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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul> |
| <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> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/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> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <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> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| |
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| <nav class="mdl-navigation"> |
| <ul> |
| <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> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/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> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <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> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
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| |
| <h1>Source code for mxnet.ndarray.sparse</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=wildcard-import, unused-wildcard-import, too-many-lines</span> |
| <span class="sd">"""Sparse NDArray API of MXNet."""</span> |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">__builtin__</span> <span class="kn">import</span> <span class="nb">slice</span> <span class="k">as</span> <span class="n">py_slice</span> |
| <span class="kn">from</span> <span class="nn">__builtin__</span> <span class="kn">import</span> <span class="nb">sum</span> <span class="k">as</span> <span class="n">py_sum</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">builtins</span> <span class="kn">import</span> <span class="nb">slice</span> <span class="k">as</span> <span class="n">py_slice</span> |
| <span class="kn">from</span> <span class="nn">builtins</span> <span class="kn">import</span> <span class="nb">sum</span> <span class="k">as</span> <span class="n">py_sum</span> |
| |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">import</span> <span class="nn">operator</span> |
| <span class="kn">from</span> <span class="nn">array</span> <span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">native_array</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"_ndarray_cls"</span><span class="p">,</span> <span class="s2">"csr_matrix"</span><span class="p">,</span> <span class="s2">"row_sparse_array"</span><span class="p">,</span> |
| <span class="s2">"BaseSparseNDArray"</span><span class="p">,</span> <span class="s2">"CSRNDArray"</span><span class="p">,</span> <span class="s2">"RowSparseNDArray"</span><span class="p">,</span> |
| <span class="s2">"add"</span><span class="p">,</span> <span class="s2">"subtract"</span><span class="p">,</span> <span class="s2">"multiply"</span><span class="p">,</span> <span class="s2">"divide"</span><span class="p">]</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">NotSupportedForSparseNDArray</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">numeric_types</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">c_array_buf</span><span class="p">,</span> <span class="n">mx_real_t</span><span class="p">,</span> <span class="n">integer_types</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">check_call</span> |
| <span class="kn">from</span> <span class="nn">..device</span> <span class="kn">import</span> <span class="n">Device</span><span class="p">,</span> <span class="n">current_device</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_internal</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">op</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">.gen_sparse</span> <span class="kn">import</span> <span class="n">retain</span> <span class="k">as</span> <span class="n">gs_retain</span> <span class="c1"># pylint: disable=redefined-builtin</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="n">gs_retain</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="kn">from</span> <span class="nn">._internal</span> <span class="kn">import</span> <span class="n">_set_ndarray_class</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span><span class="p">,</span> <span class="n">_storage_type</span><span class="p">,</span> <span class="n">dtype_np_to_mx</span><span class="p">,</span> <span class="n">dtype_mx_to_np</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">_STORAGE_TYPE_STR_TO_ID</span><span class="p">,</span> <span class="n">_STORAGE_TYPE_ROW_SPARSE</span><span class="p">,</span> <span class="n">_STORAGE_TYPE_CSR</span><span class="p">,</span> <span class="n">_int64_enabled</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">_STORAGE_TYPE_UNDEFINED</span><span class="p">,</span> <span class="n">_STORAGE_TYPE_DEFAULT</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">zeros</span> <span class="k">as</span> <span class="n">_zeros_ndarray</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">array</span> <span class="k">as</span> <span class="n">_array</span> |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">_ufunc_helper</span> |
| |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="k">as</span> <span class="nn">spsp</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="n">spsp</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="n">_STORAGE_AUX_TYPES</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'row_sparse'</span><span class="p">:</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">],</span> |
| <span class="s1">'csr'</span><span class="p">:</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">]</span> |
| <span class="p">}</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_new_alloc_handle</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">delay_alloc</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">aux_types</span><span class="p">,</span> <span class="n">aux_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a new handle with specified storage type, shape, dtype and context.</span> |
| |
| <span class="sd"> Empty handle is only used to hold results</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> handle</span> |
| <span class="sd"> A new empty ndarray handle</span> |
| <span class="sd"> """</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">aux_t</span> <span class="ow">in</span> <span class="n">aux_types</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">aux_t</span><span class="p">)</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="s2">"int64"</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"only int64 is supported for aux types"</span><span class="p">)</span> |
| <span class="n">aux_type_ids</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">dtype_np_to_mx</span><span class="p">(</span><span class="n">aux_t</span><span class="p">))</span> <span class="k">for</span> <span class="n">aux_t</span> <span class="ow">in</span> <span class="n">aux_types</span><span class="p">]</span> |
| <span class="n">aux_shapes</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,)</span> <span class="k">for</span> <span class="n">aux_t</span> <span class="ow">in</span> <span class="n">aux_types</span><span class="p">]</span> <span class="k">if</span> <span class="n">aux_shapes</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">aux_shapes</span> |
| <span class="n">aux_shape_lens</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">aux_shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">aux_shape</span> <span class="ow">in</span> <span class="n">aux_shapes</span><span class="p">]</span> |
| <span class="n">aux_shapes</span> <span class="o">=</span> <span class="n">py_sum</span><span class="p">(</span><span class="n">aux_shapes</span><span class="p">,</span> <span class="p">())</span> |
| <span class="n">num_aux</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">aux_types</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">_int64_enabled</span><span class="p">():</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayCreateSparseEx64</span><span class="p">(</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">_STORAGE_TYPE_STR_TO_ID</span><span class="p">[</span><span class="n">stype</span><span class="p">])),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int64</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'q'</span><span class="p">,</span> <span class="n">shape</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">delay_alloc</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">dtype_np_to_mx</span><span class="p">(</span><span class="n">dtype</span><span class="p">))),</span> |
| <span class="n">num_aux</span><span class="p">,</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'i'</span><span class="p">,</span> <span class="n">aux_type_ids</span><span class="p">)),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'i'</span><span class="p">,</span> <span class="n">aux_shape_lens</span><span class="p">)),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int64</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'q'</span><span class="p">,</span> <span class="n">aux_shapes</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayCreateSparseEx</span><span class="p">(</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">_STORAGE_TYPE_STR_TO_ID</span><span class="p">[</span><span class="n">stype</span><span class="p">])),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'I'</span><span class="p">,</span> <span class="n">shape</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">delay_alloc</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">dtype_np_to_mx</span><span class="p">(</span><span class="n">dtype</span><span class="p">))),</span> |
| <span class="n">num_aux</span><span class="p">,</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'i'</span><span class="p">,</span> <span class="n">aux_type_ids</span><span class="p">)),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'I'</span><span class="p">,</span> <span class="n">aux_shape_lens</span><span class="p">)),</span> |
| <span class="n">c_array_buf</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">,</span> <span class="n">native_array</span><span class="p">(</span><span class="s1">'I'</span><span class="p">,</span> <span class="n">aux_shapes</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">hdl</span> |
| |
| |
| <div class="viewcode-block" id="BaseSparseNDArray"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.BaseSparseNDArray">[docs]</a><span class="k">class</span> <span class="nc">BaseSparseNDArray</span><span class="p">(</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""The base class of an NDArray stored in a sparse storage format.</span> |
| |
| <span class="sd"> See CSRNDArray and RowSparseNDArray for more details.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns a string representation of the sparse array."""</span> |
| <span class="n">shape_info</span> <span class="o">=</span> <span class="s1">'x'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">x</span><span class="si">}</span><span class="s1">'</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">])</span> |
| <span class="c1"># The data content is not displayed since the array usually has big shape</span> |
| <span class="k">return</span> <span class="sa">f</span><span class="s1">'</span><span class="se">\n</span><span class="s1"><</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> </span><span class="si">{</span><span class="n">shape_info</span><span class="si">}</span><span class="s1"> @</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="si">}</span><span class="s1">>'</span> |
| |
| <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__div__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__iadd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__isub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__imul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">__idiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__itruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_sync_copyfrom</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_array</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_at</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="n">NotSupportedForSparseNDArray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_at</span><span class="p">,</span> <span class="s1">'[idx]'</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="n">NotSupportedForSparseNDArray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_slice</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="BaseSparseNDArray.reshape"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.BaseSparseNDArray.reshape">[docs]</a> <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="n">NotSupportedForSparseNDArray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># the `size` for a sparse ndarray is ambiguous, hence disabled.</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_aux_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Data-type of the array's ith aux data.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> numpy.dtype</span> |
| <span class="sd"> This BaseSparseNDArray's aux data type.</span> |
| <span class="sd"> """</span> |
| <span class="n">aux_type</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetAuxType</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">aux_type</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">dtype_mx_to_np</span><span class="p">(</span><span class="n">aux_type</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">_num_aux</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""The number of aux data used to help store the sparse ndarray.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">_STORAGE_AUX_TYPES</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stype</span><span class="p">])</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">_aux_types</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""The data types of the aux data for the BaseSparseNDArray.</span> |
| <span class="sd"> """</span> |
| <span class="n">aux_types</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">num_aux</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_aux</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_aux</span><span class="p">):</span> |
| <span class="n">aux_types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_type</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">aux_types</span> |
| |
| <div class="viewcode-block" id="BaseSparseNDArray.asnumpy"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.BaseSparseNDArray.asnumpy">[docs]</a> <span class="k">def</span> <span class="nf">asnumpy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a dense ``numpy.ndarray`` object with value copied from this array</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'default'</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="BaseSparseNDArray.astype"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.BaseSparseNDArray.astype">[docs]</a> <span class="k">def</span> <span class="nf">astype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a copy of the array after casting to a specified type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dtype : numpy.dtype or str</span> |
| <span class="sd"> The type of the returned array.</span> |
| <span class="sd"> copy : bool</span> |
| <span class="sd"> Default `True`. By default, astype always returns a newly</span> |
| <span class="sd"> allocated ndarray on the same context. If this is set to</span> |
| <span class="sd"> `False`, and the dtype requested is the same as the ndarray's</span> |
| <span class="sd"> dtype, the ndarray is returned instead of a copy.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.sparse.zeros('row_sparse', (2,3), dtype='float32')</span> |
| <span class="sd"> >>> y = x.astype('int32')</span> |
| <span class="sd"> >>> y.dtype</span> |
| <span class="sd"> <type 'numpy.int32'></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">copy</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="c1"># Use copyto for casting, as op.cast(self, dtype=dtype) doesn't support sparse stype</span> |
| <span class="n">res</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">res</span></div> |
| |
| <div class="viewcode-block" id="BaseSparseNDArray.copyto"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.BaseSparseNDArray.copyto">[docs]</a> <span class="k">def</span> <span class="nf">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Copies the value of this array to another array.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> other : NDArray or CSRNDArray or RowSparseNDArray or Context</span> |
| <span class="sd"> The destination array or context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray or CSRNDArray or RowSparseNDArray</span> |
| <span class="sd"> The copied array.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">handle</span> <span class="ow">is</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'You are attempting to copy an array to itself'</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">)</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">other</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Device</span><span class="p">):</span> |
| <span class="n">hret</span> <span class="o">=</span> <span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">_new_alloc_handle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stype</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> |
| <span class="kc">True</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_types</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">hret</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'copyto does not support type '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="BaseSparseNDArray.check_format"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.BaseSparseNDArray.check_format">[docs]</a> <span class="k">def</span> <span class="nf">check_format</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full_check</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Check whether the NDArray format is valid.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> full_check : bool, optional</span> |
| <span class="sd"> If `True`, rigorous check, O(N) operations. Otherwise</span> |
| <span class="sd"> basic check, O(1) operations (default True).</span> |
| <span class="sd"> """</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCheckFormat</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">(</span><span class="n">full_check</span><span class="p">)))</span></div> |
| |
| <span class="k">def</span> <span class="nf">_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A deep copy NDArray of the data array associated with the BaseSparseNDArray.</span> |
| |
| <span class="sd"> This function blocks. Do not use it in performance critical code.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wait_to_read</span><span class="p">()</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetDataNDArray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">hdl</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_aux_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Get a deep copy NDArray of the i-th aux data array associated with the</span> |
| <span class="sd"> BaseSparseNDArray.</span> |
| |
| <span class="sd"> This function blocks. Do not use it in performance critical code.</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">wait_to_read</span><span class="p">()</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetAuxNDArray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">hdl</span><span class="p">)</span></div> |
| |
| |
| <span class="c1"># pylint: disable=abstract-method</span> |
| <div class="viewcode-block" id="CSRNDArray"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray">[docs]</a><span class="k">class</span> <span class="nc">CSRNDArray</span><span class="p">(</span><span class="n">BaseSparseNDArray</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A sparse representation of 2D NDArray in the Compressed Sparse Row format.</span> |
| |
| <span class="sd"> A CSRNDArray represents an NDArray as three separate arrays: `data`,</span> |
| <span class="sd"> `indptr` and `indices`. It uses the CSR representation where the column indices for</span> |
| <span class="sd"> row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding values are stored</span> |
| <span class="sd"> in ``data[indptr[i]:indptr[i+1]]``.</span> |
| |
| <span class="sd"> The column indices for a given row are expected to be sorted in ascending order.</span> |
| <span class="sd"> Duplicate column entries for the same row are not allowed.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> a = mx.nd.array([[0, 1, 0], [2, 0, 0], [0, 0, 0], [0, 0, 3]])</span> |
| <span class="sd"> >>> a = a.tostype('csr')</span> |
| <span class="sd"> >>> a.data.asnumpy()</span> |
| <span class="sd"> array([ 1., 2., 3.], dtype=float32)</span> |
| <span class="sd"> >>> a.indices.asnumpy()</span> |
| <span class="sd"> array([1, 0, 2])</span> |
| <span class="sd"> >>> a.indptr.asnumpy()</span> |
| <span class="sd"> array([0, 1, 2, 2, 3])</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> csr_matrix: Several ways to construct a CSRNDArray</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">CSRNDArray</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,),</span> <span class="nb">super</span><span class="p">(</span><span class="n">CSRNDArray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__iadd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">+</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__isub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">-</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__imul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">*</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="nf">__idiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">/</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__itruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">/</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""x.__getitem__(i) <=> x[i]</span> |
| |
| <span class="sd"> Returns a newly created NDArray based on the indexing key.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : int or mxnet.ndarray.NDArray.slice</span> |
| <span class="sd"> Indexing key.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> indptr = np.array([0, 2, 3, 6])</span> |
| <span class="sd"> >>> indices = np.array([0, 2, 2, 0, 1, 2])</span> |
| <span class="sd"> >>> data = np.array([1, 2, 3, 4, 5, 6])</span> |
| <span class="sd"> >>> a = mx.nd.sparse.csr_matrix((data, indices, indptr), shape=(3, 3))</span> |
| <span class="sd"> >>> a.asnumpy()</span> |
| <span class="sd"> array([[ 1., 0., 2.],</span> |
| <span class="sd"> [ 0., 0., 3.],</span> |
| <span class="sd"> [ 4., 5., 6.]], dtype=float32)</span> |
| <span class="sd"> >>> a[1:2].asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> a[1].asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> a[-1].asnumpy()</span> |
| <span class="sd"> array([[ 4., 5., 6.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="n">key</span> |
| <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">begin</span><span class="o">=</span><span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="n">begin</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'CSRNDArray only supports continuous slicing on axis 0'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="k">else</span> <span class="mi">0</span> |
| <span class="n">end</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">begin</span><span class="o">=</span><span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="n">end</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Multi-dimension indexing is not supported'</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Undefined behaviour for </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">))</span> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <span class="k">def</span> <span class="fm">__setitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""x.__setitem__(i, y) <=> x[i]=y</span> |
| |
| <span class="sd"> Set self[key] to value. Only slice key [:] is supported.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : mxnet.ndarray.NDArray.slice</span> |
| <span class="sd"> The indexing key.</span> |
| <span class="sd"> value : NDArray or CSRNDArray or numpy.ndarray</span> |
| <span class="sd"> The value to set.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> src = mx.nd.sparse.zeros('csr', (3,3))</span> |
| <span class="sd"> >>> src.asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> # assign CSRNDArray with same storage type</span> |
| <span class="sd"> >>> x = mx.nd.ones((3,3)).tostype('csr')</span> |
| <span class="sd"> >>> x[:] = src</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> # assign NDArray to CSRNDArray</span> |
| <span class="sd"> >>> x[:] = mx.nd.ones((3,3)) * 2</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Failed to assign to a readonly CSRNDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Assignment with slice for CSRNDArray is not '</span> \ |
| <span class="s1">'implemented yet.'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="c1"># avoid copying to itself</span> |
| <span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">:</span> |
| <span class="n">value</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Assigning numeric types to CSRNDArray is "</span> \ |
| <span class="s2">"not implemented yet."</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span> |
| <span class="c1"># TODO(haibin/anisub) check scipy.sparse and use _sync_copy_from to</span> |
| <span class="c1"># avoid the temporary copy</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'Assigning non-NDArray object to CSRNDArray is not efficient'</span><span class="p">,</span> |
| <span class="ne">RuntimeWarning</span><span class="p">)</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="n">tmp</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'type </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">))</span><span class="si">}</span><span class="s1"> not supported'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)))</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'CSRNDArray only supports [:] for assignment'</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A deep copy NDArray of the indices array of the CSRNDArray.</span> |
| <span class="sd"> This generates a deep copy of the column indices of the current `csr` matrix.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> This CSRNDArray's indices array.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_data</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">indptr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A deep copy NDArray of the indptr array of the CSRNDArray.</span> |
| <span class="sd"> This generates a deep copy of the `indptr` of the current `csr` matrix.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> This CSRNDArray's indptr array.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A deep copy NDArray of the data array of the CSRNDArray.</span> |
| <span class="sd"> This generates a deep copy of the `data` of the current `csr` matrix.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> This CSRNDArray's data array.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">()</span> |
| |
| <span class="nd">@indices</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="nd">@indptr</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">indptr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indptr</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="nd">@data</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| |
| <div class="viewcode-block" id="CSRNDArray.tostype"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray.tostype">[docs]</a> <span class="k">def</span> <span class="nf">tostype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stype</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a copy of the array with chosen storage type.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray or CSRNDArray</span> |
| <span class="sd"> A copy of the array with the chosen storage stype</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"cast_storage from csr to row_sparse is not supported"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">cast_storage</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">stype</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="CSRNDArray.copyto"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray.copyto">[docs]</a> <span class="k">def</span> <span class="nf">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Copies the value of this array to another array.</span> |
| |
| <span class="sd"> If ``other`` is a ``NDArray`` or ``CSRNDArray`` object, then ``other.shape`` and</span> |
| <span class="sd"> ``self.shape`` should be the same. This function copies the value from</span> |
| <span class="sd"> ``self`` to ``other``.</span> |
| |
| <span class="sd"> If ``other`` is a context, a new ``CSRNDArray`` will be first created on</span> |
| <span class="sd"> the target context, and the value of ``self`` is copied.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> other : NDArray or CSRNDArray or Context</span> |
| <span class="sd"> The destination array or context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray or CSRNDArray</span> |
| <span class="sd"> The copied array. If ``other`` is an ``NDArray`` or ``CSRNDArray``, then the return</span> |
| <span class="sd"> value and ``other`` will point to the same ``NDArray`` or ``CSRNDArray``.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Device</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">CSRNDArray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">stype</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'default'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">CSRNDArray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'copyto does not support destination NDArray stype '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">stype</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'copyto does not support type '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="CSRNDArray.asscipy"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.CSRNDArray.asscipy">[docs]</a> <span class="k">def</span> <span class="nf">asscipy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.sparse.zeros('csr', (2,3))</span> |
| <span class="sd"> >>> y = x.asscipy()</span> |
| <span class="sd"> >>> type(y)</span> |
| <span class="sd"> <type 'scipy.sparse.csr.csr_matrix'></span> |
| <span class="sd"> >>> y</span> |
| <span class="sd"> <2x3 sparse matrix of type '<type 'numpy.float32'>'</span> |
| <span class="sd"> with 0 stored elements in Compressed Sparse Row format></span> |
| <span class="sd"> """</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">indptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indptr</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">spsp</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">"scipy could not be imported. "</span> |
| <span class="s2">"Please make sure that the scipy is installed."</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">spsp</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span></div></div> |
| |
| <span class="c1"># pylint: disable=abstract-method</span> |
| <div class="viewcode-block" id="RowSparseNDArray"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray">[docs]</a><span class="k">class</span> <span class="nc">RowSparseNDArray</span><span class="p">(</span><span class="n">BaseSparseNDArray</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A sparse representation of a set of NDArray row slices at given indices.</span> |
| |
| <span class="sd"> A RowSparseNDArray represents a multidimensional NDArray using two separate arrays: `data` and</span> |
| <span class="sd"> `indices`. The number of dimensions has to be at least 2.</span> |
| |
| <span class="sd"> - data: an NDArray of any dtype with shape [D0, D1, ..., Dn].</span> |
| <span class="sd"> - indices: a 1-D int64 NDArray with shape [D0] with values sorted in ascending order.</span> |
| |
| <span class="sd"> The `indices` stores the indices of the row slices with non-zeros,</span> |
| <span class="sd"> while the values are stored in `data`. The corresponding NDArray ``dense``</span> |
| <span class="sd"> represented by RowSparseNDArray ``rsp`` has</span> |
| |
| <span class="sd"> ``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``</span> |
| |
| <span class="sd"> >>> dense.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 3.],</span> |
| <span class="sd"> [ 0., 0., 0.],</span> |
| <span class="sd"> [ 4., 0., 5.],</span> |
| <span class="sd"> [ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> rsp = dense.tostype('row_sparse')</span> |
| <span class="sd"> >>> rsp.indices.asnumpy()</span> |
| <span class="sd"> array([0, 2], dtype=int64)</span> |
| <span class="sd"> >>> rsp.data.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 3.],</span> |
| <span class="sd"> [ 4., 0., 5.]], dtype=float32)</span> |
| |
| <span class="sd"> A RowSparseNDArray is typically used to represent non-zero row slices of a large NDArray</span> |
| <span class="sd"> of shape [LARGE0, D1, .. , Dn] where LARGE0 >> D0 and most row slices are zeros.</span> |
| |
| <span class="sd"> RowSparseNDArray is used principally in the definition of gradients for operations</span> |
| <span class="sd"> that have sparse gradients (e.g. sparse dot and sparse embedding).</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> row_sparse_array: Several ways to construct a RowSparseNDArray</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">RowSparseNDArray</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,),</span> <span class="nb">super</span><span class="p">(</span><span class="n">RowSparseNDArray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__iadd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">+</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__isub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">-</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__imul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">*</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="nf">__idiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">/</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__itruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="p">(</span><span class="bp">self</span> <span class="o">/</span> <span class="n">other</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""x.__getitem__(i) <=> x[i]</span> |
| |
| <span class="sd"> Returns a sliced view of this array.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : mxnet.ndarray.NDArray.slice</span> |
| <span class="sd"> Indexing key.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.sparse.zeros('row_sparse', (2, 3))</span> |
| <span class="sd"> >>> x[:].asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"__getitem__ with int key is not implemented for RowSparseNDArray yet"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'RowSparseNDArray only supports [:] for __getitem__'</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="bp">self</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Multi-dimension indexing is not supported'</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Undefined behaviour for </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="fm">__setitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""x.__setitem__(i, y) <=> x[i]=y</span> |
| |
| <span class="sd"> Set self[key] to value. Only slice key [:] is supported.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : mxnet.ndarray.NDArray.slice</span> |
| <span class="sd"> The indexing key.</span> |
| <span class="sd"> value : NDArray or numpy.ndarray</span> |
| <span class="sd"> The value to set.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> src = mx.nd.row_sparse([[1, 0, 2], [4, 5, 6]], [0, 2], (3,3))</span> |
| <span class="sd"> >>> src.asnumpy()</span> |
| <span class="sd"> array([[ 1., 0., 2.],</span> |
| <span class="sd"> [ 0., 0., 0.],</span> |
| <span class="sd"> [ 4., 5., 6.]], dtype=float32)</span> |
| <span class="sd"> >>> # assign RowSparseNDArray with same storage type</span> |
| <span class="sd"> >>> x = mx.nd.sparse.zeros('row_sparse', (3,3))</span> |
| <span class="sd"> >>> x[:] = src</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 0., 2.],</span> |
| <span class="sd"> [ 0., 0., 0.],</span> |
| <span class="sd"> [ 4., 5., 6.]], dtype=float32)</span> |
| <span class="sd"> >>> # assign NDArray to RowSparseNDArray</span> |
| <span class="sd"> >>> x[:] = mx.nd.ones((3,3))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Failed to assign to a readonly RowSparseNDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Assignment with slice for RowSparseNDArray '</span> \ |
| <span class="s1">'is not implmented yet.'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="c1"># avoid copying to itself</span> |
| <span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">:</span> |
| <span class="n">value</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_set_value</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'Assigning non-NDArray object to RowSparseNDArray is not efficient'</span><span class="p">,</span> |
| <span class="ne">RuntimeWarning</span><span class="p">)</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="n">tmp</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'type </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">))</span><span class="si">}</span><span class="s1"> not supported'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)))</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'RowSparseNDArray only supports [:] for assignment'</span><span class="p">)</span> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A deep copy NDArray of the indices array of the RowSparseNDArray.</span> |
| <span class="sd"> This generates a deep copy of the row indices of the current `row_sparse` matrix.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> This RowSparseNDArray's indices array.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A deep copy NDArray of the data array of the RowSparseNDArray.</span> |
| <span class="sd"> This generates a deep copy of the `data` of the current `row_sparse` matrix.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> This RowSparseNDArray's data array.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data</span><span class="p">()</span> |
| |
| <span class="nd">@indices</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <span class="nd">@data</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span> |
| |
| <div class="viewcode-block" id="RowSparseNDArray.tostype"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray.tostype">[docs]</a> <span class="k">def</span> <span class="nf">tostype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stype</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a copy of the array with chosen storage type.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray or RowSparseNDArray</span> |
| <span class="sd"> A copy of the array with the chosen storage stype</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'csr'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"cast_storage from row_sparse to csr is not supported"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">op</span><span class="o">.</span><span class="n">cast_storage</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">stype</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="RowSparseNDArray.copyto"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray.copyto">[docs]</a> <span class="k">def</span> <span class="nf">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Copies the value of this array to another array.</span> |
| |
| <span class="sd"> If ``other`` is a ``NDArray`` or ``RowSparseNDArray`` object, then ``other.shape``</span> |
| <span class="sd"> and ``self.shape`` should be the same. This function copies the value from</span> |
| <span class="sd"> ``self`` to ``other``.</span> |
| |
| <span class="sd"> If ``other`` is a context, a new ``RowSparseNDArray`` will be first created on</span> |
| <span class="sd"> the target context, and the value of ``self`` is copied.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> other : NDArray or RowSparseNDArray or Context</span> |
| <span class="sd"> The destination array or context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray or RowSparseNDArray</span> |
| <span class="sd"> The copied array. If ``other`` is an ``NDArray`` or ``RowSparseNDArray``, then the</span> |
| <span class="sd"> return value and ``other`` will point to the same ``NDArray`` or ``RowSparseNDArray``.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Device</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">RowSparseNDArray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">stype</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'default'</span><span class="p">,</span> <span class="s1">'row_sparse'</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">RowSparseNDArray</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'copyto does not support destination NDArray stype '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">stype</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'copyto does not support type '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="RowSparseNDArray.retain"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray.retain">[docs]</a> <span class="k">def</span> <span class="nf">retain</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Convenience fluent method for :py:func:`retain`.</span> |
| |
| <span class="sd"> The arguments are the same as for :py:func:`retain`, with</span> |
| <span class="sd"> this array as data.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">gs_retain</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">"gen_sparse could not be imported"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">gs_retain</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div> |
| |
| <span class="k">def</span> <span class="nf">_prepare_src_array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Prepare `source_array` so that it can be used to construct NDArray.</span> |
| <span class="sd"> `source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \</span> |
| <span class="sd"> nor an `np.ndarray`.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">source_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'values must be array like object'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">source_array</span> |
| |
| <span class="k">def</span> <span class="nf">_prepare_default_dtype</span><span class="p">(</span><span class="n">src_array</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray</span> |
| <span class="sd"> or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise."""</span> |
| <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">src_array</span><span class="p">,</span> <span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">src_array</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="k">elif</span> <span class="n">spsp</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">src_array</span><span class="p">,</span> <span class="n">spsp</span><span class="o">.</span><span class="n">csr</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">):</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">src_array</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">mx_real_t</span> |
| <span class="k">return</span> <span class="n">dtype</span> |
| |
| <span class="k">def</span> <span class="nf">_check_shape</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""check s1 == s2 if both are not None"""</span> |
| <span class="k">if</span> <span class="n">s1</span> <span class="ow">and</span> <span class="n">s2</span> <span class="ow">and</span> <span class="n">s1</span> <span class="o">!=</span> <span class="n">s2</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Shape mismatch detected. "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">s1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" v.s. "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">s2</span><span class="p">))</span> |
| |
| <div class="viewcode-block" id="csr_matrix"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.csr_matrix">[docs]</a><span class="k">def</span> <span class="nf">csr_matrix</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates a `CSRNDArray`, an 2D array with compressed sparse row (CSR) format.</span> |
| |
| <span class="sd"> The CSRNDArray can be instantiated in several ways:</span> |
| |
| <span class="sd"> - csr_matrix(D):</span> |
| <span class="sd"> to construct a CSRNDArray with a dense 2D array ``D``</span> |
| <span class="sd"> - **D** (*array_like*) - An object exposing the array interface, an object whose \</span> |
| <span class="sd"> `__array__` method returns an array, or any (nested) sequence.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \</span> |
| <span class="sd"> float32 otherwise.</span> |
| |
| <span class="sd"> - csr_matrix(S)</span> |
| <span class="sd"> to construct a CSRNDArray with a sparse 2D array ``S``</span> |
| <span class="sd"> - **S** (*CSRNDArray or scipy.sparse.csr.csr_matrix*) - A sparse matrix.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is ``S.dtype``.</span> |
| |
| <span class="sd"> - csr_matrix((M, N))</span> |
| <span class="sd"> to construct an empty CSRNDArray with shape ``(M, N)``</span> |
| <span class="sd"> - **M** (*int*) - Number of rows in the matrix</span> |
| <span class="sd"> - **N** (*int*) - Number of columns in the matrix</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is float32.</span> |
| |
| <span class="sd"> - csr_matrix((data, indices, indptr))</span> |
| <span class="sd"> to construct a CSRNDArray based on the definition of compressed sparse row format \</span> |
| <span class="sd"> using three separate arrays, \</span> |
| <span class="sd"> where the column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` \</span> |
| <span class="sd"> and their corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. \</span> |
| <span class="sd"> The column indices for a given row are expected to be **sorted in ascending order.** \</span> |
| <span class="sd"> Duplicate column entries for the same row are not allowed.</span> |
| <span class="sd"> - **data** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> holds all the non-zero entries of the matrix in row-major order.</span> |
| <span class="sd"> - **indices** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> stores the column index for each non-zero element in ``data``.</span> |
| <span class="sd"> - **indptr** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> stores the offset into ``data`` of the first non-zero element number of each \</span> |
| <span class="sd"> row of the matrix.</span> |
| <span class="sd"> - **shape** (*tuple of int, optional*) - The shape of the array. The default \</span> |
| <span class="sd"> shape is inferred from the indices and indptr arrays.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is ``data.dtype`` if ``data`` is an NDArray or numpy.ndarray, \</span> |
| <span class="sd"> float32 otherwise.</span> |
| |
| <span class="sd"> - csr_matrix((data, (row, col)))</span> |
| <span class="sd"> to construct a CSRNDArray based on the COOrdinate format \</span> |
| <span class="sd"> using three seperate arrays, \</span> |
| <span class="sd"> where ``row[i]`` is the row index of the element, \</span> |
| <span class="sd"> ``col[i]`` is the column index of the element \</span> |
| <span class="sd"> and ``data[i]`` is the data corresponding to the element. All the missing \</span> |
| <span class="sd"> elements in the input are taken to be zeroes.</span> |
| <span class="sd"> - **data** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> holds all the non-zero entries of the matrix in COO format.</span> |
| <span class="sd"> - **row** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> stores the row index for each non zero element in ``data``.</span> |
| <span class="sd"> - **col** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> stores the col index for each non zero element in ``data``.</span> |
| <span class="sd"> - **shape** (*tuple of int, optional*) - The shape of the array. The default \</span> |
| <span class="sd"> shape is inferred from the ``row`` and ``col`` arrays.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is float32.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> arg1: tuple of int, tuple of array_like, array_like, CSRNDArray, scipy.sparse.csr_matrix, \</span> |
| <span class="sd"> scipy.sparse.coo_matrix, tuple of int or tuple of array_like</span> |
| <span class="sd"> The argument to help instantiate the csr matrix. See above for further details.</span> |
| <span class="sd"> shape : tuple of int, optional</span> |
| <span class="sd"> The shape of the csr matrix.</span> |
| <span class="sd"> ctx: Context, optional</span> |
| <span class="sd"> Device context (default is the current default context).</span> |
| <span class="sd"> dtype: str or numpy.dtype, optional</span> |
| <span class="sd"> The data type of the output array.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> CSRNDArray</span> |
| <span class="sd"> A `CSRNDArray` with the `csr` storage representation.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> a = mx.nd.sparse.csr_matrix(([1, 2, 3], [1, 0, 2], [0, 1, 2, 2, 3]), shape=(4, 3))</span> |
| <span class="sd"> >>> a.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 0.],</span> |
| <span class="sd"> [ 2., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 3.]], dtype=float32)</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> CSRNDArray : MXNet NDArray in compressed sparse row format.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># construct a csr matrix from (M, N) or (data, indices, indptr)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">arg_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg1</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">arg_len</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="c1"># construct a sparse csr matrix from</span> |
| <span class="c1"># scipy coo matrix if input format is coo</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span> <span class="o">=</span> <span class="n">arg1</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">row</span> <span class="o">=</span> <span class="n">row</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">col</span> <span class="o">=</span> <span class="n">col</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">spsp</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">"scipy could not be imported. "</span> |
| <span class="s2">"Please make sure that the scipy is installed."</span><span class="p">)</span> |
| <span class="n">coo</span> <span class="o">=</span> <span class="n">spsp</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">coo</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="n">csr</span> <span class="o">=</span> <span class="n">coo</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">array</span><span class="p">(</span><span class="n">csr</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># empty matrix with shape</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">empty</span><span class="p">(</span><span class="s1">'csr'</span><span class="p">,</span> <span class="n">arg1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">arg_len</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> |
| <span class="c1"># data, indices, indptr</span> |
| <span class="k">return</span> <span class="n">_csr_matrix_from_definition</span><span class="p">(</span><span class="n">arg1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">arg1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">arg1</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> |
| <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unexpected length of input tuple: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_len</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># construct a csr matrix from a sparse / dense one</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">CSRNDArray</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">spsp</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">spsp</span><span class="o">.</span><span class="n">csr</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">)):</span> |
| <span class="c1"># construct a csr matrix from scipy or CSRNDArray</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">arg1</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">array</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">RowSparseNDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unexpected input type: RowSparseNDArray"</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># construct a csr matrix from a dense one</span> |
| <span class="c1"># prepare default ctx and dtype since mx.nd.array doesn't use default values</span> |
| <span class="c1"># based on source_array</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">_prepare_default_dtype</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="c1"># create dns array with provided dtype. ctx is not passed since copy across</span> |
| <span class="c1"># ctx requires dtype to be the same</span> |
| <span class="n">dns</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">dns</span><span class="o">.</span><span class="n">context</span> <span class="o">!=</span> <span class="n">ctx</span><span class="p">:</span> |
| <span class="n">dns</span> <span class="o">=</span> <span class="n">dns</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">dns</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">dns</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'csr'</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_csr_matrix_from_definition</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">indptr</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indices_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indptr_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Create a `CSRNDArray` based on data, indices and indptr"""</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="n">storage_type</span> <span class="o">=</span> <span class="s1">'csr'</span> |
| <span class="c1"># context</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</span> <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">ctx</span> |
| <span class="c1"># types</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">_prepare_default_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="n">indptr_type</span> <span class="o">=</span> <span class="n">_STORAGE_AUX_TYPES</span><span class="p">[</span><span class="n">storage_type</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">indptr_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">indptr_type</span> |
| <span class="n">indices_type</span> <span class="o">=</span> <span class="n">_STORAGE_AUX_TYPES</span><span class="p">[</span><span class="n">storage_type</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">indices_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">indices_type</span> |
| <span class="c1"># prepare src array and types</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">_prepare_src_array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="n">indptr</span> <span class="o">=</span> <span class="n">_prepare_src_array</span><span class="p">(</span><span class="n">indptr</span><span class="p">,</span> <span class="n">indptr_type</span><span class="p">)</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">_prepare_src_array</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">indices_type</span><span class="p">)</span> |
| |
| <span class="c1"># TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays</span> |
| <span class="c1"># if they are not for now. In the future, we should provide a c-api</span> |
| <span class="c1"># to accept np.ndarray types to copy from to result.data and aux_data</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indptr</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">indptr</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">indptr</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">indptr_type</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">indices_type</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'invalid shape'</span><span class="p">)</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indptr</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">op</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="c1"># verify shapes</span> |
| <span class="n">aux_shapes</span> <span class="o">=</span> <span class="p">[</span><span class="n">indptr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">indptr</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">indices</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> \ |
| <span class="n">indptr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'invalid shape'</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">CSRNDArray</span><span class="p">(</span><span class="n">_new_alloc_handle</span><span class="p">(</span><span class="n">storage_type</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">indptr_type</span><span class="p">,</span> <span class="n">indices_type</span><span class="p">],</span> <span class="n">aux_shapes</span><span class="p">))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyFromNDArray</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyFromNDArray</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">indptr</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyFromNDArray</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">indices</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">result</span> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="row_sparse_array"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.row_sparse_array">[docs]</a><span class="k">def</span> <span class="nf">row_sparse_array</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \</span> |
| <span class="sd"> tensor slices at given indices.</span> |
| |
| <span class="sd"> The RowSparseNDArray can be instantiated in several ways:</span> |
| |
| <span class="sd"> - row_sparse_array(D):</span> |
| <span class="sd"> to construct a RowSparseNDArray with a dense ndarray ``D``</span> |
| <span class="sd"> - **D** (*array_like*) - An object exposing the array interface, an object whose \</span> |
| <span class="sd"> `__array__` method returns an array, or any (nested) sequence.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \</span> |
| <span class="sd"> float32 otherwise.</span> |
| |
| <span class="sd"> - row_sparse_array(S)</span> |
| <span class="sd"> to construct a RowSparseNDArray with a sparse ndarray ``S``</span> |
| <span class="sd"> - **S** (*RowSparseNDArray*) - A sparse ndarray.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is ``S.dtype``.</span> |
| |
| <span class="sd"> - row_sparse_array((D0, D1 .. Dn))</span> |
| <span class="sd"> to construct an empty RowSparseNDArray with shape ``(D0, D1, ... Dn)``</span> |
| <span class="sd"> - **D0, D1 .. Dn** (*int*) - The shape of the ndarray</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is float32.</span> |
| |
| <span class="sd"> - row_sparse_array((data, indices))</span> |
| <span class="sd"> to construct a RowSparseNDArray based on the definition of row sparse format \</span> |
| <span class="sd"> using two separate arrays, \</span> |
| <span class="sd"> where the `indices` stores the indices of the row slices with non-zeros,</span> |
| <span class="sd"> while the values are stored in `data`. The corresponding NDArray ``dense``</span> |
| <span class="sd"> represented by RowSparseNDArray ``rsp`` has \</span> |
| <span class="sd"> ``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``</span> |
| <span class="sd"> The row indices for are expected to be **sorted in ascending order.** \</span> |
| <span class="sd"> - **data** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> holds all the non-zero row slices of the array.</span> |
| <span class="sd"> - **indices** (*array_like*) - An object exposing the array interface, which \</span> |
| <span class="sd"> stores the row index for each row slice with non-zero elements.</span> |
| <span class="sd"> - **shape** (*tuple of int, optional*) - The shape of the array. The default \</span> |
| <span class="sd"> shape is inferred from the indices and indptr arrays.</span> |
| <span class="sd"> - **ctx** (*Context, optional*) - Device context \</span> |
| <span class="sd"> (default is the current default context).</span> |
| <span class="sd"> - **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \</span> |
| <span class="sd"> The default dtype is float32.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> arg1 : NDArray, numpy.ndarray, RowSparseNDArray, tuple of int or tuple of array_like</span> |
| <span class="sd"> The argument to help instantiate the row sparse ndarray. See above for further details.</span> |
| <span class="sd"> shape : tuple of int, optional</span> |
| <span class="sd"> The shape of the row sparse ndarray. (Default value = None)</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context (default is the current default context).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> The data type of the output array. (Default value = None)</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> RowSparseNDArray</span> |
| <span class="sd"> An `RowSparseNDArray` with the `row_sparse` storage representation.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.nd.sparse.row_sparse_array(([[1, 2], [3, 4]], [1, 4]), shape=(6, 2))</span> |
| <span class="sd"> >>> a.asnumpy()</span> |
| <span class="sd"> array([[ 0., 0.],</span> |
| <span class="sd"> [ 1., 2.],</span> |
| <span class="sd"> [ 0., 0.],</span> |
| <span class="sd"> [ 0., 0.],</span> |
| <span class="sd"> [ 3., 4.],</span> |
| <span class="sd"> [ 0., 0.]], dtype=float32)</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> RowSparseNDArray : MXNet NDArray in row sparse format.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># construct a row sparse array from (D0, D1 ..) or (data, indices)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">arg_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg1</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">arg_len</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unexpected length of input tuple: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">arg_len</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">arg_len</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span> |
| <span class="c1"># empty ndarray with shape</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">empty</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">arg1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># len(arg1) = 2, is either shape or (data, indices)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">integer_types</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">integer_types</span><span class="p">):</span> |
| <span class="c1"># empty ndarray with shape</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">empty</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">arg1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># data, indices, indptr</span> |
| <span class="k">return</span> <span class="n">_row_sparse_ndarray_from_definition</span><span class="p">(</span><span class="n">arg1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">arg1</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="n">shape</span><span class="p">,</span> |
| <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># construct a row sparse ndarray from a dense / sparse array</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">RowSparseNDArray</span><span class="p">):</span> |
| <span class="c1"># construct a row sparse ndarray from RowSparseNDArray</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">arg1</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">array</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">CSRNDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unexpected input type: CSRNDArray"</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># construct a csr matrix from a dense one</span> |
| <span class="c1"># prepare default dtype since mx.nd.array doesn't use default values</span> |
| <span class="c1"># based on source_array</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">_prepare_default_dtype</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="c1"># create dns array with provided dtype. ctx is not passed since copy across</span> |
| <span class="c1"># ctx requires dtype to be the same</span> |
| <span class="n">dns</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">dns</span><span class="o">.</span><span class="n">context</span> <span class="o">!=</span> <span class="n">ctx</span><span class="p">:</span> |
| <span class="n">dns</span> <span class="o">=</span> <span class="n">dns</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">_check_shape</span><span class="p">(</span><span class="n">dns</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">dns</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_row_sparse_ndarray_from_definition</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indices_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Create a `RowSparseNDArray` based on data and indices"""</span> |
| <span class="n">storage_type</span> <span class="o">=</span> <span class="s1">'row_sparse'</span> |
| <span class="c1"># context</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</span> <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">ctx</span> |
| <span class="c1"># types</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">_prepare_default_dtype</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="n">indices_type</span> <span class="o">=</span> <span class="n">_STORAGE_AUX_TYPES</span><span class="p">[</span><span class="n">storage_type</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">indices_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">indices_type</span> |
| <span class="c1"># prepare src array and types</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">_prepare_src_array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">_prepare_src_array</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">indices_type</span><span class="p">)</span> |
| |
| <span class="c1"># TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays</span> |
| <span class="c1"># if they are not for now. In the future, we should provide a c-api</span> |
| <span class="c1"># to accept np.ndarray types to copy from to result.data and aux_data</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">_array</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">indices_type</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">num_indices</span> <span class="o">=</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">num_indices</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'invalid shape'</span><span class="p">)</span> |
| <span class="n">dim0</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">num_indices</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">dim0</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="c1"># verify shapes</span> |
| <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="ow">or</span> <span class="n">indices</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"invalid shape"</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">RowSparseNDArray</span><span class="p">(</span><span class="n">_new_alloc_handle</span><span class="p">(</span><span class="n">storage_type</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">indices_type</span><span class="p">],</span> <span class="p">[</span><span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">]))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyFromNDArray</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyFromNDArray</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">indices</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">result</span> |
| |
| <span class="k">def</span> <span class="nf">_ndarray_cls</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">_STORAGE_TYPE_UNDEFINED</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="n">_STORAGE_TYPE_UNDEFINED</span><span class="p">:</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">_storage_type</span><span class="p">(</span><span class="n">handle</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="n">_STORAGE_TYPE_DEFAULT</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="n">writable</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">stype</span> <span class="o">==</span> <span class="n">_STORAGE_TYPE_CSR</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">CSRNDArray</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="n">writable</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">stype</span> <span class="o">==</span> <span class="n">_STORAGE_TYPE_ROW_SPARSE</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">RowSparseNDArray</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="n">writable</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="sa">f</span><span class="s2">"unknown storage type: </span><span class="si">{</span><span class="n">stype</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| |
| |
| <span class="n">_set_ndarray_class</span><span class="p">(</span><span class="n">_ndarray_cls</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="add"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.add">[docs]</a><span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns element-wise sum of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and</span> |
| <span class="sd"> ``mx.nd.broadcast_plus(lhs, rhs)`` when shapes of lhs and rhs do not</span> |
| <span class="sd"> match. If lhs.shape == rhs.shape, this is equivalent to</span> |
| <span class="sd"> ``mx.nd.elemwise_add(lhs, rhs)``</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.abs</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> First array to be added.</span> |
| <span class="sd"> rhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> Second array to be added.</span> |
| <span class="sd"> If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise sum of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.nd.ones((2,3)).tostype('csr')</span> |
| <span class="sd"> >>> b = mx.nd.ones((2,3)).tostype('csr')</span> |
| <span class="sd"> >>> a.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> b.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (a+b).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> c = mx.nd.ones((2,3)).tostype('row_sparse')</span> |
| <span class="sd"> >>> d = mx.nd.ones((2,3)).tostype('row_sparse')</span> |
| <span class="sd"> >>> c.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> d.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (c+d).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">lhs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">rhs</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_plus_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">broadcast_add</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_plus_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <div class="viewcode-block" id="subtract"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.subtract">[docs]</a><span class="k">def</span> <span class="nf">subtract</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns element-wise difference of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and</span> |
| <span class="sd"> ``mx.nd.broadcast_minus(lhs, rhs)`` when shapes of lhs and rhs do not</span> |
| <span class="sd"> match. If lhs.shape == rhs.shape, this is equivalent to</span> |
| <span class="sd"> ``mx.nd.elemwise_sub(lhs, rhs)``</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> First array to be subtracted.</span> |
| <span class="sd"> rhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> Second array to be subtracted.</span> |
| <span class="sd"> If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.__spec__</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise difference of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.nd.ones((2,3)).tostype('csr')</span> |
| <span class="sd"> >>> b = mx.nd.ones((2,3)).tostype('csr')</span> |
| <span class="sd"> >>> a.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> b.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (a-b).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> c = mx.nd.ones((2,3)).tostype('row_sparse')</span> |
| <span class="sd"> >>> d = mx.nd.ones((2,3)).tostype('row_sparse')</span> |
| <span class="sd"> >>> c.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> d.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (c-d).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">lhs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">rhs</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">elemwise_sub</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">sub</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_minus_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">broadcast_sub</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">sub</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_minus_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <div class="viewcode-block" id="multiply"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.multiply">[docs]</a><span class="k">def</span> <span class="nf">multiply</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns element-wise product of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``</span> |
| <span class="sd"> when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape,</span> |
| <span class="sd"> this is equivalent to ``mx.nd.elemwise_mul(lhs, rhs)``</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> First array to be multiplied.</span> |
| <span class="sd"> rhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> Second array to be multiplied.</span> |
| <span class="sd"> If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise multiplication of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3)).tostype('csr')</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(3)</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([ 0., 1., 2.], dtype=float32)</span> |
| <span class="sd"> >>> (x*2).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (x*y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.sparse.multiply(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x*z).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 0., 1., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.sparse.multiply(x, z).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 0., 1., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> z = z.reshape((1, 3))</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (x*z).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 0., 1., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.sparse.multiply(x, z).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 0., 1., 2.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">lhs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">rhs</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">mul</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_mul_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">broadcast_mul</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">mul</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_mul_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <div class="viewcode-block" id="divide"><a class="viewcode-back" href="../../../api/legacy/ndarray/sparse/index.html#mxnet.ndarray.sparse.divide">[docs]</a><span class="k">def</span> <span class="nf">divide</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns element-wise division of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``</span> |
| <span class="sd"> when shapes of lhs and rhs do not match. If lhs.shape == rhs.shape,</span> |
| <span class="sd"> this is equivalent to ``mx.nd.elemwise_div(lhs, rhs)``</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> First array in division.</span> |
| <span class="sd"> rhs : scalar or mxnet.ndarray.sparse.array</span> |
| <span class="sd"> Second array in division.</span> |
| <span class="sd"> The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise division of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = (mx.nd.ones((2,3))*6).tostype('csr')</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1)) + 1</span> |
| <span class="sd"> >>> z = mx.nd.arange(3) + 1</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 6., 6., 6.],</span> |
| <span class="sd"> [ 6., 6., 6.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 1.],</span> |
| <span class="sd"> [ 2.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([ 1., 2., 3.], dtype=float32)</span> |
| <span class="sd"> >>> x/2</span> |
| <span class="sd"> <NDArray 2x3 @cpu(0)></span> |
| <span class="sd"> >>> (x/3).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (x/y).asnumpy()</span> |
| <span class="sd"> array([[ 6., 6., 6.],</span> |
| <span class="sd"> [ 3., 3., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.sparse.divide(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 6., 6., 6.],</span> |
| <span class="sd"> [ 3., 3., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> (x/z).asnumpy()</span> |
| <span class="sd"> array([[ 6., 3., 2.],</span> |
| <span class="sd"> [ 6., 3., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.sprase.divide(x,z).asnumpy()</span> |
| <span class="sd"> array([[ 6., 3., 2.],</span> |
| <span class="sd"> [ 6., 3., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> z = z.reshape((1,3))</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> (x/z).asnumpy()</span> |
| <span class="sd"> array([[ 6., 3., 2.],</span> |
| <span class="sd"> [ 6., 3., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.sparse.divide(x,z).asnumpy()</span> |
| <span class="sd"> array([[ 6., 3., 2.],</span> |
| <span class="sd"> [ 6., 3., 2.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">)</span> <span class="ow">and</span> <span class="n">lhs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">rhs</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">elemwise_div</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">truediv</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_div_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">op</span><span class="o">.</span><span class="n">broadcast_div</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">truediv</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_div_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <span class="k">def</span> <span class="nf">zeros</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a new array of given shape and type, filled with zeros.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> stype: string</span> |
| <span class="sd"> The storage type of the empty array, such as 'row_sparse', 'csr', etc</span> |
| <span class="sd"> shape : int or tuple of int</span> |
| <span class="sd"> The shape of the empty array</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> An optional device context (default is the current default context)</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> An optional value type (default is `float32`)</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> RowSparseNDArray or CSRNDArray</span> |
| <span class="sd"> A created array</span> |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.sparse.zeros('csr', (1,2))</span> |
| <span class="sd"> <CSRNDArray 1x2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.sparse.zeros('row_sparse', (1,2), ctx=mx.cpu(), dtype='float16').asnumpy()</span> |
| <span class="sd"> array([[ 0., 0.]], dtype=float16)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_zeros_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">mx_real_t</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtype</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">):</span> |
| <span class="n">aux_types</span> <span class="o">=</span> <span class="n">_STORAGE_AUX_TYPES</span><span class="p">[</span><span class="n">stype</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"unknown storage type: "</span> <span class="o">+</span> <span class="n">stype</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">_new_alloc_handle</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">aux_types</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <span class="k">def</span> <span class="nf">empty</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns a new array of given shape and type, without initializing entries.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> stype: string</span> |
| <span class="sd"> The storage type of the empty array, such as 'row_sparse', 'csr', etc</span> |
| <span class="sd"> shape : int or tuple of int</span> |
| <span class="sd"> The shape of the empty array.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> An optional device context (default is the current default context).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> An optional value type (default is `float32`).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> CSRNDArray or RowSparseNDArray</span> |
| <span class="sd"> A created array.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">)</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">mx_real_t</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">stype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'csr'</span><span class="p">,</span> <span class="s1">'row_sparse'</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">zeros</span><span class="p">(</span><span class="n">stype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"unknown stype : "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">stype</span><span class="p">))</span> |
| |
| |
| <span class="k">def</span> <span class="nf">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates a sparse array from any object exposing the array interface.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix</span> |
| <span class="sd"> The source sparse array</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> The default context is ``source_array.context`` if ``source_array`` is an NDArray. \</span> |
| <span class="sd"> The current default context otherwise.</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> The data type of the output array. The default dtype is ``source_array.dtype``</span> |
| <span class="sd"> if `source_array` is an `NDArray`, `numpy.ndarray` or `scipy.sparse.csr.csr_matrix`, \</span> |
| <span class="sd"> `float32` otherwise.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> RowSparseNDArray or CSRNDArray</span> |
| <span class="sd"> An array with the same contents as the `source_array`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import scipy.sparse as spsp</span> |
| <span class="sd"> >>> csr = spsp.csr_matrix((2, 100))</span> |
| <span class="sd"> >>> mx.nd.sparse.array(csr)</span> |
| <span class="sd"> <CSRNDArray 2x100 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.sparse.array(mx.nd.sparse.zeros('csr', (3, 2)))</span> |
| <span class="sd"> <CSRNDArray 3x2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.sparse.array(mx.nd.sparse.zeros('row_sparse', (3, 2)))</span> |
| <span class="sd"> <RowSparseNDArray 3x2 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</span> <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">ctx</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">source_array</span><span class="o">.</span><span class="n">stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">),</span> \ |
| <span class="s2">"Please use `tostype` to create RowSparseNDArray or CSRNDArray from an NDArray"</span> |
| <span class="c1"># prepare dtype and ctx based on source_array, if not provided</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">_prepare_default_dtype</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="c1"># if both dtype and ctx are different from source_array, we cannot copy directly</span> |
| <span class="k">if</span> <span class="n">source_array</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">dtype</span> <span class="ow">and</span> <span class="n">source_array</span><span class="o">.</span><span class="n">context</span> <span class="o">!=</span> <span class="n">ctx</span><span class="p">:</span> |
| <span class="n">arr</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="n">source_array</span><span class="o">.</span><span class="n">stype</span><span class="p">,</span> <span class="n">source_array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">source_array</span> |
| <span class="n">arr</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">arr</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="n">source_array</span><span class="o">.</span><span class="n">stype</span><span class="p">,</span> <span class="n">source_array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">source_array</span> |
| <span class="k">return</span> <span class="n">arr</span> |
| <span class="k">elif</span> <span class="n">spsp</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">spsp</span><span class="o">.</span><span class="n">csr</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">):</span> |
| <span class="c1"># TODO(haibin) implement `_sync_copy_from` with scipy csr object to reduce a copy</span> |
| <span class="c1"># preprocess scipy csr to canonical form</span> |
| <span class="n">csr</span> <span class="o">=</span> <span class="n">source_array</span><span class="o">.</span><span class="n">sorted_indices</span><span class="p">()</span> |
| <span class="n">csr</span><span class="o">.</span><span class="n">sum_duplicates</span><span class="p">()</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">_prepare_default_dtype</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">csr_matrix</span><span class="p">((</span><span class="n">csr</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">csr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> \ |
| <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Please use mx.nd.array to create an NDArray with source_array of type "</span><span class="p">,</span> |
| <span class="nb">type</span><span class="p">(</span><span class="n">source_array</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unexpected source_array type: "</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">source_array</span><span class="p">))</span> |
| </pre></div> |
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