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    # exercise 4.3.2
    
    from matplotlib.pyplot import figure, subplot, plot, legend, show,  xlabel, ylabel, xticks, yticks
    import numpy as np
    from scipy.io import loadmat
    from scipy.stats import zscore
    
    # Load Matlab data file and extract variables of interest
    mat_data = loadmat('../Data/wine.mat')
    X = mat_data['X']
    y = np.squeeze(mat_data['y'])
    C = mat_data['C'][0,0]
    M = mat_data['M'][0,0]
    N = mat_data['N'][0,0]
    
    attributeNames = [name[0][0] for name in mat_data['attributeNames']]
    classNames = [cls[0] for cls in mat_data['classNames'][0]]
        
    # The histograms show that there are a few very extreme values in these
    # three attributes. To identify these values as outliers, we must use our
    # knowledge about the data set and the attributes. Say we expect volatide
    # acidity to be around 0-2 g/dm^3, density to be close to 1 g/cm^3, and
    # alcohol percentage to be somewhere between 5-20 % vol. Then we can safely
    # identify the following outliers, which are a factor of 10 greater than
    # the largest we expect.
    outlier_mask = (X[:,1]>20) | (X[:,7]>10) | (X[:,10]>200)
    valid_mask = np.logical_not(outlier_mask)
    
    # Finally we will remove these from the data set
    X = X[valid_mask,:]
    y = y[valid_mask]
    N = len(y)
    Xnorm = zscore(X, ddof=1)
    
    ## Next we plot a number of atttributes
    Attributes = [1,4,5,6]
    NumAtr = len(Attributes)
    
    figure(figsize=(12,12))
    for m1 in range(NumAtr):
        for m2 in range(NumAtr):
            subplot(NumAtr, NumAtr, m1*NumAtr + m2 + 1)
            for c in range(C):
                class_mask = (y==c)
                plot(X[class_mask,Attributes[m2]], X[class_mask,Attributes[m1]], '.')
                if m1==NumAtr-1:
                    xlabel(attributeNames[Attributes[m2]])
                else:
                    xticks([])
                if m2==0:
                    ylabel(attributeNames[Attributes[m1]])
                else:
                    yticks([])
                #ylim(0,X.max()*1.1)
                #xlim(0,X.max()*1.1)
    legend(classNames)
    show()
    
    print('Ran Exercise 4.3.2')