def z_score():
    matrix = [[[0] * len(attributes)] * numOfRecords] * numOfClasses
    for i in range(len(attributes)):
        attr = []
        for j in range(numOfClasses):
            for k in range(numOfRecords):
                attr.append(classes[j][k][i])
        attr_normalized = zscore(attr)
        id = 0
        print(attr_normalized)
        for j in range(numOfClasses):
            for k in range(numOfRecords):
                if math.isnan(attr_normalized[id]):
                    matrix[j][k][i] = 0
                else:
                    matrix[j][k][i] = attr_normalized[id]
                id += 1

    histogram(matrix, "After z-score normalization")
    return matrix