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| <h1>SymbolRandomAPIBase</h1><h3><span class="morelinks"><div>Related Doc: |
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| <ol><li name="org.apache.mxnet.SymbolRandomAPIBase#<init>" visbl="pub" data-isabs="false" fullComment="no" group="Ungrouped"> |
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| <ol><li name="org.apache.mxnet.SymbolRandomAPIBase#exponential" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="exponential[T](lam:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$35:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$36:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="exponential[T](Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">exponential</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="lam">lam: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@exponential[T](lam:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$35:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$36:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from an exponential distribution. |
| |
| Samples are distributed according to an exponential distribution parametrized by *lambda* (rate). |
| |
| Example:: |
| |
| exponential(lam=<span class="num">4</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">0.0097189</span> , <span class="num">0.08999364</span>], |
| [ <span class="num">0.04146638</span>, <span class="num">0.31715935</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L137</pre></div><dl class="paramcmts block"><dt class="param">lam</dt><dd class="cmt"><p>Lambda parameter (rate) of the exponential distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#exponential_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="exponential_like[T](lam:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$15:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$16:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="exponential_like[T](Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">exponential_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="lam">lam: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.exponential_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@exponential_like[T](lam:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$15:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$16:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from an exponential distribution according to the input array shape. |
| |
| Samples are distributed according to an exponential distribution parametrized by *lambda* (rate). |
| |
| Example:: |
| |
| exponential(lam=<span class="num">4</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">0.0097189</span> , <span class="num">0.08999364</span>], |
| [ <span class="num">0.04146638</span>, <span class="num">0.31715935</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L243</pre></div><dl class="paramcmts block"><dt class="param">lam</dt><dd class="cmt"><p>Lambda parameter (rate) of the exponential distribution.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#gamma" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="gamma[T](alpha:Option[T],beta:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$39:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$40:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="gamma[T](Option[T],Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">gamma</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma.T">T</span>] = <span class="symbol">None</span></span>, <span name="beta">beta: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@gamma[T](alpha:Option[T],beta:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$39:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$40:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a gamma distribution. |
| |
| Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). |
| |
| Example:: |
| |
| gamma(alpha=<span class="num">9</span>, beta=<span class="num">0.5</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">7.10486984</span>, <span class="num">3.37695289</span>], |
| [ <span class="num">3.91697288</span>, <span class="num">3.65933681</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L125</pre></div><dl class="paramcmts block"><dt class="param">alpha</dt><dd class="cmt"><p>Alpha parameter (shape) of the gamma distribution.</p></dd><dt class="param">beta</dt><dd class="cmt"><p>Beta parameter (scale) of the gamma distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#gamma_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="gamma_like[T](alpha:Option[T],beta:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$21:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$22:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="gamma_like[T](Option[T],Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">gamma_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="beta">beta: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.gamma_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@gamma_like[T](alpha:Option[T],beta:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$21:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$22:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a gamma distribution according to the input array shape. |
| |
| Samples are distributed according to a gamma distribution parametrized by *alpha* (shape) and *beta* (scale). |
| |
| Example:: |
| |
| gamma(alpha=<span class="num">9</span>, beta=<span class="num">0.5</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">7.10486984</span>, <span class="num">3.37695289</span>], |
| [ <span class="num">3.91697288</span>, <span class="num">3.65933681</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L232</pre></div><dl class="paramcmts block"><dt class="param">alpha</dt><dd class="cmt"><p>Alpha parameter (shape) of the gamma distribution.</p></dd><dt class="param">beta</dt><dd class="cmt"><p>Beta parameter (scale) of the gamma distribution.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#generalized_negative_binomial" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="generalized_negative_binomial[T](mu:Option[T],alpha:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$13:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$14:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="generalized_negative_binomial[T](Option[T],Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">generalized_negative_binomial</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="mu">mu: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@generalized_negative_binomial[T](mu:Option[T],alpha:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$13:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$14:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a generalized negative binomial distribution. |
| |
| Samples are distributed according to a generalized negative binomial distribution parametrized by |
| *mu* (mean) and *alpha* (dispersion). *alpha* is defined as *<span class="num">1</span>/k* where *k* is the failure limit of the |
| number of unsuccessful experiments (generalized to real numbers). |
| Samples will always be returned as a floating point data <span class="kw">type</span>. |
| |
| Example:: |
| |
| generalized_negative_binomial(mu=<span class="num">2.0</span>, alpha=<span class="num">0.3</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">2.</span>, <span class="num">1.</span>], |
| [ <span class="num">6.</span>, <span class="num">4.</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L179</pre></div><dl class="paramcmts block"><dt class="param">mu</dt><dd class="cmt"><p>Mean of the negative binomial distribution.</p></dd><dt class="param">alpha</dt><dd class="cmt"><p>Alpha (dispersion) parameter of the negative binomial distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#generalized_negative_binomial_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="generalized_negative_binomial_like[T](mu:Option[T],alpha:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$7:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$8:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="generalized_negative_binomial_like[T](Option[T],Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">generalized_negative_binomial_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="mu">mu: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.generalized_negative_binomial_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@generalized_negative_binomial_like[T](mu:Option[T],alpha:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$7:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$8:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a generalized negative binomial distribution according to the |
| input array shape. |
| |
| Samples are distributed according to a generalized negative binomial distribution parametrized by |
| *mu* (mean) and *alpha* (dispersion). *alpha* is defined as *<span class="num">1</span>/k* where *k* is the failure limit of the |
| number of unsuccessful experiments (generalized to real numbers). |
| Samples will always be returned as a floating point data <span class="kw">type</span>. |
| |
| Example:: |
| |
| generalized_negative_binomial(mu=<span class="num">2.0</span>, alpha=<span class="num">0.3</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">2.</span>, <span class="num">1.</span>], |
| [ <span class="num">6.</span>, <span class="num">4.</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L284</pre></div><dl class="paramcmts block"><dt class="param">mu</dt><dd class="cmt"><p>Mean of the negative binomial distribution.</p></dd><dt class="param">alpha</dt><dd class="cmt"><p>Alpha (dispersion) parameter of the negative binomial distribution.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#multinomial" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="multinomial[T](data:Option[T],shape:Option[org.apache.mxnet.Shape],get_prob:Option[Boolean],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$33:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$34:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="multinomial[T](Option[T],Option[Shape],Option[Boolean],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">multinomial</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.multinomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="get_prob">get_prob: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.multinomial.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.multinomial.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@multinomial[T](data:Option[T],shape:Option[org.apache.mxnet.Shape],get_prob:Option[Boolean],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$33:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$34:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Concurrent sampling from multiple multinomial distributions. |
| |
| *data* is an *n* dimensional array whose last dimension has length *k*, where |
| *k* is the number of possible outcomes of each multinomial distribution. This |
| operator will draw *shape* samples from each distribution. If shape is empty |
| one sample will be drawn from each distribution. |
| |
| If *get_prob* is <span class="kw">true</span>, a second array containing log likelihood of the drawn |
| samples will also be returned. This is usually used <span class="kw">for</span> reinforcement learning |
| where you can provide reward as head gradient <span class="kw">for</span> <span class="kw">this</span> array to estimate |
| gradient. |
| |
| Note that the input distribution must be normalized, i.e. *data* must sum to |
| <span class="num">1</span> along its last axis. |
| |
| Examples:: |
| |
| probs = `[ [<span class="num">0</span>, <span class="num">0.1</span>, <span class="num">0.2</span>, <span class="num">0.3</span>, <span class="num">0.4</span>], [<span class="num">0.4</span>, <span class="num">0.3</span>, <span class="num">0.2</span>, <span class="num">0.1</span>, <span class="num">0</span>] ] |
| |
| <span class="cmt">// Draw a single sample for each distribution</span> |
| sample_multinomial(probs) = [<span class="num">3</span>, <span class="num">0</span>] |
| |
| <span class="cmt">// Draw a vector containing two samples for each distribution</span> |
| sample_multinomial(probs, shape=(<span class="num">2</span>)) = `[ [<span class="num">4</span>, <span class="num">2</span>], |
| [<span class="num">0</span>, <span class="num">0</span>] ] |
| |
| <span class="cmt">// requests log likelihood</span> |
| sample_multinomial(probs, get_prob=True) = [<span class="num">2</span>, <span class="num">1</span>], [<span class="num">0.2</span>, <span class="num">0.3</span>]</pre></div><dl class="paramcmts block"><dt class="param">data</dt><dd class="cmt"><p>Distribution probabilities. Must sum to one on the last axis.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape to be sampled from each random distribution.</p></dd><dt class="param">get_prob</dt><dd class="cmt"><p>Whether to also return the log probability of sampled result. This is usually used for differentiating through stochastic variables, e.g. in reinforcement learning.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#negative_binomial" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="negative_binomial[T](k:Option[T],p:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$29:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$30:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="negative_binomial[T](Option[T],Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">negative_binomial</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="k">k: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="p">p: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@negative_binomial[T](k:Option[T],p:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$29:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$30:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a negative binomial distribution. |
| |
| Samples are distributed according to a negative binomial distribution parametrized by |
| *k* (limit of unsuccessful experiments) and *p* (failure probability in each experiment). |
| Samples will always be returned as a floating point data <span class="kw">type</span>. |
| |
| Example:: |
| |
| negative_binomial(k=<span class="num">3</span>, p=<span class="num">0.4</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">4.</span>, <span class="num">7.</span>], |
| [ <span class="num">2.</span>, <span class="num">5.</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L164</pre></div><dl class="paramcmts block"><dt class="param">k</dt><dd class="cmt"><p>Limit of unsuccessful experiments.</p></dd><dt class="param">p</dt><dd class="cmt"><p>Failure probability in each experiment.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#negative_binomial_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="negative_binomial_like[T](k:Option[T],p:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$31:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$32:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="negative_binomial_like[T](Option[T],Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">negative_binomial_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="k">k: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="p">p: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.negative_binomial_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@negative_binomial_like[T](k:Option[T],p:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$31:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$32:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a negative binomial distribution according to the input array shape. |
| |
| Samples are distributed according to a negative binomial distribution parametrized by |
| *k* (limit of unsuccessful experiments) and *p* (failure probability in each experiment). |
| Samples will always be returned as a floating point data <span class="kw">type</span>. |
| |
| Example:: |
| |
| negative_binomial(k=<span class="num">3</span>, p=<span class="num">0.4</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">4.</span>, <span class="num">7.</span>], |
| [ <span class="num">2.</span>, <span class="num">5.</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L268</pre></div><dl class="paramcmts block"><dt class="param">k</dt><dd class="cmt"><p>Limit of unsuccessful experiments.</p></dd><dt class="param">p</dt><dd class="cmt"><p>Failure probability in each experiment.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#normal" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="normal[T](mu:Option[T],sigma:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$11:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$12:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="normal[T](Option[T],Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">normal</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="mu">mu: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal.T">T</span>] = <span class="symbol">None</span></span>, <span name="sigma">sigma: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@normal[T](mu:Option[T],sigma:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$11:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$12:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a normal (Gaussian) distribution. |
| |
| .. note:: The existing alias ``normal`` is deprecated. |
| |
| Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* |
| (standard deviation). |
| |
| Example:: |
| |
| normal(loc=<span class="num">0</span>, scale=<span class="num">1</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">1.89171135</span>, -<span class="num">1.16881478</span>], |
| [-<span class="num">1.23474145</span>, <span class="num">1.55807114</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L113</pre></div><dl class="paramcmts block"><dt class="param">mu</dt><dd class="cmt"><p>Mean of the distribution.</p></dd><dt class="param">sigma</dt><dd class="cmt"><p>Standard deviation of the distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#normal_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="normal_like[T](mu:Option[T],sigma:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$43:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$44:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="normal_like[T](Option[T],Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">normal_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="mu">mu: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="sigma">sigma: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.normal_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@normal_like[T](mu:Option[T],sigma:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$43:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$44:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a normal (Gaussian) distribution according to the input array shape. |
| |
| Samples are distributed according to a normal distribution parametrized by *loc* (mean) and *scale* |
| (standard deviation). |
| |
| Example:: |
| |
| normal(loc=<span class="num">0</span>, scale=<span class="num">1</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">1.89171135</span>, -<span class="num">1.16881478</span>], |
| [-<span class="num">1.23474145</span>, <span class="num">1.55807114</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L221</pre></div><dl class="paramcmts block"><dt class="param">mu</dt><dd class="cmt"><p>Mean of the distribution.</p></dd><dt class="param">sigma</dt><dd class="cmt"><p>Standard deviation of the distribution.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_dirichlet" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_dirichlet[T](sample:Option[T],alpha:Option[T],is_log:Option[Boolean],name:String,attr:Map[String,String])(implicitevidence$23:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$24:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_dirichlet[T](Option[T],Option[T],Option[Boolean],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_dirichlet</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_dirichlet.T">T</span>] = <span class="symbol">None</span></span>, <span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_dirichlet.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_dirichlet.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_dirichlet.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_dirichlet[T](sample:Option[T],alpha:Option[T],is_log:Option[Boolean],name:String,attr:Map[String,String])(implicitevidence$23:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$24:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| Dirichlet distributions <span class="kw">with</span> parameter *alpha*. |
| |
| The shape of *alpha* must <span class="kw">match</span> the leftmost subshape of *sample*. That is, *sample* |
| can have the same shape as *alpha*, in which <span class="kw">case</span> the output contains one density per |
| distribution, or *sample* can be a tensor of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> |
| the output is a tensor of densities such that the densities at index *i* in the output |
| are given by the samples at index *i* in *sample* parameterized by the value of *alpha* |
| at index *i*. |
| |
| Examples:: |
| |
| random_pdf_dirichlet(sample=`[ [<span class="num">1</span>,<span class="num">2</span>],[<span class="num">2</span>,<span class="num">3</span>],[<span class="num">3</span>,<span class="num">4</span>] ], alpha=[<span class="num">2.5</span>, <span class="num">2.5</span>]) = |
| [<span class="num">38.413498</span>, <span class="num">199.60245</span>, <span class="num">564.56085</span>] |
| |
| sample = `[ `[ [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>], [<span class="num">10</span>, <span class="num">20</span>, <span class="num">30</span>], [<span class="num">100</span>, <span class="num">200</span>, <span class="num">300</span>] ], |
| `[ [<span class="num">0.1</span>, <span class="num">0.2</span>, <span class="num">0.3</span>], [<span class="num">0.01</span>, <span class="num">0.02</span>, <span class="num">0.03</span>], [<span class="num">0.001</span>, <span class="num">0.002</span>, <span class="num">0.003</span>] ] ] |
| |
| random_pdf_dirichlet(sample=sample, alpha=[<span class="num">0.1</span>, <span class="num">0.4</span>, <span class="num">0.9</span>]) = |
| `[ [<span class="num">2.3257459e-02</span>, <span class="num">5.8420084e-04</span>, <span class="num">1.4674458e-05</span>], |
| [<span class="num">9.2589635e-01</span>, <span class="num">3.6860607e+01</span>, <span class="num">1.4674468e+03</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L316</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">alpha</dt><dd class="cmt"><p>Concentration parameters of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_exponential" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_exponential[T](sample:Option[T],lam:Option[T],is_log:Option[Boolean],name:String,attr:Map[String,String])(implicitevidence$5:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$6:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_exponential[T](Option[T],Option[T],Option[Boolean],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_exponential</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_exponential.T">T</span>] = <span class="symbol">None</span></span>, <span name="lam">lam: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_exponential.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_exponential.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_exponential.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_exponential[T](sample:Option[T],lam:Option[T],is_log:Option[Boolean],name:String,attr:Map[String,String])(implicitevidence$5:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$6:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| exponential distributions <span class="kw">with</span> parameters *lam* (rate). |
| |
| The shape of *lam* must <span class="kw">match</span> the leftmost subshape of *sample*. That is, *sample* |
| can have the same shape as *lam*, in which <span class="kw">case</span> the output contains one density per |
| distribution, or *sample* can be a tensor of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> |
| the output is a tensor of densities such that the densities at index *i* in the output |
| are given by the samples at index *i* in *sample* parameterized by the value of *lam* |
| at index *i*. |
| |
| Examples:: |
| |
| random_pdf_exponential(sample=`[ [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>] ], lam=[<span class="num">1</span>]) = |
| `[ [<span class="num">0.36787945</span>, <span class="num">0.13533528</span>, <span class="num">0.04978707</span>] ] |
| |
| sample = `[ [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>], |
| [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>], |
| [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>] ] |
| |
| random_pdf_exponential(sample=sample, lam=[<span class="num">1</span>,<span class="num">0.5</span>,<span class="num">0.25</span>]) = |
| `[ [<span class="num">0.36787945</span>, <span class="num">0.13533528</span>, <span class="num">0.04978707</span>], |
| [<span class="num">0.30326533</span>, <span class="num">0.18393973</span>, <span class="num">0.11156508</span>], |
| [<span class="num">0.1947002</span>, <span class="num">0.15163267</span>, <span class="num">0.11809164</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L305</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">lam</dt><dd class="cmt"><p>Lambda (rate) parameters of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_gamma" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_gamma[T](sample:Option[T],alpha:Option[T],is_log:Option[Boolean],beta:Option[T],name:String,attr:Map[String,String])(implicitevidence$9:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$10:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_gamma[T](Option[T],Option[T],Option[Boolean],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_gamma</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_gamma.T">T</span>] = <span class="symbol">None</span></span>, <span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_gamma.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="beta">beta: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_gamma.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_gamma.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_gamma.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_gamma[T](sample:Option[T],alpha:Option[T],is_log:Option[Boolean],beta:Option[T],name:String,attr:Map[String,String])(implicitevidence$9:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$10:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| gamma distributions <span class="kw">with</span> parameters *alpha* (shape) and *beta* (rate). |
| |
| *alpha* and *beta* must have the same shape, which must <span class="kw">match</span> the leftmost subshape |
| of *sample*. That is, *sample* can have the same shape as *alpha* and *beta*, in which |
| <span class="kw">case</span> the output contains one density per distribution, or *sample* can be a tensor |
| of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> the output is a tensor of densities such that |
| the densities at index *i* in the output are given by the samples at index *i* in *sample* |
| parameterized by the values of *alpha* and *beta* at index *i*. |
| |
| Examples:: |
| |
| random_pdf_gamma(sample=`[ [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>,<span class="num">5</span>] ], alpha=[<span class="num">5</span>], beta=[<span class="num">1</span>]) = |
| `[ [<span class="num">0.01532831</span>, <span class="num">0.09022352</span>, <span class="num">0.16803136</span>, <span class="num">0.19536681</span>, <span class="num">0.17546739</span>] ] |
| |
| sample = `[ [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>, <span class="num">4</span>, <span class="num">5</span>], |
| [<span class="num">2</span>, <span class="num">3</span>, <span class="num">4</span>, <span class="num">5</span>, <span class="num">6</span>], |
| [<span class="num">3</span>, <span class="num">4</span>, <span class="num">5</span>, <span class="num">6</span>, <span class="num">7</span>] ] |
| |
| random_pdf_gamma(sample=sample, alpha=[<span class="num">5</span>,<span class="num">6</span>,<span class="num">7</span>], beta=[<span class="num">1</span>,<span class="num">1</span>,<span class="num">1</span>]) = |
| `[ [<span class="num">0.01532831</span>, <span class="num">0.09022352</span>, <span class="num">0.16803136</span>, <span class="num">0.19536681</span>, <span class="num">0.17546739</span>], |
| [<span class="num">0.03608941</span>, <span class="num">0.10081882</span>, <span class="num">0.15629345</span>, <span class="num">0.17546739</span>, <span class="num">0.16062315</span>], |
| [<span class="num">0.05040941</span>, <span class="num">0.10419563</span>, <span class="num">0.14622283</span>, <span class="num">0.16062315</span>, <span class="num">0.14900276</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L303</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">alpha</dt><dd class="cmt"><p>Alpha (shape) parameters of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt class="param">beta</dt><dd class="cmt"><p>Beta (scale) parameters of the distributions.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_generalized_negative_binomial" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_generalized_negative_binomial[T](sample:Option[T],mu:Option[T],is_log:Option[Boolean],alpha:Option[T],name:String,attr:Map[String,String])(implicitevidence$27:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$28:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_generalized_negative_binomial[T](Option[T],Option[T],Option[Boolean],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_generalized_negative_binomial</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_generalized_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="mu">mu: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_generalized_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="alpha">alpha: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_generalized_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_generalized_negative_binomial.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_generalized_negative_binomial.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_generalized_negative_binomial[T](sample:Option[T],mu:Option[T],is_log:Option[Boolean],alpha:Option[T],name:String,attr:Map[String,String])(implicitevidence$27:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$28:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| generalized negative binomial distributions <span class="kw">with</span> parameters *mu* (mean) |
| and *alpha* (dispersion). This can be understood as a reparameterization of |
| the negative binomial, where *k* = *<span class="num">1</span> / alpha* and *p* = *<span class="num">1</span> / (mu \* alpha + <span class="num">1</span>)*. |
| |
| *mu* and *alpha* must have the same shape, which must <span class="kw">match</span> the leftmost subshape |
| of *sample*. That is, *sample* can have the same shape as *mu* and *alpha*, in which |
| <span class="kw">case</span> the output contains one density per distribution, or *sample* can be a tensor |
| of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> the output is a tensor of densities such that |
| the densities at index *i* in the output are given by the samples at index *i* in *sample* |
| parameterized by the values of *mu* and *alpha* at index *i*. |
| |
| Examples:: |
| |
| random_pdf_generalized_negative_binomial(sample=`[ [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>, <span class="num">4</span>] ], alpha=[<span class="num">1</span>], mu=[<span class="num">1</span>]) = |
| `[ [<span class="num">0.25</span>, <span class="num">0.125</span>, <span class="num">0.0625</span>, <span class="num">0.03125</span>] ] |
| |
| sample = `[ [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>], |
| [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>] ] |
| random_pdf_generalized_negative_binomial(sample=sample, alpha=[<span class="num">1</span>, <span class="num">0.6666</span>], mu=[<span class="num">1</span>, <span class="num">1.5</span>]) = |
| `[ [<span class="num">0.25</span>, <span class="num">0.125</span>, <span class="num">0.0625</span>, <span class="num">0.03125</span> ], |
| [<span class="num">0.26517063</span>, <span class="num">0.16573331</span>, <span class="num">0.09667706</span>, <span class="num">0.05437994</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L314</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">mu</dt><dd class="cmt"><p>Means of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt class="param">alpha</dt><dd class="cmt"><p>Alpha (dispersion) parameters of the distributions.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_negative_binomial" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_negative_binomial[T](sample:Option[T],k:Option[T],is_log:Option[Boolean],p:Option[T],name:String,attr:Map[String,String])(implicitevidence$37:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$38:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_negative_binomial[T](Option[T],Option[T],Option[Boolean],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_negative_binomial</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="k">k: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="p">p: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_negative_binomial.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_negative_binomial.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_negative_binomial.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_negative_binomial[T](sample:Option[T],k:Option[T],is_log:Option[Boolean],p:Option[T],name:String,attr:Map[String,String])(implicitevidence$37:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$38:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of samples of |
| negative binomial distributions <span class="kw">with</span> parameters *k* (failure limit) and *p* (failure probability). |
| |
| *k* and *p* must have the same shape, which must <span class="kw">match</span> the leftmost subshape |
| of *sample*. That is, *sample* can have the same shape as *k* and *p*, in which |
| <span class="kw">case</span> the output contains one density per distribution, or *sample* can be a tensor |
| of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> the output is a tensor of densities such that |
| the densities at index *i* in the output are given by the samples at index *i* in *sample* |
| parameterized by the values of *k* and *p* at index *i*. |
| |
| Examples:: |
| |
| random_pdf_negative_binomial(sample=`[ [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>] ], k=[<span class="num">1</span>], p=a[<span class="num">0.5</span>]) = |
| `[ [<span class="num">0.25</span>, <span class="num">0.125</span>, <span class="num">0.0625</span>, <span class="num">0.03125</span>] ] |
| |
| # Note that k may be real-valued |
| sample = `[ [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>], |
| [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>] ] |
| random_pdf_negative_binomial(sample=sample, k=[<span class="num">1</span>, <span class="num">1.5</span>], p=[<span class="num">0.5</span>, <span class="num">0.5</span>]) = |
| `[ [<span class="num">0.25</span>, <span class="num">0.125</span>, <span class="num">0.0625</span>, <span class="num">0.03125</span> ], |
| [<span class="num">0.26516506</span>, <span class="num">0.16572815</span>, <span class="num">0.09667476</span>, <span class="num">0.05437956</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L310</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">k</dt><dd class="cmt"><p>Limits of unsuccessful experiments.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt class="param">p</dt><dd class="cmt"><p>Failure probabilities in each experiment.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_normal" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_normal[T](sample:Option[T],mu:Option[T],is_log:Option[Boolean],sigma:Option[T],name:String,attr:Map[String,String])(implicitevidence$45:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$46:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_normal[T](Option[T],Option[T],Option[Boolean],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_normal</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_normal.T">T</span>] = <span class="symbol">None</span></span>, <span name="mu">mu: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_normal.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="sigma">sigma: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_normal.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_normal.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_normal.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_normal[T](sample:Option[T],mu:Option[T],is_log:Option[Boolean],sigma:Option[T],name:String,attr:Map[String,String])(implicitevidence$45:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$46:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| normal distributions <span class="kw">with</span> parameters *mu* (mean) and *sigma* (standard deviation). |
| |
| *mu* and *sigma* must have the same shape, which must <span class="kw">match</span> the leftmost subshape |
| of *sample*. That is, *sample* can have the same shape as *mu* and *sigma*, in which |
| <span class="kw">case</span> the output contains one density per distribution, or *sample* can be a tensor |
| of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> the output is a tensor of densities such that |
| the densities at index *i* in the output are given by the samples at index *i* in *sample* |
| parameterized by the values of *mu* and *sigma* at index *i*. |
| |
| Examples:: |
| |
| sample = `[ [-<span class="num">2</span>, -<span class="num">1</span>, <span class="num">0</span>, <span class="num">1</span>, <span class="num">2</span>] ] |
| random_pdf_normal(sample=sample, mu=[<span class="num">0</span>], sigma=[<span class="num">1</span>]) = |
| `[ [<span class="num">0.05399097</span>, <span class="num">0.24197073</span>, <span class="num">0.3989423</span>, <span class="num">0.24197073</span>, <span class="num">0.05399097</span>] ] |
| |
| random_pdf_normal(sample=sample*<span class="num">2</span>, mu=[<span class="num">0</span>,<span class="num">0</span>], sigma=[<span class="num">1</span>,<span class="num">2</span>]) = |
| `[ [<span class="num">0.05399097</span>, <span class="num">0.24197073</span>, <span class="num">0.3989423</span>, <span class="num">0.24197073</span>, <span class="num">0.05399097</span>], |
| [<span class="num">0.12098537</span>, <span class="num">0.17603266</span>, <span class="num">0.19947115</span>, <span class="num">0.17603266</span>, <span class="num">0.12098537</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L300</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">mu</dt><dd class="cmt"><p>Means of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt class="param">sigma</dt><dd class="cmt"><p>Standard deviations of the distributions.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_poisson" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_poisson[T](sample:Option[T],lam:Option[T],is_log:Option[Boolean],name:String,attr:Map[String,String])(implicitevidence$47:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$48:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_poisson[T](Option[T],Option[T],Option[Boolean],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_poisson</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_poisson.T">T</span>] = <span class="symbol">None</span></span>, <span name="lam">lam: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_poisson.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_poisson.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_poisson.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_poisson[T](sample:Option[T],lam:Option[T],is_log:Option[Boolean],name:String,attr:Map[String,String])(implicitevidence$47:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$48:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| Poisson distributions <span class="kw">with</span> parameters *lam* (rate). |
| |
| The shape of *lam* must <span class="kw">match</span> the leftmost subshape of *sample*. That is, *sample* |
| can have the same shape as *lam*, in which <span class="kw">case</span> the output contains one density per |
| distribution, or *sample* can be a tensor of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> |
| the output is a tensor of densities such that the densities at index *i* in the output |
| are given by the samples at index *i* in *sample* parameterized by the value of *lam* |
| at index *i*. |
| |
| Examples:: |
| |
| random_pdf_poisson(sample=`[ [<span class="num">0</span>,<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>] ], lam=[<span class="num">1</span>]) = |
| `[ [<span class="num">0.36787945</span>, <span class="num">0.36787945</span>, <span class="num">0.18393973</span>, <span class="num">0.06131324</span>] ] |
| |
| sample = `[ [<span class="num">0</span>,<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>], |
| [<span class="num">0</span>,<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>], |
| [<span class="num">0</span>,<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>] ] |
| |
| random_pdf_poisson(sample=sample, lam=[<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>]) = |
| `[ [<span class="num">0.36787945</span>, <span class="num">0.36787945</span>, <span class="num">0.18393973</span>, <span class="num">0.06131324</span>], |
| [<span class="num">0.13533528</span>, <span class="num">0.27067056</span>, <span class="num">0.27067056</span>, <span class="num">0.18044704</span>], |
| [<span class="num">0.04978707</span>, <span class="num">0.14936121</span>, <span class="num">0.22404182</span>, <span class="num">0.22404182</span>] ] |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L307</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">lam</dt><dd class="cmt"><p>Lambda (rate) parameters of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#pdf_uniform" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="pdf_uniform[T](sample:Option[T],low:Option[T],is_log:Option[Boolean],high:Option[T],name:String,attr:Map[String,String])(implicitevidence$3:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$4:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="pdf_uniform[T](Option[T],Option[T],Option[Boolean],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">pdf_uniform</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="sample">sample: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_uniform.T">T</span>] = <span class="symbol">None</span></span>, <span name="low">low: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_uniform.T">T</span>] = <span class="symbol">None</span></span>, <span name="is_log">is_log: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Boolean">Boolean</span>] = <span class="symbol">None</span></span>, <span name="high">high: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_uniform.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_uniform.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.pdf_uniform.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@pdf_uniform[T](sample:Option[T],low:Option[T],is_log:Option[Boolean],high:Option[T],name:String,attr:Map[String,String])(implicitevidence$3:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$4:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Computes the value of the PDF of *sample* of |
| uniform distributions on the intervals given by *[low,high)*. |
| |
| *low* and *high* must have the same shape, which must <span class="kw">match</span> the leftmost subshape |
| of *sample*. That is, *sample* can have the same shape as *low* and *high*, in which |
| <span class="kw">case</span> the output contains one density per distribution, or *sample* can be a tensor |
| of tensors <span class="kw">with</span> that shape, in which <span class="kw">case</span> the output is a tensor of densities such that |
| the densities at index *i* in the output are given by the samples at index *i* in *sample* |
| parameterized by the values of *low* and *high* at index *i*. |
| |
| Examples:: |
| |
| random_pdf_uniform(sample=`[ [<span class="num">1</span>,<span class="num">2</span>,<span class="num">3</span>,<span class="num">4</span>] ], low=[<span class="num">0</span>], high=[<span class="num">10</span>]) = [<span class="num">0.1</span>, <span class="num">0.1</span>, <span class="num">0.1</span>, <span class="num">0.1</span>] |
| |
| sample = `[ `[ [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>], |
| [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>] ], |
| `[ [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>], |
| [<span class="num">1</span>, <span class="num">2</span>, <span class="num">3</span>] ] ] |
| low = `[ [<span class="num">0</span>, <span class="num">0</span>], |
| [<span class="num">0</span>, <span class="num">0</span>] ] |
| high = `[ [ <span class="num">5</span>, <span class="num">10</span>], |
| [<span class="num">15</span>, <span class="num">20</span>] ] |
| random_pdf_uniform(sample=sample, low=low, high=high) = |
| `[ `[ [<span class="num">0.2</span>, <span class="num">0.2</span>, <span class="num">0.2</span> ], |
| [<span class="num">0.1</span>, <span class="num">0.1</span>, <span class="num">0.1</span> ] ], |
| `[ [<span class="num">0.06667</span>, <span class="num">0.06667</span>, <span class="num">0.06667</span>], |
| [<span class="num">0.05</span>, <span class="num">0.05</span>, <span class="num">0.05</span> ] ] ] |
| |
| |
| |
| Defined in src/operator/random/pdf_op.cc:L298</pre></div><dl class="paramcmts block"><dt class="param">sample</dt><dd class="cmt"><p>Samples from the distributions.</p></dd><dt class="param">low</dt><dd class="cmt"><p>Lower bounds of the distributions.</p></dd><dt class="param">is_log</dt><dd class="cmt"><p>If set, compute the density of the log-probability instead of the probability.</p></dd><dt class="param">high</dt><dd class="cmt"><p>Upper bounds of the distributions.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#poisson" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="poisson[T](lam:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$17:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$18:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="poisson[T](Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">poisson</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="lam">lam: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@poisson[T](lam:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$17:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$18:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a Poisson distribution. |
| |
| Samples are distributed according to a Poisson distribution parametrized by *lambda* (rate). |
| Samples will always be returned as a floating point data <span class="kw">type</span>. |
| |
| Example:: |
| |
| poisson(lam=<span class="num">4</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">5.</span>, <span class="num">2.</span>], |
| [ <span class="num">4.</span>, <span class="num">6.</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L150</pre></div><dl class="paramcmts block"><dt class="param">lam</dt><dd class="cmt"><p>Lambda parameter (rate) of the Poisson distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#poisson_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="poisson_like[T](lam:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$25:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$26:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="poisson_like[T](Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">poisson_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="lam">lam: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.poisson_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@poisson_like[T](lam:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$25:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$26:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a Poisson distribution according to the input array shape. |
| |
| Samples are distributed according to a Poisson distribution parametrized by *lambda* (rate). |
| Samples will always be returned as a floating point data <span class="kw">type</span>. |
| |
| Example:: |
| |
| poisson(lam=<span class="num">4</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">5.</span>, <span class="num">2.</span>], |
| [ <span class="num">4.</span>, <span class="num">6.</span>] ] |
| |
| |
| Defined in src/operator/random/sample_op.cc:L255</pre></div><dl class="paramcmts block"><dt class="param">lam</dt><dd class="cmt"><p>Lambda parameter (rate) of the Poisson distribution.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#randint" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="randint[T](low:Long,high:Long,shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$41:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$42:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="randint[T](Long,Long,Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">randint</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="low">low: <span class="extype" name="scala.Long">Long</span></span>, <span name="high">high: <span class="extype" name="scala.Long">Long</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.randint.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.randint.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@randint[T](low:Long,high:Long,shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$41:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$42:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a discrete uniform distribution. |
| |
| Samples are uniformly distributed over the half-open interval *[low, high)* |
| (includes *low*, but excludes *high*). |
| |
| Example:: |
| |
| randint(low=<span class="num">0</span>, high=<span class="num">5</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">0</span>, <span class="num">2</span>], |
| [ <span class="num">3</span>, <span class="num">1</span>] ] |
| |
| |
| |
| Defined in src/operator/random/sample_op.cc:L194</pre></div><dl class="paramcmts block"><dt class="param">low</dt><dd class="cmt"><p>Lower bound of the distribution.</p></dd><dt class="param">high</dt><dd class="cmt"><p>Upper bound of the distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to int32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#uniform" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="uniform[T](low:Option[T],high:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$19:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$20:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="uniform[T](Option[T],Option[T],Option[Shape],Option[String],Option[String],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">uniform</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="low">low: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform.T">T</span>] = <span class="symbol">None</span></span>, <span name="high">high: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform.T">T</span>] = <span class="symbol">None</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="ctx">ctx: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="dtype">dtype: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@uniform[T](low:Option[T],high:Option[T],shape:Option[org.apache.mxnet.Shape],ctx:Option[String],dtype:Option[String],name:String,attr:Map[String,String])(implicitevidence$19:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$20:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a uniform distribution. |
| |
| .. note:: The existing alias ``uniform`` is deprecated. |
| |
| Samples are uniformly distributed over the half-open interval *[low, high)* |
| (includes *low*, but excludes *high*). |
| |
| Example:: |
| |
| uniform(low=<span class="num">0</span>, high=<span class="num">1</span>, shape=(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">0.60276335</span>, <span class="num">0.85794562</span>], |
| [ <span class="num">0.54488319</span>, <span class="num">0.84725171</span>] ] |
| |
| |
| |
| Defined in src/operator/random/sample_op.cc:L96</pre></div><dl class="paramcmts block"><dt class="param">low</dt><dd class="cmt"><p>Lower bound of the distribution.</p></dd><dt class="param">high</dt><dd class="cmt"><p>Upper bound of the distribution.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>Shape of the output.</p></dd><dt class="param">ctx</dt><dd class="cmt"><p>Context of output, in format [cpu|gpu|cpu_pinned](n). Only used for imperative calls.</p></dd><dt class="param">dtype</dt><dd class="cmt"><p>DType of the output in case this can't be inferred. Defaults to float32 if not defined (dtype=None).</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#uniform_like" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="uniform_like[T](low:Option[T],high:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$49:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$50:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="uniform_like[T](Option[T],Option[T],Option[T],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">uniform_like</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="low">low: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="high">high: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="data">data: <span class="extype" name="scala.Option">Option</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform_like.T">T</span>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform_like.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.uniform_like.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@uniform_like[T](low:Option[T],high:Option[T],data:Option[T],name:String,attr:Map[String,String])(implicitevidence$49:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$50:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from a uniform distribution according to the input array shape. |
| |
| Samples are uniformly distributed over the half-open interval *[low, high)* |
| (includes *low*, but excludes *high*). |
| |
| Example:: |
| |
| uniform(low=<span class="num">0</span>, high=<span class="num">1</span>, data=ones(<span class="num">2</span>,<span class="num">2</span>)) = `[ [ <span class="num">0.60276335</span>, <span class="num">0.85794562</span>], |
| [ <span class="num">0.54488319</span>, <span class="num">0.84725171</span>] ] |
| |
| |
| |
| Defined in src/operator/random/sample_op.cc:L209</pre></div><dl class="paramcmts block"><dt class="param">low</dt><dd class="cmt"><p>Lower bound of the distribution.</p></dd><dt class="param">high</dt><dd class="cmt"><p>Upper bound of the distribution.</p></dd><dt class="param">data</dt><dd class="cmt"><p>The input</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li><li name="org.apache.mxnet.SymbolRandomAPIBase#unique_zipfian" visbl="pub" data-isabs="true" fullComment="yes" group="Ungrouped"> |
| <a id="unique_zipfian[T](range_max:T,shape:Option[org.apache.mxnet.Shape],name:String,attr:Map[String,String])(implicitevidence$1:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$2:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol"></a> |
| <a id="unique_zipfian[T](T,Option[Shape],String,Map[String,String])(SymbolOrScalar[T],ClassTag[T]):Symbol"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">abstract </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">unique_zipfian</span><span class="tparams">[<span name="T">T</span>]</span><span class="params">(<span name="range_max">range_max: <span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.unique_zipfian.T">T</span></span>, <span name="shape">shape: <span class="extype" name="scala.Option">Option</span>[<a href="Shape.html" class="extype" name="org.apache.mxnet.Shape">Shape</a>] = <span class="symbol">None</span></span>, <span name="name">name: <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">null</span></span>, <span name="attr">attr: <span class="extype" name="scala.Predef.Map">Map</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="scala.Predef.String">String</span>] = <span class="symbol">null</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <a href="SymbolOrScalar.html" class="extype" name="org.apache.mxnet.SymbolOrScalar">SymbolOrScalar</a>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.unique_zipfian.T">T</span>]</span>, <span name="arg1">arg1: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mxnet.SymbolRandomAPIBase.unique_zipfian.T">T</span>]</span>)</span><span class="result">: <a href="Symbol.html" class="extype" name="org.apache.mxnet.Symbol">Symbol</a></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@unique_zipfian[T](range_max:T,shape:Option[org.apache.mxnet.Shape],name:String,attr:Map[String,String])(implicitevidence$1:org.apache.mxnet.SymbolOrScalar[T],implicitevidence$2:scala.reflect.ClassTag[T]):org.apache.mxnet.Symbol" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <p class="shortcomment cmt"></p><div class="fullcomment"><div class="comment cmt"><p></p><pre>Draw random samples from an an approximately log-uniform |
| or Zipfian distribution without replacement. |
| |
| This operation takes a <span class="num">2</span>-D shape `(batch_size, num_sampled)`, |
| and randomly generates *num_sampled* samples from the range of integers [<span class="num">0</span>, range_max) |
| <span class="kw">for</span> each instance in the batch. |
| |
| The elements in each instance are drawn without replacement from the base distribution. |
| The base distribution <span class="kw">for</span> <span class="kw">this</span> operator is an approximately log-uniform or Zipfian distribution: |
| |
| P(<span class="kw">class</span>) = (log(<span class="kw">class</span> + <span class="num">2</span>) - log(<span class="kw">class</span> + <span class="num">1</span>)) / log(range_max + <span class="num">1</span>) |
| |
| Additionaly, it also returns the number of trials used to obtain `num_sampled` samples <span class="kw">for</span> |
| each instance in the batch. |
| |
| Example:: |
| |
| samples, trials = _sample_unique_zipfian(<span class="num">750000</span>, shape=(<span class="num">4</span>, <span class="num">8192</span>)) |
| unique(samples[<span class="num">0</span>]) = <span class="num">8192</span> |
| unique(samples[<span class="num">3</span>]) = <span class="num">8192</span> |
| trials[<span class="num">0</span>] = <span class="num">16435</span> |
| |
| |
| |
| Defined in src/operator/random/unique_sample_op.cc:L66</pre></div><dl class="paramcmts block"><dt class="param">range_max</dt><dd class="cmt"><p>The number of possible classes.</p></dd><dt class="param">shape</dt><dd class="cmt"><p>2-D shape of the output, where shape[0] is the batch size, and shape[1] is the number of candidates to sample for each batch.</p></dd><dt>returns</dt><dd class="cmt"><p>org.apache.mxnet.Symbol</p></dd></dl><dl class="attributes block"> <dt>Annotations</dt><dd> |
| <span class="name">@<a href="annotation/Experimental.html" class="extype" name="org.apache.mxnet.annotation.Experimental">Experimental</a></span><span class="args">()</span> |
| |
| </dd></dl></div> |
| </li></ol> |
| </div> |
| |
| <div id="values" class="values members"> |
| <h3>Concrete Value Members</h3> |
| <ol><li name="scala.AnyRef#!=" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="!=(x$1:Any):Boolean"></a> |
| <a id="!=(Any):Boolean"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">final </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span title="gt4s: $bang$eq" class="name">!=</span><span class="params">(<span name="arg0">arg0: <span class="extype" name="scala.Any">Any</span></span>)</span><span class="result">: <span class="extype" name="scala.Boolean">Boolean</span></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@!=(x$1:Any):Boolean" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div> |
| </li><li name="scala.AnyRef###" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="##():Int"></a> |
| <a id="##():Int"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">final </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span title="gt4s: $hash$hash" class="name">##</span><span class="params">()</span><span class="result">: <span class="extype" name="scala.Int">Int</span></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@##():Int" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div> |
| </li><li name="scala.AnyRef#==" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="==(x$1:Any):Boolean"></a> |
| <a id="==(Any):Boolean"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">final </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span title="gt4s: $eq$eq" class="name">==</span><span class="params">(<span name="arg0">arg0: <span class="extype" name="scala.Any">Any</span></span>)</span><span class="result">: <span class="extype" name="scala.Boolean">Boolean</span></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@==(x$1:Any):Boolean" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef → Any</dd></dl></div> |
| </li><li name="scala.Any#asInstanceOf" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="asInstanceOf[T0]:T0"></a> |
| <a id="asInstanceOf[T0]:T0"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier">final </span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">asInstanceOf</span><span class="tparams">[<span name="T0">T0</span>]</span><span class="result">: <span class="extype" name="scala.Any.asInstanceOf.T0">T0</span></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@asInstanceOf[T0]:T0" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>Any</dd></dl></div> |
| </li><li name="scala.AnyRef#clone" visbl="prt" data-isabs="false" fullComment="yes" group="Ungrouped"> |
| <a id="clone():Object"></a> |
| <a id="clone():AnyRef"></a> |
| <h4 class="signature"> |
| <span class="modifier_kind"> |
| <span class="modifier"></span> |
| <span class="kind">def</span> |
| </span> |
| <span class="symbol"> |
| <span class="name">clone</span><span class="params">()</span><span class="result">: <span class="extype" name="scala.AnyRef">AnyRef</span></span> |
| </span> |
| </h4><span class="permalink"> |
| <a href="../../../index.html#org.apache.mxnet.SymbolRandomAPIBase@clone():Object" title="Permalink" target="_top"> |
| <img src="../../../lib/permalink.png" alt="Permalink" /> |
| </a> |
| </span> |
| <div class="fullcomment"><dl class="attributes block"> <dt>Attributes</dt><dd>protected[<a href="../../../java$lang.html" class="extype" name="java.lang">java.lang</a>] </dd><dt>Definition Classes</dt><dd>AnyRef</dd><dt>Annotations</dt><dd> |
| <span class="name">@throws</span><span class="args">(<span> |
| |
| <span class="defval" name="classOf[java.lang.CloneNotSupportedException]">...</span> |
| </span>)</span> |
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