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    # exercise 4.1.7
    
    from matplotlib.pyplot import (figure, subplot, imshow, xticks, yticks, title,
                                   cm, show)
    import numpy as np
    from scipy.io import loadmat
    
    # Digits to include in analysis (to include all, n = range(10) )
    n = [1]
    
    # Number of digits to generate from normal distributions
    ngen = 10
    
    # Load Matlab data file to python dict structure
    # and extract variables of interest
    traindata = loadmat('../Data/zipdata.mat')['traindata']
    X = traindata[:,1:]
    y = traindata[:,0]
    N, M = np.shape(X) #or X.shape
    C = len(n)
    
    # Remove digits that are not to be inspected
    class_mask = np.zeros(N).astype(bool)
    for v in n:
        cmsk = (y==v)
        class_mask = class_mask | cmsk
    X = X[class_mask,:]
    y = y[class_mask]
    N = np.shape(X)[0] # or X.shape[0]
    
    mu = X.mean(axis=0)
    s = X.std(ddof=1, axis=0)
    S = np.cov(X, rowvar=0, ddof=1)
    
    # Generate 10 samples from 1-D normal distribution
    Xgen = np.random.randn(ngen,256)
    for i in range(ngen):
        Xgen[i] = np.multiply(Xgen[i],s) + mu
    
    # Plot images
    figure()
    for k in range(ngen):
        subplot(2, int(np.ceil(ngen/2.)), k+1)
        I = np.reshape(Xgen[k,:], (16,16))
        imshow(I, cmap=cm.gray_r);
        xticks([]); yticks([])
        if k==1: title('Digits: 1-D Normal')
    
    
    # Generate 10 samples from multivariate normal distribution
    Xmvgen = np.random.multivariate_normal(mu, S, ngen)
    # Note if you are investigating a single class, then you may get: 
    # """RuntimeWarning: covariance is not positive-semidefinite."""
    # Which in general is troublesome, but here is due to numerical imprecission
    
    
    # Plot images
    figure()
    for k in range(ngen):
        subplot(2, int(np.ceil(ngen/2.)), k+1)
        I = np.reshape(Xmvgen[k,:], (16,16))
        imshow(I, cmap=cm.gray_r);
        xticks([]); yticks([])
        if k==1: title('Digits: Multivariate Normal')
    
    show()
    
    print('Ran Exercise 4.1.7')