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<h1>Source code for mxnet.ndarray.random</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="sd">"""Random distribution generator NDArray API of MXNet."""</span>
<span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">_Null</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">.</span> <span class="kn">import</span> <span class="n">_internal</span>
<span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'uniform'</span><span class="p">,</span> <span class="s1">'normal'</span><span class="p">,</span> <span class="s1">'poisson'</span><span class="p">,</span> <span class="s1">'exponential'</span><span class="p">,</span> <span class="s1">'gamma'</span><span class="p">,</span> <span class="s1">'multinomial'</span><span class="p">,</span>
<span class="s1">'negative_binomial'</span><span class="p">,</span> <span class="s1">'generalized_negative_binomial'</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_random_helper</span><span class="p">(</span><span class="n">random</span><span class="p">,</span> <span class="n">sampler</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Helper function for random generators."""</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">NDArray</span><span class="p">):</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">),</span> \
<span class="s2">"Distribution parameters must all have the same type, but got "</span> \
<span class="s2">"both </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
<span class="k">return</span> <span class="n">sampler</span><span class="p">(</span><span class="o">*</span><span class="n">params</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">numeric_types</span><span class="p">):</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="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="n">_Null</span> <span class="ow">and</span> <span class="n">out</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">shape</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">),</span> \
<span class="s2">"Distribution parameters must all have the same type, but got "</span> \
<span class="s2">"both </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
<span class="k">return</span> <span class="n">random</span><span class="p">(</span><span class="o">*</span><span class="n">params</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</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="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Distribution parameters must be either NDArray or numbers, "</span>
<span class="s2">"but got </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<div class="viewcode-block" id="uniform"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.uniform">[docs]</a><span class="k">def</span> <span class="nf">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Draw random samples from a uniform distribution.</span>
<span class="sd"> Samples are uniformly distributed over the half-open interval *[low, high)*</span>
<span class="sd"> (includes *low*, but excludes *high*).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> low : float or NDArray</span>
<span class="sd"> Lower boundary of the output interval. All values generated will be</span>
<span class="sd"> greater than or equal to low. The default value is 0.</span>
<span class="sd"> high : float or NDArray</span>
<span class="sd"> Upper boundary of the output interval. All values generated will be</span>
<span class="sd"> less than high. The default value is 1.0.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and</span>
<span class="sd"> `high` are scalars, output shape will be `(m, n)`. If `low` and `high`</span>
<span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `low.context` when `low` is an NDArray.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.uniform(0, 1)</span>
<span class="sd"> [ 0.54881352]</span>
<span class="sd"> <NDArray 1 @cpu(0)</span>
<span class="sd"> >>>> mx.nd.random.uniform(0, 1, ctx=mx.gpu(0))</span>
<span class="sd"> [ 0.92514056]</span>
<span class="sd"> <NDArray 1 @gpu(0)></span>
<span class="sd"> >>> mx.nd.random.uniform(-1, 1, shape=(2,))</span>
<span class="sd"> [[ 0.71589124 0.08976638]</span>
<span class="sd"> [ 0.69450343 -0.15269041]]</span>
<span class="sd"> <NDArray 2x2 @cpu(0)></span>
<span class="sd"> >>> low = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> high = mx.nd.array([2,3,4])</span>
<span class="sd"> >>> mx.nd.random.uniform(low, high, shape=2)</span>
<span class="sd"> [[ 1.78653979 1.93707538]</span>
<span class="sd"> [ 2.01311183 2.37081361]</span>
<span class="sd"> [ 3.30491424 3.69977832]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_uniform</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_uniform</span><span class="p">,</span>
<span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="normal"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.normal">[docs]</a><span class="k">def</span> <span class="nf">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Draw random samples from a normal (Gaussian) distribution.</span>
<span class="sd"> Samples are distributed according to a normal distribution parametrized</span>
<span class="sd"> by *loc* (mean) and *scale* (standard deviation).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> loc : float or NDArray</span>
<span class="sd"> Mean (centre) of the distribution.</span>
<span class="sd"> scale : float or NDArray</span>
<span class="sd"> Standard deviation (spread or width) of the distribution.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and</span>
<span class="sd"> `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`</span>
<span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `loc.context` when `loc` is an NDArray.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.normal(0, 1)</span>
<span class="sd"> [ 2.21220636]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>>> mx.nd.random.normal(0, 1, ctx=mx.gpu(0))</span>
<span class="sd"> [ 0.29253659]</span>
<span class="sd"> <NDArray 1 @gpu(0)></span>
<span class="sd"> >>> mx.nd.random.normal(-1, 1, shape=(2,))</span>
<span class="sd"> [-0.2259962 -0.51619542]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> loc = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> scale = mx.nd.array([2,3,4])</span>
<span class="sd"> >>> mx.nd.random.normal(loc, scale, shape=2)</span>
<span class="sd"> [[ 0.55912292 3.19566321]</span>
<span class="sd"> [ 1.91728961 2.47706747]</span>
<span class="sd"> [ 2.79666662 5.44254589]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_normal</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_normal</span><span class="p">,</span>
<span class="p">[</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="poisson"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.poisson">[docs]</a><span class="k">def</span> <span class="nf">poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Draw random samples from a Poisson distribution.</span>
<span class="sd"> Samples are distributed according to a Poisson distribution parametrized</span>
<span class="sd"> by *lambda* (rate). Samples will always be returned as a floating point data type.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> lam : float or NDArray</span>
<span class="sd"> Expectation of interval, should be >= 0.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `lam` is</span>
<span class="sd"> a scalar, output shape will be `(m, n)`. If `lam`</span>
<span class="sd"> is an NDArray with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `lam`.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `lam.context` when `lam` is an NDArray.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.poisson(1)</span>
<span class="sd"> [ 1.]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.poisson(1, shape=(2,))</span>
<span class="sd"> [ 0. 2.]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> lam = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> mx.nd.random.poisson(lam, shape=2)</span>
<span class="sd"> [[ 1. 3.]</span>
<span class="sd"> [ 3. 2.]</span>
<span class="sd"> [ 2. 3.]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_poisson</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_poisson</span><span class="p">,</span>
<span class="p">[</span><span class="n">lam</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="exponential"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.exponential">[docs]</a><span class="k">def</span> <span class="nf">exponential</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Draw samples from an exponential distribution.</span>
<span class="sd"> Its probability density function is</span>
<span class="sd"> f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),</span>
<span class="sd"> for x > 0 and 0 elsewhere. \beta is the scale parameter, which is the</span>
<span class="sd"> inverse of the rate parameter \lambda = 1/\beta.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> scale : float or NDArray</span>
<span class="sd"> The scale parameter, \beta = 1/\lambda.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `scale` is</span>
<span class="sd"> a scalar, output shape will be `(m, n)`. If `scale`</span>
<span class="sd"> is an NDArray with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `scale`.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `scale.context` when `scale` is an NDArray.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.exponential(1)</span>
<span class="sd"> [ 0.79587454]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.exponential(1, shape=(2,))</span>
<span class="sd"> [ 0.89856035 1.25593066]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> scale = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> mx.nd.random.exponential(scale, shape=2)</span>
<span class="sd"> [[ 0.41063145 0.42140478]</span>
<span class="sd"> [ 2.59407091 10.12439728]</span>
<span class="sd"> [ 2.42544937 1.14260709]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_exponential</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_exponential</span><span class="p">,</span>
<span class="p">[</span><span class="mf">1.0</span><span class="o">/</span><span class="n">scale</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="gamma"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.gamma">[docs]</a><span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Draw random samples from a gamma distribution.</span>
<span class="sd"> Samples are distributed according to a gamma distribution parametrized</span>
<span class="sd"> by *alpha* (shape) and *beta* (scale).</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> alpha : float or NDArray</span>
<span class="sd"> The shape of the gamma distribution. Should be greater than zero.</span>
<span class="sd"> beta : float or NDArray</span>
<span class="sd"> The scale of the gamma distribution. Should be greater than zero.</span>
<span class="sd"> Default is equal to 1.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `alpha` and</span>
<span class="sd"> `beta` are scalars, output shape will be `(m, n)`. If `alpha` and `beta`</span>
<span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[alpha, beta)` pair.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `alpha.context` when `alpha` is an NDArray.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.gamma(1, 1)</span>
<span class="sd"> [ 1.93308783]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.gamma(1, 1, shape=(2,))</span>
<span class="sd"> [ 0.48216391 2.09890771]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> alpha = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> beta = mx.nd.array([2,3,4])</span>
<span class="sd"> >>> mx.nd.random.gamma(alpha, beta, shape=2)</span>
<span class="sd"> [[ 3.24343276 0.94137681]</span>
<span class="sd"> [ 3.52734375 0.45568955]</span>
<span class="sd"> [ 14.26264095 14.0170126 ]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_gamma</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_gamma</span><span class="p">,</span>
<span class="p">[</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="negative_binomial"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.negative_binomial">[docs]</a><span class="k">def</span> <span class="nf">negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Draw random samples from a negative binomial distribution.</span>
<span class="sd"> Samples are distributed according to a negative binomial distribution</span>
<span class="sd"> parametrized by *k* (limit of unsuccessful experiments) and *p* (failure</span>
<span class="sd"> probability in each experiment). Samples will always be returned as a</span>
<span class="sd"> floating point data type.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> k : float or NDArray</span>
<span class="sd"> Limit of unsuccessful experiments, > 0.</span>
<span class="sd"> p : float or NDArray</span>
<span class="sd"> Failure probability in each experiment, >= 0 and <=1.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `k` and</span>
<span class="sd"> `p` are scalars, output shape will be `(m, n)`. If `k` and `p`</span>
<span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[k, p)` pair.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</span>
<span class="sd"> ctx : Context</span>
<span class="sd"> Device context of output. Default is current context. Overridden by</span>
<span class="sd"> `k.context` when `k` is an NDArray.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.negative_binomial(10, 0.5)</span>
<span class="sd"> [ 4.]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.negative_binomial(10, 0.5, shape=(2,))</span>
<span class="sd"> [ 3. 4.]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> k = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> p = mx.nd.array([0.2,0.4,0.6])</span>
<span class="sd"> >>> mx.nd.random.negative_binomial(k, p, shape=2)</span>
<span class="sd"> [[ 3. 2.]</span>
<span class="sd"> [ 4. 4.]</span>
<span class="sd"> [ 0. 5.]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_negative_binomial</span><span class="p">,</span>
<span class="n">_internal</span><span class="o">.</span><span class="n">_sample_negative_binomial</span><span class="p">,</span>
<span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="generalized_negative_binomial"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.generalized_negative_binomial">[docs]</a><span class="k">def</span> <span class="nf">generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</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="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Draw random samples from a generalized negative binomial distribution.</span>
<span class="sd"> Samples are distributed according to a generalized negative binomial</span>
<span class="sd"> distribution parametrized by *mu* (mean) and *alpha* (dispersion).</span>
<span class="sd"> *alpha* is defined as *1/k* where *k* is the failure limit of the</span>
<span class="sd"> number of unsuccessful experiments (generalized to real numbers).</span>
<span class="sd"> Samples will always be returned as a floating point data type.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> mu : float or NDArray</span>
<span class="sd"> Mean of the negative binomial distribution.</span>
<span class="sd"> alpha : float or NDArray</span>
<span class="sd"> Alpha (dispersion) parameter of the negative binomial distribution.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `mu` and</span>
<span class="sd"> `alpha` are scalars, output shape will be `(m, n)`. If `mu` and `alpha`</span>
<span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span>
<span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[mu, alpha)` pair.</span>
<span class="sd"> dtype : {'float16','float32', 'float64'}</span>
<span class="sd"> Data type of output samples. Default is 'float32'</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"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> mx.nd.random.generalized_negative_binomial(10, 0.5)</span>
<span class="sd"> [ 19.]</span>
<span class="sd"> <NDArray 1 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.generalized_negative_binomial(10, 0.5, shape=(2,))</span>
<span class="sd"> [ 30. 21.]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> mu = mx.nd.array([1,2,3])</span>
<span class="sd"> >>> alpha = mx.nd.array([0.2,0.4,0.6])</span>
<span class="sd"> >>> mx.nd.random.generalized_negative_binomial(mu, alpha, shape=2)</span>
<span class="sd"> [[ 4. 0.]</span>
<span class="sd"> [ 3. 2.]</span>
<span class="sd"> [ 6. 2.]]</span>
<span class="sd"> <NDArray 3x2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_generalized_negative_binomial</span><span class="p">,</span>
<span class="n">_internal</span><span class="o">.</span><span class="n">_sample_generalized_negative_binomial</span><span class="p">,</span>
<span class="p">[</span><span class="n">mu</span><span class="p">,</span> <span class="n">alpha</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="multinomial"><a class="viewcode-back" href="../../../api/python/ndarray/random.html#mxnet.ndarray.random.multinomial">[docs]</a><span class="k">def</span> <span class="nf">multinomial</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">get_prob</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Concurrent sampling from multiple multinomial distributions.</span>
<span class="sd"> .. note:: The input distribution must be normalized, i.e. `data` must sum to</span>
<span class="sd"> 1 along its last dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : NDArray</span>
<span class="sd"> An *n* dimensional array whose last dimension has length `k`, where</span>
<span class="sd"> `k` is the number of possible outcomes of each multinomial distribution.</span>
<span class="sd"> For example, data with shape `(m, n, k)` specifies `m*n` multinomial</span>
<span class="sd"> distributions each with `k` possible outcomes.</span>
<span class="sd"> shape : int or tuple of ints</span>
<span class="sd"> The number of samples to draw from each distribution. If shape is empty</span>
<span class="sd"> one sample will be drawn from each distribution.</span>
<span class="sd"> get_prob : bool</span>
<span class="sd"> If true, a second array containing log likelihood of the drawn</span>
<span class="sd"> samples will also be returned.</span>
<span class="sd"> This is usually used for reinforcement learning, where you can provide</span>
<span class="sd"> reward as head gradient w.r.t. this array to estimate gradient.</span>
<span class="sd"> out : NDArray</span>
<span class="sd"> Store output to an existing NDArray.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> probs = mx.nd.array([[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]])</span>
<span class="sd"> >>> mx.nd.random.multinomial(probs)</span>
<span class="sd"> [3 1]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.multinomial(probs, shape=2)</span>
<span class="sd"> [[4 4]</span>
<span class="sd"> [1 2]]</span>
<span class="sd"> <NDArray 2x2 @cpu(0)></span>
<span class="sd"> >>> mx.nd.random.multinomial(probs, get_prob=True)</span>
<span class="sd"> [3 2]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> [-1.20397282 -1.60943794]</span>
<span class="sd"> <NDArray 2 @cpu(0)></span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_multinomial</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">get_prob</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
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