def de_mean_matrix(A):
"""returns the result of subtracting from everty value in A the
mean value of its column, the resulting matri has mean 0 in every column"""
nr, nc = matrix.shape(A)
column_means, __ = scale(A)
return matrix.make_mtr(nr, nc, lambda i, j: A[i][j] -
column_means[j])
def rescale(data_matrix):
"""rescales the input data_raw so that each column has mean 0 and
stdev1"""
means, stdevs = scale(data_matrix)
def rescaled(i,j):
if stdevs[j]>0:
return (data_matrix[i][j] - means[j])/stdevs[j]
else:
data_matrix[i][j]
num_rows, num_cols = matrix.shape(data_matrix)
return matrix.make_mtr(num_rows, num_cols, rescaled)
