dpnp.identity

dpnp.identity(n, /, dtype=None, *, like=None, device=None, usm_type='device', sycl_queue=None)[source]

Return the identity array.

The identity array is a square array with ones on the main diagonal.

For full documentation refer to numpy.identity.

Parameters:

  • n (int) -- Number of rows (and columns) in n x n output.

  • dtype ({None, dtype}, optional) -- The desired dtype for the array, e.g., dpnp.int32. Default is the default floating point data type for the device where input array is allocated.

  • 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 is "device".

  • sycl_queue ({None, SyclQueue}, optional) -- A SYCL queue to use for output array allocation and copying.

Returns:

out -- n x n array with its main diagonal set to one, and all other elements 0.

Return type:

dpnp.ndarray

Limitations

Parameter like is currently not supported. Otherwise, the function raises NotImplementedError exception.

See also

dpnp.eye

Return a 2-D array with ones on the diagonal and zeros elsewhere.

dpnp.ones

Return a new array setting values to one.

dpnp.diag

Return diagonal 2-D array from an input 1-D array.

Examples

>>> import dpnp as np
>>> np.identity(3)
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])

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

>>> x = np.identity(3) # default case
>>> x, x.device, x.usm_type
(array([[1.,  0.,  0.],
        [0.,  1.,  0.],
        [0.,  0.,  1.]]), Device(level_zero:gpu:0), 'device')

>>> y = np.identity(3, device="cpu")
>>> y, y.device, y.usm_type
(array([[1.,  0.,  0.],
        [0.,  1.,  0.],
        [0.,  0.,  1.]]), Device(opencl:cpu:0), 'device')

>>> z = np.identity(3, usm_type="host")
>>> z, z.device, z.usm_type
(array([[1.,  0.,  0.],
        [0.,  1.,  0.],
        [0.,  0.,  1.]]), Device(level_zero:gpu:0), 'host')