def separation(X,y,samples=False):
    """ return the sum of the between-class squared distance"""
    # pdb.set_trace()
    num_classes = len(np.unique(y))
    total_dist = (X.max()-X.min())**2
    if samples:
        # return intra-class distance for each sample
        separation = np.zeros(y.shape)
        for label in np.unique(y):
            for outsider in np.unique(y[y!=label]):
                separation[y==label] += (X[y==label] - np.mean(X[y==outsider])) ** 2

        #normalize between 0 and 1
        print('separation:',separation)
        print('num_classes:',num_classes)
        print('total_dist:',total_dist)
        separation = separation#/separation.max()

        print('separation after normalization:',separation)

    else:
        # return aggregate score
        separation = 0
        for i,label in enumerate(np.unique(y)):
            for outsider in np.unique(y[y!=label]):
                separation += np.sum((X[y==label] - np.mean(X[y==outsider])) ** 2)/len(y[y==label])
        separation = separation/len(np.unique(y))

    return separation
return the sum of the between-class squared distance