import numpy as np


def evaluate_measures(sample):
    n_samples = len(sample)
    n_classes = 0
    classes = dict()
    for c in sample:
        if c in classes:
            classes[c] += 1
        else:
            classes[c] = 1
            n_classes += 1
    probs = np.zeros(n_classes)
    for i, c in zip(range(n_classes), classes):
        probs[i] = classes[c] / n_samples
    gini = 1
    entropy = 0
    error = 1
    for p in probs:
        gini -= p * p
        entropy -= p * np.log(p)
        error = min(error, 1 - p)
    measures = {'gini': gini, 'entropy': entropy, 'error': error}
    return measures