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| <h1>Source code for dpnp.dpnp_iface_counting</h1><div class="highlight"><pre> | ||
| <span></span><span class="c1"># -*- coding: utf-8 -*-</span> | ||
| <span class="c1"># *****************************************************************************</span> | ||
| <span class="c1"># Copyright (c) 2016-2024, Intel Corporation</span> | ||
| <span class="c1"># All rights reserved.</span> | ||
| <span class="c1">#</span> | ||
| <span class="c1"># Redistribution and use in source and binary forms, with or without</span> | ||
| <span class="c1"># modification, are permitted provided that the following conditions are met:</span> | ||
| <span class="c1"># - Redistributions of source code must retain the above copyright notice,</span> | ||
| <span class="c1"># this list of conditions and the following disclaimer.</span> | ||
| <span class="c1"># - Redistributions in binary form must reproduce the above copyright notice,</span> | ||
| <span class="c1"># this list of conditions and the following disclaimer in the documentation</span> | ||
| <span class="c1"># and/or other materials provided with the distribution.</span> | ||
| <span class="c1">#</span> | ||
| <span class="c1"># THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"</span> | ||
| <span class="c1"># AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE</span> | ||
| <span class="c1"># IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE</span> | ||
| <span class="c1"># ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE</span> | ||
| <span class="c1"># LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR</span> | ||
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| <span class="c1"># ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF</span> | ||
| <span class="c1"># THE POSSIBILITY OF SUCH DAMAGE.</span> | ||
| <span class="c1"># *****************************************************************************</span> | ||
|
|
||
| <span class="sd">"""</span> | ||
| <span class="sd">Interface of the counting function of the dpnp</span> | ||
|
|
||
| <span class="sd">Notes</span> | ||
| <span class="sd">-----</span> | ||
| <span class="sd">This module is a face or public interface file for the library</span> | ||
| <span class="sd">it contains:</span> | ||
| <span class="sd"> - Interface functions</span> | ||
| <span class="sd"> - documentation for the functions</span> | ||
| <span class="sd"> - The functions parameters check</span> | ||
|
|
||
| <span class="sd">"""</span> | ||
|
|
||
| <span class="kn">import</span> <span class="nn">dpctl.tensor</span> <span class="k">as</span> <span class="nn">dpt</span> | ||
|
|
||
| <span class="kn">import</span> <span class="nn">dpnp</span> | ||
|
|
||
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"count_nonzero"</span><span class="p">]</span> | ||
|
|
||
|
|
||
| <div class="viewcode-block" id="count_nonzero"> | ||
| <a class="viewcode-back" href="../../reference/generated/dpnp.count_nonzero.html#dpnp.count_nonzero">[docs]</a> | ||
| <span class="k">def</span> <span class="nf">count_nonzero</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> | ||
| <span class="w"> </span><span class="sd">"""</span> | ||
| <span class="sd"> Counts the number of non-zero values in the array `a`.</span> | ||
|
|
||
| <span class="sd"> For full documentation refer to :obj:`numpy.count_nonzero`.</span> | ||
|
|
||
| <span class="sd"> Parameters</span> | ||
| <span class="sd"> ----------</span> | ||
| <span class="sd"> a : {dpnp.ndarray, usm_ndarray}</span> | ||
| <span class="sd"> The array for which to count non-zeros.</span> | ||
| <span class="sd"> axis : {None, int, tuple}, optional</span> | ||
| <span class="sd"> Axis or tuple of axes along which to count non-zeros.</span> | ||
| <span class="sd"> Default value means that non-zeros will be counted along a flattened</span> | ||
| <span class="sd"> version of `a`.</span> | ||
| <span class="sd"> Default: ``None``.</span> | ||
| <span class="sd"> keepdims : bool, optional</span> | ||
| <span class="sd"> If this is set to ``True``, the axes that are counted are left in the</span> | ||
| <span class="sd"> result as dimensions with size one. With this option, the result will</span> | ||
| <span class="sd"> broadcast correctly against the input array.</span> | ||
| <span class="sd"> Default: ``False``.</span> | ||
| <span class="sd"> out : {None, dpnp.ndarray, usm_ndarray}, optional</span> | ||
| <span class="sd"> The array into which the result is written. The data type of `out` must</span> | ||
| <span class="sd"> match the expected shape and the expected data type of the result.</span> | ||
| <span class="sd"> If ``None`` then a new array is returned.</span> | ||
| <span class="sd"> Default: ``None``.</span> | ||
|
|
||
| <span class="sd"> Returns</span> | ||
| <span class="sd"> -------</span> | ||
| <span class="sd"> out : dpnp.ndarray</span> | ||
| <span class="sd"> Number of non-zero values in the array along a given axis.</span> | ||
| <span class="sd"> Otherwise, a zero-dimensional array with the total number of non-zero</span> | ||
| <span class="sd"> values in the array is returned.</span> | ||
|
|
||
| <span class="sd"> See Also</span> | ||
| <span class="sd"> --------</span> | ||
| <span class="sd"> :obj:`dpnp.nonzero` : Return the coordinates of all the non-zero values.</span> | ||
|
|
||
| <span class="sd"> Examples</span> | ||
| <span class="sd"> --------</span> | ||
| <span class="sd"> >>> import dpnp as np</span> | ||
| <span class="sd"> >>> np.count_nonzero(np.eye(4))</span> | ||
| <span class="sd"> array(4)</span> | ||
| <span class="sd"> >>> a = np.array([[0, 1, 7, 0],</span> | ||
| <span class="sd"> [3, 0, 2, 19]])</span> | ||
| <span class="sd"> >>> np.count_nonzero(a)</span> | ||
| <span class="sd"> array(5)</span> | ||
| <span class="sd"> >>> np.count_nonzero(a, axis=0)</span> | ||
| <span class="sd"> array([1, 1, 2, 1])</span> | ||
| <span class="sd"> >>> np.count_nonzero(a, axis=1)</span> | ||
| <span class="sd"> array([2, 3])</span> | ||
| <span class="sd"> >>> np.count_nonzero(a, axis=1, keepdims=True)</span> | ||
| <span class="sd"> array([[2],</span> | ||
| <span class="sd"> [3]])</span> | ||
|
|
||
| <span class="sd"> """</span> | ||
|
|
||
| <span class="n">usm_a</span> <span class="o">=</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">get_usm_ndarray</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> | ||
| <span class="n">usm_out</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">get_usm_ndarray</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> | ||
|
|
||
| <span class="n">usm_res</span> <span class="o">=</span> <span class="n">dpt</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span> | ||
| <span class="n">usm_a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="n">keepdims</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">usm_out</span> | ||
| <span class="p">)</span> | ||
| <span class="k">return</span> <span class="n">dpnp</span><span class="o">.</span><span class="n">get_result_array</span><span class="p">(</span><span class="n">usm_res</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span></div> | ||
|
|
||
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