Python numpy.broadcast_arrays方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False):
    # Broadcast two shapes against each other and check that the data layout
    # is the same as if a ufunc did the broadcasting.

    x0 = np.zeros(shape0, dtype=int)
    # Note that multiply.reduce's identity element is 1.0, so when shape1==(),
    # this gives the desired n==1.
    n = int(np.multiply.reduce(shape1))
    x1 = np.arange(n).reshape(shape1)
    if transposed:
        x0 = x0.T
        x1 = x1.T
    if flipped:
        x0 = x0[::-1]
        x1 = x1[::-1]
    # Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the
    # result should be exactly the same as the broadcasted view of x1.
    y = x0 + x1
    b0, b1 = broadcast_arrays(x0, x1)
    assert_array_equal(y, b1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_writeable():
    # broadcast_to should return a readonly array
    original = np.array([1, 2, 3])
    result = broadcast_to(original, (2, 3))
    assert_equal(result.flags.writeable, False)
    assert_raises(ValueError, result.__setitem__, slice(None), 0)

    # but the result of broadcast_arrays needs to be writeable (for now), to
    # preserve backwards compatibility
    for results in [broadcast_arrays(original),
                    broadcast_arrays(0, original)]:
        for result in results:
            assert_equal(result.flags.writeable, True)
    # keep readonly input readonly
    original.flags.writeable = False
    _, result = broadcast_arrays(0, original)
    assert_equal(result.flags.writeable, False)

    # regression test for GH6491
    shape = (2,)
    strides = [0]
    tricky_array = as_strided(np.array(0), shape, strides)
    other = np.zeros((1,))
    first, second = broadcast_arrays(tricky_array, other)
    assert_(first.shape == second.shape) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def loglike(self, y, f):
        r"""
        Bernoulli log likelihood.

        Parameters
        ----------
        y: ndarray
            array of 0, 1 valued integers of targets
        f: ndarray
            latent function from the GLM prior (:math:`\mathbf{f} =
            \boldsymbol\Phi \mathbf{w}`)

        Returns
        -------
        logp: ndarray
            the log likelihood of each y given each f under this
            likelihood.
        """
        # way faster than calling bernoulli.logpmf
        y, f = np.broadcast_arrays(y, f)
        ll = y * f - softplus(f)
        return ll 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def df(self, y, f):
        r"""
        Derivative of Bernoulli log likelihood w.r.t.\  f.

        Parameters
        ----------
        y: ndarray
            array of 0, 1 valued integers of targets
        f: ndarray
            latent function from the GLM prior (:math:`\mathbf{f} =
            \boldsymbol\Phi \mathbf{w}`)

        Returns
        -------
        df: ndarray
            the derivative :math:`\partial \log p(y|f) / \partial f`
        """
        y, f = np.broadcast_arrays(y, f)
        return y - expit(f) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def __setitem__(self, index, x):
        # Process arrays from IndexMixin
        i, j = self._unpack_index(index)
        i, j = self._index_to_arrays(i, j)

        if isspmatrix(x):
            x = x.toarray()

        # Make x and i into the same shape
        x = np.asarray(x, dtype=self.dtype)
        x, _ = np.broadcast_arrays(x, i)

        if x.shape != i.shape:
            raise ValueError("shape mismatch in assignment")

        # Set values
        for ii, jj, xx in zip(i.ravel(), j.ravel(), x.ravel()):
            self._set_one(ii, jj, xx) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def _prep_smooth(t, y, dy, span, t_out, span_out, period):
    """Private function to prepare & check variables for smooth utilities"""

    # If period is provided, sort by phases. Otherwise sort by t
    if period:
        t = t % period
        if t_out is not None:
            t_out = t_out % period

    t, y, dy = validate_inputs(t, y, dy, sort_by=t)

    if span_out is not None:
        if t_out is None:
            raise ValueError("Must specify t_out when span_out is given")
        if span is not None:
            raise ValueError("Must specify only one of span, span_out")
        span, t_out = np.broadcast_arrays(span_out, t_out)
        indices = np.searchsorted(t, t_out)
    elif span is None:
        raise ValueError("Must specify either span_out or span")
    else:
        indices = None

    return t, y, dy, span, t_out, span_out, indices 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_square_rescale_manual(self):
        points = np.array([(0,0), (0,100), (10,100), (10,0), (1, 5)], dtype=np.double)
        points_rescaled = np.array([(0,0), (0,1), (1,1), (1,0), (0.1, 0.05)], dtype=np.double)
        values = np.array([1., 2., -3., 5., 9.], dtype=np.double)

        xx, yy = np.broadcast_arrays(np.linspace(0, 10, 14)[:,None],
                                     np.linspace(0, 100, 14)[None,:])
        xx = xx.ravel()
        yy = yy.ravel()
        xi = np.array([xx, yy]).T.copy()

        for method in ('nearest', 'linear', 'cubic'):
            msg = method
            zi = griddata(points_rescaled, values, xi/np.array([10, 100.]),
                          method=method)
            zi_rescaled = griddata(points, values, xi, method=method,
                                   rescale=True)
            assert_allclose(zi, zi_rescaled, err_msg=msg,
                            atol=1e-12) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_square_rescale(self):
        # Test barycentric interpolation on a rectangle with rescaling
        # agaings the same implementation without rescaling

        points = np.array([(0,0), (0,100), (10,100), (10,0)], dtype=np.double)
        values = np.array([1., 2., -3., 5.], dtype=np.double)

        xx, yy = np.broadcast_arrays(np.linspace(0, 10, 14)[:,None],
                                     np.linspace(0, 100, 14)[None,:])
        xx = xx.ravel()
        yy = yy.ravel()
        xi = np.array([xx, yy]).T.copy()
        zi = interpnd.LinearNDInterpolator(points, values)(xi)
        zi_rescaled = interpnd.LinearNDInterpolator(points, values,
                rescale=True)(xi)

        assert_almost_equal(zi, zi_rescaled) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def broadcast_arrays(ys, *args):
  d[args] = tuple(tangent.unbroadcast_to(dy, numpy.shape(arg))
                  for arg, dy in zip(args, d[ys])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_sparse(self):
        grid_full   = mgrid[-1:1:10j, -2:2:10j]
        grid_sparse = ogrid[-1:1:10j, -2:2:10j]

        # sparse grids can be made dense by broadcasting
        grid_broadcast = np.broadcast_arrays(*grid_sparse)
        for f, b in zip(grid_full, grid_broadcast):
            assert_equal(f, b) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def assert_shapes_correct(input_shapes, expected_shape):
    # Broadcast a list of arrays with the given input shapes and check the
    # common output shape.

    inarrays = [np.zeros(s) for s in input_shapes]
    outarrays = broadcast_arrays(*inarrays)
    outshapes = [a.shape for a in outarrays]
    expected = [expected_shape] * len(inarrays)
    assert_equal(outshapes, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def assert_incompatible_shapes_raise(input_shapes):
    # Broadcast a list of arrays with the given (incompatible) input shapes
    # and check that they raise a ValueError.

    inarrays = [np.zeros(s) for s in input_shapes]
    assert_raises(ValueError, broadcast_arrays, *inarrays) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_broadcast_kwargs():
    # ensure that a TypeError is appropriately raised when
    # np.broadcast_arrays() is called with any keyword
    # argument other than 'subok'
    x = np.arange(10)
    y = np.arange(10)

    with assert_raises_regex(TypeError,
                             r'broadcast_arrays\(\) got an unexpected keyword*'):
        broadcast_arrays(x, y, dtype='float64') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_one_off():
    x = np.array([[1, 2, 3]])
    y = np.array([[1], [2], [3]])
    bx, by = broadcast_arrays(x, y)
    bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]])
    by0 = bx0.T
    assert_array_equal(bx0, bx)
    assert_array_equal(by0, by) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import broadcast_arrays [as 別名]
def test_broadcast_shape():
    # broadcast_shape is already exercized indirectly by broadcast_arrays
    assert_equal(_broadcast_shape(), ())
    assert_equal(_broadcast_shape([1, 2]), (2,))
    assert_equal(_broadcast_shape(np.ones((1, 1))), (1, 1))
    assert_equal(_broadcast_shape(np.ones((1, 1)), np.ones((3, 4))), (3, 4))
    assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 32)), (1, 2))
    assert_equal(_broadcast_shape(*([np.ones((1, 2))] * 100)), (1, 2))

    # regression tests for gh-5862
    assert_equal(_broadcast_shape(*([np.ones(2)] * 32 + [1])), (2,))
    bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32
    assert_raises(ValueError, lambda: _broadcast_shape(*bad_args))