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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/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
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| <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> |
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| <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-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <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> |
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| <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/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> |
| </ul> |
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| <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> |
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| <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> |
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| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
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| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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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/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-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-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</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/gluon_migration_guide.html">Gluon2.0: Migration Guide</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-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <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/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> |
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| <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/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> |
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| <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/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> |
| </ul> |
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| <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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
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| </ul> |
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| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
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| <div class="section" id="module-mxnet.ndarray.random"> |
| <span id="ndarray-random"></span><h1>ndarray.random<a class="headerlink" href="#module-mxnet.ndarray.random" title="Permalink to this headline">¶</a></h1> |
| <p>Random distribution generator NDArray API of MXNet.</p> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.uniform" title="mxnet.ndarray.random.uniform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uniform</span></code></a>([low, high, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a uniform distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.normal" title="mxnet.ndarray.random.normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normal</span></code></a>([loc, scale, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a normal (Gaussian) distribution.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.randn" title="mxnet.ndarray.random.randn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randn</span></code></a>(*shape, **kwargs)</p></td> |
| <td><p>Draw random samples from a normal (Gaussian) distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.poisson" title="mxnet.ndarray.random.poisson"><code class="xref py py-obj docutils literal notranslate"><span class="pre">poisson</span></code></a>([lam, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a Poisson distribution.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.exponential" title="mxnet.ndarray.random.exponential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exponential</span></code></a>([scale, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw samples from an exponential distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.gamma" title="mxnet.ndarray.random.gamma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gamma</span></code></a>([alpha, beta, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a gamma distribution.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.binomial" title="mxnet.ndarray.random.binomial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binomial</span></code></a>([n, p, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a binomial distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.categorical" title="mxnet.ndarray.random.categorical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">categorical</span></code></a>(data[, shape, get_prob, out, dtype])</p></td> |
| <td><p>Concurrent sampling from multiple categorical distributions.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.multinomial" title="mxnet.ndarray.random.multinomial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multinomial</span></code></a>([n, p, shape, dtype, ctx, out])</p></td> |
| <td><p>Concurrent sampling from multiple multinomial distributions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.negative_binomial" title="mxnet.ndarray.random.negative_binomial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">negative_binomial</span></code></a>([k, p, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a negative binomial distribution.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.generalized_negative_binomial" title="mxnet.ndarray.random.generalized_negative_binomial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generalized_negative_binomial</span></code></a>([mu, alpha, …])</p></td> |
| <td><p>Draw random samples from a generalized negative binomial distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.shuffle" title="mxnet.ndarray.random.shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shuffle</span></code></a>(data, **kwargs)</p></td> |
| <td><p>Shuffle the elements randomly.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.randint" title="mxnet.ndarray.random.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a>(low, high[, shape, dtype, ctx, out])</p></td> |
| <td><p>Draw random samples from a discrete uniform distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.binomial_like" title="mxnet.ndarray.random.binomial_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binomial_like</span></code></a>([out, name])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param out</dt> |
| <dd class="field-odd"><p>The output NDArray to hold the result.</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.exponential_like" title="mxnet.ndarray.random.exponential_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exponential_like</span></code></a>([data, lam, out, name])</p></td> |
| <td><p>Draw random samples from an exponential distribution according to the input array shape.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.gamma_like" title="mxnet.ndarray.random.gamma_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gamma_like</span></code></a>([data, alpha, beta, out, name])</p></td> |
| <td><p>Draw random samples from a gamma distribution according to the input array shape.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.generalized_negative_binomial_like" title="mxnet.ndarray.random.generalized_negative_binomial_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generalized_negative_binomial_like</span></code></a>([data, …])</p></td> |
| <td><p>Draw random samples from a generalized negative binomial distribution according to the input array shape.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.negative_binomial_like" title="mxnet.ndarray.random.negative_binomial_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">negative_binomial_like</span></code></a>([data, k, p, out, name])</p></td> |
| <td><p>Draw random samples from a negative binomial distribution according to the input array shape.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.normal_like" title="mxnet.ndarray.random.normal_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normal_like</span></code></a>([data, loc, scale, out, name])</p></td> |
| <td><p>Draw random samples from a normal (Gaussian) distribution according to the input array shape.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.ndarray.random.poisson_like" title="mxnet.ndarray.random.poisson_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">poisson_like</span></code></a>([data, lam, out, name])</p></td> |
| <td><p>Draw random samples from a Poisson distribution according to the input array shape.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.ndarray.random.uniform_like" title="mxnet.ndarray.random.uniform_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uniform_like</span></code></a>([data, low, high, out, name])</p></td> |
| <td><p>Draw random samples from a uniform distribution according to the input array shape.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.uniform"> |
| <code class="sig-name descname">uniform</code><span class="sig-paren">(</span><em class="sig-param">low=0</em>, <em class="sig-param">high=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#uniform"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.uniform" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a uniform distribution.</p> |
| <p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em> |
| (includes <em>low</em>, but excludes <em>high</em>).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>low</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Lower boundary of the output interval. All values generated will be |
| greater than or equal to low. The default value is 0.</p></li> |
| <li><p><strong>high</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Upper boundary of the output interval. All values generated will be |
| less than high. The default value is 1.0.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>low</cite> and |
| <cite>high</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>low</cite> and <cite>high</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[low, high)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>low.context</cite> when <cite>low</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>An NDArray of type <cite>dtype</cite>. If input <cite>shape</cite> has shape, e.g., |
| <cite>(m, n)</cite> and <cite>low</cite> and <cite>high</cite> are scalars, output shape will be <cite>(m, n)</cite>. |
| If <cite>low</cite> and <cite>high</cite> are NDArrays with shape, e.g., <cite>(x, y)</cite>, then the |
| return NDArray will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> uniformly distributed |
| samples are drawn for each <cite>[low, high)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="go">[ 0.54881352]</span> |
| <span class="go"><NDArray 1 @cpu(0)</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="go">[ 0.92514056]</span> |
| <span class="go"><NDArray 1 @gpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 0.71589124 0.08976638]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">low</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">high</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</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="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 1.78653979 1.93707538]</span> |
| <span class="go"> [ 2.01311183 2.37081361]</span> |
| <span class="go"> [ 3.30491424 3.69977832]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.normal"> |
| <code class="sig-name descname">normal</code><span class="sig-paren">(</span><em class="sig-param">loc=0</em>, <em class="sig-param">scale=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#normal"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.normal" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a normal (Gaussian) distribution.</p> |
| <p>Samples are distributed according to a normal distribution parametrized |
| by <em>loc</em> (mean) and <em>scale</em> (standard deviation).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>loc</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Mean (centre) of the distribution.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Standard deviation (spread or width) of the distribution.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>loc</cite> and |
| <cite>scale</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>loc</cite> and <cite>scale</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[loc, scale)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>loc.context</cite> when <cite>loc</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>An NDArray of type <cite>dtype</cite>. If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and |
| <cite>loc</cite> and <cite>scale</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>loc</cite> and |
| <cite>scale</cite> are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[loc, scale)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="go">[ 2.21220636]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="go">[ 0.29253659]</span> |
| <span class="go"><NDArray 1 @gpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[-0.2259962 -0.51619542]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">loc</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">scale</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</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="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 0.55912292 3.19566321]</span> |
| <span class="go"> [ 1.91728961 2.47706747]</span> |
| <span class="go"> [ 2.79666662 5.44254589]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.randn"> |
| <code class="sig-name descname">randn</code><span class="sig-paren">(</span><em class="sig-param">*shape</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#randn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.randn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a normal (Gaussian) distribution.</p> |
| <p>Samples are distributed according to a normal distribution parametrized |
| by <em>loc</em> (mean) and <em>scale</em> (standard deviation).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>loc</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Mean (centre) of the distribution.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Standard deviation (spread or width) of the distribution.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>loc</cite> and |
| <cite>scale</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>loc</cite> and <cite>scale</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[loc, scale)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em>) – Device context of output. Default is current context. Overridden by |
| <cite>loc.context</cite> when <cite>loc</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>loc</cite> and <cite>scale</cite> are scalars, output |
| shape will be <cite>(m, n)</cite>. If <cite>loc</cite> and <cite>scale</cite> are NDArrays with shape, e.g., <cite>(x, y)</cite>, |
| then output will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for |
| each <cite>[loc, scale)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">()</span> |
| <span class="go">2.21220636</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[-1.856082 -1.9768796 ]</span> |
| <span class="go">[-0.20801921 0.2444218 ]]</span> |
| <span class="go"><NDArray 2x2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">5</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="go">[[4.19962 4.8311777 5.936328 ]</span> |
| <span class="go">[5.357444 5.7793283 3.9896927]]</span> |
| <span class="go"><NDArray 2x3 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.poisson"> |
| <code class="sig-name descname">poisson</code><span class="sig-paren">(</span><em class="sig-param">lam=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#poisson"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.poisson" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a Poisson distribution.</p> |
| <p>Samples are distributed according to a Poisson distribution parametrized |
| by <em>lambda</em> (rate). Samples will always be returned as a floating point data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lam</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Expectation of interval, should be >= 0.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>lam</cite> is |
| a scalar, output shape will be <cite>(m, n)</cite>. If <cite>lam</cite> |
| is an NDArray with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each entry in <cite>lam</cite>.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>lam.context</cite> when <cite>lam</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>lam</cite> is |
| a scalar, output shape will be <cite>(m, n)</cite>. If <cite>lam</cite> |
| is an NDArray with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each entry in <cite>lam</cite>.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">[ 1.]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 0. 2.]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">lam</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 1. 3.]</span> |
| <span class="go"> [ 3. 2.]</span> |
| <span class="go"> [ 2. 3.]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.exponential"> |
| <code class="sig-name descname">exponential</code><span class="sig-paren">(</span><em class="sig-param">scale=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#exponential"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.exponential" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw samples from an exponential distribution.</p> |
| <p>Its probability density function is</p> |
| <div class="math notranslate nohighlight"> |
| \[f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),\]</div> |
| <p>for x > 0 and 0 elsewhere. beta is the scale parameter, which is the |
| inverse of the rate parameter lambda = 1/beta.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>scale</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The scale parameter, beta = 1/lambda.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>scale</cite> is |
| a scalar, output shape will be <cite>(m, n)</cite>. If <cite>scale</cite> |
| is an NDArray with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each entry in <cite>scale</cite>.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>scale.context</cite> when <cite>scale</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>scale</cite> is a scalar, output shape will |
| be <cite>(m, n)</cite>. If <cite>scale</cite> is an NDArray with shape, e.g., <cite>(x, y)</cite>, then <cite>output</cite> |
| will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each entry in scale.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">exponential</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">[ 0.79587454]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">exponential</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 0.89856035 1.25593066]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">scale</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">exponential</span><span class="p">(</span><span class="n">scale</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 0.41063145 0.42140478]</span> |
| <span class="go"> [ 2.59407091 10.12439728]</span> |
| <span class="go"> [ 2.42544937 1.14260709]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.gamma"> |
| <code class="sig-name descname">gamma</code><span class="sig-paren">(</span><em class="sig-param">alpha=1</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#gamma"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.gamma" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a gamma distribution.</p> |
| <p>Samples are distributed according to a gamma distribution parametrized |
| by <em>alpha</em> (shape) and <em>beta</em> (scale).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>alpha</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The shape of the gamma distribution. Should be greater than zero.</p></li> |
| <li><p><strong>beta</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The scale of the gamma distribution. Should be greater than zero. |
| Default is equal to 1.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>alpha</cite> and |
| <cite>beta</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>alpha</cite> and <cite>beta</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[alpha, beta)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>alpha.context</cite> when <cite>alpha</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>alpha</cite> and <cite>beta</cite> are scalars, output |
| shape will be <cite>(m, n)</cite>. If <cite>alpha</cite> and <cite>beta</cite> are NDArrays with shape, e.g., |
| <cite>(x, y)</cite>, then output will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are |
| drawn for each <cite>[alpha, beta)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="go">[ 1.93308783]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">gamma</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 0.48216391 2.09890771]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">alpha</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">beta</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">gamma</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="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 3.24343276 0.94137681]</span> |
| <span class="go"> [ 3.52734375 0.45568955]</span> |
| <span class="go"> [ 14.26264095 14.0170126 ]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.binomial"> |
| <code class="sig-name descname">binomial</code><span class="sig-paren">(</span><em class="sig-param">n=1</em>, <em class="sig-param">p=0.5</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#binomial"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.binomial" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a binomial distribution.</p> |
| <p>Samples are distributed according to a binomial distribution parametrized |
| by <em>n</em> (number of trials) and <em>p</em> (success probability).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>n</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Number of experiments, > 0.</p></li> |
| <li><p><strong>p</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Success probability in each experiment, >= 0 and <= 1.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>n</cite> and |
| <cite>p</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>n</cite> and <cite>p</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[n, p)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>n.context</cite> when <cite>n</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>n</cite> and <cite>p</cite> are scalars, output |
| shape will be <cite>(m, n)</cite>. If <cite>n</cite> and <cite>p</cite> are NDArrays with shape, e.g., |
| <cite>(x, y)</cite>, then output will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are |
| drawn for each <cite>[n, p)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span> |
| <span class="go">[ 1.]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 4. 6.]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">n</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">p</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.3</span><span class="p">,</span><span class="mf">0.4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 1. 4.]</span> |
| <span class="go"> [ 0. 2.]</span> |
| <span class="go"> [ 1. 1.]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.categorical"> |
| <code class="sig-name descname">categorical</code><span class="sig-paren">(</span><em class="sig-param">data</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">get_prob=False</em>, <em class="sig-param">out=None</em>, <em class="sig-param">dtype='int32'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#categorical"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.categorical" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Concurrent sampling from multiple categorical distributions.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>The input distribution must be normalized, i.e. <cite>data</cite> must sum to |
| 1 along its last dimension.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – An <em>n</em> dimensional array whose last dimension has length <cite>k</cite>, where |
| <cite>k</cite> is the number of possible outcomes of each categorical distribution. |
| For example, data with shape <cite>(m, n, k)</cite> specifies <cite>m*n</cite> categorical |
| distributions each with <cite>k</cite> possible outcomes.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw from each distribution. If shape is empty |
| one sample will be drawn from each distribution.</p></li> |
| <li><p><strong>get_prob</strong> (<em>bool</em><em>, </em><em>optional</em>) – If true, a second array containing log likelihood of the drawn |
| samples will also be returned. |
| This is usually used for reinforcement learning, where you can provide |
| reward as head gradient w.r.t. this array to estimate gradient.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| <li><p><strong>dtype</strong> (<em>str</em><em> or </em><em>numpy.dtype</em><em>, </em><em>optional</em>) – Data type of the sample output array. The default is int32. |
| Note that the data type of the log likelihood array is the same with that of <cite>data</cite>.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><p>For input <cite>data</cite> with <cite>n</cite> dimensions and shape <cite>(d1, d2, …, dn-1, k)</cite>, and input |
| <cite>shape</cite> with shape <cite>(s1, s2, …, sx)</cite>, returns an NDArray with shape |
| <cite>(d1, d2, … dn-1, s1, s2, …, sx)</cite>. The <cite>s1, s2, … sx</cite> dimensions of the |
| returned NDArray consist of 0-indexed values sampled from each respective categorical |
| distribution provided in the <cite>k</cite> dimension of <cite>data</cite>.</p> |
| <p>For the case <cite>n`=1, and `x`=1 (one shape dimension), returned NDArray has shape `(s1,)</cite>.</p> |
| <p>If <cite>get_prob</cite> is set to True, this function returns a list of format: |
| <cite>[ndarray_output, log_likelihood_output]</cite>, where <cite>log_likelihood_output</cite> is an NDArray of the |
| same shape as the sampled outputs.</p> |
| </p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>List, or <a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">probs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">categorical</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> |
| <span class="go">[3]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">probs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">categorical</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> |
| <span class="go">[3 1]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">categorical</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[4 4]</span> |
| <span class="go"> [1 2]]</span> |
| <span class="go"><NDArray 2x2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">categorical</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">get_prob</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="go">[3 2]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="go">[-1.20397282 -1.60943794]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.multinomial"> |
| <code class="sig-name descname">multinomial</code><span class="sig-paren">(</span><em class="sig-param">n=[1], p=[[1.0]], shape=_Null, dtype='float32', ctx=None, out=None, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#multinomial"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.multinomial" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Concurrent sampling from multiple multinomial distributions.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>The input distribution must be normalized, i.e. <cite>p</cite> must sum to |
| 1 along its last dimension.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>n</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – An <em>n</em> dimensional array containing the number of trials of each |
| multinomial distribution.</p></li> |
| <li><p><strong>p</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – An <em>n+1</em> dimensional array containing the probabilities of each multinomial |
| distribution. Its last dimension has length <cite>k</cite>, where <cite>k</cite> is the number |
| of possible outcomes of each multinomial distribution. |
| For example, p with shape <cite>(m, n, k)</cite> specifies <cite>m*n</cite> multinomial |
| distributions each with <cite>k</cite> possible outcomes.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw from each distribution. If shape is empty |
| one sample will be drawn from each distribution.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>n.context</cite> when <cite>n</cite> is an NDArray.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>n</cite> and <cite>p</cite> are a scalar and an array of length k |
| respectively, output shape will be <cite>(m, n, k)</cite>. If <cite>n</cite> and <cite>p</cite> are NDArrays with shape, e.g., |
| <cite>(x, y)</cite> and <cite>(x, y, k)</cite>, then output will have shape <cite>(x, y, m, n, k)</cite>, where <cite>m*n</cite> |
| samples are drawn for each <cite>[n, p)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">]),</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]]))</span> |
| <span class="go">[[ 1. 9.]]</span> |
| <span class="go"><NDArray 1x2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">]),</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]]),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[[[ 5. 5.]</span> |
| <span class="go"> [ 6. 4.]]]</span> |
| <span class="go"><NDArray 1x2x2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">n</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">p</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> |
| <span class="go">[[ 2. 8.]</span> |
| <span class="go"> [ 1. 1.]</span> |
| <span class="go"> [ 1. 2.]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.negative_binomial"> |
| <code class="sig-name descname">negative_binomial</code><span class="sig-paren">(</span><em class="sig-param">k=1</em>, <em class="sig-param">p=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#negative_binomial"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.negative_binomial" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a negative binomial distribution.</p> |
| <p>Samples are distributed according to a negative binomial distribution |
| parametrized by <em>k</em> (limit of unsuccessful experiments) and <em>p</em> (failure |
| probability in each experiment). Samples will always be returned as a |
| floating point data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>k</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Limit of unsuccessful experiments, > 0.</p></li> |
| <li><p><strong>p</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Failure probability in each experiment, >= 0 and <=1.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>k</cite> and |
| <cite>p</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>k</cite> and <cite>p</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[k, p)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>k.context</cite> when <cite>k</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>k</cite> and <cite>p</cite> are scalars, output shape |
| will be <cite>(m, n)</cite>. If <cite>k</cite> and <cite>p</cite> are NDArrays with shape, e.g., <cite>(x, y)</cite>, then |
| output will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[k, p)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">negative_binomial</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| <span class="go">[ 4.]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">negative_binomial</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 3. 4.]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">k</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">p</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.4</span><span class="p">,</span><span class="mf">0.6</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">negative_binomial</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="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 3. 2.]</span> |
| <span class="go"> [ 4. 4.]</span> |
| <span class="go"> [ 0. 5.]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.generalized_negative_binomial"> |
| <code class="sig-name descname">generalized_negative_binomial</code><span class="sig-paren">(</span><em class="sig-param">mu=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#generalized_negative_binomial"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.generalized_negative_binomial" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a generalized negative binomial distribution.</p> |
| <p>Samples are distributed according to a generalized negative binomial |
| distribution parametrized by <em>mu</em> (mean) and <em>alpha</em> (dispersion). |
| <em>alpha</em> is defined as <em>1/k</em> where <em>k</em> is the failure limit of the |
| number of unsuccessful experiments (generalized to real numbers). |
| Samples will always be returned as a floating point data type.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>mu</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Mean of the negative binomial distribution.</p></li> |
| <li><p><strong>alpha</strong> (<em>float</em><em> or </em><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Alpha (dispersion) parameter of the negative binomial distribution.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>mu</cite> and |
| <cite>alpha</cite> are scalars, output shape will be <cite>(m, n)</cite>. If <cite>mu</cite> and <cite>alpha</cite> |
| are NDArrays with shape, e.g., <cite>(x, y)</cite>, then output will have shape |
| <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for each <cite>[mu, alpha)</cite> pair.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘float32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>mu.context</cite> when <cite>mu</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>If input <cite>shape</cite> has shape, e.g., <cite>(m, n)</cite> and <cite>mu</cite> and <cite>alpha</cite> are scalars, output |
| shape will be <cite>(m, n)</cite>. If <cite>mu</cite> and <cite>alpha</cite> are NDArrays with shape, e.g., <cite>(x, y)</cite>, |
| then output will have shape <cite>(x, y, m, n)</cite>, where <cite>m*n</cite> samples are drawn for |
| each <cite>[mu, alpha)</cite> pair.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">generalized_negative_binomial</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span> |
| <span class="go">[ 19.]</span> |
| <span class="go"><NDArray 1 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">generalized_negative_binomial</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ 30. 21.]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mu</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">alpha</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.4</span><span class="p">,</span><span class="mf">0.6</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">generalized_negative_binomial</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="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">[[ 4. 0.]</span> |
| <span class="go"> [ 3. 2.]</span> |
| <span class="go"> [ 6. 2.]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.shuffle"> |
| <code class="sig-name descname">shuffle</code><span class="sig-paren">(</span><em class="sig-param">data</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#shuffle"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.shuffle" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Shuffle the elements randomly.</p> |
| <p>This shuffles the array along the first axis. |
| The order of the elements in each subarray does not change. |
| For example, if a 2D array is given, the order of the rows randomly changes, |
| but the order of the elements in each row does not change.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – Input data array.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Array to store the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A new NDArray with the same shape and type as input <cite>data</cite>, but |
| with items in the first axis of the returned NDArray shuffled randomly. |
| The original input <cite>data</cite> is not modified.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</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="go">[[ 0. 1. 2.]</span> |
| <span class="go"> [ 6. 7. 8.]</span> |
| <span class="go"> [ 3. 4. 5.]]</span> |
| <span class="go"><NDArray 2x3 @cpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</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="go">[[ 3. 4. 5.]</span> |
| <span class="go"> [ 0. 1. 2.]</span> |
| <span class="go"> [ 6. 7. 8.]]</span> |
| <span class="go"><NDArray 2x3 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.randint"> |
| <code class="sig-name descname">randint</code><span class="sig-paren">(</span><em class="sig-param">low</em>, <em class="sig-param">high</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">ctx=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/ndarray/random.html#randint"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.random.randint" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a discrete uniform distribution.</p> |
| <p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em> |
| (includes <em>low</em>, but excludes <em>high</em>).</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>low</strong> (<em>int</em><em>, </em><em>required</em>) – Lower boundary of the output interval. All values generated will be |
| greater than or equal to low.</p></li> |
| <li><p><strong>high</strong> (<em>int</em><em>, </em><em>required</em>) – Upper boundary of the output interval. All values generated will be |
| less than high.</p></li> |
| <li><p><strong>shape</strong> (<em>int</em><em> or </em><em>tuple of ints</em><em>, </em><em>optional</em>) – The number of samples to draw. If shape is, e.g., <cite>(m, n)</cite> and <cite>low</cite> and |
| <cite>high</cite> are scalars, output shape will be <cite>(m, n)</cite>.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'int32'</em><em>, </em><em>'int64'}</em><em>, </em><em>optional</em>) – Data type of output samples. Default is ‘int32’</p></li> |
| <li><p><strong>ctx</strong> (<em>Context</em><em>, </em><em>optional</em>) – Device context of output. Default is current context. Overridden by |
| <cite>low.context</cite> when <cite>low</cite> is an NDArray.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – Store output to an existing NDArray.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>An NDArray of type <cite>dtype</cite>. If input <cite>shape</cite> has shape, e.g., |
| <cite>(m, n)</cite>, the returned NDArray will shape will be <cite>(m, n)</cite>. Contents |
| of the returned NDArray will be samples from the interval <cite>[low, high)</cite>.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> |
| <span class="go">[ 90]</span> |
| <span class="go"><NDArray 1 @cpu(0)</span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> |
| <span class="go">[ -8]</span> |
| <span class="go"><NDArray 1 @gpu(0)></span> |
| <span class="gp">>>> </span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,))</span> |
| <span class="go">[ -5 4]</span> |
| <span class="go"><NDArray 2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.binomial_like"> |
| <code class="sig-name descname">binomial_like</code><span class="sig-paren">(</span><em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.binomial_like" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.exponential_like"> |
| <code class="sig-name descname">exponential_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">lam=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.exponential_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from an exponential distribution according to the input array shape.</p> |
| <p>Samples are distributed according to an exponential distribution parametrized by <em>lambda</em> (rate).</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">exponential</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.0097189</span> <span class="p">,</span> <span class="mf">0.08999364</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mf">0.04146638</span><span class="p">,</span> <span class="mf">0.31715935</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L263</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lam</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Lambda parameter (rate) of the exponential distribution.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.gamma_like"> |
| <code class="sig-name descname">gamma_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">alpha=_Null</em>, <em class="sig-param">beta=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.gamma_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a gamma distribution according to the input array shape.</p> |
| <p>Samples are distributed according to a gamma distribution parametrized by <em>alpha</em> (shape) and <em>beta</em> (scale).</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">gamma</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">7.10486984</span><span class="p">,</span> <span class="mf">3.37695289</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mf">3.91697288</span><span class="p">,</span> <span class="mf">3.65933681</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L251</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Alpha parameter (shape) of the gamma distribution.</p></li> |
| <li><p><strong>beta</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Beta parameter (scale) of the gamma distribution.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.generalized_negative_binomial_like"> |
| <code class="sig-name descname">generalized_negative_binomial_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">mu=_Null</em>, <em class="sig-param">alpha=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.generalized_negative_binomial_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a generalized negative binomial distribution according to the |
| input array shape.</p> |
| <p>Samples are distributed according to a generalized negative binomial distribution parametrized by |
| <em>mu</em> (mean) and <em>alpha</em> (dispersion). <em>alpha</em> is defined as <em>1/k</em> where <em>k</em> is the failure limit of the |
| number of unsuccessful experiments (generalized to real numbers). |
| Samples will always be returned as a floating point data type.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">generalized_negative_binomial</span><span class="p">(</span><span class="n">mu</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L307</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>mu</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Mean of the negative binomial distribution.</p></li> |
| <li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Alpha (dispersion) parameter of the negative binomial distribution.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.negative_binomial_like"> |
| <code class="sig-name descname">negative_binomial_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">k=_Null</em>, <em class="sig-param">p=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.negative_binomial_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a negative binomial distribution according to the input array shape.</p> |
| <p>Samples are distributed according to a negative binomial distribution parametrized by |
| <em>k</em> (limit of unsuccessful experiments) and <em>p</em> (failure probability in each experiment). |
| Samples will always be returned as a floating point data type.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">negative_binomial</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">7.</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L290</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>k</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Limit of unsuccessful experiments.</p></li> |
| <li><p><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Failure probability in each experiment.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.normal_like"> |
| <code class="sig-name descname">normal_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">loc=_Null</em>, <em class="sig-param">scale=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.normal_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a normal (Gaussian) distribution according to the input array shape.</p> |
| <p>Samples are distributed according to a normal distribution parametrized by <em>loc</em> (mean) and <em>scale</em> |
| (standard deviation).</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">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">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">1.89171135</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.16881478</span><span class="p">],</span> |
| <span class="p">[</span><span class="o">-</span><span class="mf">1.23474145</span><span class="p">,</span> <span class="mf">1.55807114</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L239</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>loc</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Mean of the distribution.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Standard deviation of the distribution.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.poisson_like"> |
| <code class="sig-name descname">poisson_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">lam=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.poisson_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a Poisson distribution according to the input array shape.</p> |
| <p>Samples are distributed according to a Poisson distribution parametrized by <em>lambda</em> (rate). |
| Samples will always be returned as a floating point data type.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">poisson</span><span class="p">(</span><span class="n">lam</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L276</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lam</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Lambda parameter (rate) of the Poisson distribution.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.ndarray.random.uniform_like"> |
| <code class="sig-name descname">uniform_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">low=_Null</em>, <em class="sig-param">high=_Null</em>, <em class="sig-param">out=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.ndarray.random.uniform_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from a uniform distribution according to the input array shape.</p> |
| <p>Samples are uniformly distributed over the half-open interval <em>[low, high)</em> |
| (includes <em>low</em>, but excludes <em>high</em>).</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">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">data</span><span class="o">=</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[</span> <span class="mf">0.60276335</span><span class="p">,</span> <span class="mf">0.85794562</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mf">0.54488319</span><span class="p">,</span> <span class="mf">0.84725171</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/random/sample_op.cc:L226</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>low</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Lower bound of the distribution.</p></li> |
| <li><p><strong>high</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Upper bound of the distribution.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – The input</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><em>optional</em>) – The output NDArray to hold the result.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>out</strong> – The output of this function.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a> or list of NDArrays</p> |
| </dd> |
| </dl> |
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