def scale(data_matrix):
    num_rows, num_cols = shape(data_matrix)
    means = [mean(get_column(data_matrix,j))
             for j in range(num_cols)]
    stdevs = [standard_deviation(get_column(data_matrix,j))
              for j in range(num_cols)]
    return means, stdevs
def correlation_matrix(data):
    """returns the num_columns x num_columns matrix whose (i, j)th entry
    is the correlation between columns i and j of data"""

    _, num_columns = shape(data)

    def matrix_entry(i, j):
        return correlation(get_column(data, i), get_column(data, j))

    return make_matrix(num_columns, num_columns, matrix_entry)
def rescale(data_matrix):
    """rescales the input data so that each column
    has mean 0 and standard deviation 1
    ignores columns with no deviation"""
    means, stdevs = scale(data_matrix)

    def rescaled(i, j):
        if stdevs[j] > 0:
            return (data_matrix[i][j] - means[j]) / stdevs[j]
        else:
            return data_matrix[i][j]

    num_rows, num_cols = shape(data_matrix)
    return make_matrix(num_rows, num_cols, rescaled)
def make_scatterplot_matrix():

    
    print("Matrix correlational relations!")
    num_points = 100

    def random_row():
        row = [None, None, None, None]
        row[0] = random_normal()
        row[1] = -5 * row[0] + random_normal()
        row[2] = row[0] + row[1] + 5 * random_normal()
        row[3] = 6 if row[2] > -2 else 0
        return row
    random.seed(0)
    data = [random_row()
            for _ in range(num_points)]

    _, num_columns = shape(data)
    fig, ax = plt.subplots(num_columns, num_columns)
    for i in range(num_columns):
        for j in range(num_columns):

            # scatter column_j on the x-axis vs column_i on the y-axis
            if i != j: ax[i][j].scatter(get_column(data, j), get_column(data, i))

            # unless i == j, in which case show the series name
            else: ax[i][j].annotate("series " + str(i), (0.5, 0.5),
                                    xycoords='axes fraction',
                                    ha="center", va="center")

            # then hide axis labels except left and bottom charts
            if i < num_columns - 1: ax[i][j].xaxis.set_visible(False)
            if j > 0: ax[i][j].yaxis.set_visible(False)

    # fix the bottom right and top left axis labels, which are wrong because
    # their charts only have text in them
    ax[-1][-1].set_xlim(ax[0][-1].get_xlim())
    ax[0][0].set_ylim(ax[0][1].get_ylim())
    plt.show()
def de_mean_matrix(A):
    """returns the result of subtracting from every value in A the mean
    value of its column. the resulting matrix has mean 0 in every column"""
    nr, nc = shape(A)
    column_means, _ = scale(A)
    return make_matrix(nr, nc, lambda i, j: A[i][j] - column_means[j])