Python numpy.broadcast_to() Examples

def unreduce_array(array, shape, axis, keepdims):
  """Reverse summing over a dimension, NumPy implementation.

  Args:
    array: The array that was reduced.
    shape: The original shape of the array before reduction.
    axis: The axis or axes that were summed.
    keepdims: Whether these axes were kept as singleton axes.

  Returns:
    An array with axes broadcast to match the shape of the original array.
  """
  # NumPy uses a special default value for keepdims, which is equivalent to
  # False.
  if axis is not None and (not keepdims or keepdims is numpy._NoValue):  # pylint: disable=protected-access
    if isinstance(axis, int):
      axis = axis,
    for ax in sorted(axis):
      array = numpy.expand_dims(array, ax)
  return numpy.broadcast_to(array, shape)


# The values are unary functions. 

def _broadcast_shape(*args):
    """Returns the shape of the arrays that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        return ()
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

def test_max_unbounded(self):
        n_batch = 7
        ndim_action = 3
        mu = np.random.randn(n_batch, ndim_action).astype(np.float32)
        mat = np.broadcast_to(
            np.eye(ndim_action, dtype=np.float32)[None],
            (n_batch, ndim_action, ndim_action))
        v = np.random.randn(n_batch).astype(np.float32)
        q_out = action_value.QuadraticActionValue(
            chainer.Variable(mu),
            chainer.Variable(mat),
            chainer.Variable(v))

        v_out = q_out.max
        self.assertIsInstance(v_out, chainer.Variable)
        v_out = v_out.array

        np.testing.assert_almost_equal(v_out, v) 

def random_warp( image ):
    assert image.shape == (256,256,3)
    range_ = numpy.linspace( 128-80, 128+80, 5 )
    mapx = numpy.broadcast_to( range_, (5,5) )
    mapy = mapx.T

    mapx = mapx + numpy.random.normal( size=(5,5), scale=5 )
    mapy = mapy + numpy.random.normal( size=(5,5), scale=5 )

    interp_mapx = cv2.resize( mapx, (80,80) )[8:72,8:72].astype('float32')
    interp_mapy = cv2.resize( mapy, (80,80) )[8:72,8:72].astype('float32')

    warped_image = cv2.remap( image, interp_mapx, interp_mapy, cv2.INTER_LINEAR )

    src_points = numpy.stack( [ mapx.ravel(), mapy.ravel() ], axis=-1 )
    dst_points = numpy.mgrid[0:65:16,0:65:16].T.reshape(-1,2)
    mat = umeyama( src_points, dst_points, True )[0:2]

    target_image = cv2.warpAffine( image, mat, (64,64) )

    return warped_image, target_image 

def to_grayscale(img, num_output_channels=1):
    """Convert image to grayscale version of image.
    Args:
        img (numpy ndarray): Image to be converted to grayscale.
    Returns:
        numpy ndarray: Grayscale version of the image.
            if num_output_channels = 1 : returned image is single channel
            if num_output_channels = 3 : returned image is 3 channel with r = g = b
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy ndarray. Got {}'.format(type(img)))

    if num_output_channels==1:
        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis]
    elif num_output_channels==3:
        # much faster than doing cvtColor to go back to gray
        img = np.broadcast_to(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis], img.shape) 
    return img 

def test_getitem(self):
        n_batch = 7
        ndim_action = 3
        mu = np.random.randn(n_batch, ndim_action).astype(np.float32)
        mat = np.broadcast_to(
            np.eye(ndim_action, dtype=np.float32)[None],
            (n_batch, ndim_action, ndim_action))
        v = np.random.randn(n_batch).astype(np.float32)
        min_action, max_action = -1, 1
        qout = action_value.QuadraticActionValue(
            chainer.Variable(mu),
            chainer.Variable(mat),
            chainer.Variable(v),
            min_action,
            max_action,
        )
        sliced = qout[:3]
        np.testing.assert_equal(sliced.mu.array, mu[:3])
        np.testing.assert_equal(sliced.mat.array, mat[:3])
        np.testing.assert_equal(sliced.v.array, v[:3])
        np.testing.assert_equal(sliced.min_action, min_action)
        np.testing.assert_equal(sliced.max_action, max_action) 

def _prepare_value_nd(self, value, vshape):
        """Given value and vshape, create an `NDArray` from value with the same
        context and dtype as the current one and broadcast it to vshape."""
        if isinstance(value, numeric_types):
            value_nd = full(shape=vshape, val=value, ctx=self.context, dtype=self.dtype)
        elif isinstance(value, NDArray):
            value_nd = value.as_in_context(self.context)
            if value_nd.dtype != self.dtype:
                value_nd = value_nd.astype(self.dtype)
        else:
            try:
                value_nd = array(value, ctx=self.context, dtype=self.dtype)
            except:
                raise TypeError('NDArray does not support assignment with non-array-like'
                                ' object %s of type %s' % (str(value), str(type(value))))
        if value_nd.shape != vshape:
            value_nd = value_nd.broadcast_to(vshape)
        return value_nd 

def _dominant_set_sparse(s, k, is_thresh=False, norm=False):
    """Compute dominant set for a sparse matrix."""
    if is_thresh:
        mask = s > k
        idx, data = np.where(mask), s[mask]
        s = ssp.coo_matrix((data, idx), shape=s.shape)

    else:  # keep top k
        nr, nc = s.shape
        idx = np.argpartition(s, nc - k, axis=1)
        col = idx[:, -k:].ravel()  # idx largest
        row = np.broadcast_to(np.arange(nr)[:, None], (nr, k)).ravel()
        data = s[row, col].ravel()
        s = ssp.coo_matrix((data, (row, col)), shape=s.shape)

    if norm:
        s.data /= s.sum(axis=1).A1[s.row]

    return s.tocsr(copy=False) 

def to_grayscale(img, num_output_channels=1):
    """Convert image to grayscale version of image.
    Args:
        img (numpy ndarray): Image to be converted to grayscale.
    Returns:
        numpy ndarray: Grayscale version of the image.
            if num_output_channels = 1 : returned image is single channel
            if num_output_channels = 3 : returned image is 3 channel with r = g = b
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy ndarray. Got {}'.format(type(img)))

    if num_output_channels==1:
        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis]
    elif num_output_channels==3:
        # much faster than doing cvtColor to go back to gray
        img = np.broadcast_to(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis], img.shape)
    return img 

def make_batch_mask(mb_size, n_head, max_length_1, max_length_2, 
                    key_seq_lengths=None,
                    future_mask=False,
                    mask_value=-10000):
    
    if future_mask:
        assert max_length_1 == max_length_2
        mask = np.array(
                np.broadcast_to(( (-mask_value) * (np.tri(max_length_1, dtype = np.float32)-1))[None,None,:,:], 
                                (mb_size, n_head, max_length_1, max_length_2))
                )
    else:
        mask = np.zeros((mb_size, n_head, max_length_1, max_length_2), dtype = np.float32)
        
    if key_seq_lengths is not None:
        assert mb_size == len(key_seq_lengths)
        assert min(key_seq_lengths) > 0
        assert max(key_seq_lengths) <= max_length_2
        for num_batch, length in enumerate(key_seq_lengths):
            mask[num_batch, :, :, length:] = mask_value

    return mask 

def testBroadcastConcat(self):
    b = builder_lib.ModelBuilderBase()
    p = b._BroadcastConcat('p', b._ArgIdx('arg0', [0]), b._ArgIdx('arg1', [1]))
    l = p.Instantiate()

    # 2 x 1 x 3
    x1 = np.asarray([[[1, 2, 3]], [[4, 5, 6]]])
    self.assertEqual(x1.shape, (2, 1, 3))
    # 1 x 4 x 2
    x2 = np.asarray([[[7, 8], [9, 10], [11, 12], [13, 14]]])
    self.assertEqual(x2.shape, (1, 4, 2))

    y = l.FPropDefaultTheta(tf.constant(x1), tf.constant(x2))

    expected_y = np.concatenate([
        np.broadcast_to(x1, [2, 4, 3]),
        np.broadcast_to(x2, [2, 4, 2]),
    ], axis=-1)  # pyformat: disable
    with self.session():
      actual_y = self.evaluate(y)
      # x1 will be broadcasted to [2, 4, 3]
      # x2 will be broadcasted to [2, 4, 2]
      # Concatenation on axis=-1 should result in a tensor of shape [2, 4, 5]
      self.assertEqual(actual_y.shape, (2, 4, 5))
      self.assertAllEqual(actual_y, expected_y) 

def _broadcast_shape(*args):
    """Returns the shape of the arrays that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        return ()
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

def _broadcast_shape(*args):
    """Returns the shape of the arrays that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        return ()
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

def __getitem__(self, items):
        # To make life easier, we only wrap when `items` is a tuple of slices.
        # It'd be nifty if they could be integers or work on arbitrary arrays,
        # but that's more work and it won't be used.
        if not (isinstance(items, tuple) and len(items) == 2 and
                isinstance(items[0], slice) and isinstance(items[1], slice)):
            return super().__getitem__(items)
        rows, cols = items
        rows = np.arange(
            rows.start or 0,
            rows.stop if rows.stop is not None else self.shape[0],
            rows.step or 1
        ) % self.shape[0]
        cols = np.arange(
            cols.start or 0,
            cols.stop if cols.stop is not None else self.shape[0],
            cols.step or 1
        ) % self.shape[1]
        nrows = len(rows)
        ncols = len(cols)
        rows = np.broadcast_to(rows[:,None], (nrows, ncols))
        cols = np.broadcast_to(cols[None,:], (nrows, ncols))
        return super().__getitem__((rows, cols)) 

def __call__(self, x, update=True):
        """Normalize mean and variance of values based on emprical values.
        Args:
            x (ndarray or Variable): Input values
            update (bool): Flag to learn the input values
        Returns:
            ndarray or Variable: Normalized output values
        """
        if self.count == 0:
            return x

        mean = np.broadcast_to(self._mean, x.shape)
        std_inv = np.broadcast_to(self._std_inverse, x.shape)

        if update:
            self.experience(x)

        normalized = (x - mean) * std_inv
        if self.clip_threshold is not None:
            normalized = np.clip(
                normalized, -self.clip_threshold, self.clip_threshold)
        return normalized 

def _broadcast_shape(*args):
    """Returns the shape of the ararys that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

def render_board(board, goals=0, orientation=0, edit_loc=None, edit_color=0):
    """
    Just render the board itself. Doesn't require game state.
    """
    if edit_loc and (edit_loc[0] >= board.shape[0] or edit_loc[1] >= board.shape[1]):
        edit_loc = None
    goals = np.broadcast_to(goals, board.shape)

    screen = np.empty((board.shape[0]+2, board.shape[1]+3,), dtype=object)
    screen[:] = ''
    screen[0] = screen[-1] = ' -'
    screen[:,0] = screen[:,-2] = ' |'
    screen[:,-1] = '\n'
    screen[0,0] = screen[0,-2] = screen[-1,0] = screen[-1,-2] = ' +'
    screen[1:-1,1:-2] = render_cell(board, goals, orientation)

    if edit_loc:
        x1, y1 = edit_loc
        val = render_cell(board[y1, x1], goals[y1, x1], orientation, edit_color)
        screen[y1+1, x1+1] = str(val)
    return ''.join(screen.ravel()) 

def _deduplicate_training_data(X, y):
        # type: (ndarray, ndarray) -> Tuple[ndarray, ndarray]
        """Make sure every training example in X occurs exactly once."""

        # we need to concat X and y to make sure that
        # we do NOT throw out contradicting examples
        # (same featurization but different labels).
        # appends y to X so it appears to be just another feature
        casted_y = np.broadcast_to(
                np.reshape(y, (y.shape[0], 1, 1)), (y.shape[0], X.shape[1], 1))
        concatenated = np.concatenate((X, casted_y), axis=2)
        t_data = np.unique(concatenated, axis=0)
        X_unique = t_data[:, :, :-1]
        y_unique = np.array(t_data[:, 0, -1], dtype=casted_y.dtype)
        return X_unique, y_unique 

def _discretize_env(self, env):
    """Generates a discrete bounded spec and a linspace for the given limits.

    Args:
      env: An array to discretize.

    Returns:
      Tuple with the discrete_spec along with a list of lists mapping actions.
    Raises:
      ValueError: If not all limits value are >=2 or maximum or minimum of boxes
      is equal to +- infinity.
    """
    if not np.all(self._num_actions >= 2):
      raise ValueError("num_actions should all be at least size 2.")

    if (math.isinf(np.min(env.action_space.low)) or
        math.isinf(np.max(env.action_space.high))):
      raise ValueError(
          """Minimum of boxes is {} and maximum of boxes is {},
          but we expect that finite values are provided.""".
          format(np.min(env.action_space.low),
                 np.max(env.action_space.high)))

    limits = np.broadcast_to(self._num_actions,
                             env.action_space.shape)
    minimum = np.broadcast_to(np.min(env.action_space.low),
                              env.action_space.shape)
    maximum = np.broadcast_to(np.max(env.action_space.high),
                              env.action_space.shape)

    action_map = [
        np.linspace(env_min, env_max, num=n_actions)
        for env_min, env_max, n_actions in zip(
            np.nditer(minimum), np.nditer(maximum), np.nditer(limits))
    ]

    return action_map 

def apply_array_image_mask(array, mask, fill_value=0):
    """Applies a mask of (y,x) to an image array of (bands, y, x). Replaces any masked pixels with fill_value
    Mask is an a 2 dimensional array of 1 ( unmasked) and 0 (masked)"""
    stacked_mask = np.broadcast_to(mask, array.shape)
    return np.where(stacked_mask == 1, array, fill_value) 

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

def broadcast_to(array, shape, subok=False):
        return _broadcast_to(array, shape, subok=subok, readonly=True) 

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

def test_max_bounded(self):
        n_batch = 20
        ndim_action = 3
        mu = np.random.randn(n_batch, ndim_action).astype(np.float32)
        mat = np.broadcast_to(
            np.eye(ndim_action, dtype=np.float32)[None],
            (n_batch, ndim_action, ndim_action))
        v = np.random.randn(n_batch).astype(np.float32)
        min_action, max_action = -1.3, 1.3
        q_out = action_value.QuadraticActionValue(
            chainer.Variable(mu),
            chainer.Variable(mat),
            chainer.Variable(v),
            min_action, max_action)

        v_out = q_out.max
        self.assertIsInstance(v_out, chainer.Variable)
        v_out = v_out.array

        # If mu[i] is an valid action, v_out[i] should be v[i]
        mu_is_allowed = np.all(
            (min_action < mu) * (mu < max_action),
            axis=1)
        np.testing.assert_almost_equal(v_out[mu_is_allowed], v[mu_is_allowed])

        # Otherwise, v_out[i] should be less than v[i]
        mu_is_not_allowed = ~np.all(
            (min_action - 1e-2 < mu) * (mu < max_action + 1e-2),
            axis=1)
        np.testing.assert_array_less(
            v_out[mu_is_not_allowed],
            v[mu_is_not_allowed]) 

def test_indexing_array_weird_strides(self):
        # See also gh-6221
        # the shapes used here come from the issue and create the correct
        # size for the iterator buffering size.
        x = np.ones(10)
        x2 = np.ones((10, 2))
        ind = np.arange(10)[:, None, None, None]
        ind = np.broadcast_to(ind, (10, 55, 4, 4))

        # single advanced index case
        assert_array_equal(x[ind], x[ind.copy()])
        # higher dimensional advanced index
        zind = np.zeros(4, dtype=np.intp)
        assert_array_equal(x2[ind, zind], x2[ind.copy(), zind]) 

def AttenProbs(self, key, query, paddings):
    assert query.ndim == 4
    assert paddings.ndim == 2
    assert key.shape == query.shape

    batch, seqlen = query.shape[:2]
    tgtlen, srclen = seqlen, seqlen
    assert query.shape[2] == self._num_heads
    assert query.shape[3] == self._atten_dim
    assert paddings.shape == query.shape[:2]

    # [B, N, T, T]
    logits = np.zeros((batch, self._num_heads, tgtlen, srclen))
    # [B, N, T, T]
    probs = np.zeros((batch, self._num_heads, tgtlen, srclen))

    paddings = np.broadcast_to(
        np.reshape(paddings, (batch, 1, 1, seqlen)),
        (batch, self._num_heads, seqlen, seqlen))

    def Normalize(vec):
      expx = np.exp(vec)
      expxsum = np.sum(expx, axis=-1)
      return expx / expxsum

    for b in range(batch):
      for h in range(self._num_heads):
        for i in range(tgtlen):
          for j in range(srclen):
            logits[b][h][i][j] = np.dot(query[b][i][h],
                                        key[b][j][h] + self.GetKeyEmb(i, j, h))
          logits[b][h][i] = np.where(paddings[b][h][i] > 0,
                                     np.finfo(np.float32).max * (-0.7),
                                     logits[b][h][i])
          probs[b][h][i] = Normalize(logits[b][h][i])
    return probs 

def step(self):
        s = self.S[all_agents, self.idx]
        na = self.NA[all_agents, self.idx]

        a, u = self.act(s, na)
        s_, r, na_, done, info = self.env.step(a)

        self.A[all_agents, self.idx] = a
        self.R[all_agents, self.idx] = r
        self.U[all_agents, self.idx] = u

        for i in np.where(done)[0]:     # truncate & store the finished episode i
            idx = self.idx[i]+1

            _s = self.S[i, :idx].copy()
            _a = self.A[i, :idx].copy()
            _r = self.R[i, :idx].copy()
            _u = self.U[i, :idx].copy()
            _na = self.NA[i, :idx].copy()

            # extract the true state
            _x = np.broadcast_to(self.env.x[i].copy(), (idx, config.FEATURE_DIM))
            _y = np.repeat(self.env.y[i], idx)

            self.pool.put( (_s, _a, _r, _u, _na, _x, _y) )

        self.idx = (done == 0) * (self.idx + 1)     # advance idx by 1 and reset to 0 for finished episodes

        self.NA[all_agents, self.idx] = na_     # unavailable actions
        self.S[all_agents,  self.idx] = s_

        return s, a, r, s_, done, info 

def get_example(self, i):
        img = Image.open(os.path.join(self.directory, self.files[i]))
        img = img.crop(self.random_box(img.size, 16))

        size = 2**(2+self.depth)
        img = img.resize((size, size))
        
        img = np.array(img, dtype=np.float32) / 256
        if len(img.shape) == 2:
            img = np.broadcast_to(img, (3, img.shape[0], img.shape[1]))
        else:
            img = np.transpose(img, (2, 0, 1))
        
        return img 

def test_pool_global_average_3d(ndarray_1x1x4x4):
    x = np.broadcast_to(ndarray_1x1x4x4, (1, 1, 4, 4, 4))

    node = onnx.helper.make_node('GlobalAveragePool', inputs=['x'], outputs=['y'])
    y = np.array([18.5], dtype=np.float32).reshape(1, 1, 1, 1, 1)
    ng_results = run_node(node, [x])
    assert np.array_equal(ng_results, [y])