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 least_squares_fit(x, y):
""" given training values for x,y computes the least squares values for
beta_0 and beta_1"""
beta_1 = covariance(x,y)/variance(x)
beta_0 = mean(y) - beta_1 * mean(x)
return beta_0, beta_1
