def yule_walker(x,lag):
number_of_rows = number_of_cols = lag
sum_matrix = np.zeros(number_of_rows**2).reshape((number_of_rows,number_of_rows))
for i in range(0,number_of_rows):
for j in range(0,number_of_cols):
if(i==j):
sum_matrix[i][j] = 1.0
else:
sum_matrix[i][j] = stat.autocorr(x,abs(i-j))
return sum_matrix
def yule_matrix_rhs(x,lag):
number_of_rows = number_of_cols = lag
rhs_matrix = np.zeros(number_of_rows).reshape((number_of_rows,1))
for i in range(0,number_of_rows-1):
rhs_matrix[i] = stat.autocorr(x,i+1)
return rhs_matrix