def test_compare_to_sklearn(self):
        for n in range(2, 100):
            # Multi-label classification notation with binary label indicators
            y_true = np.random.randint(2, size=(n,n))
            y_pred = np.random.randint(2, size=(n,n))
            # Hamming accuracy by hand
            hamming_loss = 0.
            for i in range(n):
                for j in range(n):
                    hamming_loss += (y_true[i,j] != y_pred[i,j])
            # Compare both
            acc_sklearn = 1. - hamming_loss/n**2
            acc_hamming = accuracy.hamming_accuracy(y_true, y_pred)
            assert_equals(acc_hamming, acc_sklearn)
create the instance to be tested, called before every test
dataset/ETHPy150Open kashefy/nideep/nideep/eval/test_accuracy.py/TestHammingAccuracy.test_compare_to_sklearn--crossover with--dataset/ETHPy150Open NeuralEnsemble/python-neo/neo/test/coretest/test_base.py/TestBaseNeoContainerTypes.setUp