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<h1>Source code for mxnet.ndarray.contrib</h1><div class="highlight"><pre>
<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
<span class="c1"># distributed with this work for additional information</span>
<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
<span class="c1"># "License"); you may not use this file except in compliance</span>
<span class="c1"># with the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
<span class="c1"># software distributed under the License is distributed on an</span>
<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
<span class="c1"># KIND, either express or implied. See the License for the</span>
<span class="c1"># specific language governing permissions and limitations</span>
<span class="c1"># under the License.</span>
<span class="c1"># coding: utf-8</span>
<span class="c1"># pylint: disable=wildcard-import, unused-wildcard-import</span>
<span class="sd">"""Contrib NDArray API of MXNet."""</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">..context</span> <span class="kn">import</span> <span class="n">current_context</span>
<span class="kn">from</span> <span class="nn">..random</span> <span class="kn">import</span> <span class="n">uniform</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">.gen_contrib</span> <span class="kn">import</span> <span class="o">*</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="k">pass</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"rand_zipfian"</span><span class="p">]</span>
<span class="c1"># pylint: disable=line-too-long</span>
<div class="viewcode-block" id="rand_zipfian"><a class="viewcode-back" href="../../../api/python/ndarray/contrib.html#mxnet.ndarray.contrib.rand_zipfian">[docs]</a><span class="k">def</span> <span class="nf">rand_zipfian</span><span class="p">(</span><span class="n">true_classes</span><span class="p">,</span> <span class="n">num_sampled</span><span class="p">,</span> <span class="n">range_max</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Draw random samples from an approximately log-uniform or Zipfian distribution.</span>
<span class="sd"> This operation randomly samples *num_sampled* candidates the range of integers [0, range_max).</span>
<span class="sd"> The elements of sampled_candidates are drawn with replacement from the base distribution.</span>
<span class="sd"> The base distribution for this operator is an approximately log-uniform or Zipfian distribution:</span>
<span class="sd"> P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)</span>
<span class="sd"> This sampler is useful when the true classes approximately follow such a distribution.</span>
<span class="sd"> For example, if the classes represent words in a lexicon sorted in decreasing order of \</span>
<span class="sd"> frequency. If your classes are not ordered by decreasing frequency, do not use this op.</span>
<span class="sd"> Additionaly, it also returns the number of times each of the \</span>
<span class="sd"> true classes and the sampled classes is expected to occur.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> true_classes : NDArray</span>
<span class="sd"> A 1-D NDArray of the target classes.</span>
<span class="sd"> num_sampled: int</span>
<span class="sd"> The number of classes to randomly sample.</span>
<span class="sd"> range_max: int</span>
<span class="sd"> The number of possible classes.</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `mu.context` when `mu` is an NDArray.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> samples: NDArray</span>
<span class="sd"> The sampled candidate classes in 1-D `int64` dtype.</span>
<span class="sd"> expected_count_true: NDArray</span>
<span class="sd"> The expected count for true classes in 1-D `float64` dtype.</span>
<span class="sd"> expected_count_sample: NDArray</span>
<span class="sd"> The expected count for sampled candidates in 1-D `float64` dtype.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> true_cls = mx.nd.array([3])</span>
<span class="sd"> >>> samples, exp_count_true, exp_count_sample = mx.nd.contrib.rand_zipfian(true_cls, 4, 5)</span>
<span class="sd"> >>> samples</span>
<span class="sd"> [1 3 3 3]</span>
<span class="sd"> <NDArray 4 @cpu(0)></span>
<span class="sd"> >>> exp_count_true</span>
<span class="sd"> [ 0.12453879]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>> exp_count_sample</span>
<span class="sd"> [ 0.22629439 0.12453879 0.12453879 0.12453879]</span>
<span class="sd"> <NDArray 4 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">ctx</span> <span class="o">=</span> <span class="n">current_context</span><span class="p">()</span>
<span class="n">log_range</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">range_max</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">rand</span> <span class="o">=</span> <span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">log_range</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_sampled</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float64'</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span>
<span class="c1"># make sure sampled_classes are in the range of [0, range_max)</span>
<span class="n">sampled_classes</span> <span class="o">=</span> <span class="p">(</span><span class="n">rand</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int64'</span><span class="p">)</span> <span class="o">%</span> <span class="n">range_max</span>
<span class="n">true_cls</span> <span class="o">=</span> <span class="n">true_classes</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span>
<span class="n">expected_count_true</span> <span class="o">=</span> <span class="p">((</span><span class="n">true_cls</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_cls</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">/</span> <span class="n">log_range</span> <span class="o">*</span> <span class="n">num_sampled</span>
<span class="c1"># cast sampled classes to fp64 to avoid interget division</span>
<span class="n">sampled_cls_fp64</span> <span class="o">=</span> <span class="n">sampled_classes</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float64'</span><span class="p">)</span>
<span class="n">expected_prob_sampled</span> <span class="o">=</span> <span class="p">((</span><span class="n">sampled_cls_fp64</span> <span class="o">+</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sampled_cls_fp64</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">/</span> <span class="n">log_range</span>
<span class="n">expected_count_sampled</span> <span class="o">=</span> <span class="n">expected_prob_sampled</span> <span class="o">*</span> <span class="n">num_sampled</span>
<span class="k">return</span> <span class="n">sampled_classes</span><span class="p">,</span> <span class="n">expected_count_true</span><span class="p">,</span> <span class="n">expected_count_sampled</span></div>
<span class="c1"># pylint: enable=line-too-long</span>
</pre></div>
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Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
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"Copyright © 2017-2018, The Apache Software Foundation
Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation."
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