Python numpy 模块，broadcast_to() 实例源码

def get_local_wavenumbermesh(self, scaled=True, broadcast=False,
                                 eliminate_highest_freq=False):
        kx = fftfreq(self.N[0], 1./self.N[0])
        ky = rfftfreq(self.N[1], 1./self.N[1])
        if eliminate_highest_freq:
            for i, k in enumerate((kx, ky)):
                if self.N[i] % 2 == 0:
                    k[self.N[i]//2] = 0

        Ks = np.meshgrid(kx, ky[self.rank*self.Np[1]//2:(self.rank*self.Np[1]//2+self.Npf)], indexing='ij', sparse=True)
        if scaled is True:
            Lp = 2*np.pi/self.L
            Ks[0] *= Lp[0]
            Ks[1] *= Lp[1]
        K = Ks
        if broadcast is True:
            K = [np.broadcast_to(k, self.complex_shape()) for k in Ks]
        return K

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 get_scipy_batch_logpdf(self, idx):
        if not self.scipy_arg_fn:
            return
        dist_params = self.get_dist_params(idx, wrap_tensor=False)
        dist_params_wrapped = self.get_dist_params(idx)
        dist_params = self._convert_logits_to_ps(dist_params)
        test_data = self.get_test_data(idx, wrap_tensor=False)
        test_data_wrapped = self.get_test_data(idx)
        shape = self.pyro_dist.shape(test_data_wrapped, **dist_params_wrapped)
        batch_log_pdf = []
        for i in range(len(test_data)):
            batch_params = {}
            for k in dist_params:
                param = np.broadcast_to(dist_params[k], shape)
                batch_params[k] = param[i]
            args, kwargs = self.scipy_arg_fn(**batch_params)
            if self.is_discrete:
                batch_log_pdf.append(self.scipy_dist.logpmf(test_data[i],
                                                            *args,
                                                            **kwargs))
            else:
                batch_log_pdf.append(self.scipy_dist.logpdf(test_data[i],
                                                            *args,
                                                            **kwargs))
        return batch_log_pdf

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.data

        np.testing.assert_almost_equal(v_out, v)

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 compute_convolution_nd(data, kernel, dimension: int, mode=ConvolutionMode.valid, element_wise: bool=False):
    mode_string = __get_convolution_mode_string(mode)
    result = []
    data_prefix_shape = data.shape[:-dimension]
    kernel_prefix_shape = kernel.shape[:-dimension]
    if element_wise:
        final_shape = element_wise_shape(data_prefix_shape, kernel_prefix_shape)[0]
        data = numpy.broadcast_to(data, final_shape + data.shape[-2:])
        kernel = numpy.broadcast_to(kernel, final_shape + kernel.shape[-2:])
        if final_shape:
            for index in array_index_traversal(final_shape):
                result.append(__compute_convolution_nd(data[index], kernel[index], dimension, mode_string))
            return numpy.array(result).reshape(final_shape + result[0].shape)
        else:
            return __compute_convolution_nd(data, kernel, dimension, mode_string)
    else:
        if kernel_prefix_shape:
            final_shape = data_prefix_shape + kernel_prefix_shape + basic_convolution_shape(data.shape[-dimension:], kernel.shape[-dimension:], dimension, mode_string)
            result = numpy.zeros(final_shape)
            for kernel_index in array_index_traversal(kernel_prefix_shape):
                sub_result_index = tuple(slice(None) for _ in data_prefix_shape) + kernel_index + tuple(slice(None) for _ in range(dimension))
                result[sub_result_index] = __compute_convolution_nd(data, kernel[kernel_index], dimension, mode_string)
            return result
        else:
            return __compute_convolution_nd(data, kernel, dimension, mode_string)

def test_One(backend, M, N, K, alpha, beta, forward):
    x = indigo.util.rand64c(K,N)
    y = indigo.util.rand64c(M,N)
    B = backend()
    if getattr(B.onemm, '__isabstractmethod__', False):
        pytest.skip("backed <%s> doesn't implement onemm" % backend.__name__)
    if not hasattr(B, 'onemm'):
        pytest.skip("backend doesn't implement onemm")
    O = B.One((M,K), dtype=np.complex64)

    if forward:
        u, v = x, y
    else:
        v, u = x, y

    u_d = B.copy_array(u)
    v_d = B.copy_array(v)
    exp = beta * v + \
        np.broadcast_to(alpha*u.sum(axis=0,keepdims=True), v.shape)
    O.eval(v_d, u_d, alpha=alpha, beta=beta, forward=forward)
    act = v_d.to_host()
    np.testing.assert_allclose(act, exp, rtol=1e-5)

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')
    if len(args) == 1:
        # a single argument does not work with np.broadcast
        return np.asarray(args[0]).shape
    # 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 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 broadcast_to(self, shape):
        """
        Performs the equivalent of np.broadcast_to for COO.
        Parameters
        ----------
        shape : tuple[int]
            The shape to broadcast the data to.
        Returns
        -------
            The broadcasted sparse array.
        Raises
        ------
        ValueError
            If the operand cannot be broadcast to the given shape.
        """
        result_shape = self._get_broadcast_shape(self.shape, shape, is_result=True)
        params = self._get_broadcast_parameters(self.shape, result_shape)
        coords, data = self._get_expanded_coords_data(self.coords, self.data, params, result_shape)

        return COO(coords, data, shape=result_shape, has_duplicates=self.has_duplicates,
                   sorted=self.sorted)

def _broadcast_shape(*args):
    """Returns the shape of the arrays 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 test_repeat_tile(self):

        initial_shape = (8, 4)

        repeats = ((3, 1, 1),
                   (3, 3, 3),
                   (1, 2, 1),
                   (2, 2, 2, 2))

        def _generate_noncontiguous_input():

            out = np.broadcast_to(np.random.random((1, 4)),
                                  initial_shape)

            assert not (out.flags.c_contiguous or out.flags.f_contiguous)

            return out

        for repeat in repeats:
            for tensor in (torch.from_numpy(np.random.random(initial_shape)),
                           torch.from_numpy(_generate_noncontiguous_input()),):

                self.assertEqual(tensor.repeat(*repeat).numpy(),
                                 np.tile(tensor.numpy(), repeat))

def ordinal_loss(y, mask):
    xp = cuda.get_array_module(y.data)
    volatile = y.volatile
    b, c, n = y.data.shape
    max_y = F.broadcast_to(F.max(y, axis=1, keepdims=True), y.data.shape)
    y = y - max_y
    sum_y = F.broadcast_to(F.expand_dims(F.sum(y, axis=1), 1), y.data.shape)
    down_tri = np.tri(c, dtype=np.float32)
    up_tri = down_tri.T
    w1 = Variable(xp.asarray(down_tri.reshape(c, c, 1, 1)), volatile=volatile)
    w2 = Variable(xp.asarray(up_tri.reshape(c, c, 1, 1)), volatile=volatile)
    h = F.exp(F.expand_dims(y, -1))
    h1 = F.convolution_2d(h, w1)
    h1 = F.convolution_2d(F.log(h1), w1)
    h2 = F.convolution_2d(h, w2)
    h2 = F.convolution_2d(F.log(h2), w2)
    h = F.reshape(h1 + h2, (b, c, n))
    return F.sum((h - sum_y - y) * mask) / b

def __forward(self, batch_x, batch_t, weight, train=True):
        xp = self.xp
        x = Variable(xp.asarray(batch_x), volatile=not train)
        t = Variable(xp.asarray(batch_t), volatile=not train)
        y = self.net(x, train=train)

        b, c, n = y.data.shape
        mask = Variable(xp.asarray(np.broadcast_to(weight.reshape(-1, 1, 1), (b, c, n)) * loss_mask(batch_t, self.net.rating_num)), volatile=not train)
        if self.ordinal_weight == 0:
            loss = F.sum(-F.log_softmax(y) * mask) / b
        elif self.ordinal_weight == 1:
            loss = ordinal_loss(y, mask)
        else:
            loss = (1 - self.ordinal_weight) * F.sum(-F.log_softmax(y) * mask) / b + self.ordinal_weight * ordinal_loss(y, mask)

        acc = self.__accuracy(y, t)
        return loss, acc

def broadcast(vec: T.Tensor, matrix: T.Tensor) -> T.Tensor:
    """
    Broadcasts vec into the shape of matrix following numpy rules:

    vec ~ (N, 1) broadcasts to matrix ~ (N, M)
    vec ~ (1, N) and (N,) broadcast to matrix ~ (M, N)

    Args:
        vec: A vector (either flat, row, or column).
        matrix: A matrix (i.e., a 2D tensor).

    Returns:
        tensor: A tensor of the same size as matrix containing the elements
                of the vector.

    Raises:
        BroadcastError

    """
    try:
        return numpy.broadcast_to(vec, shape(matrix))
    except ValueError:
        raise BroadcastError('cannot broadcast vector of dimension {} \
        onto matrix of dimension {}'.format(shape(vec), shape(matrix)))

def test_lmatvec(b0, b1, quad, format, axis, k0, k1):
    """Test matrix-vector product"""
    global c, c1, d, d1
    b0 = b0(N, quad=quad)
    b1 = b1(N, quad=quad)
    mat = shenfun.spectralbase.inner_product((b0, k0), (b1, k1))
    c = mat.matvec(a, c, format='csr')
    c1 = mat.matvec(a, c1, format=format)
    assert np.allclose(c, c1)

    d.fill(0)
    d1.fill(0)
    d = mat.matvec(b, d, format='csr', axis=axis)
    d1 = mat.matvec(b, d1, format=format, axis=axis)
    assert np.allclose(d, d1)

    # Test multidimensional with axis equals 1D case
    d1.fill(0)
    bc = [np.newaxis,]*3
    bc[axis] = slice(None)
    fj = np.broadcast_to(a[bc], (N,)*3).copy()
    d1 = mat.matvec(fj, d1, format=format, axis=axis)
    cc = [0,]*3
    cc[axis] = slice(None)
    assert np.allclose(c, d1[cc])

def test_axis(ST, quad, axis):
    ST = ST(N, quad=quad, plan=True)
    points, weights = ST.points_and_weights(N)
    f_hat = np.random.random(N)

    B = inner_product((ST, 0), (ST, 0))

    c = np.zeros_like(f_hat)
    c = B.solve(f_hat, c)

    # Multidimensional version
    bc = [np.newaxis,]*3
    bc[axis] = slice(None)
    fk = np.broadcast_to(f_hat[bc], (N,)*3).copy()
    ST.plan((N,)*3, axis, np.float, {})
    if hasattr(ST, 'bc'):
        ST.bc.set_tensor_bcs(ST) # To set Dirichlet boundary conditions on multidimensional array
    ck = np.zeros_like(fk)
    ck = B.solve(fk, ck, axis=axis)
    cc = [0,]*3
    cc[axis] = slice(None)
    assert np.allclose(ck[cc], c)

#test_axis(cbases.ShenDirichletBasis, "GC", 1)

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 get_local_mesh(self):
        """Returns the local decomposed physical mesh"""
        X = np.ogrid[self.rank*self.Np[0]:(self.rank+1)*self.Np[0],
                     :self.N[1], :self.N[2]]
        X[0] = (X[0]*self.L[0]/self.N[0]).astype(self.float)
        X[1] = (X[1]*self.L[1]/self.N[1]).astype(self.float)
        X[2] = (X[2]*self.L[2]/self.N[2]).astype(self.float)
        X = [np.broadcast_to(x, self.real_shape()) for x in X]
        return X

def get_local_wavenumbermesh(self, scaled=False, broadcast=False, eliminate_highest_freq=False):
        """Returns (scaled) local decomposed wavenumbermesh

        If scaled is True, then the wavenumbermesh is scaled with physical mesh
        size. This takes care of mapping the physical domain to a computational
        cube of size (2pi)**3.

        If eliminate_highest_freq is True, then the Nyquist frequency is set to zero.
        """
        kx, ky, kz = self.complex_local_wavenumbers()
        if eliminate_highest_freq:
            ky = fftfreq(self.N[1], 1./self.N[1])
            for i, k in enumerate((kx, ky, kz)):
                if self.N[i] % 2 == 0:
                    k[self.N[i]//2] = 0
            ky = ky[self.complex_local_slice()[1]]

        Ks = np.meshgrid(kx, ky, kz, indexing='ij', sparse=True)
        if scaled:
            Lp = 2*np.pi/self.L
            for i in range(3):
                Ks[i] *= Lp[i]
        K = Ks
        if broadcast is True:
            K = [np.broadcast_to(k, self.complex_shape()) for k in Ks]
        return K

def get_local_mesh(self):
        xzrank = self.comm0.Get_rank() # Local rank in xz-plane
        xyrank = self.comm1.Get_rank() # Local rank in xy-plane

        # Create the physical mesh
        x1 = slice(xzrank * self.N1[0], (xzrank+1) * self.N1[0], 1)
        x2 = slice(xyrank * self.N2[1], (xyrank+1) * self.N2[1], 1)
        X = np.ogrid[x1, x2, :self.N[2]]

        X[0] = (X[0]*self.L[0]/self.N[0]).astype(self.float)
        X[1] = (X[1]*self.L[1]/self.N[1]).astype(self.float)
        X[2] = (X[2]*self.L[2]/self.N[2]).astype(self.float)
        X = [np.broadcast_to(x, self.real_shape()) for x in X]
        return X

def get_local_wavenumbermesh(self, scaled=False, broadcast=False,
                                 eliminate_highest_freq=False):
        """Returns (scaled) local decomposed wavenumbermesh

        If scaled is True, then the wavenumbermesh is scaled with physical mesh
        size. This takes care of mapping the physical domain to a computational
        cube of size (2pi)**3


        """
        s = self.complex_local_slice()
        kx = fftfreq(self.N[0], 1./self.N[0]).astype(int)
        ky = fftfreq(self.N[1], 1./self.N[1]).astype(int)
        kz = rfftfreq(self.N[2], 1./self.N[2]).astype(int)
        if eliminate_highest_freq:
            for i, k in enumerate((kx, ky, kz)):
                if self.N[i] % 2 == 0:
                    k[self.N[i]//2] = 0
        kx = kx[s[0]]
        kz = kz[s[2]]
        Ks = np.meshgrid(kx, ky, kz, indexing='ij', sparse=True)
        if scaled is True:
            Lp = 2*np.pi/self.L
            for i in range(3):
                Ks[i] = (Ks[i]*Lp[i]).astype(self.float)
        K = Ks
        if broadcast is True:
            K = [np.broadcast_to(k, self.complex_shape()) for k in Ks]
        return K

def scalar_broadcast_match(a, b):
    """ Returns arguments as np.array, if one is a scalar it will broadcast the other one's shape.
    """
    a, b = np.atleast_1d(a, b)
    if a.size == 1 and b.size != 1:
        a = np.broadcast_to(a, b.shape)
    elif b.size == 1 and a.size != 1:
        b = np.broadcast_to(b, a.shape)
    return a, b

def predict(self, input_x):
        if isinstance(input_x, chainer.Variable):
            device = cuda.get_device(input_x.data)
        else:
            device = cuda.get_device(input_x)
        xp = self.predictor.xp
        with device:
            output = self.predictor(input_x)
            batch_size, input_channel, input_h, input_w = input_x.shape
            batch_size, _, grid_h, grid_w = output.shape
            x, y, w, h, conf, prob = F.split_axis(F.reshape(output, (batch_size, self.predictor.n_boxes, self.predictor.n_classes+5, grid_h, grid_w)), (1, 2, 3, 4, 5), axis=2)
            x = F.sigmoid(x)
            y = F.sigmoid(y)
            conf = F.sigmoid(conf)
            prob = F.transpose(prob, (0, 2, 1, 3, 4))
            prob = F.softmax(prob)
            prob = F.transpose(prob, (0, 2, 1, 3, 4))


            # convert coordinates to those on the image
            x_shift = xp.asarray(np.broadcast_to(np.arange(grid_w, dtype=np.float32), x.shape))
            y_shift = xp.asarray(np.broadcast_to(np.arange(grid_h, dtype=np.float32).reshape(grid_h, 1), y.shape))
            w_anchor = xp.asarray(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 0], (self.predictor.n_boxes, 1, 1, 1)), w.shape))
            h_anchor = xp.asarray(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 1], (self.predictor.n_boxes, 1, 1, 1)), h.shape))
            box_x = (x + x_shift) / grid_w
            box_y = (y + y_shift) / grid_h
            box_w = F.exp(w) * w_anchor / grid_w
            box_h = F.exp(h) * h_anchor / grid_h

            return box_x, box_y, box_w, box_h, conf, prob

def predict(self, input_x):
        if isinstance(input_x, chainer.Variable):
            device = cuda.get_device(input_x.data)
        else:
            device = cuda.get_device(input_x)
        xp = self.predictor.xp
        with device:
            output = self.predictor(input_x)
            batch_size, input_channel, input_h, input_w = input_x.shape
            batch_size, _, grid_h, grid_w = output.shape
            x, y, w, h, conf, prob = F.split_axis(F.reshape(output, (batch_size, self.predictor.n_boxes, self.predictor.n_classes+5, grid_h, grid_w)), (1, 2, 3, 4, 5), axis=2)
            x = F.sigmoid(x)
            y = F.sigmoid(y)
            conf = F.sigmoid(conf)
            prob = F.transpose(prob, (0, 2, 1, 3, 4))
            prob = F.softmax(prob)
            prob = F.transpose(prob, (0, 2, 1, 3, 4))


            # convert coordinates to those on the image
            x_shift = xp.asarray(np.broadcast_to(np.arange(grid_w, dtype=np.float32), x.shape))
            y_shift = xp.asarray(np.broadcast_to(np.arange(grid_h, dtype=np.float32).reshape(grid_h, 1), y.shape))
            w_anchor = xp.asarray(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 0], (self.predictor.n_boxes, 1, 1, 1)), w.shape))
            h_anchor = xp.asarray(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 1], (self.predictor.n_boxes, 1, 1, 1)), h.shape))
            box_x = (x + x_shift) / grid_w
            box_y = (y + y_shift) / grid_h
            box_w = F.exp(w) * w_anchor / grid_w
            box_h = F.exp(h) * h_anchor / grid_h

            return box_x, box_y, box_w, box_h, conf, prob

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 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.data

        # 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_pool_average_3d(ndarray_1x1x4x4):
    x = np.broadcast_to(ndarray_1x1x4x4, (1, 1, 4, 4, 4))

    node = onnx.helper.make_node('AveragePool', inputs=['x'], outputs=['y'],
                                 kernel_shape=(2, 2, 2), strides=(2, 2, 2))
    y = np.array([[[13.5, 15.5],
                   [21.5, 23.5]],

                  [[13.5, 15.5],
                   [21.5, 23.5]]], dtype=np.float32).reshape(1, 1, 2, 2, 2)
    ng_results = convert_and_calculate(node, [x], [y])

    assert np.array_equal(ng_results, [y])

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 = convert_and_calculate(node, [x], [y])
    assert np.array_equal(ng_results, [y])

def get_tgt_vec(self,r):
        """
        Computes the target vector `g` in the above description
        """
        r0,r1 = r
        g0 = 1/self.rsqrt0*r0
        if self.wvar_pos:
            gout = 1/self.wsqrt*np.broadcast_to(self.b,self.shape1)
            g1 = 1/self.rsqrt1*r1            
            g = np.hstack((gout.ravel(),g0.ravel(),g1.ravel()))
        else:
            g1 = 1/self.rsqrt1*(r1-self.b)
            g = np.hstack((g0.ravel(),g1.ravel()))
        return g

def forward_cpu(self, inputs):
        x, t = inputs
        if chainer.is_debug():
            self._check_input_values(x, t)

        log_y = log_softmax._log_softmax(x, self.use_cudnn)
        if self.cache_score:
            self.y = numpy.exp(log_y)
        if self.class_weight is not None:
            if self.class_weight.shape != x.shape:
                shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)]
                self.class_weight = numpy.broadcast_to(
                    self.class_weight.reshape(shape), x.shape)
            log_y *= self.class_weight
        log_yd = numpy.rollaxis(log_y, 1)
        log_yd = log_yd.reshape(len(log_yd), -1)
        log_p = log_yd[numpy.maximum(t.ravel(), 0), numpy.arange(t.size)]

        # deal with the case where the SoftmaxCrossEntropy is
        # unpickled from the old version
        if self.normalize:
            count = (t != self.ignore_label).sum()
        else:
            count = len(x)
        self._coeff = 1.0 / max(count, 1)

        y = (log_p * (t.ravel() != self.ignore_label)).sum(keepdims=True) \
            * (-self._coeff)
        return y.reshape(()),

def forward_gpu(self, inputs):
        cupy = cuda.cupy
        x, t = inputs
        if chainer.is_debug():
            self._check_input_values(x, t)

        log_y = log_softmax._log_softmax(x, self.use_cudnn)
        if self.cache_score:
            self.y = cupy.exp(log_y)
        if self.class_weight is not None:
            shape = [1 if d != 1 else -1 for d in six.moves.range(x.ndim)]
            log_y *= cupy.broadcast_to(
                self.class_weight.reshape(shape), x.shape)
        if self.normalize:
            coeff = cupy.maximum(1, (t != self.ignore_label).sum())
        else:
            coeff = max(1, len(t))
        self._coeff = cupy.divide(1.0, coeff, dtype=x.dtype)

        log_y = cupy.rollaxis(log_y, 1, log_y.ndim)
        ret = cuda.reduce(
            'S t, raw T log_y, int32 n_channel, raw T coeff', 'T out',
            't == -1 ? T(0) : log_y[_j * n_channel + t]',
            'a + b', 'out = a * -coeff[0]', '0', 'crossent_fwd'
        )(t, log_y.reduced_view(), log_y.shape[-1], self._coeff)
        return ret,

def backward_cpu(self, inputs, grad_outputs):
        x, t = inputs
        gloss = grad_outputs[0]
        if hasattr(self, 'y'):
            y = self.y.copy()
        else:
            y = log_softmax._log_softmax(x, self.use_cudnn)
            numpy.exp(y, out=y)
        if y.ndim == 2:
            gx = y
            gx[numpy.arange(len(t)), numpy.maximum(t, 0)] -= 1
            if self.class_weight is not None:
                c = self.class_weight[
                    numpy.arange(len(t)), numpy.maximum(t, 0)]
                gx *= numpy.broadcast_to(numpy.expand_dims(c, 1), gx.shape)
            gx *= (t != self.ignore_label).reshape((len(t), 1))
        else:
            # in the case where y.ndim is higher than 2,
            # we think that a current implementation is inefficient
            # because it yields two provisional arrays for indexing.
            n_unit = t.size // len(t)
            gx = y.reshape(y.shape[0], y.shape[1], -1)
            fst_index = numpy.arange(t.size) // n_unit
            trd_index = numpy.arange(t.size) % n_unit
            gx[fst_index, numpy.maximum(t.ravel(), 0), trd_index] -= 1
            if self.class_weight is not None:
                c = self.class_weight.reshape(gx.shape)
                c = c[fst_index, numpy.maximum(t.ravel(), 0), trd_index]
                c = c.reshape(y.shape[0], 1, -1)
                gx *= numpy.broadcast_to(c, gx.shape)
            gx *= (t != self.ignore_label).reshape((len(t), 1, -1))
            gx = gx.reshape(y.shape)
        gx *= gloss * self._coeff
        return gx, None

def broadcast_mgrid(arrays):
    shape = tuple(map(len, arrays))
    ndim = len(shape)
    result = []
    for i, arr in enumerate(arrays, start=1):
        reshaped = np.broadcast_to(arr[[...] + [np.newaxis] * (ndim - i)],
                                   shape)
        result.append(reshaped)
    return result

def numba_csgraph(csr, node_props=None):
    if node_props is None:
        node_props = np.broadcast_to(1., csr.shape[0])
        node_props.flags.writeable = True
    return CSGraph(csr.indptr, csr.indices, csr.data,
                   np.array(csr.shape, dtype=np.int32), node_props)

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 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 _fd(self, xi, idx, delta):
        """Calculate the derivative along the given index using central finite difference.

        Parameters
        ----------
        xi : array_like
            The coordinates to evaluate, with shape (..., ndim)
        idx : int
            The index to calculate the derivative on.
        delta : float
            The finite difference step size.

        Returns
        -------
        val : np.multiarray.ndarray
            The derivatives at the given coordinates.
        """
        if idx < 0 or idx >= self.ndim:
            raise ValueError('Invalid derivative index: %d' % idx)

        xi = np.asarray(xi, dtype=float)
        if xi.shape[-1] != self.ndim:
            raise ValueError("The requested sample points xi have dimension %d, "
                             "but this interpolator has dimension %d" % (xi.shape[-1], self.ndim))

        # use broadcasting to evaluate two points at once
        xtest = np.broadcast_to(xi, (2,) + xi.shape).copy()
        xtest[0, ..., idx] += delta / 2.0
        xtest[1, ..., idx] -= delta / 2.0
        val = self(xtest)
        ans = (val[0] - val[1]) / delta  # type: np.ndarray

        if ans.size == 1 and not np.isscalar(ans):
            return ans[0]
        return ans

def _fd_jacobian(self, xi, delta_list):
        """Calculate the Jacobian matrix using central finite difference.

        Parameters
        ----------
        xi : array_like
            The coordinates to evaluate, with shape (..., ndim)
        delta_list : List[float]
            list of finite difference step sizes for each input.

        Returns
        -------
        val : np.multiarray.ndarray
            The Jacobian matrices at the given coordinates.
        """
        xi = np.asarray(xi, dtype=float)
        if xi.shape[-1] != self.ndim:
            raise ValueError("The requested sample points xi have dimension %d, "
                             "but this interpolator has dimension %d" % (xi.shape[-1], self.ndim))

        # use broadcasting to evaluate all points at once
        xtest = np.broadcast_to(xi, (2 * self.ndim,) + xi.shape).copy()
        for idx, delta in enumerate(delta_list):
            xtest[2 * idx, ..., idx] += delta / 2.0
            xtest[2 * idx + 1, ..., idx] -= delta / 2.0

        val = self(xtest)
        ans = np.empty(xi.shape)
        for idx, delta in enumerate(delta_list):
            ans[..., idx] = (val[2 * idx, ...] - val[2 * idx + 1, ...]) / delta
        return ans

def _fix_priors_shape(self):
        # If priors are numbers, this function will make them into a
        # matrix of proper shape
        self.weights_prior = np.broadcast_to(
            self.weights_prior, (self.n_components, self.n_mix)).copy()
        self.means_prior = np.broadcast_to(
            self.means_prior,
            (self.n_components, self.n_mix, self.n_features)).copy()
        self.means_weight = np.broadcast_to(
            self.means_weight,
            (self.n_components, self.n_mix)).copy()

        if self.covariance_type == "full":
            self.covars_prior = np.broadcast_to(
                self.covars_prior,
                (self.n_components, self.n_mix,
                 self.n_features, self.n_features)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight, (self.n_components, self.n_mix)).copy()
        elif self.covariance_type == "tied":
            self.covars_prior = np.broadcast_to(
                self.covars_prior,
                (self.n_components, self.n_features, self.n_features)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight, self.n_components).copy()
        elif self.covariance_type == "diag":
            self.covars_prior = np.broadcast_to(
                self.covars_prior,
                (self.n_components, self.n_mix, self.n_features)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight,
                (self.n_components, self.n_mix, self.n_features)).copy()
        elif self.covariance_type == "spherical":
            self.covars_prior = np.broadcast_to(
                self.covars_prior, (self.n_components, self.n_mix)).copy()
            self.covars_weight = np.broadcast_to(
                self.covars_weight, (self.n_components, self.n_mix)).copy()

def _grid_distance(self, index):
        """
        Calculate the distance grid for a single index position.

        This is pre-calculated for fast neighborhood calculations
        later on (see _calc_influence).
        """
        # Take every dimension but the first in reverse
        # then reverse that list again.
        dimensions = np.cumprod(self.map_dimensions[1::][::-1])[::-1]

        coord = []
        for idx, dim in enumerate(dimensions):
            if idx != 0:
                value = (index % dimensions[idx-1]) // dim
            else:
                value = index // dim
            coord.append(value)

        coord.append(index % self.map_dimensions[-1])

        for idx, (width, row) in enumerate(zip(self.map_dimensions, coord)):
            x = np.abs(np.arange(width) - row) ** 2
            dims = self.map_dimensions[::-1]
            if idx:
                dims = dims[:-idx]
            x = np.broadcast_to(x, dims).T
            if idx == 0:
                distance = np.copy(x)
            else:
                distance += x.T

        return distance

def patCycles(s0, s1=50, return_x=False):
    arr = np.zeros(s0)
    p = 1
    t = 1
    c = 0
    x,y = [],[]
    while True:
        arr[p:p+t] = 1
        p+=t
        x.append(2*t)
        y.append(p+0.5*t)
        if c > s1:
            t+=1

        c += 2
        p+=t

        if p>s0:
            break

    arr = arr[::-1]
    arr = np.broadcast_to(arr, (s1, s0))
    if return_x:
        #cycles/px:
        x =np.interp(np.arange(s0), y, x)  
        return arr,1/x[::-1]
    else:
        return arr

def compute_max_unpooling_nd(data, pooling, size, step, dimension: int):
    result = []
    data_prefix_shape = data.shape[:-dimension]
    kernel_prefix_shape = pooling.shape[:-dimension]
    final_shape = element_wise_shape(data_prefix_shape, kernel_prefix_shape)[0]
    data = numpy.broadcast_to(data, final_shape + data.shape[-dimension:])
    pooling = numpy.broadcast_to(pooling, final_shape + pooling.shape[-dimension:])
    if final_shape:
        for key in array_index_traversal(final_shape):
            result.append(__compute_max_unpooling_nd(data[key], pooling[key], size, step, dimension))
        return numpy.array(result).reshape(final_shape + result[0].shape)
    else:
        return __compute_max_unpooling_nd(data, pooling, size, step, dimension)

def compute(self, node, input_vals, output_val, use_numpy=True):
        assert len(input_vals) == 2
        if use_numpy:
            output_val[:] = np.broadcast_to(input_vals[0], input_vals[1].shape)
        else:
            gpu_op.broadcast_to(input_vals[0], output_val)

def test_broadcast_to():
    ctx = ndarray.gpu(0)
    shape = (200, 300)
    to_shape = (130, 200, 300)
    x = np.random.uniform(-1, 1, shape).astype(np.float32)
    arr_x = ndarray.array(x, ctx=ctx)
    arr_y = ndarray.empty(to_shape, ctx=ctx)
    gpu_op.broadcast_to(arr_x, arr_y)
    y = arr_y.asnumpy()
    np.testing.assert_allclose(np.broadcast_to(x, to_shape), y)