5ffd5d3838
## What changes were proposed in this pull request? Made DataFrame-based API primary * Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html * mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html * ml-guide.html includes a "maintenance mode" announcement about the RDD-based API * **Reviewers: please check this carefully** * (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix * Moved migration guide to ml-guide from mllib-guide * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides * **Reviewers**: I did not change any of the content of the migration guides. Reorganized DataFrame-based guide: * ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc. * Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html * **Reviewers**: I did not change the content of these guides, except some intro text. * Sidebar remains the same, but with pipeline and tuning sections added Other: * ml-classification-regression.html: Moved text about linear methods to new section in page ## How was this patch tested? Generated docs locally Author: Joseph K. Bradley <joseph@databricks.com> Closes #14213 from jkbradley/ml-guide-2.0.
235 lines
6.7 KiB
Markdown
235 lines
6.7 KiB
Markdown
---
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layout: global
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title: Clustering
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displayTitle: Clustering
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---
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This page describes clustering algorithms in MLlib.
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The [guide for clustering in the RDD-based API](mllib-clustering.html) also has relevant information
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about these algorithms.
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**Table of Contents**
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* This will become a table of contents (this text will be scraped).
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{:toc}
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## K-means
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[k-means](http://en.wikipedia.org/wiki/K-means_clustering) is one of the
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most commonly used clustering algorithms that clusters the data points into a
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predefined number of clusters. The MLlib implementation includes a parallelized
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variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method
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called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf).
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`KMeans` is implemented as an `Estimator` and generates a `KMeansModel` as the base model.
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### Input Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>featuresCol</td>
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<td>Vector</td>
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<td>"features"</td>
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<td>Feature vector</td>
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</tr>
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</tbody>
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</table>
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### Output Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>predictionCol</td>
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<td>Int</td>
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<td>"prediction"</td>
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<td>Predicted cluster center</td>
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</tr>
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</tbody>
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</table>
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### Example
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.KMeans) for more details.
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{% include_example scala/org/apache/spark/examples/ml/KMeansExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/KMeans.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaKMeansExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.KMeans) for more details.
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{% include_example python/ml/kmeans_example.py %}
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</div>
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</div>
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## Latent Dirichlet allocation (LDA)
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`LDA` is implemented as an `Estimator` that supports both `EMLDAOptimizer` and `OnlineLDAOptimizer`,
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and generates a `LDAModel` as the base model. Expert users may cast a `LDAModel` generated by
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`EMLDAOptimizer` to a `DistributedLDAModel` if needed.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.LDA) for more details.
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{% include_example scala/org/apache/spark/examples/ml/LDAExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/LDA.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaLDAExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.LDA) for more details.
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{% include_example python/ml/lda_example.py %}
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</div>
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</div>
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## Bisecting k-means
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Bisecting k-means is a kind of [hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) using a
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divisive (or "top-down") approach: all observations start in one cluster, and splits are performed recursively as one
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moves down the hierarchy.
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Bisecting K-means can often be much faster than regular K-means, but it will generally produce a different clustering.
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`BisectingKMeans` is implemented as an `Estimator` and generates a `BisectingKMeansModel` as the base model.
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### Example
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.BisectingKMeans) for more details.
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{% include_example scala/org/apache/spark/examples/ml/BisectingKMeansExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/BisectingKMeans.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.BisectingKMeans) for more details.
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{% include_example python/ml/bisecting_k_means_example.py %}
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</div>
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</div>
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## Gaussian Mixture Model (GMM)
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A [Gaussian Mixture Model](http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model)
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represents a composite distribution whereby points are drawn from one of *k* Gaussian sub-distributions,
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each with its own probability. The `spark.ml` implementation uses the
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[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
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algorithm to induce the maximum-likelihood model given a set of samples.
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`GaussianMixture` is implemented as an `Estimator` and generates a `GaussianMixtureModel` as the base
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model.
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### Input Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>featuresCol</td>
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<td>Vector</td>
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<td>"features"</td>
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<td>Feature vector</td>
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</tr>
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</tbody>
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</table>
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### Output Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>predictionCol</td>
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<td>Int</td>
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<td>"prediction"</td>
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<td>Predicted cluster center</td>
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</tr>
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<tr>
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<td>probabilityCol</td>
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<td>Vector</td>
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<td>"probability"</td>
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<td>Probability of each cluster</td>
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</tr>
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</tbody>
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</table>
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### Example
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.GaussianMixture) for more details.
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{% include_example scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/GaussianMixture.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.GaussianMixture) for more details.
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{% include_example python/ml/gaussian_mixture_example.py %}
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</div>
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</div>
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