Python numpy.long() Examples

The following are 30 code examples of numpy.long(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: test_model_wrappers.py    From ngraph-python with Apache License 2.0 6 votes vote down vote up
def test_attribute_wrapper():
    def attribute_value_test(attribute_value):
        node = make_node('Abs', ['X'], [], name='test_node', test_attribute=attribute_value)
        model = make_model(make_graph([node], 'test_graph', [
            make_tensor_value_info('X', onnx.TensorProto.FLOAT, [1, 2]),
        ], []), producer_name='ngraph')
        wrapped_attribute = ModelWrapper(model).graph.node[0].get_attribute('test_attribute')
        return wrapped_attribute.get_value()

    tensor = make_tensor('test_tensor', onnx.TensorProto.FLOAT, [1], [1])

    assert attribute_value_test(1) == 1
    assert type(attribute_value_test(1)) == np.long
    assert attribute_value_test(1.0) == 1.0
    assert type(attribute_value_test(1.0)) == np.float
    assert attribute_value_test('test') == 'test'
    assert attribute_value_test(tensor)._proto == tensor

    assert attribute_value_test([1, 2, 3]) == [1, 2, 3]
    assert attribute_value_test([1.0, 2.0, 3.0]) == [1.0, 2.0, 3.0]
    assert attribute_value_test(['test1', 'test2']) == ['test1', 'test2']
    assert attribute_value_test([tensor, tensor])[1]._proto == tensor 
Example #2
Source File: test_random.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_equal(sample.dtype, np.dtype(dt))

        for dt in (bool, int, np.long):
            lbnd = 0 if dt is bool else np.iinfo(dt).min
            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_(not hasattr(sample, 'dtype'))
            assert_equal(type(sample), dt) 
Example #3
Source File: test_random.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_equal(sample.dtype, np.dtype(dt))

        for dt in (bool, int, np.long):
            lbnd = 0 if dt is bool else np.iinfo(dt).min
            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_(not hasattr(sample, 'dtype'))
            assert_equal(type(sample), dt) 
Example #4
Source File: makePathFromOutline.py    From fontgoggles with Apache License 2.0 6 votes vote down vote up
def makePathFromArrays(points, tags, contours):
    n_contours = len(contours)
    n_points = len(tags)
    assert len(points) >= n_points
    assert points.shape[1:] == (2,)
    if points.dtype != numpy.long:
        points = numpy.floor(points + [0.5, 0.5])
        points = points.astype(numpy.long)
    assert tags.dtype == numpy.byte
    assert contours.dtype == numpy.short
    path = objc.objc_object(
        c_void_p=_makePathFromArrays(
            n_contours,
            n_points,
            points.ctypes.data_as(FT_Vector_p),
            tags.ctypes.data_as(c_char_p),
            contours.ctypes.data_as(c_short_p)))
    # See comment in makePathFromOutline()
    path.release()
    return path 
Example #5
Source File: MNIST_gold_only.py    From glc with Apache License 2.0 6 votes vote down vote up
def prepare_data(corruption_matrix, gold_fraction=0.5, merge_valset=True):
    np.random.seed(1)

    mnist_images = np.copy(mnist.train.images)
    mnist_labels = np.copy(mnist.train.labels)
    if merge_valset:
        mnist_images = np.concatenate([mnist_images, np.copy(mnist.validation.images)], axis=0)
        mnist_labels = np.concatenate([mnist_labels, np.copy(mnist.validation.labels)])

    indices = np.arange(len(mnist_labels))
    np.random.shuffle(indices)

    mnist_images = mnist_images[indices]
    mnist_labels = mnist_labels[indices].astype(np.long)

    num_gold = int(len(mnist_labels)*gold_fraction)
    num_silver = len(mnist_labels) - num_gold

    for i in range(num_silver):
        mnist_labels[i] = np.random.choice(num_classes, p=corruption_matrix[mnist_labels[i]])

    dataset = {'x': mnist_images, 'y': mnist_labels}
    gold = {'x': dataset['x'][num_silver:], 'y': dataset['y'][num_silver:]}

    return dataset, gold, num_gold, num_silver 
Example #6
Source File: MNIST_experiments_pytorch.py    From glc with Apache License 2.0 6 votes vote down vote up
def prepare_data(corruption_matrix, gold_fraction=0.5, merge_valset=True):
    np.random.seed(1)

    mnist_images = np.copy(mnist.train.images)
    mnist_labels = np.copy(mnist.train.labels)
    if merge_valset:
        mnist_images = np.concatenate([mnist_images, np.copy(mnist.validation.images)], axis=0)
        mnist_labels = np.concatenate([mnist_labels, np.copy(mnist.validation.labels)])

    indices = np.arange(len(mnist_labels))
    np.random.shuffle(indices)

    mnist_images = mnist_images[indices]
    mnist_labels = mnist_labels[indices].astype(np.long)

    num_gold = int(len(mnist_labels)*gold_fraction)
    num_silver = len(mnist_labels) - num_gold

    for i in range(num_silver):
        mnist_labels[i] = np.random.choice(num_classes, p=corruption_matrix[mnist_labels[i]])

    dataset = {'x': mnist_images, 'y': mnist_labels}
    gold = {'x': dataset['x'][num_silver:], 'y': dataset['y'][num_silver:]}

    return dataset, gold, num_gold, num_silver 
Example #7
Source File: test_random.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_equal(sample.dtype, np.dtype(dt))

        for dt in (bool, int, np.long):
            lbnd = 0 if dt is bool else np.iinfo(dt).min
            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_(not hasattr(sample, 'dtype'))
            assert_equal(type(sample), dt) 
Example #8
Source File: Twitter_gold_only.py    From glc with Apache License 2.0 6 votes vote down vote up
def prepare_data(corruption_matrix, gold_fraction=0.5, merge_valset=True):
    np.random.seed(1)

    twitter_tweets = np.copy(X_train)
    twitter_labels = np.copy(Y_train)
    if merge_valset:
        twitter_tweets = np.concatenate([twitter_tweets, np.copy(X_dev)], axis=0)
        twitter_labels = np.concatenate([twitter_labels, np.copy(Y_dev)])

    indices = np.arange(len(twitter_labels))
    np.random.shuffle(indices)

    twitter_tweets = twitter_tweets[indices]
    twitter_labels = twitter_labels[indices].astype(np.long)

    num_gold = int(len(twitter_labels)*gold_fraction)
    num_silver = len(twitter_labels) - num_gold

    for i in range(num_silver):
        twitter_labels[i] = np.random.choice(num_classes, p=corruption_matrix[twitter_labels[i]])

    dataset = {'x': twitter_tweets, 'y': twitter_labels}
    gold = {'x': dataset['x'][num_silver:], 'y': dataset['y'][num_silver:]}

    return dataset, gold, num_gold, num_silver 
Example #9
Source File: Twitter_experiments_pytorch.py    From glc with Apache License 2.0 6 votes vote down vote up
def prepare_data(corruption_matrix, gold_fraction=0.5, merge_valset=True):
    np.random.seed(1)

    twitter_tweets = np.copy(X_train)
    twitter_labels = np.copy(Y_train)
    if merge_valset:
        twitter_tweets = np.concatenate([twitter_tweets, np.copy(X_dev)], axis=0)
        twitter_labels = np.concatenate([twitter_labels, np.copy(Y_dev)])

    indices = np.arange(len(twitter_labels))
    np.random.shuffle(indices)

    twitter_tweets = twitter_tweets[indices]
    twitter_labels = twitter_labels[indices].astype(np.long)

    num_gold = int(len(twitter_labels)*gold_fraction)
    num_silver = len(twitter_labels) - num_gold

    for i in range(num_silver):
        twitter_labels[i] = np.random.choice(num_classes, p=corruption_matrix[twitter_labels[i]])

    dataset = {'x': twitter_tweets, 'y': twitter_labels}
    gold = {'x': dataset['x'][num_silver:], 'y': dataset['y'][num_silver:]}

    return dataset, gold, num_gold, num_silver 
Example #10
Source File: data_helper.py    From HGNN with MIT License 6 votes vote down vote up
def load_ft(data_dir, feature_name='GVCNN'):
    data = scio.loadmat(data_dir)
    lbls = data['Y'].astype(np.long)
    if lbls.min() == 1:
        lbls = lbls - 1
    idx = data['indices'].item()

    if feature_name == 'MVCNN':
        fts = data['X'][0].item().astype(np.float32)
    elif feature_name == 'GVCNN':
        fts = data['X'][1].item().astype(np.float32)
    else:
        print(f'wrong feature name{feature_name}!')
        raise IOError

    idx_train = np.where(idx == 1)[0]
    idx_test = np.where(idx == 0)[0]
    return fts, lbls, idx_train, idx_test 
Example #11
Source File: sudoku_solver.py    From dgl with Apache License 2.0 6 votes vote down vote up
def solve_sudoku(puzzle):
    """
    Solve sudoku puzzle using RRN.
    :param puzzle: an array-like data with shape [9, 9], blank positions are filled with 0
    :return: a [9, 9] shaped numpy array
    """
    puzzle = np.array(puzzle, dtype=np.long).reshape([-1])
    model_path = 'ckpt'
    if not os.path.exists(model_path):
        os.mkdir(model_path)

    model_filename = os.path.join(model_path, 'rrn-sudoku.pkl')
    if not os.path.exists(model_filename):
        print('Downloading model...')
        url = 'https://data.dgl.ai/models/rrn-sudoku.pkl'
        urllib.request.urlretrieve(url, model_filename)

    model = torch.load(model_filename, map_location='cpu')

    g = _basic_sudoku_graph()
    sudoku_indices = np.arange(0, 81)
    rows = sudoku_indices // 9
    cols = sudoku_indices % 9

    g.ndata['row'] = torch.tensor(rows, dtype=torch.long)
    g.ndata['col'] = torch.tensor(cols, dtype=torch.long)
    g.ndata['q'] = torch.tensor(puzzle, dtype=torch.long)
    g.ndata['a'] = torch.tensor(puzzle, dtype=torch.long)

    pred, _ = model(g, False)
    pred = pred.cpu().data.numpy().reshape([9, 9])
    return pred 
Example #12
Source File: fixes.py    From skutil with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def _is_integer(x):
    """Determine whether some object ``x`` is an
    integer type (int, long, etc). This is part of the 
    ``fixes`` module, since Python 3 removes the long
    datatype, we have to check the version major.

    Parameters
    ----------

    x : object
        The item to assess whether is an integer.


    Returns
    -------

    bool
        True if ``x`` is an integer type
    """
    return (not isinstance(x, (bool, np.bool))) and \
        isinstance(x, (numbers.Integral, int, np.int, np.long, long))  # no long type in python 3 
Example #13
Source File: test_random.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            self.assertEqual(sample.dtype, np.dtype(dt))

        for dt in (np.bool, np.int, np.long):
            lbnd = 0 if dt is np.bool else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            self.assertFalse(hasattr(sample, 'dtype'))
            self.assertEqual(type(sample), dt) 
Example #14
Source File: test_random.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_equal(sample.dtype, np.dtype(dt))

        for dt in (bool, int, np.long):
            lbnd = 0 if dt is bool else np.iinfo(dt).min
            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_(not hasattr(sample, 'dtype'))
            assert_equal(type(sample), dt) 
Example #15
Source File: test_random.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def test_respect_dtype_singleton(self):
        # See gh-7203
        for dt in self.itype:
            lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
            ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1

            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_equal(sample.dtype, np.dtype(dt))

        for dt in (bool, int, np.long):
            lbnd = 0 if dt is bool else np.iinfo(dt).min
            ubnd = 2 if dt is bool else np.iinfo(dt).max + 1

            # gh-7284: Ensure that we get Python data types
            sample = self.rfunc(lbnd, ubnd, dtype=dt)
            assert_(not hasattr(sample, 'dtype'))
            assert_equal(type(sample), dt) 
Example #16
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_array_side_effect(self):
        # The second use of itemsize was throwing an exception because in
        # ctors.c, discover_itemsize was calling PyObject_Length without
        # checking the return code.  This failed to get the length of the
        # number 2, and the exception hung around until something checked
        # PyErr_Occurred() and returned an error.
        assert_equal(np.dtype('S10').itemsize, 10)
        np.array([['abc', 2], ['long   ', '0123456789']], dtype=np.string_)
        assert_equal(np.dtype('S10').itemsize, 10) 
Example #17
Source File: stata.py    From Computable with MIT License 5 votes vote down vote up
def __init__(self, offset, value):
        self._value = value
        if type(value) is int or type(value) is long:
            self._str = value - offset is 1 and \
                '.' or ('.' + chr(value - offset + 96))
        else:
            self._str = '.' 
Example #18
Source File: test_regression.py    From Computable with MIT License 5 votes vote down vote up
def test_object_array_self_reference(self):
        # Object arrays with references to themselves can cause problems
        a = np.array(0, dtype=object)
        a[()] = a
        assert_raises(TypeError, int, a)
        assert_raises(TypeError, long, a)
        assert_raises(TypeError, float, a)
        assert_raises(TypeError, oct, a)
        assert_raises(TypeError, hex, a)

        # This was causing a to become like the above
        a = np.array(0, dtype=object)
        a[...] += 1
        assert_equal(a, 1) 
Example #19
Source File: test_regression.py    From Computable with MIT License 5 votes vote down vote up
def test_array_side_effect(self):
        assert_equal(np.dtype('S10').itemsize, 10)

        A = np.array([['abc', 2], ['long   ', '0123456789']], dtype=np.string_)

        # This was throwing an exception because in ctors.c,
        # discover_itemsize was calling PyObject_Length without checking
        # the return code.  This failed to get the length of the number 2,
        # and the exception hung around until something checked
        # PyErr_Occurred() and returned an error.
        assert_equal(np.dtype('S10').itemsize, 10) 
Example #20
Source File: test_ufunc.py    From Computable with MIT License 5 votes vote down vote up
def test_matrix_multiply(self):
        self.compare_matrix_multiply_results(np.long)
        self.compare_matrix_multiply_results(np.double) 
Example #21
Source File: datasets.py    From kaggle-google-quest with MIT License 5 votes vote down vote up
def __init__(self, x_features, question_ids, answer_ids, seg_question_ids, 
                 seg_answer_ids, idxs, targets=None):
        self.question_ids = question_ids[idxs].astype(np.long)
        self.answer_ids = answer_ids[idxs].astype(np.long)
        self.seg_question_ids = seg_question_ids[idxs].astype(np.long)
        self.seg_answer_ids = seg_answer_ids[idxs].astype(np.long)
        self.x_features = x_features[idxs].astype(np.float32)
        if targets is not None: self.targets = targets[idxs].astype(np.float32)
        else: self.targets = np.zeros((self.x_features.shape[0], N_TARGETS), dtype=np.float32) 
Example #22
Source File: test_regression.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_array_side_effect(self):
        # The second use of itemsize was throwing an exception because in
        # ctors.c, discover_itemsize was calling PyObject_Length without
        # checking the return code.  This failed to get the length of the
        # number 2, and the exception hung around until something checked
        # PyErr_Occurred() and returned an error.
        assert_equal(np.dtype('S10').itemsize, 10)
        np.array([['abc', 2], ['long   ', '0123456789']], dtype=np.string_)
        assert_equal(np.dtype('S10').itemsize, 10) 
Example #23
Source File: test_regression.py    From Computable with MIT License 5 votes vote down vote up
def test_signed_integer_division_overflow(self):
        """Ticket #1317."""
        def test_type(t):
            min = np.array([np.iinfo(t).min])
            min //= -1

        with np.errstate(divide="ignore"):
            for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
                test_type(t) 
Example #24
Source File: test_regression.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_signed_integer_division_overflow(self):
        # Ticket #1317.
        def test_type(t):
            min = np.array([np.iinfo(t).min])
            min //= -1

        with np.errstate(divide="ignore"):
            for t in (np.int8, np.int16, np.int32, np.int64, int, np.long):
                test_type(t) 
Example #25
Source File: generator.py    From crnn.pytorch with Apache License 2.0 5 votes vote down vote up
def __getitem__(self, item):
        image, indices, target_len = self.gen_image()
        if self.direction == 'horizontal':
            image = np.transpose(image[:, :, np.newaxis], axes=(2, 1, 0))  # [H,W,C]=>[C,W,H]
        else:
            image = np.transpose(image[:, :, np.newaxis], axes=(2, 0, 1))  # [H,W,C]=>[C,H,W]
        # 标准化
        image = image.astype(np.float32) / 255.
        image -= 0.5
        image /= 0.5

        target = np.zeros(shape=(self.max_len,), dtype=np.long)
        target[:target_len] = indices
        input_len = self.im_w // 4 - 3
        return image, target, input_len, target_len 
Example #26
Source File: test_ufunc.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up
def test_matrix_multiply(self):
        self.compare_matrix_multiply_results(np.long)
        self.compare_matrix_multiply_results(np.double) 
Example #27
Source File: test_random.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_random_integers_max_int(self):
        # Tests whether random_integers can generate the
        # maximum allowed Python int that can be converted
        # into a C long. Previous implementations of this
        # method have thrown an OverflowError when attempting
        # to generate this integer.
        with suppress_warnings() as sup:
            w = sup.record(DeprecationWarning)
            actual = np.random.random_integers(np.iinfo('l').max,
                                               np.iinfo('l').max)
            assert_(len(w) == 1)

        desired = np.iinfo('l').max
        assert_equal(actual, desired) 
Example #28
Source File: test_ufunc.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_matrix_multiply(self):
        self.compare_matrix_multiply_results(np.long)
        self.compare_matrix_multiply_results(np.double) 
Example #29
Source File: test_regression.py    From Mastering-Elasticsearch-7.0 with MIT License 5 votes vote down vote up
def test_signed_integer_division_overflow(self):
        # Ticket #1317.
        def test_type(t):
            min = np.array([np.iinfo(t).min])
            min //= -1

        with np.errstate(divide="ignore"):
            for t in (np.int8, np.int16, np.int32, np.int64, int, np.long):
                test_type(t) 
Example #30
Source File: stata.py    From Computable with MIT License 5 votes vote down vote up
def __init__(self, fname, data, convert_dates=None, write_index=True,
                 encoding="latin-1", byteorder=None):
        super(StataWriter, self).__init__(encoding)
        self._convert_dates = convert_dates
        self._write_index = write_index
        # attach nobs, nvars, data, varlist, typlist
        self._prepare_pandas(data)

        if byteorder is None:
            byteorder = sys.byteorder
        self._byteorder = _set_endianness(byteorder)
        self._file = _open_file_binary_write(
            fname, self._encoding or self._default_encoding
        )
        self.type_converters = {253: np.long, 252: int}