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How to use arrays method in hypothesis

Best Python code snippet using hypothesis

shape_base.py

Source:shape_base.py

1from __future__ import division, absolute_import, print_function2__all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'block', 'hstack',3           'stack', 'vstack']4from . import numeric as _nx5from .numeric import array, asanyarray, newaxis6from .multiarray import normalize_axis_index7def atleast_1d(*arys):8    """9    Convert inputs to arrays with at least one dimension.10    Scalar inputs are converted to 1-dimensional arrays, whilst11    higher-dimensional inputs are preserved.12    Parameters13    ----------14    arys1, arys2, ... : array_like15        One or more input arrays.16    Returns17    -------18    ret : ndarray19        An array, or list of arrays, each with ``a.ndim >= 1``.20        Copies are made only if necessary.21    See Also22    --------23    atleast_2d, atleast_3d24    Examples25    --------26    >>> np.atleast_1d(1.0)27    array([ 1.])28    >>> x = np.arange(9.0).reshape(3,3)29    >>> np.atleast_1d(x)30    array([[ 0.,  1.,  2.],31           [ 3.,  4.,  5.],32           [ 6.,  7.,  8.]])33    >>> np.atleast_1d(x) is x34    True35    >>> np.atleast_1d(1, [3, 4])36    [array([1]), array([3, 4])]37    """38    res = []39    for ary in arys:40        ary = asanyarray(ary)41        if ary.ndim == 0:42            result = ary.reshape(1)43        else:44            result = ary45        res.append(result)46    if len(res) == 1:47        return res[0]48    else:49        return res50def atleast_2d(*arys):51    """52    View inputs as arrays with at least two dimensions.53    Parameters54    ----------55    arys1, arys2, ... : array_like56        One or more array-like sequences.  Non-array inputs are converted57        to arrays.  Arrays that already have two or more dimensions are58        preserved.59    Returns60    -------61    res, res2, ... : ndarray62        An array, or list of arrays, each with ``a.ndim >= 2``.63        Copies are avoided where possible, and views with two or more64        dimensions are returned.65    See Also66    --------67    atleast_1d, atleast_3d68    Examples69    --------70    >>> np.atleast_2d(3.0)71    array([[ 3.]])72    >>> x = np.arange(3.0)73    >>> np.atleast_2d(x)74    array([[ 0.,  1.,  2.]])75    >>> np.atleast_2d(x).base is x76    True77    >>> np.atleast_2d(1, [1, 2], [[1, 2]])78    [array([[1]]), array([[1, 2]]), array([[1, 2]])]79    """80    res = []81    for ary in arys:82        ary = asanyarray(ary)83        if ary.ndim == 0:84            result = ary.reshape(1, 1)85        elif ary.ndim == 1:86            result = ary[newaxis,:]87        else:88            result = ary89        res.append(result)90    if len(res) == 1:91        return res[0]92    else:93        return res94def atleast_3d(*arys):95    """96    View inputs as arrays with at least three dimensions.97    Parameters98    ----------99    arys1, arys2, ... : array_like100        One or more array-like sequences.  Non-array inputs are converted to101        arrays.  Arrays that already have three or more dimensions are102        preserved.103    Returns104    -------105    res1, res2, ... : ndarray106        An array, or list of arrays, each with ``a.ndim >= 3``.  Copies are107        avoided where possible, and views with three or more dimensions are108        returned.  For example, a 1-D array of shape ``(N,)`` becomes a view109        of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a110        view of shape ``(M, N, 1)``.111    See Also112    --------113    atleast_1d, atleast_2d114    Examples115    --------116    >>> np.atleast_3d(3.0)117    array([[[ 3.]]])118    >>> x = np.arange(3.0)119    >>> np.atleast_3d(x).shape120    (1, 3, 1)121    >>> x = np.arange(12.0).reshape(4,3)122    >>> np.atleast_3d(x).shape123    (4, 3, 1)124    >>> np.atleast_3d(x).base is x.base  # x is a reshape, so not base itself125    True126    >>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):127    ...     print(arr, arr.shape)128    ...129    [[[1]130      [2]]] (1, 2, 1)131    [[[1]132      [2]]] (1, 2, 1)133    [[[1 2]]] (1, 1, 2)134    """135    res = []136    for ary in arys:137        ary = asanyarray(ary)138        if ary.ndim == 0:139            result = ary.reshape(1, 1, 1)140        elif ary.ndim == 1:141            result = ary[newaxis,:, newaxis]142        elif ary.ndim == 2:143            result = ary[:,:, newaxis]144        else:145            result = ary146        res.append(result)147    if len(res) == 1:148        return res[0]149    else:150        return res151def vstack(tup):152    """153    Stack arrays in sequence vertically (row wise).154    This is equivalent to concatenation along the first axis after 1-D arrays155    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by156    `vsplit`.157    This function makes most sense for arrays with up to 3 dimensions. For158    instance, for pixel-data with a height (first axis), width (second axis),159    and r/g/b channels (third axis). The functions `concatenate`, `stack` and160    `block` provide more general stacking and concatenation operations.161    Parameters162    ----------163    tup : sequence of ndarrays164        The arrays must have the same shape along all but the first axis.165        1-D arrays must have the same length.166    Returns167    -------168    stacked : ndarray169        The array formed by stacking the given arrays, will be at least 2-D.170    See Also171    --------172    stack : Join a sequence of arrays along a new axis.173    hstack : Stack arrays in sequence horizontally (column wise).174    dstack : Stack arrays in sequence depth wise (along third dimension).175    concatenate : Join a sequence of arrays along an existing axis.176    vsplit : Split array into a list of multiple sub-arrays vertically.177    block : Assemble arrays from blocks.178    Examples179    --------180    >>> a = np.array([1, 2, 3])181    >>> b = np.array([2, 3, 4])182    >>> np.vstack((a,b))183    array([[1, 2, 3],184           [2, 3, 4]])185    >>> a = np.array([[1], [2], [3]])186    >>> b = np.array([[2], [3], [4]])187    >>> np.vstack((a,b))188    array([[1],189           [2],190           [3],191           [2],192           [3],193           [4]])194    """195    return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)196def hstack(tup):197    """198    Stack arrays in sequence horizontally (column wise).199    This is equivalent to concatenation along the second axis, except for 1-D200    arrays where it concatenates along the first axis. Rebuilds arrays divided201    by `hsplit`.202    This function makes most sense for arrays with up to 3 dimensions. For203    instance, for pixel-data with a height (first axis), width (second axis),204    and r/g/b channels (third axis). The functions `concatenate`, `stack` and205    `block` provide more general stacking and concatenation operations.206    Parameters207    ----------208    tup : sequence of ndarrays209        The arrays must have the same shape along all but the second axis,210        except 1-D arrays which can be any length.211    Returns212    -------213    stacked : ndarray214        The array formed by stacking the given arrays.215    See Also216    --------217    stack : Join a sequence of arrays along a new axis.218    vstack : Stack arrays in sequence vertically (row wise).219    dstack : Stack arrays in sequence depth wise (along third axis).220    concatenate : Join a sequence of arrays along an existing axis.221    hsplit : Split array along second axis.222    block : Assemble arrays from blocks.223    Examples224    --------225    >>> a = np.array((1,2,3))226    >>> b = np.array((2,3,4))227    >>> np.hstack((a,b))228    array([1, 2, 3, 2, 3, 4])229    >>> a = np.array([[1],[2],[3]])230    >>> b = np.array([[2],[3],[4]])231    >>> np.hstack((a,b))232    array([[1, 2],233           [2, 3],234           [3, 4]])235    """236    arrs = [atleast_1d(_m) for _m in tup]237    # As a special case, dimension 0 of 1-dimensional arrays is "horizontal"238    if arrs and arrs[0].ndim == 1:239        return _nx.concatenate(arrs, 0)240    else:241        return _nx.concatenate(arrs, 1)242def stack(arrays, axis=0, out=None):243    """244    Join a sequence of arrays along a new axis.245    The `axis` parameter specifies the index of the new axis in the dimensions246    of the result. For example, if ``axis=0`` it will be the first dimension247    and if ``axis=-1`` it will be the last dimension.248    .. versionadded:: 1.10.0249    Parameters250    ----------251    arrays : sequence of array_like252        Each array must have the same shape.253    axis : int, optional254        The axis in the result array along which the input arrays are stacked.255    out : ndarray, optional256        If provided, the destination to place the result. The shape must be257        correct, matching that of what stack would have returned if no258        out argument were specified.259    Returns260    -------261    stacked : ndarray262        The stacked array has one more dimension than the input arrays.263    See Also264    --------265    concatenate : Join a sequence of arrays along an existing axis.266    split : Split array into a list of multiple sub-arrays of equal size.267    block : Assemble arrays from blocks.268    Examples269    --------270    >>> arrays = [np.random.randn(3, 4) for _ in range(10)]271    >>> np.stack(arrays, axis=0).shape272    (10, 3, 4)273    >>> np.stack(arrays, axis=1).shape274    (3, 10, 4)275    >>> np.stack(arrays, axis=2).shape276    (3, 4, 10)277    >>> a = np.array([1, 2, 3])278    >>> b = np.array([2, 3, 4])279    >>> np.stack((a, b))280    array([[1, 2, 3],281           [2, 3, 4]])282    >>> np.stack((a, b), axis=-1)283    array([[1, 2],284           [2, 3],285           [3, 4]])286    """287    arrays = [asanyarray(arr) for arr in arrays]288    if not arrays:289        raise ValueError('need at least one array to stack')290    shapes = set(arr.shape for arr in arrays)291    if len(shapes) != 1:292        raise ValueError('all input arrays must have the same shape')293    result_ndim = arrays[0].ndim + 1294    axis = normalize_axis_index(axis, result_ndim)295    sl = (slice(None),) * axis + (_nx.newaxis,)296    expanded_arrays = [arr[sl] for arr in arrays]297    return _nx.concatenate(expanded_arrays, axis=axis, out=out)298def _block_check_depths_match(arrays, parent_index=[]):299    """300    Recursive function checking that the depths of nested lists in `arrays`301    all match. Mismatch raises a ValueError as described in the block302    docstring below.303    The entire index (rather than just the depth) needs to be calculated304    for each innermost list, in case an error needs to be raised, so that305    the index of the offending list can be printed as part of the error.306    The parameter `parent_index` is the full index of `arrays` within the307    nested lists passed to _block_check_depths_match at the top of the308    recursion.309    The return value is a pair. The first item returned is the full index310    of an element (specifically the first element) from the bottom of the311    nesting in `arrays`. An empty list at the bottom of the nesting is312    represented by a `None` index.313    The second item is the maximum of the ndims of the arrays nested in314    `arrays`.315    """316    def format_index(index):317        idx_str = ''.join('[{}]'.format(i) for i in index if i is not None)318        return 'arrays' + idx_str319    if type(arrays) is tuple:320        # not strictly necessary, but saves us from:321        #  - more than one way to do things - no point treating tuples like322        #    lists323        #  - horribly confusing behaviour that results when tuples are324        #    treated like ndarray325        raise TypeError(326            '{} is a tuple. '327            'Only lists can be used to arrange blocks, and np.block does '328            'not allow implicit conversion from tuple to ndarray.'.format(329                format_index(parent_index)330            )331        )332    elif type(arrays) is list and len(arrays) > 0:333        idxs_ndims = (_block_check_depths_match(arr, parent_index + [i])334                      for i, arr in enumerate(arrays))335        first_index, max_arr_ndim = next(idxs_ndims)336        for index, ndim in idxs_ndims:337            if ndim > max_arr_ndim:338                max_arr_ndim = ndim339            if len(index) != len(first_index):340                raise ValueError(341                    "List depths are mismatched. First element was at depth "342                    "{}, but there is an element at depth {} ({})".format(343                        len(first_index),344                        len(index),345                        format_index(index)346                    )347                )348        return first_index, max_arr_ndim349    elif type(arrays) is list and len(arrays) == 0:350        # We've 'bottomed out' on an empty list351        return parent_index + [None], 0352    else:353        # We've 'bottomed out' - arrays is either a scalar or an array354        return parent_index, _nx.ndim(arrays)355def _block(arrays, max_depth, result_ndim):356    """357    Internal implementation of block. `arrays` is the argument passed to358    block. `max_depth` is the depth of nested lists within `arrays` and359    `result_ndim` is the greatest of the dimensions of the arrays in360    `arrays` and the depth of the lists in `arrays` (see block docstring361    for details).362    """363    def atleast_nd(a, ndim):364        # Ensures `a` has at least `ndim` dimensions by prepending365        # ones to `a.shape` as necessary366        return array(a, ndmin=ndim, copy=False, subok=True)367    def block_recursion(arrays, depth=0):368        if depth < max_depth:369            if len(arrays) == 0:370                raise ValueError('Lists cannot be empty')371            arrs = [block_recursion(arr, depth+1) for arr in arrays]372            return _nx.concatenate(arrs, axis=-(max_depth-depth))373        else:374            # We've 'bottomed out' - arrays is either a scalar or an array375            # type(arrays) is not list376            return atleast_nd(arrays, result_ndim)377    try:378        return block_recursion(arrays)379    finally:380        # recursive closures have a cyclic reference to themselves, which381        # requires gc to collect (gh-10620). To avoid this problem, for382        # performance and PyPy friendliness, we break the cycle:383        block_recursion = None384def block(arrays):385    """386    Assemble an nd-array from nested lists of blocks.387    Blocks in the innermost lists are concatenated (see `concatenate`) along388    the last dimension (-1), then these are concatenated along the389    second-last dimension (-2), and so on until the outermost list is reached.390    Blocks can be of any dimension, but will not be broadcasted using the normal391    rules. Instead, leading axes of size 1 are inserted, to make ``block.ndim``392    the same for all blocks. This is primarily useful for working with scalars,393    and means that code like ``np.block([v, 1])`` is valid, where394    ``v.ndim == 1``.395    When the nested list is two levels deep, this allows block matrices to be396    constructed from their components.397    .. versionadded:: 1.13.0398    Parameters399    ----------400    arrays : nested list of array_like or scalars (but not tuples)401        If passed a single ndarray or scalar (a nested list of depth 0), this402        is returned unmodified (and not copied).403        Elements shapes must match along the appropriate axes (without404        broadcasting), but leading 1s will be prepended to the shape as405        necessary to make the dimensions match.406    Returns407    -------408    block_array : ndarray409        The array assembled from the given blocks.410        The dimensionality of the output is equal to the greatest of:411        * the dimensionality of all the inputs412        * the depth to which the input list is nested413    Raises414    ------415    ValueError416        * If list depths are mismatched - for instance, ``[[a, b], c]`` is417          illegal, and should be spelt ``[[a, b], [c]]``418        * If lists are empty - for instance, ``[[a, b], []]``419    See Also420    --------421    concatenate : Join a sequence of arrays together.422    stack : Stack arrays in sequence along a new dimension.423    hstack : Stack arrays in sequence horizontally (column wise).424    vstack : Stack arrays in sequence vertically (row wise).425    dstack : Stack arrays in sequence depth wise (along third dimension).426    vsplit : Split array into a list of multiple sub-arrays vertically.427    Notes428    -----429    When called with only scalars, ``np.block`` is equivalent to an ndarray430    call. So ``np.block([[1, 2], [3, 4]])`` is equivalent to431    ``np.array([[1, 2], [3, 4]])``.432    This function does not enforce that the blocks lie on a fixed grid.433    ``np.block([[a, b], [c, d]])`` is not restricted to arrays of the form::434        AAAbb435        AAAbb436        cccDD437    But is also allowed to produce, for some ``a, b, c, d``::438        AAAbb439        AAAbb440        cDDDD441    Since concatenation happens along the last axis first, `block` is _not_442    capable of producing the following directly::443        AAAbb444        cccbb445        cccDD446    Matlab's "square bracket stacking", ``[A, B, ...; p, q, ...]``, is447    equivalent to ``np.block([[A, B, ...], [p, q, ...]])``.448    Examples449    --------450    The most common use of this function is to build a block matrix451    >>> A = np.eye(2) * 2452    >>> B = np.eye(3) * 3453    >>> np.block([454    ...     [A,               np.zeros((2, 3))],455    ...     [np.ones((3, 2)), B               ]456    ... ])457    array([[ 2.,  0.,  0.,  0.,  0.],458           [ 0.,  2.,  0.,  0.,  0.],459           [ 1.,  1.,  3.,  0.,  0.],460           [ 1.,  1.,  0.,  3.,  0.],461           [ 1.,  1.,  0.,  0.,  3.]])462    With a list of depth 1, `block` can be used as `hstack`463    >>> np.block([1, 2, 3])              # hstack([1, 2, 3])464    array([1, 2, 3])465    >>> a = np.array([1, 2, 3])466    >>> b = np.array([2, 3, 4])467    >>> np.block([a, b, 10])             # hstack([a, b, 10])468    array([1, 2, 3, 2, 3, 4, 10])469    >>> A = np.ones((2, 2), int)470    >>> B = 2 * A471    >>> np.block([A, B])                 # hstack([A, B])472    array([[1, 1, 2, 2],473           [1, 1, 2, 2]])474    With a list of depth 2, `block` can be used in place of `vstack`:475    >>> a = np.array([1, 2, 3])476    >>> b = np.array([2, 3, 4])477    >>> np.block([[a], [b]])             # vstack([a, b])478    array([[1, 2, 3],479           [2, 3, 4]])480    >>> A = np.ones((2, 2), int)481    >>> B = 2 * A482    >>> np.block([[A], [B]])             # vstack([A, B])483    array([[1, 1],484           [1, 1],485           [2, 2],486           [2, 2]])487    It can also be used in places of `atleast_1d` and `atleast_2d`488    >>> a = np.array(0)489    >>> b = np.array([1])490    >>> np.block([a])                    # atleast_1d(a)491    array([0])492    >>> np.block([b])                    # atleast_1d(b)493    array([1])494    >>> np.block([[a]])                  # atleast_2d(a)495    array([[0]])496    >>> np.block([[b]])                  # atleast_2d(b)497    array([[1]])498    """499    bottom_index, arr_ndim = _block_check_depths_match(arrays)500    list_ndim = len(bottom_index)...

test_convert.py

Source:test_convert.py

...10    def check_device(self, array, device):11        if device is not None:12            self.assertIsInstance(array, cuda.ndarray)13            self.assertEqual(array.device.id, device)14    def check_concat_arrays(self, arrays, device=None):15        array = dataset.concat_examples(arrays, device)16        self.assertEqual(array.shape, (len(arrays),) + arrays[0].shape)17        self.check_device(array, device)18        for x, y in zip(array, arrays):19            numpy.testing.assert_array_equal(20                cuda.to_cpu(x), cuda.to_cpu(y))21    def test_concat_arrays_cpu(self):22        arrays = self.get_arrays_to_concat(numpy)23        self.check_concat_arrays(arrays)24    @attr.gpu25    def test_concat_arrays_gpu(self):26        arrays = self.get_arrays_to_concat(cuda.cupy)27        self.check_concat_arrays(arrays)28    @attr.gpu29    def test_concat_arrays_to_gpu(self):30        arrays = self.get_arrays_to_concat(numpy)31        self.check_concat_arrays(arrays, cuda.Device().id)32    def get_tuple_arrays_to_concat(self, xp):33        return [(xp.random.rand(2, 3), xp.random.rand(3, 4))34                for _ in range(5)]35    def check_concat_tuples(self, tuples, device=None):36        arrays = dataset.concat_examples(tuples, device)37        self.assertEqual(len(arrays), len(tuples[0]))38        for i in range(len(arrays)):39            shape = (len(tuples),) + tuples[0][i].shape40            self.assertEqual(arrays[i].shape, shape)41            self.check_device(arrays[i], device)42            for x, y in zip(arrays[i], tuples):43                numpy.testing.assert_array_equal(44                    cuda.to_cpu(x), cuda.to_cpu(y[i]))45    def test_concat_tuples_cpu(self):46        tuples = self.get_tuple_arrays_to_concat(numpy)47        self.check_concat_tuples(tuples)48    @attr.gpu49    def test_concat_tuples_gpu(self):50        tuples = self.get_tuple_arrays_to_concat(cuda.cupy)51        self.check_concat_tuples(tuples)52    @attr.gpu53    def test_concat_tuples_to_gpu(self):54        tuples = self.get_tuple_arrays_to_concat(numpy)55        self.check_concat_tuples(tuples, cuda.Device().id)56    def get_dict_arrays_to_concat(self, xp):57        return [{'x': xp.random.rand(2, 3), 'y': xp.random.rand(3, 4)}58                for _ in range(5)]59    def check_concat_dicts(self, dicts, device=None):60        arrays = dataset.concat_examples(dicts, device)61        self.assertEqual(frozenset(arrays.keys()), frozenset(dicts[0].keys()))62        for key in arrays:63            shape = (len(dicts),) + dicts[0][key].shape64            self.assertEqual(arrays[key].shape, shape)65            self.check_device(arrays[key], device)66            for x, y in zip(arrays[key], dicts):67                numpy.testing.assert_array_equal(68                    cuda.to_cpu(x), cuda.to_cpu(y[key]))69    def test_concat_dicts_cpu(self):70        dicts = self.get_dict_arrays_to_concat(numpy)71        self.check_concat_dicts(dicts)72    @attr.gpu73    def test_concat_dicts_gpu(self):74        dicts = self.get_dict_arrays_to_concat(cuda.cupy)75        self.check_concat_dicts(dicts)76    @attr.gpu77    def test_concat_dicts_to_gpu(self):78        dicts = self.get_dict_arrays_to_concat(numpy)79        self.check_concat_dicts(dicts, cuda.Device().id)80class TestConcatExamplesWithPadding(unittest.TestCase):81    def check_concat_arrays_padding(self, xp):82        arrays = [xp.random.rand(3, 4),83                  xp.random.rand(2, 5),84                  xp.random.rand(4, 3)]85        array = dataset.concat_examples(arrays, padding=0)86        self.assertEqual(array.shape, (3, 4, 5))87        self.assertEqual(type(array), type(arrays[0]))88        arrays = [cuda.to_cpu(a) for a in arrays]89        array = cuda.to_cpu(array)90        numpy.testing.assert_array_equal(array[0, :3, :4], arrays[0])91        numpy.testing.assert_array_equal(array[0, 3:, :], 0)92        numpy.testing.assert_array_equal(array[0, :, 4:], 0)93        numpy.testing.assert_array_equal(array[1, :2, :5], arrays[1])94        numpy.testing.assert_array_equal(array[1, 2:, :], 0)95        numpy.testing.assert_array_equal(array[2, :4, :3], arrays[2])96        numpy.testing.assert_array_equal(array[2, :, 3:], 0)97    def test_concat_arrays_padding_cpu(self):98        self.check_concat_arrays_padding(numpy)99    @attr.gpu100    def test_concat_arrays_padding_gpu(self):101        self.check_concat_arrays_padding(cuda.cupy)102    def check_concat_tuples_padding(self, xp):103        tuples = [104            (xp.random.rand(3, 4), xp.random.rand(2, 5)),105            (xp.random.rand(4, 4), xp.random.rand(3, 4)),106            (xp.random.rand(2, 5), xp.random.rand(2, 6)),107        ]108        arrays = dataset.concat_examples(tuples, padding=0)109        self.assertEqual(len(arrays), 2)110        self.assertEqual(arrays[0].shape, (3, 4, 5))111        self.assertEqual(arrays[1].shape, (3, 3, 6))112        self.assertEqual(type(arrays[0]), type(tuples[0][0]))113        self.assertEqual(type(arrays[1]), type(tuples[0][1]))114        for i in range(len(tuples)):115            tuples[i] = cuda.to_cpu(tuples[i][0]), cuda.to_cpu(tuples[i][1])116        arrays = tuple(cuda.to_cpu(array) for array in arrays)117        numpy.testing.assert_array_equal(arrays[0][0, :3, :4], tuples[0][0])118        numpy.testing.assert_array_equal(arrays[0][0, 3:, :], 0)119        numpy.testing.assert_array_equal(arrays[0][0, :, 4:], 0)120        numpy.testing.assert_array_equal(arrays[0][1, :4, :4], tuples[1][0])121        numpy.testing.assert_array_equal(arrays[0][1, :, 4:], 0)122        numpy.testing.assert_array_equal(arrays[0][2, :2, :5], tuples[2][0])123        numpy.testing.assert_array_equal(arrays[0][2, 2:, :], 0)124        numpy.testing.assert_array_equal(arrays[1][0, :2, :5], tuples[0][1])125        numpy.testing.assert_array_equal(arrays[1][0, 2:, :], 0)126        numpy.testing.assert_array_equal(arrays[1][0, :, 5:], 0)127        numpy.testing.assert_array_equal(arrays[1][1, :3, :4], tuples[1][1])128        numpy.testing.assert_array_equal(arrays[1][1, 3:, :], 0)129        numpy.testing.assert_array_equal(arrays[1][1, :, 4:], 0)130        numpy.testing.assert_array_equal(arrays[1][2, :2, :6], tuples[2][1])131        numpy.testing.assert_array_equal(arrays[1][2, 2:, :], 0)132    def test_concat_tuples_padding_cpu(self):133        self.check_concat_tuples_padding(numpy)134    @attr.gpu135    def test_concat_tuples_padding_gpu(self):136        self.check_concat_tuples_padding(cuda.cupy)137    def check_concat_dicts_padding(self, xp):138        dicts = [139            {'x': xp.random.rand(3, 4), 'y': xp.random.rand(2, 5)},140            {'x': xp.random.rand(4, 4), 'y': xp.random.rand(3, 4)},141            {'x': xp.random.rand(2, 5), 'y': xp.random.rand(2, 6)},142        ]143        arrays = dataset.concat_examples(dicts, padding=0)144        self.assertIn('x', arrays)145        self.assertIn('y', arrays)146        self.assertEqual(arrays['x'].shape, (3, 4, 5))147        self.assertEqual(arrays['y'].shape, (3, 3, 6))148        self.assertEqual(type(arrays['x']), type(dicts[0]['x']))149        self.assertEqual(type(arrays['y']), type(dicts[0]['y']))150        for d in dicts:151            d['x'] = cuda.to_cpu(d['x'])152            d['y'] = cuda.to_cpu(d['y'])153        arrays = {'x': cuda.to_cpu(arrays['x']), 'y': cuda.to_cpu(arrays['y'])}154        numpy.testing.assert_array_equal(arrays['x'][0, :3, :4], dicts[0]['x'])155        numpy.testing.assert_array_equal(arrays['x'][0, 3:, :], 0)156        numpy.testing.assert_array_equal(arrays['x'][0, :, 4:], 0)157        numpy.testing.assert_array_equal(arrays['x'][1, :4, :4], dicts[1]['x'])158        numpy.testing.assert_array_equal(arrays['x'][1, :, 4:], 0)159        numpy.testing.assert_array_equal(arrays['x'][2, :2, :5], dicts[2]['x'])160        numpy.testing.assert_array_equal(arrays['x'][2, 2:, :], 0)161        numpy.testing.assert_array_equal(arrays['y'][0, :2, :5], dicts[0]['y'])162        numpy.testing.assert_array_equal(arrays['y'][0, 2:, :], 0)163        numpy.testing.assert_array_equal(arrays['y'][0, :, 5:], 0)164        numpy.testing.assert_array_equal(arrays['y'][1, :3, :4], dicts[1]['y'])165        numpy.testing.assert_array_equal(arrays['y'][1, 3:, :], 0)166        numpy.testing.assert_array_equal(arrays['y'][1, :, 4:], 0)167        numpy.testing.assert_array_equal(arrays['y'][2, :2, :6], dicts[2]['y'])168        numpy.testing.assert_array_equal(arrays['y'][2, 2:, :], 0)169    def test_concat_dicts_padding_cpu(self):170        self.check_concat_dicts_padding(numpy)171    @attr.gpu172    def test_concat_dicts_padding_gpu(self):173        self.check_concat_dicts_padding(cuda.cupy)174@testing.parameterize(175    {'padding': None},176    {'padding': 0},177)178class TestConcatExamplesWithBuiltInTypes(unittest.TestCase):179    int_arrays = [1, 2, 3]180    float_arrays = [1.0, 2.0, 3.0]181    def check_device(self, array, device):182        if device is not None and device >= 0:183            self.assertIsInstance(array, cuda.ndarray)184            self.assertEqual(array.device.id, device)185        else:186            self.assertIsInstance(array, numpy.ndarray)187    def check_concat_arrays(self, arrays, device, expected_type):188        array = dataset.concat_examples(arrays, device, self.padding)189        self.assertEqual(array.shape, (len(arrays),))190        self.check_device(array, device)191        for x, y in zip(array, arrays):192            if cuda.get_array_module(x) == numpy:193                numpy.testing.assert_array_equal(194                    numpy.array(x),195                    numpy.array(y, dtype=expected_type))196            else:197                numpy.testing.assert_array_equal(198                    cuda.to_cpu(x),199                    numpy.array(y, dtype=expected_type))200    def test_concat_arrays_cpu(self):201        for device in (-1, None):202            self.check_concat_arrays(self.int_arrays,203                                     device=device,204                                     expected_type=numpy.int64)205            self.check_concat_arrays(self.float_arrays,206                                     device=device,207                                     expected_type=numpy.float64)208    @attr.gpu209    def test_concat_arrays_gpu(self):210        self.check_concat_arrays(self.int_arrays,211                                 device=cuda.Device().id,212                                 expected_type=numpy.int64)213        self.check_concat_arrays(self.float_arrays,214                                 device=cuda.Device().id,215                                 expected_type=numpy.float64)216def get_xp(gpu):217    if gpu:218        return cuda.cupy219    else:220        return numpy221@testing.parameterize(222    {'device': None, 'src_gpu': False, 'dst_gpu': False},223    {'device': -1, 'src_gpu': False, 'dst_gpu': False},224)225class TestToDeviceCPU(unittest.TestCase):226    def test_to_device(self):227        src_xp = get_xp(self.src_gpu)...

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