_Data_LR_HR.py
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import numpy as np
import matplotlib.pyplot as plt
from statistics import mean
import xlsxwriter
mu1 = 0.8 #Mean parameter of the first Gaussian distribution
sigma1 = 0.4 #Standard deviation parameter of the first Gaussian distribution
mu2 = 0.7 # Mean parameter of the second Gaussian distribution
sigma2 = 0.6 #Standard deviation parameter of the second Gaussian distribution
def simulate_Data_LR_HR(mu1, sigma1, mu2, sigma2):
Featurematrix = [];
max_Featurematrix=[];
AV_Featurematrix=[];
c1 = int(input("Select the number of class 1 graphs: "))
while not c1:
c1 = int(input('Please give a number: '))
while c1 < 5:
c1 = int(input('Please choose a number > 4: '))
c2 = int(input('Select the number of class 2 graphs: '))
while not c2:
c2 = int(input('Please give a number: '))
while c2 < 5:
c2 = int(input('Please choose a number > 4: '))
m = int(input('Select the number of nodes (i.e. ROI\'s for brain graphs): '))
while not m:
m = int(input('Please give a number: '))
while (m < 20) | (m%4 != 0):
m = int(input('Please choose a number > 20 and a multiple of 4: '))
N = c1 + c2
datac1 = np.random.normal(mu1, sigma1, [c1, m, m]) # Normal random distribution
datac2 = np.random.normal(mu2, sigma2, [c2, m, m]) # Normal random distribution
data1 = np.concatenate((datac1, datac2), axis=0)
# Creating a 1D vector from the entries of datac1 and datac2 vectors in order to draw a histogram graph.
datac1_vector = datac1.flatten()
datac2_vector = datac2.flatten()
# Drawing samples from two different distributions to simulate both classes
plt.hist(datac1_vector, bins = 115, edgecolor=(0, 1, 0, 1), fc=(0, 1, 0, 0.8))
plt.hist(datac2_vector, bins = 115, edgecolor=(0, 0, 1, 0.4), fc=(0, 0, 1, 0.3))
plt.show()
V = np.zeros((int(m/4), 4, 4))
AV = np.zeros((N, int(m/4), int(m/4)))
maxi = np.zeros((N, int(m/4), int(m/4)))
Featurematrix = np.zeros((0,m*m))
z = np.zeros((1,1))
for i in range(0,N):
x = np.zeros((0,1))
LR = np.zeros((int(m/4), 4, 4))
data1[i][:][:] = np.squeeze(data1[i][:][:]) - np.diag(np.diag(np.squeeze(data1[i][:][:]))) #Eliminating self-symmetry
data1[i][:][:] = np.true_divide((np.squeeze(data1[i][:][:]) + np.transpose(np.squeeze(data1[i][:][:]))),2) #Insure data symmetry
t = np.triu(np.squeeze(data1[i,:,:]),1) #Upper triangular matrix constructed
#t is vectorized:
for ii in range(0,m):
for jj in range(0,m):
z[0,0] = t[jj,ii]
x = np.append(x, z, axis=0)
x1 = np.transpose(x) #transpose of x is x1
Featurematrix = np.append(Featurematrix, x1, axis=0)
jp=0
jc=3
r=-1
H = np.zeros((int(m/4), int(m/4), 4, 4))
while jp < m:
r = r + 1
for v in range(jp, jc+1, 4):
ip = 0
ic = 3
o = 0
while (ic < m):
mm = 0
for k in range(ip, ic+1):
n = 0
for l in range(jp, jc+1):
LR[o][mm][n] = np.squeeze(data1[i][k][l])
n = n+1
mm = mm+1
ip = ic+1
ic = ip+3
o = o+1
for p in range(0, int(m/4)):
for j in range(0, 4):
for s in range(0, 4):
H[r][p][j][s] = LR[p][j][s]
jp = jc+1
jc = jp+3
for a1 in range(0, int(m/4)):
for g in range(0, int(m/4)):
for j in range(0, 4):
for s in range(0, 4):
V[g][j][s] = H[a1][g][j][s]
for b1 in range(0,int(m/4)):
q1 = np.squeeze(V[b1][:][:])
maxi[i][b1][a1] = np.amax(np.amax(q1[:][:], axis=0), axis=0)
AV[i][b1][a1] = np.mean(np.mean(q1, axis=0), axis = 0)
def pooling_LR_data(max_av):
t = np.triu(np.squeeze(max_av[i,:,:]),1) # Upper triangular part of matrix
#vectorizing t (triangular matrix) into 1 column vector
x = np.zeros((0,1))
for ii in range(0,int(m/4)):
for jj in range(0,int(m/4)):
z[0,0] = t[jj,ii]
x = np.append(x, z, axis=0)
x = np.reshape(x, (1,len(x)))
max_av_Featurematrix = np.zeros((0, len(t)*len(t)))
max_av_Featurematrix = np.append(max_av_Featurematrix, x, axis=0)
LR_data_x = max_av
return max_av_Featurematrix, LR_data_x
max_Featurematrix, LR_data_max_x = pooling_LR_data(maxi)
LR_data_max_Featurematrix = Featurematrix
LR_data_max_Labels = np.concatenate((np.ones((c1,1)),-1*np.ones((c2,1))), axis=0)
AV_Featurematrix, LR_data_av_x = pooling_LR_data(AV)
LR_average_Featurematrix = Featurematrix
LR_data_av_Labels = np.concatenate((np.ones((c1,1)),-1*np.ones((c2,1))), axis=0)
HR_data_Featurematrix = Featurematrix
HR_data_x = data1
HR_data_Labels = np.concatenate((np.ones((c1,1)),-1*np.ones((c2,1))), axis=0)
return LR_data_max_Featurematrix, LR_data_max_x, LR_data_max_Labels, LR_average_Featurematrix, LR_data_av_x, LR_data_av_Labels, HR_data_Featurematrix, HR_data_x, HR_data_Labels, c1, c2