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| <a href="random_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> * or more contributor license agreements. See the NOTICE file</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * distributed with this work for additional information</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * regarding copyright ownership. The ASF licenses this file</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * to you under the Apache License, Version 2.0 (the</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * "License"); you may not use this file except in compliance</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * with the License. You may obtain a copy of the License at</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> *</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * http://www.apache.org/licenses/LICENSE-2.0</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> *</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> * Unless required by applicable law or agreed to in writing,</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * software distributed under the License is distributed on an</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> * KIND, either express or implied. See the License for the</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * specific language governing permissions and limitations</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * under the License.</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> */</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#ifndef MSHADOW_RANDOM_H_</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#define MSHADOW_RANDOM_H_</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include <cstdlib></span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include <algorithm></span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <random></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="3rdparty_2mshadow_2mshadow_2base_8h.html">./base.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="tensor_8h.html">./tensor.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="tensor__container_8h.html">./tensor_container.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#if MSHADOW_IN_CXX11</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <random></span> <span class="comment">// use cxx11 random by default</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#if _MSC_VER</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#define rand_r(x) rand()</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> </div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">namespace </span><a class="code" href="namespacemshadow.html">mshadow</a> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="keyword">template</span><<span class="keyword">typename</span> Device, <span class="keyword">typename</span> DType MSHADOW_DEFAULT_DTYPE></div><div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random.html"> 52</a></span> <span class="keyword">class </span><a class="code" href="classmshadow_1_1Random.html">Random</a> {};</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html"> 56</a></span> <span class="keyword">class </span><a class="code" href="classmshadow_1_1Random.html">Random</a><<a class="code" href="structmshadow_1_1cpu.html">cpu</a>, DType> {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a468829a8a58919ec4aadb1e75a6174ff"> 62</a></span>  <span class="keyword">explicit</span> <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a468829a8a58919ec4aadb1e75a6174ff">Random</a>(<span class="keywordtype">int</span> seed) {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  this->Seed(seed);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(<a class="code" href="namespacemshadow.html#ac4cba6b672478463350f7d84b47e99e3">kRandBufferSize</a>));</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a37b7e8cbe08d7c0f6699039cc0bad8b2"> 66</a></span>  <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a37b7e8cbe08d7c0f6699039cc0bad8b2">~Random</a>(<span class="keywordtype">void</span>) {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  }</div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#aff2ec8923288cea076f5fa94897c0bfe"> 72</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#aff2ec8923288cea076f5fa94897c0bfe">Seed</a>(<span class="keywordtype">int</span> seed) {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="preprocessor">#if MSHADOW_IN_CXX11</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  rnd_engine_.seed(seed);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  this->rseed_ = <span class="keyword">static_cast<</span><span class="keywordtype">unsigned</span><span class="keyword">></span>(seed);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a41b3d2a322bbd6fc2679275b80c27342"> 82</a></span>  <span class="keyword">inline</span> <span class="keywordtype">unsigned</span> <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a41b3d2a322bbd6fc2679275b80c27342">GetSeed</a>()<span class="keyword"> const </span>{</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">return</span> rseed_;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a0096231388917db869409e0f587b1cf3"> 89</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a0096231388917db869409e0f587b1cf3">set_stream</a>(<a class="code" href="structmshadow_1_1Stream.html">Stream<cpu></a> *stream) {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  }</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="comment">// These samplers are only avail in C++11.</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="preprocessor">#if MSHADOW_IN_CXX11</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">inline</span> <span class="keywordtype">unsigned</span> GetRandInt() {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">return</span> rnd_engine_();</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  }</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GetRandInt(<span class="keyword">const</span> <a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, 1, unsigned></a>& dst) {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  std::generate_n(dst.<a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(0), [&](){ <span class="keywordflow">return</span> rnd_engine_(); });</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">class</span> Sampler></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> SampleDistribution(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst, Sampler sampler) {</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">if</span> (dst-><a class="code" href="structmshadow_1_1Tensor.html#a9cc7d01a1eb0825d7a3fcdabc8e58f07">CheckContiguous</a>()) {</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  std::generate_n(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>.Size(), sampler);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, 2, DType></a> mat = dst-><a class="code" href="structmshadow_1_1Tensor.html#a48a5927e810fbc45e43e92cfe397d9f2">FlatTo2D</a>();</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> i = 0; i < mat.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(0); ++i) {</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  std::generate_n(mat[i].dptr_, mat.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(1), sampler);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  }</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">SampleUniform</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  PType a = 0.0f , PType b = 1.0f ) {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// Ensure that half_t is handled correctly.</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_floating_point<DType>::value,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  DType, <span class="keywordtype">double</span>>::type FType;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_integral<DType>::value,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  std::uniform_int_distribution<DType>,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  std::uniform_real_distribution<FType>>::type GType;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  GType dist_uniform(a, b);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  SampleDistribution(dst, [&](){ <span class="keywordflow">return</span> dist_uniform(rnd_engine_);});</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">SampleGaussian</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst,</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  PType mu = 0.0f, PType sigma = 1.0f ) {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keywordflow">if</span> (sigma <= 0) {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  *dst = mu; <span class="keywordflow">return</span>;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  }</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_floating_point<DType>::value,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  DType, <span class="keywordtype">double</span>>::type GType;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  std::normal_distribution<GType> dist_normal(mu, sigma);</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  SampleDistribution(dst, [&](){ <span class="keywordflow">return</span> dist_normal(rnd_engine_);});</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">SampleGamma</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  PType alpha, PType beta) {</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_floating_point<DType>::value,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  DType, <span class="keywordtype">double</span>>::type GType;</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  std::gamma_distribution<GType> dist_gamma(alpha, beta);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  SampleDistribution(dst, [&](){ <span class="keywordflow">return</span> dist_gamma(rnd_engine_);});</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">SampleExponential</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst, PType lambda ) {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_floating_point<DType>::value,</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  DType, <span class="keywordtype">double</span>>::type GType;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  std::exponential_distribution<GType> dist_exp(lambda);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  SampleDistribution(dst, [&](){ <span class="keywordflow">return</span> dist_exp(rnd_engine_);});</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">SamplePoisson</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst, PType lambda) {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_integral<DType>::value, DType, <span class="keywordtype">int</span>>::type GType;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  std::poisson_distribution<GType> dist_poisson(lambda);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  SampleDistribution(dst, [&](){ <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>DType<span class="keyword">></span>(dist_poisson(rnd_engine_));});</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  }</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType1, <span class="keyword">typename</span> PType2></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> SampleNegativeBinomial(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst, PType1 k, PType2 p) {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_integral<DType>::value, DType, <span class="keywordtype">int</span>>::type GType;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  std::negative_binomial_distribution<GType> dist_negbinomial(k, p);</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  SampleDistribution(dst, [&](){ <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>DType<span class="keyword">></span>(dist_negbinomial(rnd_engine_));});</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim, <span class="keyword">typename</span> PType></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> SampleGeneralizedNegativeBinomial(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst,</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  PType mu, PType alpha) {</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">if</span> (alpha == PType(0)) {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">SamplePoisson</a>(dst, mu); <span class="comment">// limit of Poisson</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  PType r(PType(1) / alpha);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  PType beta = mu * alpha;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  std::gamma_distribution<> dist_gamma(r, beta);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keyword">typedef</span> <span class="keyword">typename</span> std::conditional<std::is_integral<DType>::value, DType, <span class="keywordtype">int</span>>::type GType;</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  SampleDistribution(dst,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  [&](){ std::poisson_distribution<GType> dist_poisson(dist_gamma(rnd_engine_));</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>DType<span class="keyword">></span>(dist_poisson(rnd_engine_));});</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  }</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">inline</span> <a class="code" href="structmshadow_1_1expr_1_1ReshapeExp.html">expr::ReshapeExp<Tensor<cpu, 1, DType></a>, DType, dim, 1></div><div class="line"><a name="l00262"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a4ca9b27ca3752795017372a05cfce5c3"> 262</a></span>  <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a4ca9b27ca3752795017372a05cfce5c3">gaussian</a>(<a class="code" href="structmshadow_1_1Shape.html">Shape<dim></a> shape) {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(shape.<a class="code" href="structmshadow_1_1Shape.html#ac6d667fde1a8180b64f475f5d33ea58f">Size</a>()));</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  this-><a class="code" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">SampleGaussian</a>(&buffer_, 0.0f, 1.0f);</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <span class="keywordflow">return</span> <a class="code" href="namespacemshadow_1_1expr.html#a73862619baed02a20e49897decf13fc2">expr::reshape</a>(buffer_, shape);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  }</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">inline</span> expr::ReshapeExp<Tensor<cpu, 1, DType>, DType, dim, 1></div><div class="line"><a name="l00280"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a4eae04b06525d65b839f2c9c5428c436"> 280</a></span>  <a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a4eae04b06525d65b839f2c9c5428c436">uniform</a>(<a class="code" href="structmshadow_1_1Shape.html">Shape<dim></a> shape) {</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(shape.<a class="code" href="structmshadow_1_1Shape.html#ac6d667fde1a8180b64f475f5d33ea58f">Size</a>()));</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  this-><a class="code" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">SampleUniform</a>(&buffer_, 0.0f, 1.0f);</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordflow">return</span> <a class="code" href="namespacemshadow_1_1expr.html#a73862619baed02a20e49897decf13fc2">expr::reshape</a>(buffer_, shape);</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  }</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a02bf4cce9c068da452494452432fcf7f"> 286</a></span>  std::mt19937 &<a class="code" href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a02bf4cce9c068da452494452432fcf7f">GetRndEngine</a>() {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keywordflow">return</span> rnd_engine_;</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  }</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span> <span class="preprocessor">#if MSHADOW_IN_CXX11</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  std::mt19937 rnd_engine_;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keywordtype">unsigned</span> rseed_;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordtype">unsigned</span> rseed_;</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <span class="comment">// functions</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">SampleUniform</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst,</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  DType a = 0.0f, DType b = 1.0f) {</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keywordflow">if</span> (dst-><a class="code" href="structmshadow_1_1Tensor.html#a9cc7d01a1eb0825d7a3fcdabc8e58f07">CheckContiguous</a>()) {</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  this->GenUniform(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>.Size(), a, b);</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, 2, DType></a> mat = dst-><a class="code" href="structmshadow_1_1Tensor.html#a48a5927e810fbc45e43e92cfe397d9f2">FlatTo2D</a>();</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> i = 0; i < mat.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(0); ++i) {</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  this->GenUniform(mat[i].dptr_, mat.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(1), a, b);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  }</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  }</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  }</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">SampleGaussian</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, dim, DType></a> *dst,</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  DType mu = 0.0f, DType sigma = 1.0f) {</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordflow">if</span> (sigma <= 0.0f) {</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  *dst = mu; <span class="keywordflow">return</span>;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  }</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordflow">if</span> (dst-><a class="code" href="structmshadow_1_1Tensor.html#a9cc7d01a1eb0825d7a3fcdabc8e58f07">CheckContiguous</a>()) {</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  this->GenGaussian(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>.Size(), mu, sigma);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="structmshadow_1_1Tensor.html">Tensor<cpu, 2, DType></a> mat = dst-><a class="code" href="structmshadow_1_1Tensor.html#a48a5927e810fbc45e43e92cfe397d9f2">FlatTo2D</a>();</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> i = 0; i < mat.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(0); ++i) {</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  this->GenGaussian(mat[i].dptr_, mat.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(1), mu, sigma);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  }</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenUniform(<span class="keywordtype">float</span> *dptr, <a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> size, <span class="keywordtype">float</span> a, <span class="keywordtype">float</span> b) {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> j = 0; j < size; ++j) {</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  dptr[j] = <span class="keyword">static_cast<</span><span class="keywordtype">float</span><span class="keyword">></span>(RandNext()) * (b - a) + a;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  }</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenUniform(<span class="keywordtype">double</span> *dptr, <a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> size, <span class="keywordtype">double</span> a, <span class="keywordtype">double</span> b) {</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> j = 0; j < size; ++j) {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  dptr[j] = <span class="keyword">static_cast<</span><span class="keywordtype">double</span><span class="keyword">></span>(RandNext()) * (b - a) + a;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  }</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  }</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenGaussian(<span class="keywordtype">float</span> *dptr, <a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> size, <span class="keywordtype">float</span> mu, <span class="keywordtype">float</span> sigma) {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  this->GenGaussianX(dptr, size, mu, sigma);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenGaussian(<span class="keywordtype">double</span> *dptr, <a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> size, <span class="keywordtype">double</span> mu, <span class="keywordtype">double</span> sigma) {</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  this->GenGaussianX(dptr, size, mu, sigma);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenGaussianX(DType *dptr, <a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> size, DType mu, DType sigma) {</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  DType g1 = 0.0f, g2 = 0.0f;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordflow">for</span> (<a class="code" href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">index_t</a> j = 0; j < size; ++j) {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">if</span> ((j & 1) == 0) {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  this->SampleNormal2D(&g1, &g2);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  dptr[j] = mu + g1 * sigma;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  dptr[j] = mu + g2 * sigma;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  }</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keyword">inline</span> DType RandNext(<span class="keywordtype">void</span>) {</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordflow">return</span> <span class="keyword">static_cast<</span>DType<span class="keyword">></span>(rand_r(&rseed_)) /</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  (static_cast<DType>(RAND_MAX) + 1.0f);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  }</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  <span class="keyword">inline</span> DType RandNext2(<span class="keywordtype">void</span>) {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">return</span> (static_cast<DType>(rand_r(&rseed_)) + 1.0f) /</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  (<span class="keyword">static_cast<</span>DType<span class="keyword">></span>(RAND_MAX) + 2.0f);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  }</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> SampleNormal2D(DType *xx_, DType *yy_) {</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  DType &xx = *xx_, &yy = *yy_;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  DType x, y, s;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="keywordflow">do</span> {</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  x = 2.0f * RandNext2() - 1.0f;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  y = 2.0f * RandNext2() - 1.0f;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  s = x * x + y * y;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  } <span class="keywordflow">while</span> (s >= 1.0f || s == 0.0f);</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  DType t = std::sqrt(-2.0f * std::log(s) / s);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  xx = x * t; yy = y * t;</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  }</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> <span class="preprocessor">#endif</span></div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="classmshadow_1_1TensorContainer.html">TensorContainer<cpu, 1, DType></a> buffer_;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> }; <span class="comment">// class Random<cpu, DType></span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> <span class="comment">// only allow GPU PRNG when cuda is enabled</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> <span class="preprocessor">#if MSHADOW_USE_CUDA</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div><div class="line"><a name="l00391"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html"> 391</a></span> <span class="keyword">class </span><a class="code" href="classmshadow_1_1Random.html">Random</a><<a class="code" href="structmshadow_1_1gpu.html">gpu</a>, DType> {</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00397"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a47236e16630bfafbf207d1ac57547b18"> 397</a></span>  <span class="keyword">explicit</span> <a class="code" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a47236e16630bfafbf207d1ac57547b18">Random</a>(<span class="keywordtype">int</span> seed) : gen_(NULL) {</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  this->Seed(seed);</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(<a class="code" href="namespacemshadow.html#ac4cba6b672478463350f7d84b47e99e3">kRandBufferSize</a>));</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  }</div><div class="line"><a name="l00401"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a6600fb034b3c840dd568458b9cea397c"> 401</a></span>  <a class="code" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a6600fb034b3c840dd568458b9cea397c">~Random</a>(<span class="keywordtype">void</span>) <a class="code" href="3rdparty_2mshadow_2mshadow_2base_8h.html#a53a342d1f8cc22d36ed49e4c51c19f0c">MSHADOW_THROW_EXCEPTION</a> {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  DeleteGenerator();</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div><div class="line"><a name="l00408"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#ac813f60f1d2d7c7e5cd4a4cc15068667"> 408</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#ac813f60f1d2d7c7e5cd4a4cc15068667">set_stream</a>(<a class="code" href="structmshadow_1_1Stream_3_01gpu_01_4.html">Stream<gpu></a> *stream) {</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  curandStatus_t status;</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  status = curandSetStream(gen_, <a class="code" href="structmshadow_1_1Stream.html">Stream<gpu>::GetStream</a>(stream));</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> </div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"set_stream CURAND failed"</span>;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  }</div><div class="line"><a name="l00418"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a997d28fe4cfa9a1f36b88160ce61ec9a"> 418</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a997d28fe4cfa9a1f36b88160ce61ec9a">Seed</a>(<span class="keywordtype">int</span> seed) {</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="comment">// Create a new rng, either initially or if the RNG type can't reset its offset.</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">if</span> (gen_ == NULL || (curandSetGeneratorOffset(gen_, 0ULL) != CURAND_STATUS_SUCCESS))</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  CreateGenerator();</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <span class="comment">// Now set the seed.</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  curandStatus_t status;</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  status = curandSetPseudoRandomGeneratorSeed(gen_, static_cast<uint64_t>(seed));</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"Set CURAND seed failed."</span>;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  }</div><div class="line"><a name="l00430"></a><span class="lineno"><a class="line" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a8c3a2f7df5bc62093b4a55b9d42bb60e"> 430</a></span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a8c3a2f7df5bc62093b4a55b9d42bb60e">GetRandInt</a>(<span class="keyword">const</span> <a class="code" href="structmshadow_1_1Tensor.html">Tensor<gpu, 1, unsigned></a>& dst) {</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  curandStatus_t status = curandGenerate(gen_, dst.<a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst.<a class="code" href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">size</a>(0));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"CURAND Gen rand ints failed."</span>;</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  }</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">SampleUniform</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<gpu, dim, DType></a> *dst,</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  DType a = 0.0f, DType b = 1.0f);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">SampleGaussian</a>(<a class="code" href="structmshadow_1_1Tensor.html">Tensor<gpu, dim, DType></a> *dst,</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  DType mu = 0.0f, DType sigma = 1.0f);</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <span class="keyword">inline</span> <a class="code" href="structmshadow_1_1expr_1_1ReshapeExp.html">expr::ReshapeExp<Tensor<gpu, 1, DType></a>, DType, dim, 1></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  gaussian(<a class="code" href="structmshadow_1_1Shape.html">Shape<dim></a> shape, DType mu = 0.0f, DType sigma = 1.0f);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">inline</span> expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  uniform(<a class="code" href="structmshadow_1_1Shape.html">Shape<dim></a> shape);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> </div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenGaussian(<span class="keywordtype">float</span> *dptr, <span class="keywordtype">size_t</span> size, <span class="keywordtype">float</span> mu, <span class="keywordtype">float</span> sigma) {</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  curandStatus_t status;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  status = curandGenerateNormal(gen_, dptr, size, mu, sigma);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"CURAND Gen Normal float failed."</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  << <span class="stringliteral">" size = "</span> << size</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  << <span class="stringliteral">",mu = "</span> << mu</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  << <span class="stringliteral">",sigma = "</span> << sigma;</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  }</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenGaussian(<span class="keywordtype">double</span> *dptr, <span class="keywordtype">size_t</span> size, <span class="keywordtype">double</span> mu, <span class="keywordtype">double</span> sigma) {</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  curandStatus_t status;</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  status = curandGenerateNormalDouble(gen_, dptr, size, mu, sigma);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"CURAND Gen Normal double failed."</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  << <span class="stringliteral">" size = "</span> << size</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  << <span class="stringliteral">",mu = "</span> << mu</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  << <span class="stringliteral">",sigma = "</span> << sigma;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  }</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenUniform(<span class="keywordtype">float</span> *dptr, <span class="keywordtype">size_t</span> size) {</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  curandStatus_t status;</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  status = curandGenerateUniform(gen_, dptr, size);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"CURAND Gen Uniform float failed."</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  << <span class="stringliteral">" size = "</span> << size;</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  }</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> GenUniform(<span class="keywordtype">double</span> *dptr, <span class="keywordtype">size_t</span> size) {</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  curandStatus_t status;</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  status = curandGenerateUniformDouble(gen_, dptr, size);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"CURAND Gen Uniform double failed."</span></div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  << <span class="stringliteral">" size = "</span> << size;</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  }</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> CreateGenerator() {</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="keywordflow">if</span> (gen_ != NULL)</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  DeleteGenerator();</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  curandStatus_t status;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  status = curandCreateGenerator(&gen_, CURAND_RNG_PSEUDO_DEFAULT);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"Cannot create CURAND Generator"</span>;</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  }</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="keyword">inline</span> <span class="keywordtype">void</span> DeleteGenerator() {</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <span class="keywordflow">if</span> (gen_ != NULL) {</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  curandStatus_t status;</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  status = curandDestroyGenerator(gen_);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  CHECK_EQ(status, CURAND_STATUS_SUCCESS) << <span class="stringliteral">"Destory CURAND Gen failed"</span>;</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  gen_ = NULL;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  }</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  }</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  curandGenerator_t gen_;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <a class="code" href="classmshadow_1_1TensorContainer.html">TensorContainer<gpu, 1, DType></a> buffer_;</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> }; <span class="comment">// class Random<gpu, DType></span></div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> <span class="preprocessor">#endif // MSHADOW_USE_CUDA</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span> </div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> <span class="preprocessor">#ifdef __CUDACC__</span></div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span> <span class="comment">// implementations that depends on cuda kernels</span></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">Random<gpu, DType>::SampleUniform</a>(</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <a class="code" href="structmshadow_1_1Tensor.html">Tensor<gpu, dim, DType></a> *dst, DType a, DType b) {</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <span class="keywordflow">if</span> (a == 0.0f && b == 1.0f) {</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="keywordflow">if</span> (dst-><a class="code" href="structmshadow_1_1Tensor.html#a9cc7d01a1eb0825d7a3fcdabc8e58f07">CheckContiguous</a>()) {</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  this->GenUniform(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>.Size());</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  *dst = this->uniform(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>);</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  }</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  *dst = this->uniform(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>) * (b - a) + a;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  }</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> }</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> <span class="keyword">inline</span> <span class="keywordtype">void</span> <a class="code" href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">Random<gpu, DType>::SampleGaussian</a>(</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="structmshadow_1_1Tensor.html">Tensor<gpu, dim, DType></a> *dst, DType mu, DType sigma) {</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <span class="comment">// We need to check whether the shape size is even since CuRand supports only normal distribution</span></div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="comment">// generation of even number of elements.</span></div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keywordflow">if</span> (dst-><a class="code" href="structmshadow_1_1Tensor.html#a9cc7d01a1eb0825d7a3fcdabc8e58f07">CheckContiguous</a>() && (dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>.Size() % 2 == 0)) {</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  this->GenGaussian(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">dptr_</a>, dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>.Size(), mu, sigma);</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  *dst = this->gaussian(dst-><a class="code" href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">shape_</a>, mu, sigma);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  }</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> }</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span> </div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span> <span class="keyword">inline</span> <a class="code" href="structmshadow_1_1expr_1_1ReshapeExp.html">expr::ReshapeExp<Tensor<gpu, 1, DType></a>, DType, dim, 1></div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> <a class="code" href="classmshadow_1_1Random.html">Random<gpu, DType>::gaussian</a>(<a class="code" href="structmshadow_1_1Shape.html">Shape<dim></a> shape, DType mu, DType sigma) {</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="keywordtype">size_t</span> aligned_sz = ((shape.<a class="code" href="structmshadow_1_1Shape.html#ac6d667fde1a8180b64f475f5d33ea58f">Size</a>() + 1UL) >> 1) << 1;</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  <span class="comment">// allocate alligned size</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(aligned_sz));</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(shape.<a class="code" href="structmshadow_1_1Shape.html#ac6d667fde1a8180b64f475f5d33ea58f">Size</a>()));</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  this->GenGaussian(buffer_.dptr_, aligned_sz, mu, sigma);</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  <span class="keywordflow">return</span> <a class="code" href="namespacemshadow_1_1expr.html#a73862619baed02a20e49897decf13fc2">expr::reshape</a>(buffer_, shape);</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> }</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> <span class="keyword">template</span><<span class="keyword">typename</span> DType></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> <span class="keyword">template</span><<span class="keywordtype">int</span> dim></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> <span class="keyword">inline</span> expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1></div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> <a class="code" href="classmshadow_1_1Random.html">Random<gpu, DType>::uniform</a>(<a class="code" href="structmshadow_1_1Shape.html">Shape<dim></a> shape) {</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  buffer_.Resize(<a class="code" href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">Shape1</a>(shape.<a class="code" href="structmshadow_1_1Shape.html#ac6d667fde1a8180b64f475f5d33ea58f">Size</a>()));</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  this->GenUniform(buffer_.dptr_, buffer_.size(0));</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keywordflow">return</span> <a class="code" href="namespacemshadow_1_1expr.html#a73862619baed02a20e49897decf13fc2">expr::reshape</a>(buffer_, shape);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> }</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> <span class="preprocessor">#endif // __CUDACC__</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> } <span class="comment">// namespace mshadow</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> <span class="preprocessor">#endif // MSHADOW_RANDOM_H_</span></div><div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a4ca9b27ca3752795017372a05cfce5c3"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a4ca9b27ca3752795017372a05cfce5c3">mshadow::Random< cpu, DType >::gaussian</a></div><div class="ttdeci">expr::ReshapeExp< Tensor< cpu, 1, DType >, DType, dim, 1 > gaussian(Shape< dim > shape)</div><div class="ttdoc">return a temporal expression storing standard gaussian random variables the temporal tensor is only v...</div><div class="ttdef"><b>Definition:</b> random.h:262</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_html"><div class="ttname"><a href="classmshadow_1_1Random.html">mshadow::Random</a></div><div class="ttdoc">random number generator </div><div class="ttdef"><b>Definition:</b> random.h:52</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01gpu_00_01DType_01_4_html_a997d28fe4cfa9a1f36b88160ce61ec9a"><div class="ttname"><a href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a997d28fe4cfa9a1f36b88160ce61ec9a">mshadow::Random< gpu, DType >::Seed</a></div><div class="ttdeci">void Seed(int seed)</div><div class="ttdoc">seed random number generator using this seed </div><div class="ttdef"><b>Definition:</b> random.h:418</div></div> |
| <div class="ttc" id="structmshadow_1_1Tensor_html_ad86d6759c585efb5229b3a0659973838"><div class="ttname"><a href="structmshadow_1_1Tensor.html#ad86d6759c585efb5229b3a0659973838">mshadow::Tensor::dptr_</a></div><div class="ttdeci">DType * dptr_</div><div class="ttdoc">pointer to the data </div><div class="ttdef"><b>Definition:</b> tensor.h:434</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a41b3d2a322bbd6fc2679275b80c27342"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a41b3d2a322bbd6fc2679275b80c27342">mshadow::Random< cpu, DType >::GetSeed</a></div><div class="ttdeci">unsigned GetSeed() const</div><div class="ttdoc">get random seed used in random generator </div><div class="ttdef"><b>Definition:</b> random.h:82</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_aff2ec8923288cea076f5fa94897c0bfe"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#aff2ec8923288cea076f5fa94897c0bfe">mshadow::Random< cpu, DType >::Seed</a></div><div class="ttdeci">void Seed(int seed)</div><div class="ttdoc">seed random number generator using this seed </div><div class="ttdef"><b>Definition:</b> random.h:72</div></div> |
| <div class="ttc" id="structmshadow_1_1Shape_html"><div class="ttname"><a href="structmshadow_1_1Shape.html">mshadow::Shape< dim ></a></div></div> |
| <div class="ttc" id="structmshadow_1_1Stream_3_01gpu_01_4_html"><div class="ttname"><a href="structmshadow_1_1Stream_3_01gpu_01_4.html">mshadow::Stream< gpu ></a></div><div class="ttdef"><b>Definition:</b> stream_gpu-inl.h:37</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01gpu_00_01DType_01_4_html_a6600fb034b3c840dd568458b9cea397c"><div class="ttname"><a href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a6600fb034b3c840dd568458b9cea397c">mshadow::Random< gpu, DType >::~Random</a></div><div class="ttdeci">~Random(void) MSHADOW_THROW_EXCEPTION</div><div class="ttdef"><b>Definition:</b> random.h:401</div></div> |
| <div class="ttc" id="structmshadow_1_1Tensor_html_ad10c7414c5948e789e8761df2083c4e5"><div class="ttname"><a href="structmshadow_1_1Tensor.html#ad10c7414c5948e789e8761df2083c4e5">mshadow::Tensor::shape_</a></div><div class="ttdeci">Shape< dimension > shape_</div><div class="ttdoc">shape of the tensor </div><div class="ttdef"><b>Definition:</b> tensor.h:436</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a37b7e8cbe08d7c0f6699039cc0bad8b2"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a37b7e8cbe08d7c0f6699039cc0bad8b2">mshadow::Random< cpu, DType >::~Random</a></div><div class="ttdeci">~Random(void)</div><div class="ttdef"><b>Definition:</b> random.h:66</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a4eae04b06525d65b839f2c9c5428c436"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a4eae04b06525d65b839f2c9c5428c436">mshadow::Random< cpu, DType >::uniform</a></div><div class="ttdeci">expr::ReshapeExp< Tensor< cpu, 1, DType >, DType, dim, 1 > uniform(Shape< dim > shape)</div><div class="ttdoc">return a temporal expression storing standard uniform [0,1) the temporal tensor is only valid before ...</div><div class="ttdef"><b>Definition:</b> random.h:280</div></div> |
| <div class="ttc" id="tensor_8h_html"><div class="ttname"><a href="tensor_8h.html">tensor.h</a></div><div class="ttdoc">header file of tensor data structure and functions This lib requires explicit memory allocation and d...</div></div> |
| <div class="ttc" id="structmshadow_1_1cpu_html"><div class="ttname"><a href="structmshadow_1_1cpu.html">mshadow::cpu</a></div><div class="ttdoc">device name CPU </div><div class="ttdef"><b>Definition:</b> tensor.h:39</div></div> |
| <div class="ttc" id="structmshadow_1_1gpu_html"><div class="ttname"><a href="structmshadow_1_1gpu.html">mshadow::gpu</a></div><div class="ttdoc">device name GPU </div><div class="ttdef"><b>Definition:</b> tensor.h:46</div></div> |
| <div class="ttc" id="namespacemshadow_html_ac4cba6b672478463350f7d84b47e99e3"><div class="ttname"><a href="namespacemshadow.html#ac4cba6b672478463350f7d84b47e99e3">mshadow::kRandBufferSize</a></div><div class="ttdeci">const unsigned kRandBufferSize</div><div class="ttdoc">buffer size for each random number generator </div><div class="ttdef"><b>Definition:</b> base.h:336</div></div> |
| <div class="ttc" id="namespacemxnet_html_a51f9b094369a349c05463de2be9f0a31"><div class="ttname"><a href="namespacemxnet.html#a51f9b094369a349c05463de2be9f0a31">mxnet::SampleExponential</a></div><div class="ttdeci">void SampleExponential(real_t lambda, NDArray *out)</div><div class="ttdoc">Sample exponential distribution for each elements of out. </div></div> |
| <div class="ttc" id="namespacemshadow_html_adcbc2e1131386fccb1474b0bdf045926"><div class="ttname"><a href="namespacemshadow.html#adcbc2e1131386fccb1474b0bdf045926">mshadow::index_t</a></div><div class="ttdeci">int32_t index_t</div><div class="ttdoc">type that will be used for index </div><div class="ttdef"><b>Definition:</b> base.h:343</div></div> |
| <div class="ttc" id="namespacemshadow_1_1expr_html_a73862619baed02a20e49897decf13fc2"><div class="ttname"><a href="namespacemshadow_1_1expr.html#a73862619baed02a20e49897decf13fc2">mshadow::expr::reshape</a></div><div class="ttdeci">ReshapeExp< SrcExp, DType, dimdst, ExpInfo< SrcExp >::kDim > reshape(const Exp< SrcExp, DType, etype > &src, Shape< dimdst > oshape)</div><div class="ttdoc">a expression that reshapes a tensor to another shape </div><div class="ttdef"><b>Definition:</b> reshape.h:66</div></div> |
| <div class="ttc" id="namespacemxnet_html_a5a28062f52ca576a126599e7ad487077"><div class="ttname"><a href="namespacemxnet.html#a5a28062f52ca576a126599e7ad487077">mxnet::SampleGaussian</a></div><div class="ttdeci">void SampleGaussian(real_t mu, real_t sigma, NDArray *out)</div><div class="ttdoc">Sample gaussian distribution for each elements of out. </div></div> |
| <div class="ttc" id="structmshadow_1_1Tensor_html_a48a5927e810fbc45e43e92cfe397d9f2"><div class="ttname"><a href="structmshadow_1_1Tensor.html#a48a5927e810fbc45e43e92cfe397d9f2">mshadow::Tensor::FlatTo2D</a></div><div class="ttdeci">MSHADOW_XINLINE Tensor< Device, 2, DType > FlatTo2D(void) const</div><div class="ttdoc">flatten the tensor to 2 dimension, collapse the higher dimensions together </div><div class="ttdef"><b>Definition:</b> tensor.h:519</div></div> |
| <div class="ttc" id="classmshadow_1_1TensorContainer_html"><div class="ttname"><a href="classmshadow_1_1TensorContainer.html">mshadow::TensorContainer</a></div><div class="ttdoc">tensor container that does memory allocation and resize like STL, use it to save the lines of FreeSpa...</div><div class="ttdef"><b>Definition:</b> tensor_container.h:40</div></div> |
| <div class="ttc" id="structmshadow_1_1Tensor_html_a88cbcae11653307bfa4c99804320b638"><div class="ttname"><a href="structmshadow_1_1Tensor.html#a88cbcae11653307bfa4c99804320b638">mshadow::Tensor::size</a></div><div class="ttdeci">MSHADOW_XINLINE index_t size(int idx) const</div><div class="ttdoc">return size of i-th dimension, start counting from highest dimension </div><div class="ttdef"><b>Definition:</b> tensor.h:505</div></div> |
| <div class="ttc" id="structmshadow_1_1Tensor_html_a9cc7d01a1eb0825d7a3fcdabc8e58f07"><div class="ttname"><a href="structmshadow_1_1Tensor.html#a9cc7d01a1eb0825d7a3fcdabc8e58f07">mshadow::Tensor::CheckContiguous</a></div><div class="ttdeci">MSHADOW_XINLINE bool CheckContiguous(void) const</div><div class="ttdef"><b>Definition:</b> tensor.h:491</div></div> |
| <div class="ttc" id="namespacemshadow_html_a05e468ef4d8882fccce53ae951b7bdbd"><div class="ttname"><a href="namespacemshadow.html#a05e468ef4d8882fccce53ae951b7bdbd">mshadow::Shape1</a></div><div class="ttdeci">MSHADOW_XINLINE Shape< 1 > Shape1(index_t s0)</div><div class="ttdoc">construct a one dimension shape, stride will equal s0 </div><div class="ttdef"><b>Definition:</b> tensor.h:206</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01gpu_00_01DType_01_4_html_a8c3a2f7df5bc62093b4a55b9d42bb60e"><div class="ttname"><a href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a8c3a2f7df5bc62093b4a55b9d42bb60e">mshadow::Random< gpu, DType >::GetRandInt</a></div><div class="ttdeci">void GetRandInt(const Tensor< gpu, 1, unsigned > &dst)</div><div class="ttdoc">get a set of random integers </div><div class="ttdef"><b>Definition:</b> random.h:430</div></div> |
| <div class="ttc" id="namespacemxnet_html_a2846f4556c9ca9bd0f567504ce60f274"><div class="ttname"><a href="namespacemxnet.html#a2846f4556c9ca9bd0f567504ce60f274">mxnet::SampleUniform</a></div><div class="ttdeci">void SampleUniform(real_t begin, real_t end, NDArray *out)</div><div class="ttdoc">Sample uniform distribution for each elements of out. </div></div> |
| <div class="ttc" id="structmshadow_1_1expr_1_1ReshapeExp_html"><div class="ttname"><a href="structmshadow_1_1expr_1_1ReshapeExp.html">mshadow::expr::ReshapeExp</a></div><div class="ttdoc">reshape the content to another shape input: Tensor<Device,dimsrc>: ishape output: Tensor<Device...</div><div class="ttdef"><b>Definition:</b> reshape.h:39</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a02bf4cce9c068da452494452432fcf7f"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a02bf4cce9c068da452494452432fcf7f">mshadow::Random< cpu, DType >::GetRndEngine</a></div><div class="ttdeci">std::mt19937 & GetRndEngine()</div><div class="ttdef"><b>Definition:</b> random.h:286</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01gpu_00_01DType_01_4_html_a47236e16630bfafbf207d1ac57547b18"><div class="ttname"><a href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#a47236e16630bfafbf207d1ac57547b18">mshadow::Random< gpu, DType >::Random</a></div><div class="ttdeci">Random(int seed)</div><div class="ttdoc">constructor of random engine </div><div class="ttdef"><b>Definition:</b> random.h:397</div></div> |
| <div class="ttc" id="tensor__container_8h_html"><div class="ttname"><a href="tensor__container_8h.html">tensor_container.h</a></div><div class="ttdoc">tensor container that does memory allocation and resize like STL </div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a0096231388917db869409e0f587b1cf3"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a0096231388917db869409e0f587b1cf3">mshadow::Random< cpu, DType >::set_stream</a></div><div class="ttdeci">void set_stream(Stream< cpu > *stream)</div><div class="ttdoc">set the stream of computation </div><div class="ttdef"><b>Definition:</b> random.h:89</div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01cpu_00_01DType_01_4_html_a468829a8a58919ec4aadb1e75a6174ff"><div class="ttname"><a href="classmshadow_1_1Random_3_01cpu_00_01DType_01_4.html#a468829a8a58919ec4aadb1e75a6174ff">mshadow::Random< cpu, DType >::Random</a></div><div class="ttdeci">Random(int seed)</div><div class="ttdoc">constructor of random engine </div><div class="ttdef"><b>Definition:</b> random.h:62</div></div> |
| <div class="ttc" id="namespacemshadow_html"><div class="ttname"><a href="namespacemshadow.html">mshadow</a></div><div class="ttdoc">overloaded + operator between half_t and bf16_t </div><div class="ttdef"><b>Definition:</b> base.h:334</div></div> |
| <div class="ttc" id="3rdparty_2mshadow_2mshadow_2base_8h_html_a53a342d1f8cc22d36ed49e4c51c19f0c"><div class="ttname"><a href="3rdparty_2mshadow_2mshadow_2base_8h.html#a53a342d1f8cc22d36ed49e4c51c19f0c">MSHADOW_THROW_EXCEPTION</a></div><div class="ttdeci">#define MSHADOW_THROW_EXCEPTION</div><div class="ttdef"><b>Definition:</b> base.h:260</div></div> |
| <div class="ttc" id="structmshadow_1_1Tensor_html"><div class="ttname"><a href="structmshadow_1_1Tensor.html">mshadow::Tensor</a></div><div class="ttdoc">general tensor </div><div class="ttdef"><b>Definition:</b> tensor.h:420</div></div> |
| <div class="ttc" id="namespacemxnet_html_aed55e8197182b7c66126902b2a43739a"><div class="ttname"><a href="namespacemxnet.html#aed55e8197182b7c66126902b2a43739a">mxnet::SamplePoisson</a></div><div class="ttdeci">void SamplePoisson(real_t lambda, NDArray *out)</div><div class="ttdoc">Sample Poisson distribution for each elements of out. </div></div> |
| <div class="ttc" id="3rdparty_2mshadow_2mshadow_2base_8h_html"><div class="ttname"><a href="3rdparty_2mshadow_2mshadow_2base_8h.html">base.h</a></div></div> |
| <div class="ttc" id="classmshadow_1_1Random_3_01gpu_00_01DType_01_4_html_ac813f60f1d2d7c7e5cd4a4cc15068667"><div class="ttname"><a href="classmshadow_1_1Random_3_01gpu_00_01DType_01_4.html#ac813f60f1d2d7c7e5cd4a4cc15068667">mshadow::Random< gpu, DType >::set_stream</a></div><div class="ttdeci">void set_stream(Stream< gpu > *stream)</div><div class="ttdoc">set the stream of computation </div><div class="ttdef"><b>Definition:</b> random.h:408</div></div> |
| <div class="ttc" id="structmshadow_1_1Shape_html_ac6d667fde1a8180b64f475f5d33ea58f"><div class="ttname"><a href="structmshadow_1_1Shape.html#ac6d667fde1a8180b64f475f5d33ea58f">mshadow::Shape::Size</a></div><div class="ttdeci">MSHADOW_XINLINE index_t Size(void) const</div><div class="ttdef"><b>Definition:</b> tensor.h:144</div></div> |
| <div class="ttc" id="namespacemxnet_html_a050beaa505f11e0b844deb91efe0cac2"><div class="ttname"><a href="namespacemxnet.html#a050beaa505f11e0b844deb91efe0cac2">mxnet::SampleGamma</a></div><div class="ttdeci">void SampleGamma(real_t alpha, real_t beta, NDArray *out)</div><div class="ttdoc">Sample gamma distribution for each elements of out. </div></div> |
| <div class="ttc" id="structmshadow_1_1Stream_html"><div class="ttname"><a href="structmshadow_1_1Stream.html">mshadow::Stream</a></div><div class="ttdoc">computaion stream structure, used for asynchronous computations </div><div class="ttdef"><b>Definition:</b> tensor.h:383</div></div> |
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