Auto Cross covariance Python
Auto Cross Covariance for converting protein sequence descriptor to single length vector import numpy as np # z1 z2 and z3 descriptor was used to represent the protein sequence # Index j was used for the z-scales (j = 1, 2, 3), # n is the number of amino acids in a sequence, # index i is the amino acid position (i = 1, 2, ...n) # l is the lag (l = 1, 2, ...L). # a short range of lags (L= 1, 2, 3, 4, 5) Z = np.random.rand(3,80) print(Z) #Z = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) n = Z.shape[1] n = n-1 print(n) # Autocovariance column = [] for j in range(0,3): row = [] for l in range(0,5): summ = 0 for i in range(0,n-l): rightsum = (Z[j,i]*Z[j,i+1])/(n-l) summ = summ + rightsum row.append(summ) column.append(row) R = np.array(column) print(R) # Cross Covariance ja = [0,1,2,0,1,2] ka = [1,0,0,2,2,1] column = [] for j,k in zip(ja,ka): row = [] for l in range(0,5): summ = 0 for i in range