dpnp.vander

dpnp.vander(x, /, N=None, increasing=False, *, device=None, usm_type=None, sycl_queue=None)[source]

Generate a Vandermonde matrix.

For full documentation refer to numpy.vander.

Parameters:

  • x (array_like) -- 1-D input array, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays.

  • N ({None, int}, optional) -- Number of columns in the output. If N is not specified, a square array is returned (N = len(x)).

  • increasing (bool, optional) -- Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed.

  • device ({None, string, SyclDevice, SyclQueue}, optional) -- An array API concept of device where the output array is created. The device can be None (the default), an OneAPI filter selector string, an instance of dpctl.SyclDevice corresponding to a non-partitioned SYCL device, an instance of dpctl.SyclQueue, or a Device object returned by dpnp.dpnp_array.dpnp_array.device property.

  • usm_type ({None, "device", "shared", "host"}, optional) -- The type of SYCL USM allocation for the output array. Default: None.

  • sycl_queue ({None, SyclQueue}, optional) -- A SYCL queue to use for output array allocation and copying. The sycl_queue can be passed as None (the default), which means to get the SYCL queue from device keyword if present or to use a default queue. Default: None.

Returns:

out -- Vandermonde matrix.

Return type:

dpnp.ndarray

Examples

>>> import dpnp as np
>>> x0 = np.array([1, 2, 3, 5])
>>> N = 3
>>> np.vander(x0, N)
array([[ 1,  1,  1],
       [ 4,  2,  1],
       [ 9,  3,  1],
       [25,  5,  1]])

>>> np.vander(x0)
array([[  1,   1,   1,   1],
       [  8,   4,   2,   1],
       [ 27,   9,   3,   1],
       [125,  25,   5,   1]])

>>> np.vander(x0, increasing=True)
array([[  1,   1,   1,   1],
       [  1,   2,   4,   8],
       [  1,   3,   9,  27],
       [  1,   5,  25, 125]])

Creating an array on a different device or with a specified usm_type

>>> x = np.vander(x0) # default case
>>> x, x.device, x.usm_type
(array([[  1,   1,   1,   1],
        [  8,   4,   2,   1],
        [ 27,   9,   3,   1],
        [125,  25,   5,   1]]), Device(level_zero:gpu:0), 'device')

>>> y = np.vander(x0, device="cpu")
>>> y, y.device, y.usm_type
(array([[  1,   1,   1,   1],
        [  8,   4,   2,   1],
        [ 27,   9,   3,   1],
        [125,  25,   5,   1]]), Device(opencl:cpu:0), 'device')

>>> z = np.vander(x0, usm_type="host")
>>> z, z.device, z.usm_type
(array([[  1,   1,   1,   1],
        [  8,   4,   2,   1],
        [ 27,   9,   3,   1],
        [125,  25,   5,   1]]), Device(level_zero:gpu:0), 'host')