def scale(data_matrix):
"""returns the mean and standard deviations of each column"""
num_rows, num_cols = algebra.shape(data_matrix)
means = [stats.mean(algebra.get_column(data_matrix, j))
for j in range(num_cols)]
stddevs = [stats.standard_deviation(algebra.get_column(data_matrix, j))
for j in range(num_cols)]
return means, stddevs
def scale(data_matrix):
"""returns the mean and standard deviations of each column"""
num_rows, num_cols = algebra.shape(data_matrix)
means = [
stats.mean(algebra.get_column(data_matrix, j)) for j in range(num_cols)
]
stddevs = [
stats.standard_deviation(algebra.get_column(data_matrix, j))
for j in range(num_cols)
]
return means, stddevs
def matrix_entry(i, j):
return stats.correlation(algebra.get_column(data, i),
algebra.get_column(data, j))
def matrix_entry(i, j):
return stats.correlation(algebra.get_column(data, i),
algebra.get_column(data, j))