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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <h1>Source code for mxnet.ndarray.random</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="sd">"""Random distribution generator NDArray API of MXNet."""</span> |
| |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">_Null</span> |
| <span class="kn">from</span> <span class="nn">..context</span> <span class="kn">import</span> <span class="n">current_context</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_internal</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span> |
| |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'uniform'</span><span class="p">,</span> <span class="s1">'normal'</span><span class="p">,</span> <span class="s1">'randn'</span><span class="p">,</span> <span class="s1">'poisson'</span><span class="p">,</span> <span class="s1">'exponential'</span><span class="p">,</span> <span class="s1">'gamma'</span><span class="p">,</span> |
| <span class="s1">'multinomial'</span><span class="p">,</span> <span class="s1">'negative_binomial'</span><span class="p">,</span> <span class="s1">'generalized_negative_binomial'</span><span class="p">,</span> |
| <span class="s1">'shuffle'</span><span class="p">,</span> <span class="s1">'randint'</span><span class="p">]</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_random_helper</span><span class="p">(</span><span class="n">random</span><span class="p">,</span> <span class="n">sampler</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function for random generators."""</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">),</span> \ |
| <span class="s2">"Distribution parameters must all have the same type, but got "</span> \ |
| <span class="s2">"both </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">sampler</span><span class="p">(</span><span class="o">*</span><span class="n">params</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_context</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="n">_Null</span> <span class="ow">and</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">),</span> \ |
| <span class="s2">"Distribution parameters must all have the same type, but got "</span> \ |
| <span class="s2">"both </span><span class="si">%s</span><span class="s2"> and </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">random</span><span class="p">(</span><span class="o">*</span><span class="n">params</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Distribution parameters must be either NDArray or numbers, "</span> |
| <span class="s2">"but got </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> |
| |
| |
| <div class="viewcode-block" id="uniform"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.uniform">[docs]</a><span class="k">def</span> <span class="nf">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a uniform distribution.</span> |
| |
| <span class="sd"> Samples are uniformly distributed over the half-open interval *[low, high)*</span> |
| <span class="sd"> (includes *low*, but excludes *high*).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> low : float or NDArray, optional</span> |
| <span class="sd"> Lower boundary of the output interval. All values generated will be</span> |
| <span class="sd"> greater than or equal to low. The default value is 0.</span> |
| <span class="sd"> high : float or NDArray, optional</span> |
| <span class="sd"> Upper boundary of the output interval. All values generated will be</span> |
| <span class="sd"> less than high. The default value is 1.0.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and</span> |
| <span class="sd"> `high` are scalars, output shape will be `(m, n)`. If `low` and `high`</span> |
| <span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)` pair.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `low.context` when `low` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An NDArray of type `dtype`. If input `shape` has shape, e.g.,</span> |
| <span class="sd"> `(m, n)` and `low` and `high` are scalars, output shape will be `(m, n)`.</span> |
| <span class="sd"> If `low` and `high` are NDArrays with shape, e.g., `(x, y)`, then the</span> |
| <span class="sd"> return NDArray will have shape `(x, y, m, n)`, where `m*n` uniformly distributed</span> |
| <span class="sd"> samples are drawn for each `[low, high)` pair.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.uniform(0, 1)</span> |
| <span class="sd"> [ 0.54881352]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)</span> |
| <span class="sd"> >>> mx.nd.random.uniform(0, 1, ctx=mx.gpu(0))</span> |
| <span class="sd"> [ 0.92514056]</span> |
| <span class="sd"> <NDArray 1 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.uniform(-1, 1, shape=(2,))</span> |
| <span class="sd"> [ 0.71589124 0.08976638]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> low = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> high = mx.nd.array([2,3,4])</span> |
| <span class="sd"> >>> mx.nd.random.uniform(low, high, shape=2)</span> |
| <span class="sd"> [[ 1.78653979 1.93707538]</span> |
| <span class="sd"> [ 2.01311183 2.37081361]</span> |
| <span class="sd"> [ 3.30491424 3.69977832]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_uniform</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_uniform</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="normal"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.normal">[docs]</a><span class="k">def</span> <span class="nf">normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a normal (Gaussian) distribution.</span> |
| |
| <span class="sd"> Samples are distributed according to a normal distribution parametrized</span> |
| <span class="sd"> by *loc* (mean) and *scale* (standard deviation).</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> loc : float or NDArray, optional</span> |
| <span class="sd"> Mean (centre) of the distribution.</span> |
| <span class="sd"> scale : float or NDArray, optional</span> |
| <span class="sd"> Standard deviation (spread or width) of the distribution.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and</span> |
| <span class="sd"> `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`</span> |
| <span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `loc.context` when `loc` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An NDArray of type `dtype`. If input `shape` has shape, e.g., `(m, n)` and</span> |
| <span class="sd"> `loc` and `scale` are scalars, output shape will be `(m, n)`. If `loc` and</span> |
| <span class="sd"> `scale` are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.normal(0, 1)</span> |
| <span class="sd"> [ 2.21220636]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.normal(0, 1, ctx=mx.gpu(0))</span> |
| <span class="sd"> [ 0.29253659]</span> |
| <span class="sd"> <NDArray 1 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.normal(-1, 1, shape=(2,))</span> |
| <span class="sd"> [-0.2259962 -0.51619542]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> loc = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> scale = mx.nd.array([2,3,4])</span> |
| <span class="sd"> >>> mx.nd.random.normal(loc, scale, shape=2)</span> |
| <span class="sd"> [[ 0.55912292 3.19566321]</span> |
| <span class="sd"> [ 1.91728961 2.47706747]</span> |
| <span class="sd"> [ 2.79666662 5.44254589]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_normal</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_normal</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="randn"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.randn">[docs]</a><span class="k">def</span> <span class="nf">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a normal (Gaussian) distribution.</span> |
| |
| <span class="sd"> Samples are distributed according to a normal distribution parametrized</span> |
| <span class="sd"> by *loc* (mean) and *scale* (standard deviation).</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> loc : float or NDArray</span> |
| <span class="sd"> Mean (centre) of the distribution.</span> |
| <span class="sd"> scale : float or NDArray</span> |
| <span class="sd"> Standard deviation (spread or width) of the distribution.</span> |
| <span class="sd"> shape : int or tuple of ints</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `loc` and</span> |
| <span class="sd"> `scale` are scalars, output shape will be `(m, n)`. If `loc` and `scale`</span> |
| <span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[loc, scale)` pair.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `loc.context` when `loc` is an NDArray.</span> |
| <span class="sd"> out : NDArray</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> If input `shape` has shape, e.g., `(m, n)` and `loc` and `scale` are scalars, output</span> |
| <span class="sd"> shape will be `(m, n)`. If `loc` and `scale` are NDArrays with shape, e.g., `(x, y)`,</span> |
| <span class="sd"> then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for</span> |
| <span class="sd"> each `[loc, scale)` pair.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.randn()</span> |
| <span class="sd"> 2.21220636</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.randn(2, 2)</span> |
| <span class="sd"> [[-1.856082 -1.9768796 ]</span> |
| <span class="sd"> [-0.20801921 0.2444218 ]]</span> |
| <span class="sd"> <NDArray 2x2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.randn(2, 3, loc=5, scale=1)</span> |
| <span class="sd"> [[4.19962 4.8311777 5.936328 ]</span> |
| <span class="sd"> [5.357444 5.7793283 3.9896927]]</span> |
| <span class="sd"> <NDArray 2x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'loc'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="n">scale</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'scale'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'dtype'</span><span class="p">,</span> <span class="n">_Null</span><span class="p">)</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'ctx'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'out'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scale</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_normal</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_normal</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="poisson"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.poisson">[docs]</a><span class="k">def</span> <span class="nf">poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a Poisson distribution.</span> |
| |
| <span class="sd"> Samples are distributed according to a Poisson distribution parametrized</span> |
| <span class="sd"> by *lambda* (rate). Samples will always be returned as a floating point data type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lam : float or NDArray, optional</span> |
| <span class="sd"> Expectation of interval, should be >= 0.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `lam` is</span> |
| <span class="sd"> a scalar, output shape will be `(m, n)`. If `lam`</span> |
| <span class="sd"> is an NDArray with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `lam`.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `lam.context` when `lam` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> If input `shape` has shape, e.g., `(m, n)` and `lam` is</span> |
| <span class="sd"> a scalar, output shape will be `(m, n)`. If `lam`</span> |
| <span class="sd"> is an NDArray with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `lam`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.poisson(1)</span> |
| <span class="sd"> [ 1.]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.poisson(1, shape=(2,))</span> |
| <span class="sd"> [ 0. 2.]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> lam = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> mx.nd.random.poisson(lam, shape=2)</span> |
| <span class="sd"> [[ 1. 3.]</span> |
| <span class="sd"> [ 3. 2.]</span> |
| <span class="sd"> [ 2. 3.]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_poisson</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_poisson</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">lam</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="exponential"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.exponential">[docs]</a><span class="k">def</span> <span class="nf">exponential</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Draw samples from an exponential distribution.</span> |
| |
| <span class="sd"> Its probability density function is</span> |
| |
| <span class="sd"> .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),</span> |
| |
| <span class="sd"> for x > 0 and 0 elsewhere. \beta is the scale parameter, which is the</span> |
| <span class="sd"> inverse of the rate parameter \lambda = 1/\beta.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> scale : float or NDArray, optional</span> |
| <span class="sd"> The scale parameter, \beta = 1/\lambda.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `scale` is</span> |
| <span class="sd"> a scalar, output shape will be `(m, n)`. If `scale`</span> |
| <span class="sd"> is an NDArray with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each entry in `scale`.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `scale.context` when `scale` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> If input `shape` has shape, e.g., `(m, n)` and `scale` is a scalar, output shape will</span> |
| <span class="sd"> be `(m, n)`. If `scale` is an NDArray with shape, e.g., `(x, y)`, then `output`</span> |
| <span class="sd"> will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each entry in scale.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.exponential(1)</span> |
| <span class="sd"> [ 0.79587454]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.exponential(1, shape=(2,))</span> |
| <span class="sd"> [ 0.89856035 1.25593066]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> scale = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> mx.nd.random.exponential(scale, shape=2)</span> |
| <span class="sd"> [[ 0.41063145 0.42140478]</span> |
| <span class="sd"> [ 2.59407091 10.12439728]</span> |
| <span class="sd"> [ 2.42544937 1.14260709]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_exponential</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_exponential</span><span class="p">,</span> |
| <span class="p">[</span><span class="mf">1.0</span><span class="o">/</span><span class="n">scale</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="gamma"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.gamma">[docs]</a><span class="k">def</span> <span class="nf">gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a gamma distribution.</span> |
| |
| <span class="sd"> Samples are distributed according to a gamma distribution parametrized</span> |
| <span class="sd"> by *alpha* (shape) and *beta* (scale).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> alpha : float or NDArray, optional</span> |
| <span class="sd"> The shape of the gamma distribution. Should be greater than zero.</span> |
| <span class="sd"> beta : float or NDArray, optional</span> |
| <span class="sd"> The scale of the gamma distribution. Should be greater than zero.</span> |
| <span class="sd"> Default is equal to 1.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `alpha` and</span> |
| <span class="sd"> `beta` are scalars, output shape will be `(m, n)`. If `alpha` and `beta`</span> |
| <span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[alpha, beta)` pair.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `alpha.context` when `alpha` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> If input `shape` has shape, e.g., `(m, n)` and `alpha` and `beta` are scalars, output</span> |
| <span class="sd"> shape will be `(m, n)`. If `alpha` and `beta` are NDArrays with shape, e.g.,</span> |
| <span class="sd"> `(x, y)`, then output will have shape `(x, y, m, n)`, where `m*n` samples are</span> |
| <span class="sd"> drawn for each `[alpha, beta)` pair.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.gamma(1, 1)</span> |
| <span class="sd"> [ 1.93308783]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.gamma(1, 1, shape=(2,))</span> |
| <span class="sd"> [ 0.48216391 2.09890771]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> alpha = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> beta = mx.nd.array([2,3,4])</span> |
| <span class="sd"> >>> mx.nd.random.gamma(alpha, beta, shape=2)</span> |
| <span class="sd"> [[ 3.24343276 0.94137681]</span> |
| <span class="sd"> [ 3.52734375 0.45568955]</span> |
| <span class="sd"> [ 14.26264095 14.0170126 ]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_gamma</span><span class="p">,</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_gamma</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="negative_binomial"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.negative_binomial">[docs]</a><span class="k">def</span> <span class="nf">negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a negative binomial distribution.</span> |
| |
| <span class="sd"> Samples are distributed according to a negative binomial distribution</span> |
| <span class="sd"> parametrized by *k* (limit of unsuccessful experiments) and *p* (failure</span> |
| <span class="sd"> probability in each experiment). Samples will always be returned as a</span> |
| <span class="sd"> floating point data type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> k : float or NDArray, optional</span> |
| <span class="sd"> Limit of unsuccessful experiments, > 0.</span> |
| <span class="sd"> p : float or NDArray, optional</span> |
| <span class="sd"> Failure probability in each experiment, >= 0 and <=1.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `k` and</span> |
| <span class="sd"> `p` are scalars, output shape will be `(m, n)`. If `k` and `p`</span> |
| <span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[k, p)` pair.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `k.context` when `k` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> If input `shape` has shape, e.g., `(m, n)` and `k` and `p` are scalars, output shape</span> |
| <span class="sd"> will be `(m, n)`. If `k` and `p` are NDArrays with shape, e.g., `(x, y)`, then</span> |
| <span class="sd"> output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for each `[k, p)` pair.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.negative_binomial(10, 0.5)</span> |
| <span class="sd"> [ 4.]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.negative_binomial(10, 0.5, shape=(2,))</span> |
| <span class="sd"> [ 3. 4.]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> k = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> p = mx.nd.array([0.2,0.4,0.6])</span> |
| <span class="sd"> >>> mx.nd.random.negative_binomial(k, p, shape=2)</span> |
| <span class="sd"> [[ 3. 2.]</span> |
| <span class="sd"> [ 4. 4.]</span> |
| <span class="sd"> [ 0. 5.]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_negative_binomial</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_negative_binomial</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="generalized_negative_binomial"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.generalized_negative_binomial">[docs]</a><span class="k">def</span> <span class="nf">generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a generalized negative binomial distribution.</span> |
| |
| <span class="sd"> Samples are distributed according to a generalized negative binomial</span> |
| <span class="sd"> distribution parametrized by *mu* (mean) and *alpha* (dispersion).</span> |
| <span class="sd"> *alpha* is defined as *1/k* where *k* is the failure limit of the</span> |
| <span class="sd"> number of unsuccessful experiments (generalized to real numbers).</span> |
| <span class="sd"> Samples will always be returned as a floating point data type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> mu : float or NDArray, optional</span> |
| <span class="sd"> Mean of the negative binomial distribution.</span> |
| <span class="sd"> alpha : float or NDArray, optional</span> |
| <span class="sd"> Alpha (dispersion) parameter of the negative binomial distribution.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `mu` and</span> |
| <span class="sd"> `alpha` are scalars, output shape will be `(m, n)`. If `mu` and `alpha`</span> |
| <span class="sd"> are NDArrays with shape, e.g., `(x, y)`, then output will have shape</span> |
| <span class="sd"> `(x, y, m, n)`, where `m*n` samples are drawn for each `[mu, alpha)` pair.</span> |
| <span class="sd"> dtype : {'float16', 'float32', 'float64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'float32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `mu.context` when `mu` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> If input `shape` has shape, e.g., `(m, n)` and `mu` and `alpha` are scalars, output</span> |
| <span class="sd"> shape will be `(m, n)`. If `mu` and `alpha` are NDArrays with shape, e.g., `(x, y)`,</span> |
| <span class="sd"> then output will have shape `(x, y, m, n)`, where `m*n` samples are drawn for</span> |
| <span class="sd"> each `[mu, alpha)` pair.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.generalized_negative_binomial(10, 0.5)</span> |
| <span class="sd"> [ 19.]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.generalized_negative_binomial(10, 0.5, shape=(2,))</span> |
| <span class="sd"> [ 30. 21.]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> mu = mx.nd.array([1,2,3])</span> |
| <span class="sd"> >>> alpha = mx.nd.array([0.2,0.4,0.6])</span> |
| <span class="sd"> >>> mx.nd.random.generalized_negative_binomial(mu, alpha, shape=2)</span> |
| <span class="sd"> [[ 4. 0.]</span> |
| <span class="sd"> [ 3. 2.]</span> |
| <span class="sd"> [ 6. 2.]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_generalized_negative_binomial</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_generalized_negative_binomial</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">mu</span><span class="p">,</span> <span class="n">alpha</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="multinomial"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.multinomial">[docs]</a><span class="k">def</span> <span class="nf">multinomial</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">get_prob</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Concurrent sampling from multiple multinomial distributions.</span> |
| |
| <span class="sd"> .. note:: The input distribution must be normalized, i.e. `data` must sum to</span> |
| <span class="sd"> 1 along its last dimension.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : NDArray</span> |
| <span class="sd"> An *n* dimensional array whose last dimension has length `k`, where</span> |
| <span class="sd"> `k` is the number of possible outcomes of each multinomial distribution.</span> |
| <span class="sd"> For example, data with shape `(m, n, k)` specifies `m*n` multinomial</span> |
| <span class="sd"> distributions each with `k` possible outcomes.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw from each distribution. If shape is empty</span> |
| <span class="sd"> one sample will be drawn from each distribution.</span> |
| <span class="sd"> get_prob : bool, optional</span> |
| <span class="sd"> If true, a second array containing log likelihood of the drawn</span> |
| <span class="sd"> samples will also be returned.</span> |
| <span class="sd"> This is usually used for reinforcement learning, where you can provide</span> |
| <span class="sd"> reward as head gradient w.r.t. this array to estimate gradient.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> Data type of the sample output array. The default is int32.</span> |
| <span class="sd"> Note that the data type of the log likelihood array is the same with that of `data`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> List, or NDArray</span> |
| <span class="sd"> For input `data` with `n` dimensions and shape `(d1, d2, ..., dn-1, k)`, and input</span> |
| <span class="sd"> `shape` with shape `(s1, s2, ..., sx)`, returns an NDArray with shape</span> |
| <span class="sd"> `(d1, d2, ... dn-1, s1, s2, ..., sx)`. The `s1, s2, ... sx` dimensions of the</span> |
| <span class="sd"> returned NDArray consist of 0-indexed values sampled from each respective multinomial</span> |
| <span class="sd"> distribution provided in the `k` dimension of `data`.</span> |
| |
| <span class="sd"> For the case `n`=1, and `x`=1 (one shape dimension), returned NDArray has shape `(s1,)`.</span> |
| |
| <span class="sd"> If `get_prob` is set to True, this function returns a list of format:</span> |
| <span class="sd"> `[ndarray_output, log_likelihood_output]`, where `log_likelihood_output` is an NDArray of the</span> |
| <span class="sd"> same shape as the sampled outputs.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> probs = mx.nd.array([0, 0.1, 0.2, 0.3, 0.4])</span> |
| <span class="sd"> >>> mx.nd.random.multinomial(probs)</span> |
| <span class="sd"> [3]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> probs = mx.nd.array([[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]])</span> |
| <span class="sd"> >>> mx.nd.random.multinomial(probs)</span> |
| <span class="sd"> [3 1]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.multinomial(probs, shape=2)</span> |
| <span class="sd"> [[4 4]</span> |
| <span class="sd"> [1 2]]</span> |
| <span class="sd"> <NDArray 2x2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.multinomial(probs, get_prob=True)</span> |
| <span class="sd"> [3 2]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> [-1.20397282 -1.60943794]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_sample_multinomial</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">get_prob</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="shuffle"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.shuffle">[docs]</a><span class="k">def</span> <span class="nf">shuffle</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Shuffle the elements randomly.</span> |
| |
| <span class="sd"> This shuffles the array along the first axis.</span> |
| <span class="sd"> The order of the elements in each subarray does not change.</span> |
| <span class="sd"> For example, if a 2D array is given, the order of the rows randomly changes,</span> |
| <span class="sd"> but the order of the elements in each row does not change.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : NDArray</span> |
| <span class="sd"> Input data array.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Array to store the result.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> A new NDArray with the same shape and type as input `data`, but</span> |
| <span class="sd"> with items in the first axis of the returned NDArray shuffled randomly.</span> |
| <span class="sd"> The original input `data` is not modified.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> data = mx.nd.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])</span> |
| <span class="sd"> >>> mx.nd.random.shuffle(data)</span> |
| <span class="sd"> [[ 0. 1. 2.]</span> |
| <span class="sd"> [ 6. 7. 8.]</span> |
| <span class="sd"> [ 3. 4. 5.]]</span> |
| <span class="sd"> <NDArray 2x3 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.shuffle(data)</span> |
| <span class="sd"> [[ 3. 4. 5.]</span> |
| <span class="sd"> [ 0. 1. 2.]</span> |
| <span class="sd"> [ 6. 7. 8.]]</span> |
| <span class="sd"> <NDArray 2x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_shuffle</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="randint"><a class="viewcode-back" href="../../../api/ndarray/random/index.html#mxnet.ndarray.random.randint">[docs]</a><span class="k">def</span> <span class="nf">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">_Null</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Draw random samples from a discrete uniform distribution.</span> |
| |
| <span class="sd"> Samples are uniformly distributed over the half-open interval *[low, high)*</span> |
| <span class="sd"> (includes *low*, but excludes *high*).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> low : int, required</span> |
| <span class="sd"> Lower boundary of the output interval. All values generated will be</span> |
| <span class="sd"> greater than or equal to low.</span> |
| <span class="sd"> high : int, required</span> |
| <span class="sd"> Upper boundary of the output interval. All values generated will be</span> |
| <span class="sd"> less than high.</span> |
| <span class="sd"> shape : int or tuple of ints, optional</span> |
| <span class="sd"> The number of samples to draw. If shape is, e.g., `(m, n)` and `low` and</span> |
| <span class="sd"> `high` are scalars, output shape will be `(m, n)`.</span> |
| <span class="sd"> dtype : {'int32', 'int64'}, optional</span> |
| <span class="sd"> Data type of output samples. Default is 'int32'</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context of output. Default is current context. Overridden by</span> |
| <span class="sd"> `low.context` when `low` is an NDArray.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Store output to an existing NDArray.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An NDArray of type `dtype`. If input `shape` has shape, e.g.,</span> |
| <span class="sd"> `(m, n)`, the returned NDArray will shape will be `(m, n)`. Contents</span> |
| <span class="sd"> of the returned NDArray will be samples from the interval `[low, high)`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.random.randint(5, 100)</span> |
| <span class="sd"> [ 90]</span> |
| <span class="sd"> <NDArray 1 @cpu(0)</span> |
| <span class="sd"> >>> mx.nd.random.randint(-10, 2, ctx=mx.gpu(0))</span> |
| <span class="sd"> [ -8]</span> |
| <span class="sd"> <NDArray 1 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.random.randint(-10, 10, shape=(2,))</span> |
| <span class="sd"> [ -5 4]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_random_helper</span><span class="p">(</span><span class="n">_internal</span><span class="o">.</span><span class="n">_random_randint</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">],</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span></div> |
| </pre></div> |
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