Python numpy.allclose方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_clip_eta_goldilocks(self):
        # Test that the clipping handles perturbations that are
        # too small, just right, and too big correctly
        eta = tf.constant([[2.], [3.], [4.]])
        assert eta.dtype == tf.float32, eta.dtype
        eps = 3.
        for ord_arg in [np.inf, 1, 2]:
            for sign in [-1., 1.]:
                clipped = clip_eta(eta * sign, ord_arg, eps)
                clipped_value = self.sess.run(clipped)
                gold = sign * np.array([[2.], [3.], [3.]])
                self.assertClose(clipped_value, gold)
                grad, = tf.gradients(clipped, eta)
                grad_value = self.sess.run(grad)
                # Note: the second 1. is debatable (the left-sided derivative
                # and the right-sided derivative do not match, so formally
                # the derivative is not defined). This test makes sure that
                # we at least handle this oddity consistently across all the
                # argument values we test
                gold = sign * np.array([[1.], [1.], [0.]])
                assert np.allclose(grad_value, gold) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_separate(test_file, configuration, backend):
    """ Test separation from raw data. """
    with tf.Session() as sess:
        instruments = MODEL_TO_INST[configuration]
        adapter = get_default_audio_adapter()
        waveform, _ = adapter.load(test_file)
        separator = Separator(configuration, stft_backend=backend)
        prediction = separator.separate(waveform, test_file)
        assert len(prediction) == len(instruments)
        for instrument in instruments:
            assert instrument in prediction
        for instrument in instruments:
            track = prediction[instrument]
            assert waveform.shape[:-1] == track.shape[:-1]
            assert not np.allclose(waveform, track)
            for compared in instruments:
                if instrument != compared:
                    assert not np.allclose(track, prediction[compared]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_naivebayes_breastcancer(self):
        # python -m unittest tests_classification.Tests_Classification.test_naivebayes_breastcancer
        from discomll.classification import naivebayes
        train_data1, test_data1 = datasets.breastcancer_disc_orange()
        train_data2, test_data2 = datasets.breastcancer_disc_discomll()

        for m in range(3):
            learner = Orange.classification.bayes.NaiveLearner(m=m)
            classifier = learner(train_data1)
            predictions1 = [classifier(inst, Orange.classification.Classifier.GetBoth) for inst in test_data1]
            predictions1_target = [v[0].value for v in predictions1]
            predictions1_probs = [v[1].values() for v in predictions1]

            fitmodel_url = naivebayes.fit(train_data2)
            predictions_url = naivebayes.predict(test_data2, fitmodel_url, m=m)
            predictions2_target = []
            predictions2_probs = []
            for k, v in result_iterator(predictions_url):
                predictions2_target.append(v[0])
                predictions2_probs.append(v[1])

            self.assertListEqual(predictions1_target, predictions2_target)
            self.assertTrue(np.allclose(predictions1_probs, predictions2_probs)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_naivebayes_breastcancer_cont(self):
        # python -m unittest tests_classification.Tests_Classification.test_naivebayes_breastcancer_cont
        from sklearn.naive_bayes import GaussianNB
        from discomll.classification import naivebayes

        x_train, y_train, x_test, y_test = datasets.breastcancer_cont(replication=1)
        train_data, test_data = datasets.breastcancer_cont_discomll(replication=1)

        clf = GaussianNB()
        probs_log1 = clf.fit(x_train, y_train).predict_proba(x_test)

        fitmodel_url = naivebayes.fit(train_data)
        prediction_url = naivebayes.predict(test_data, fitmodel_url)
        probs_log2 = [v[1] for _, v in result_iterator(prediction_url)]

        self.assertTrue(np.allclose(probs_log1, probs_log2, atol=1e-8)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_lwlr(self):
        # python -m unittest tests_regression.Tests_Regression.test_lwlr
        import locally_weighted_linear_regression as lwlr1
        from discomll.regression import locally_weighted_linear_regression as lwlr2

        x_train, y_train, x_test, y_test = datasets.regression_data()
        train_data, test_data = datasets.regression_data_discomll()

        lwlr1 = lwlr1.Locally_Weighted_Linear_Regression()
        taus = [1, 10, 25]
        sorted_indices = np.argsort([str(el) for el in x_test[:, 1].tolist()])

        for tau in taus:
            thetas1, estimation1 = lwlr1.fit(x_train, y_train, x_test, tau=tau)
            thetas1, estimation1 = np.array(thetas1)[sorted_indices], np.array(estimation1)[sorted_indices]

            results = lwlr2.fit_predict(train_data, test_data, tau=tau)
            thetas2, estimation2 = [], []

            for x_id, (est, thetas) in result_iterator(results):
                estimation2.append(est)
                thetas2.append(thetas)

            self.assertTrue(np.allclose(thetas1, thetas2, atol=1e-8))
            self.assertTrue(np.allclose(estimation1, estimation2, atol=1e-3)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_add_single_ground_truth_image_info(self):
    expected_num_gt_instances_per_class = np.array([3, 1, 2], dtype=int)
    expected_num_gt_imgs_per_class = np.array([2, 1, 2], dtype=int)
    self.assertTrue(np.array_equal(expected_num_gt_instances_per_class,
                                   self.od_eval.num_gt_instances_per_class))
    self.assertTrue(np.array_equal(expected_num_gt_imgs_per_class,
                                   self.od_eval.num_gt_imgs_per_class))
    groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
                                   [10, 10, 12, 12]], dtype=float)
    self.assertTrue(np.allclose(self.od_eval.groundtruth_boxes["img2"],
                                groundtruth_boxes2))
    groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
    self.assertTrue(np.allclose(
        self.od_eval.groundtruth_is_difficult_list["img2"],
        groundtruth_is_difficult_list2))
    groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
    self.assertTrue(np.array_equal(self.od_eval.groundtruth_class_labels[
        "img1"], groundtruth_class_labels1)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_add_single_detected_image_info(self):
    expected_scores_per_class = [[np.array([0.8, 0.7], dtype=float)], [],
                                 [np.array([0.9], dtype=float)]]
    expected_tp_fp_labels_per_class = [[np.array([0, 1], dtype=bool)], [],
                                       [np.array([0], dtype=bool)]]
    expected_num_images_correctly_detected_per_class = np.array([0, 0, 0],
                                                                dtype=int)
    for i in range(self.od_eval.num_class):
      for j in range(len(expected_scores_per_class[i])):
        self.assertTrue(np.allclose(expected_scores_per_class[i][j],
                                    self.od_eval.scores_per_class[i][j]))
        self.assertTrue(np.array_equal(expected_tp_fp_labels_per_class[i][
            j], self.od_eval.tp_fp_labels_per_class[i][j]))
    self.assertTrue(np.array_equal(
        expected_num_images_correctly_detected_per_class,
        self.od_eval.num_images_correctly_detected_per_class)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_combining_stat():
    for shape in [(), (3,), (3, 4)]:
        li = []
        rs1 = RunningStat(shape)
        rs2 = RunningStat(shape)
        rs = RunningStat(shape)
        for _ in range(5):
            val = np.random.randn(*shape)
            rs1.push(val)
            rs.push(val)
            li.append(val)
        for _ in range(9):
            rs2.push(val)
            rs.push(val)
            li.append(val)
        rs1.update(rs2)
        assert np.allclose(rs.mean, rs1.mean)
        assert np.allclose(rs.std, rs1.std) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_givens_inverse():
    r"""
    The Givens rotation in OpenFermion is defined as

    .. math::

        \begin{pmatrix}
            \cos(\theta) & -e^{i \varphi} \sin(\theta) \\
            \sin(\theta) &     e^{i \varphi} \cos(\theta)
        \end{pmatrix}.

    confirm numerically its hermitian conjugate is it's inverse
    """
    a = numpy.random.random() + 1j * numpy.random.random()
    b = numpy.random.random() + 1j * numpy.random.random()
    ab_rotation = givens_matrix_elements(a, b, which='right')

    assert numpy.allclose(ab_rotation.dot(numpy.conj(ab_rotation).T),
                          numpy.eye(2))
    assert numpy.allclose(numpy.conj(ab_rotation).T.dot(ab_rotation),
                          numpy.eye(2)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_circuit_generation_and_accuracy():
    for dim in range(2, 10):
        qubits = cirq.LineQubit.range(dim)
        u_generator = numpy.random.random(
            (dim, dim)) + 1j * numpy.random.random((dim, dim))
        u_generator = u_generator - numpy.conj(u_generator).T
        assert numpy.allclose(-1 * u_generator, numpy.conj(u_generator).T)

        unitary = scipy.linalg.expm(u_generator)
        circuit = cirq.Circuit()
        circuit.append(optimal_givens_decomposition(qubits, unitary))

        fermion_generator = QubitOperator(()) * 0.0
        for i, j in product(range(dim), repeat=2):
            fermion_generator += jordan_wigner(
                FermionOperator(((i, 1), (j, 0)), u_generator[i, j]))

        true_unitary = scipy.linalg.expm(
            get_sparse_operator(fermion_generator).toarray())
        assert numpy.allclose(true_unitary.conj().T.dot(true_unitary),
                              numpy.eye(2 ** dim, dtype=complex))

        test_unitary = cirq.unitary(circuit)
        assert numpy.isclose(
            abs(numpy.trace(true_unitary.conj().T.dot(test_unitary))), 2 ** dim) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def _eigen_components(self):
        components = [(0, np.diag([1, 1, 1, 0, 1, 0, 0, 1]))]
        nontrivial_part = np.zeros((3, 3), dtype=np.complex128)
        for ij, w in zip([(1, 2), (0, 2), (0, 1)], self.weights):
            nontrivial_part[ij] = w
            nontrivial_part[ij[::-1]] = w.conjugate()
        assert np.allclose(nontrivial_part, nontrivial_part.conj().T)
        eig_vals, eig_vecs = np.linalg.eigh(nontrivial_part)
        for eig_val, eig_vec in zip(eig_vals, eig_vecs.T):
            exp_factor = -eig_val / np.pi
            proj = np.zeros((8, 8), dtype=np.complex128)
            nontrivial_indices = np.array([3, 5, 6], dtype=np.intp)
            proj[nontrivial_indices[:, np.newaxis], nontrivial_indices] = (
                np.outer(eig_vec.conjugate(), eig_vec))
            components.append((exp_factor, proj))
        return components 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def assert_permute_consistent(gate):
    gate = gate.__copy__()
    n_qubits = gate.num_qubits()
    qubits = cirq.LineQubit.range(n_qubits)
    for pos in itertools.permutations(range(n_qubits)):
        permuted_gate = gate.__copy__()
        gate.permute(pos)
        assert permuted_gate.permuted(pos) == gate
        actual_unitary = cirq.unitary(permuted_gate)

        ops = [
            cca.LinearPermutationGate(n_qubits, dict(zip(range(n_qubits), pos)),
                                      ofc.FSWAP)(*qubits),
            gate(*qubits),
            cca.LinearPermutationGate(n_qubits, dict(zip(pos, range(n_qubits))),
                                      ofc.FSWAP)(*qubits)
        ]
        circuit = cirq.Circuit(ops)
        expected_unitary = cirq.unitary(circuit)
        assert np.allclose(actual_unitary, expected_unitary)

    with pytest.raises(ValueError):
        gate.permute(range(1, n_qubits))
    with pytest.raises(ValueError):
        gate.permute([1] * n_qubits) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def assert_interaction_operator_consistent(gate):
    interaction_op = gate.interaction_operator_generator()
    other_gate = gate.from_interaction_operator(operator=interaction_op)
    if other_gate is None:
        assert np.allclose(gate.weights, 0)
    else:
        assert cirq.approx_eq(gate, other_gate)
    interaction_op = openfermion.normal_ordered(interaction_op)
    other_interaction_op = openfermion.InteractionOperator.zero(
        interaction_op.n_qubits)
    super(type(gate),
          gate).interaction_operator_generator(operator=other_interaction_op)
    other_interaction_op = openfermion.normal_ordered(interaction_op)
    assert interaction_op == other_interaction_op

    other_interaction_op = super(type(gate),
                                 gate).interaction_operator_generator()
    other_interaction_op = openfermion.normal_ordered(interaction_op)
    assert interaction_op == other_interaction_op 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_quadratic_fermionic_simulation_gate_unitary(weights, exponent):
    generator = np.zeros((4, 4), dtype=np.complex128)
    # w0 |10><01| + h.c.
    generator[2, 1] = weights[0]
    generator[1, 2] = weights[0].conjugate()
    # w1 |11><11|
    generator[3, 3] = weights[1]
    expected_unitary = la.expm(-1j * exponent * generator)

    gate = ofc.QuadraticFermionicSimulationGate(weights, exponent=exponent)
    actual_unitary = cirq.unitary(gate)

    assert np.allclose(expected_unitary, actual_unitary)

    symbolic_gate = (ofc.QuadraticFermionicSimulationGate(
        (sympy.Symbol('w0'), sympy.Symbol('w1')), exponent=sympy.Symbol('t')))
    qubits = cirq.LineQubit.range(2)
    circuit = cirq.Circuit(symbolic_gate._decompose_(qubits))
    resolver = {'w0': weights[0], 'w1': weights[1], 't': exponent}
    resolved_circuit = cirq.resolve_parameters(circuit, resolver)
    decomp_unitary = resolved_circuit.unitary(qubit_order=qubits)

    assert np.allclose(expected_unitary, decomp_unitary) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import allclose [as 别名]
def test_cubic_fermionic_simulation_gate_consistency_docstring(
        weights, exponent):
    generator = np.zeros((8, 8), dtype=np.complex128)
    # w0 |110><101| + h.c.
    generator[6, 5] = weights[0]
    generator[5, 6] = weights[0].conjugate()
    # w1 |110><011| + h.c.
    generator[6, 3] = weights[1]
    generator[3, 6] = weights[1].conjugate()
    # w2 |101><011| + h.c.
    generator[5, 3] = weights[2]
    generator[3, 5] = weights[2].conjugate()
    expected_unitary = la.expm(-1j * exponent * generator)

    gate = ofc.CubicFermionicSimulationGate(weights, exponent=exponent)
    actual_unitary = cirq.unitary(gate)

    assert np.allclose(expected_unitary, actual_unitary)