def scale(data_matrix):
""" returns the means and stds of each column in data_matrix """
num_rows, num_cols = shape(data_matrix)
means = [mean(get_col(data_matrix, col)) for col in range(num_cols)]
stds = [standard_deviation(get_col(data_matrix, col)) for col in range(num_cols)]
return means, stds
def scale(data_matrix):
""" returns the means and stds of each column in data_matrix """
num_rows, num_cols = shape(data_matrix)
means = [mean(get_col(data_matrix, col)) for col in range(num_cols)]
stds = [
standard_deviation(get_col(data_matrix, col))
for col in range(num_cols)
]
return means, stds
def norm_matrix(data_matrix):
""" rescales the input columns to have zero mean and stdev 1. Ignores
cols with no deviation """
means, stds = scale(data_matrix)
def norm(i, j):
if stds[j] > 0:
return (data_matrix[i][j] - means[j]) / stds[j]
else:
return data_matrix[i][j]
num_rows, num_cols = shape(data_matrix)
return make_matrix(num_rows, num_cols, norm)
def norm_matrix(data_matrix):
""" rescales the input columns to have zero mean and stdev 1. Ignores
cols with no deviation """
means, stds = scale(data_matrix)
def norm(i, j):
if stds[j] > 0:
return (data_matrix[i][j] - means[j]) / stds[j]
else:
return data_matrix[i][j]
num_rows, num_cols = shape(data_matrix)
return make_matrix(num_rows, num_cols, norm)
