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| <h1>Source code for pyspark.mllib.random</h1><div class="highlight"><pre> |
| <span></span><span class="c1">#</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span> |
| <span class="c1"># contributor license agreements. See the NOTICE file distributed with</span> |
| <span class="c1"># this work for additional information regarding copyright ownership.</span> |
| <span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span> |
| <span class="c1"># (the "License"); you may not use this file except in compliance with</span> |
| <span class="c1"># 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, software</span> |
| <span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span> |
| <span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span> |
| <span class="c1"># See the License for the specific language governing permissions and</span> |
| <span class="c1"># limitations under the License.</span> |
| <span class="c1">#</span> |
| |
| <span class="sd">"""</span> |
| <span class="sd">Python package for random data generation.</span> |
| <span class="sd">"""</span> |
| |
| <span class="kn">import</span> <span class="nn">sys</span> |
| <span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">wraps</span> |
| |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">callMLlibFunc</span> |
| |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="s2">"RandomRDDs"</span><span class="p">,</span> |
| <span class="p">]</span> |
| |
| |
| <span class="k">def</span> <span class="nf">toArray</span><span class="p">(</span><span class="n">f</span><span class="p">):</span> |
| <span class="nd">@wraps</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="o">*</span><span class="n">a</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span> |
| <span class="n">rdd</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="o">*</span><span class="n">a</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">rdd</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">vec</span><span class="p">:</span> <span class="n">vec</span><span class="o">.</span><span class="n">toArray</span><span class="p">())</span> |
| |
| <span class="k">return</span> <span class="n">func</span> |
| |
| |
| <div class="viewcode-block" id="RandomRDDs"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs">[docs]</a><span class="k">class</span> <span class="nc">RandomRDDs</span><span class="p">:</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generator methods for creating RDDs comprised of i.i.d samples from</span> |
| <span class="sd"> some distribution.</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="RandomRDDs.uniformRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.uniformRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">uniformRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of i.i.d. samples from the</span> |
| <span class="sd"> uniform distribution U(0.0, 1.0).</span> |
| |
| <span class="sd"> To transform the distribution in the generated RDD from U(0.0, 1.0)</span> |
| <span class="sd"> to U(a, b), use</span> |
| <span class="sd"> ``RandomRDDs.uniformRDD(sc, n, p, seed).map(lambda v: a + (b - a) * v)``</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> used to create the RDD.</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> Size of the RDD.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of float comprised of i.i.d. samples ~ `U(0.0, 1.0)`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = RandomRDDs.uniformRDD(sc, 100).collect()</span> |
| <span class="sd"> >>> len(x)</span> |
| <span class="sd"> 100</span> |
| <span class="sd"> >>> max(x) <= 1.0 and min(x) >= 0.0</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> RandomRDDs.uniformRDD(sc, 100, 4).getNumPartitions()</span> |
| <span class="sd"> 4</span> |
| <span class="sd"> >>> parts = RandomRDDs.uniformRDD(sc, 100, seed=4).getNumPartitions()</span> |
| <span class="sd"> >>> parts == sc.defaultParallelism</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"uniformRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.normalRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.normalRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">normalRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of i.i.d. samples from the standard normal</span> |
| <span class="sd"> distribution.</span> |
| |
| <span class="sd"> To transform the distribution in the generated RDD from standard normal</span> |
| <span class="sd"> to some other normal N(mean, sigma^2), use</span> |
| <span class="sd"> ``RandomRDDs.normal(sc, n, p, seed).map(lambda v: mean + sigma * v)``</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> used to create the RDD.</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> Size of the RDD.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1)</span> |
| <span class="sd"> >>> stats = x.stats()</span> |
| <span class="sd"> >>> stats.count()</span> |
| <span class="sd"> 1000</span> |
| <span class="sd"> >>> abs(stats.mean() - 0.0) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> abs(stats.stdev() - 1.0) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"normalRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.logNormalRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.logNormalRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">logNormalRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of i.i.d. samples from the log normal</span> |
| <span class="sd"> distribution with the input mean and standard distribution.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> used to create the RDD.</span> |
| <span class="sd"> mean : float</span> |
| <span class="sd"> mean for the log Normal distribution</span> |
| <span class="sd"> std : float</span> |
| <span class="sd"> std for the log Normal distribution</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> Size of the RDD.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> RDD of float comprised of i.i.d. samples ~ log N(mean, std).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from math import sqrt, exp</span> |
| <span class="sd"> >>> mean = 0.0</span> |
| <span class="sd"> >>> std = 1.0</span> |
| <span class="sd"> >>> expMean = exp(mean + 0.5 * std * std)</span> |
| <span class="sd"> >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))</span> |
| <span class="sd"> >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2)</span> |
| <span class="sd"> >>> stats = x.stats()</span> |
| <span class="sd"> >>> stats.count()</span> |
| <span class="sd"> 1000</span> |
| <span class="sd"> >>> abs(stats.mean() - expMean) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> abs(stats.stdev() - expStd) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"logNormalRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">std</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.poissonRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.poissonRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">poissonRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of i.i.d. samples from the Poisson</span> |
| <span class="sd"> distribution with the input mean.</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> mean : float</span> |
| <span class="sd"> Mean, or lambda, for the Poisson distribution.</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> Size of the RDD.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of float comprised of i.i.d. samples ~ Pois(mean).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mean = 100.0</span> |
| <span class="sd"> >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2)</span> |
| <span class="sd"> >>> stats = x.stats()</span> |
| <span class="sd"> >>> stats.count()</span> |
| <span class="sd"> 1000</span> |
| <span class="sd"> >>> abs(stats.mean() - mean) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> abs(stats.stdev() - sqrt(mean)) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"poissonRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.exponentialRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.exponentialRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">exponentialRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of i.i.d. samples from the Exponential</span> |
| <span class="sd"> distribution with the input mean.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> mean : float</span> |
| <span class="sd"> Mean, or 1 / lambda, for the Exponential distribution.</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> Size of the RDD.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of float comprised of i.i.d. samples ~ Exp(mean).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mean = 2.0</span> |
| <span class="sd"> >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2)</span> |
| <span class="sd"> >>> stats = x.stats()</span> |
| <span class="sd"> >>> stats.count()</span> |
| <span class="sd"> 1000</span> |
| <span class="sd"> >>> abs(stats.mean() - mean) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> abs(stats.stdev() - sqrt(mean)) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"exponentialRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.gammaRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.gammaRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">gammaRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of i.i.d. samples from the Gamma</span> |
| <span class="sd"> distribution with the input shape and scale.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> shape : float</span> |
| <span class="sd"> shape (> 0) parameter for the Gamma distribution</span> |
| <span class="sd"> scale : float</span> |
| <span class="sd"> scale (> 0) parameter for the Gamma distribution</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> Size of the RDD.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of float comprised of i.i.d. samples ~ Gamma(shape, scale).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> shape = 1.0</span> |
| <span class="sd"> >>> scale = 2.0</span> |
| <span class="sd"> >>> expMean = shape * scale</span> |
| <span class="sd"> >>> expStd = sqrt(shape * scale * scale)</span> |
| <span class="sd"> >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2)</span> |
| <span class="sd"> >>> stats = x.stats()</span> |
| <span class="sd"> >>> stats.count()</span> |
| <span class="sd"> 1000</span> |
| <span class="sd"> >>> abs(stats.mean() - expMean) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> abs(stats.stdev() - expStd) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"gammaRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">scale</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.uniformVectorRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.uniformVectorRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@toArray</span> |
| <span class="k">def</span> <span class="nf">uniformVectorRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of vectors containing i.i.d. samples drawn</span> |
| <span class="sd"> from the uniform distribution U(0.0, 1.0).</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> numRows : int</span> |
| <span class="sd"> Number of Vectors in the RDD.</span> |
| <span class="sd"> numCols : int</span> |
| <span class="sd"> Number of elements in each Vector.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD.</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Seed for the RNG that generates the seed for the generator in each partition.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of Vector with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> mat = np.matrix(RandomRDDs.uniformVectorRDD(sc, 10, 10).collect())</span> |
| <span class="sd"> >>> mat.shape</span> |
| <span class="sd"> (10, 10)</span> |
| <span class="sd"> >>> mat.max() <= 1.0 and mat.min() >= 0.0</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> RandomRDDs.uniformVectorRDD(sc, 10, 10, 4).getNumPartitions()</span> |
| <span class="sd"> 4</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"uniformVectorRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.normalVectorRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.normalVectorRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@toArray</span> |
| <span class="k">def</span> <span class="nf">normalVectorRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of vectors containing i.i.d. samples drawn</span> |
| <span class="sd"> from the standard normal distribution.</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> numRows : int</span> |
| <span class="sd"> Number of Vectors in the RDD.</span> |
| <span class="sd"> numCols : int</span> |
| <span class="sd"> Number of elements in each Vector.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1).collect())</span> |
| <span class="sd"> >>> mat.shape</span> |
| <span class="sd"> (100, 100)</span> |
| <span class="sd"> >>> abs(mat.mean() - 0.0) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> abs(mat.std() - 1.0) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"normalVectorRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.logNormalVectorRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.logNormalVectorRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@toArray</span> |
| <span class="k">def</span> <span class="nf">logNormalVectorRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of vectors containing i.i.d. samples drawn</span> |
| <span class="sd"> from the log normal distribution.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> mean : float</span> |
| <span class="sd"> Mean of the log normal distribution</span> |
| <span class="sd"> std : float</span> |
| <span class="sd"> Standard Deviation of the log normal distribution</span> |
| <span class="sd"> numRows : int</span> |
| <span class="sd"> Number of Vectors in the RDD.</span> |
| <span class="sd"> numCols : int</span> |
| <span class="sd"> Number of elements in each Vector.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of Vector with vectors containing i.i.d. samples ~ log `N(mean, std)`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> from math import sqrt, exp</span> |
| <span class="sd"> >>> mean = 0.0</span> |
| <span class="sd"> >>> std = 1.0</span> |
| <span class="sd"> >>> expMean = exp(mean + 0.5 * std * std)</span> |
| <span class="sd"> >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std))</span> |
| <span class="sd"> >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect()</span> |
| <span class="sd"> >>> mat = np.matrix(m)</span> |
| <span class="sd"> >>> mat.shape</span> |
| <span class="sd"> (100, 100)</span> |
| <span class="sd"> >>> abs(mat.mean() - expMean) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> abs(mat.std() - expStd) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"logNormalVectorRDD"</span><span class="p">,</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">std</span><span class="p">),</span> |
| <span class="n">numRows</span><span class="p">,</span> |
| <span class="n">numCols</span><span class="p">,</span> |
| <span class="n">numPartitions</span><span class="p">,</span> |
| <span class="n">seed</span><span class="p">,</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.poissonVectorRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.poissonVectorRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@toArray</span> |
| <span class="k">def</span> <span class="nf">poissonVectorRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of vectors containing i.i.d. samples drawn</span> |
| <span class="sd"> from the Poisson distribution with the input mean.</span> |
| |
| <span class="sd"> .. versionadded:: 1.1.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> mean : float</span> |
| <span class="sd"> Mean, or lambda, for the Poisson distribution.</span> |
| <span class="sd"> numRows : float</span> |
| <span class="sd"> Number of Vectors in the RDD.</span> |
| <span class="sd"> numCols : int</span> |
| <span class="sd"> Number of elements in each Vector.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`)</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of Vector with vectors containing i.i.d. samples ~ Pois(mean).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> mean = 100.0</span> |
| <span class="sd"> >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1)</span> |
| <span class="sd"> >>> mat = np.mat(rdd.collect())</span> |
| <span class="sd"> >>> mat.shape</span> |
| <span class="sd"> (100, 100)</span> |
| <span class="sd"> >>> abs(mat.mean() - mean) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> abs(mat.std() - sqrt(mean)) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"poissonVectorRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.exponentialVectorRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.exponentialVectorRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@toArray</span> |
| <span class="k">def</span> <span class="nf">exponentialVectorRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of vectors containing i.i.d. samples drawn</span> |
| <span class="sd"> from the Exponential distribution with the input mean.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> mean : float</span> |
| <span class="sd"> Mean, or 1 / lambda, for the Exponential distribution.</span> |
| <span class="sd"> numRows : int</span> |
| <span class="sd"> Number of Vectors in the RDD.</span> |
| <span class="sd"> numCols : int</span> |
| <span class="sd"> Number of elements in each Vector.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`)</span> |
| <span class="sd"> seed : int, optional</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of Vector with vectors containing i.i.d. samples ~ Exp(mean).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> mean = 0.5</span> |
| <span class="sd"> >>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1)</span> |
| <span class="sd"> >>> mat = np.mat(rdd.collect())</span> |
| <span class="sd"> >>> mat.shape</span> |
| <span class="sd"> (100, 100)</span> |
| <span class="sd"> >>> abs(mat.mean() - mean) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> abs(mat.std() - sqrt(mean)) < 0.5</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"exponentialVectorRDD"</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">mean</span><span class="p">),</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">,</span> <span class="n">seed</span> |
| <span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="RandomRDDs.gammaVectorRDD"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.random.RandomRDDs.html#pyspark.mllib.random.RandomRDDs.gammaVectorRDD">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@toArray</span> |
| <span class="k">def</span> <span class="nf">gammaVectorRDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">numRows</span><span class="p">,</span> <span class="n">numCols</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Generates an RDD comprised of vectors containing i.i.d. samples drawn</span> |
| <span class="sd"> from the Gamma distribution.</span> |
| |
| <span class="sd"> .. versionadded:: 1.3.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sc : :py:class:`pyspark.SparkContext`</span> |
| <span class="sd"> SparkContext used to create the RDD.</span> |
| <span class="sd"> shape : float</span> |
| <span class="sd"> Shape (> 0) of the Gamma distribution</span> |
| <span class="sd"> scale : float</span> |
| <span class="sd"> Scale (> 0) of the Gamma distribution</span> |
| <span class="sd"> numRows : int</span> |
| <span class="sd"> Number of Vectors in the RDD.</span> |
| <span class="sd"> numCols : int</span> |
| <span class="sd"> Number of elements in each Vector.</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> Number of partitions in the RDD (default: `sc.defaultParallelism`).</span> |
| <span class="sd"> seed : int, optional,</span> |
| <span class="sd"> Random seed (default: a random long integer).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :py:class:`pyspark.RDD`</span> |
| <span class="sd"> RDD of Vector with vectors containing i.i.d. samples ~ Gamma(shape, scale).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> from math import sqrt</span> |
| <span class="sd"> >>> shape = 1.0</span> |
| <span class="sd"> >>> scale = 2.0</span> |
| <span class="sd"> >>> expMean = shape * scale</span> |
| <span class="sd"> >>> expStd = sqrt(shape * scale * scale)</span> |
| <span class="sd"> >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect())</span> |
| <span class="sd"> >>> mat.shape</span> |
| <span class="sd"> (100, 100)</span> |
| <span class="sd"> >>> abs(mat.mean() - expMean) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> abs(mat.std() - expStd) < 0.1</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"gammaVectorRDD"</span><span class="p">,</span> |
| <span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="p">,</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> |
| <span class="nb">float</span><span class="p">(</span><span class="n">scale</span><span class="p">),</span> |
| <span class="n">numRows</span><span class="p">,</span> |
| <span class="n">numCols</span><span class="p">,</span> |
| <span class="n">numPartitions</span><span class="p">,</span> |
| <span class="n">seed</span><span class="p">,</span> |
| <span class="p">)</span></div></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_test</span><span class="p">():</span> |
| <span class="kn">import</span> <span class="nn">doctest</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| |
| <span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="c1"># The small batch size here ensures that we see multiple batches,</span> |
| <span class="c1"># even in these small test examples:</span> |
| <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">"local[2]"</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">"mllib.random tests"</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"sc"</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span> |
| <span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span> |
| <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| |
| <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span> |
| <span class="n">_test</span><span class="p">()</span> |
| </pre></div> |
| |
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