dpnp.multiply
- dpnp.multiply(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)
Calculates the product for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.
For full documentation refer to
numpy.multiply
.- Parameters:
x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time. If
x1.shape != x2.shape
, they must be broadcastable to a common shape (which becomes the shape of the output).out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type. Default:
None
.order ({"C", "F", "A", "K"}, optional) -- Memory layout of the newly output array, if parameter out is
None
. Default:"K"
.
- Returns:
out -- An array containing the element-wise products. The data type of the returned array is determined by the Type Promotion Rules.
- Return type:
dpnp.ndarray
Limitations
Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise
NotImplementedError
exception will be raised.Notes
Equivalent to x1 * x2 in terms of array broadcasting.
Examples
>>> import dpnp as np >>> a = np.array([1, 2, 3, 4, 5]) >>> np.multiply(a, a) array([ 1, 4, 9, 16, 25])]
>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.]])
The
*
operator can be used as a shorthand formultiply
ondpnp.ndarray
.>>> x1 * x2 array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.]])