Python numpy.long方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
def __init__(self, dataset_path, sep=',', engine='c', header='infer'):
        data = pd.read_csv(dataset_path, sep=sep, engine=engine, header=header).to_numpy()[:, :3]
        self.items = data[:, :2].astype(np.int) - 1  # -1 because ID begins from 1
        self.targets = self.__preprocess_target(data[:, 2]).astype(np.float32)
        self.field_dims = np.max(self.items, axis=0) + 1
        self.user_field_idx = np.array((0, ), dtype=np.long)
        self.item_field_idx = np.array((1,), dtype=np.long) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
def __getitem__(self, index):
        with self.env.begin(write=False) as txn:
            np_array = np.frombuffer(
                txn.get(struct.pack('>I', index)), dtype=np.uint32).astype(dtype=np.long)
        return np_array[1:], np_array[0] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
def __init__(self, field_dims, output_dim=1):
        super().__init__()
        self.fc = torch.nn.Embedding(sum(field_dims), output_dim)
        self.bias = torch.nn.Parameter(torch.zeros((output_dim,)))
        self.offsets = np.array((0, *np.cumsum(field_dims)[:-1]), dtype=np.long) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
def __call__(self, img, msk, cat, iscrowd):
        # Random flip
        if self.random_flip:
            img, msk = self._random_flip(img, msk)

        # Adjust scale, possibly at random
        if self.random_scale is not None:
            target_size = self._random_target_size()
        else:
            target_size = self.shortest_size
        scale = self._adjusted_scale(img.size[0], img.size[1], target_size)

        out_size = tuple(int(dim * scale) for dim in img.size)
        img = img.resize(out_size, resample=Image.BILINEAR)
        msk = [m.resize(out_size, resample=Image.NEAREST) for m in msk]

        # Wrap in np.array
        cat = np.array(cat, dtype=np.int32)
        iscrowd = np.array(iscrowd, dtype=np.uint8)

        # Image transformations
        img = tfn.to_tensor(img)
        img = self._normalize_image(img)

        # Label transformations
        msk = np.stack([np.array(m, dtype=np.int32, copy=False) for m in msk], axis=0)
        msk, cat, iscrowd = self._compact_labels(msk, cat, iscrowd)

        # Convert labels to torch and extract bounding boxes
        msk = torch.from_numpy(msk.astype(np.long))
        cat = torch.from_numpy(cat.astype(np.long))
        iscrowd = torch.from_numpy(iscrowd)
        bbx = extract_boxes(msk, cat.numel())

        return dict(img=img, msk=msk, cat=cat, iscrowd=iscrowd, bbx=bbx) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
def test_matrix_multiply(self):
        self.compare_matrix_multiply_results(np.long)
        self.compare_matrix_multiply_results(np.double) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import long [as 別名]
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)