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class="sig-prename descclassname">pyspark.mllib.clustering。<gydF4y2Ba/code> <code class="sig-name descname">GaussianMixtureModel<gydF4y2Ba/code> <span class="sig-paren">(<gydF4y2Ba/span> <em class="sig-param"><span class="n">java_model<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">py4j.java_gateway.JavaObject<gydF4y2Ba/span></em> <span class="sig-paren">)<gydF4y2Ba/span> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>集群模型來自高斯混合模型方法。<gydF4y2Ba/p> <p class="rubric">例子<gydF4y2Ba/p> <div class="doctest highlight-default notranslate"> <div class="highlight"> <pre><span></span><span class="gp">> > ><gydF4y2Ba/span><span class="kn">從<gydF4y2Ba/span><span class="nn">pyspark.mllib.linalg<gydF4y2Ba/span><span class="kn">進口<gydF4y2Ba/span><span class="n">向量<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">DenseMatrix<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="kn">從<gydF4y2Ba/span><span class="nn">numpy.testing<gydF4y2Ba/span><span class="kn">進口<gydF4y2Ba/span><span class="n">assert_equal<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="kn">從<gydF4y2Ba/span><span class="nn">shutil<gydF4y2Ba/span><span class="kn">進口<gydF4y2Ba/span><span class="n">rmtree<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="kn">進口<gydF4y2Ba/span><span class="nn">操作係統<gydF4y2Ba/span><span class="o">,<gydF4y2Ba/span><span class="nn">tempfile<gydF4y2Ba/span></pre> </div> </div> <div class="doctest highlight-default notranslate"> <div class="highlight"> <pre><span></span><span class="gp">> > ><gydF4y2Ba/span><span class="n">clusterdata_1<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">sc<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">並行化<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">數組<gydF4y2Ba/span><span class="p">([<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.1<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.05<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.01<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.1<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="mf">0.9<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">0.8<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">0.75<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">0.935<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.83<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.68<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.91<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.76<gydF4y2Ba/span><span class="p">])<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">重塑<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">6<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mi">2<gydF4y2Ba/span><span class="p">),<gydF4y2Ba/span><span class="mi">2<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">GaussianMixture<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">火車<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">clusterdata_1<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mi">3<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">convergenceTol<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="mf">0.0001<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="n">maxIterations<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="mi">50<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">種子<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="mi">10<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">預測<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">clusterdata_1<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">收集<gydF4y2Ba/span><span class="p">()<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">0<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">1<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="go">假<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">1<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">2<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="go">假<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">4<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">5<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">預測<gydF4y2Ba/span><span class="p">([<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.1<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.05<gydF4y2Ba/span><span class="p">])<gydF4y2Ba/span><span class="go">0<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">softPredicted<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">predictSoft<gydF4y2Ba/span><span class="p">([<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.1<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.05<gydF4y2Ba/span><span class="p">])<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="nb">腹肌<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">softPredicted<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">0<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">1.0<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="o"><<gydF4y2Ba/span><span class="mf">0.03<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="nb">腹肌<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">softPredicted<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">1<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.0<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="o"><<gydF4y2Ba/span><span class="mf">0.03<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="nb">腹肌<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">softPredicted<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">2<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">0.0<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="o"><<gydF4y2Ba/span><span class="mf">0.03<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span></pre> </div> </div> <div class="doctest highlight-default notranslate"> <div class="highlight"> <pre><span></span><span class="gp">> > ><gydF4y2Ba/span><span class="n">路徑<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">tempfile<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">mkdtemp<gydF4y2Ba/span><span class="p">()<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">保存<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">sc<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">路徑<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">sameModel<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">GaussianMixtureModel<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">負載<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">sc<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">路徑<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">assert_equal<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">權重<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">sameModel<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">權重<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">畝<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">(開關)<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="nb">列表<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="nb">郵政編碼<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="o">*<gydF4y2Ba/span><span class="p">((<gydF4y2Ba/span><span class="n">g<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">μ<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">g<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">σ<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="k">為<gydF4y2Ba/span><span class="n">g<gydF4y2Ba/span><span class="ow">在<gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">高斯函數<gydF4y2Ba/span><span class="p">)))<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">sameMus<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">sameSigmas<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="nb">列表<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="nb">郵政編碼<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="o">*<gydF4y2Ba/span><span class="p">((<gydF4y2Ba/span><span class="n">g<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">μ<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">g<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">σ<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="k">為<gydF4y2Ba/span><span class="n">g<gydF4y2Ba/span><span class="ow">在<gydF4y2Ba/span><span class="n">sameModel<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">高斯函數<gydF4y2Ba/span><span class="p">)))<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">畝<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">sameMus<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">(開關)<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">sameSigmas<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="kn">從<gydF4y2Ba/span><span class="nn">shutil<gydF4y2Ba/span><span class="kn">進口<gydF4y2Ba/span><span class="n">rmtree<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="k">試一試<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="n">rmtree<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">路徑<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="k">除了<gydF4y2Ba/span><span class="ne">OSError<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="k">通過<gydF4y2Ba/span></pre> </div> </div> <div class="doctest highlight-default notranslate"> <div class="highlight"> <pre><span></span><span class="gp">> > ><gydF4y2Ba/span><span class="n">數據<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">數組<gydF4y2Ba/span><span class="p">([<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">5.1971<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">2.5359<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">3.8220<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">5.2211<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="o">- - - - - -<gydF4y2Ba/span><span class="mf">5.0602<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">4.7118<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="mf">6.8989<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">3.4592<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">4.6322<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="mf">5.7048<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">4.6567<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">5.5026<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="mf">4.5605<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">5.2043<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mf">6.2734<gydF4y2Ba/span><span class="p">])<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">clusterdata_2<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">sc<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">並行化<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">數據<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">重塑<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">5<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mi">3<gydF4y2Ba/span><span class="p">))<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">GaussianMixture<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">火車<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">clusterdata_2<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="mi">2<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">convergenceTol<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="mf">0.0001<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="gp">…<gydF4y2Ba/span><span class="n">maxIterations<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="mi">150年<gydF4y2Ba/span><span class="p">,<gydF4y2Ba/span><span class="n">種子<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="mi">4<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="o">=<gydF4y2Ba/span><span class="n">模型<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">預測<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="n">clusterdata_2<gydF4y2Ba/span><span class="p">)<gydF4y2Ba/span><span class="o">。<gydF4y2Ba/span><span class="n">收集<gydF4y2Ba/span><span class="p">()<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">0<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">1<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span><span class="gp">> > ><gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">2<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">3<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="o">= =<gydF4y2Ba/span><span class="n">標簽<gydF4y2Ba/span><span class="p">(<gydF4y2Ba/span><span class="mi">4<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span><span class="go">真正的<gydF4y2Ba/span></pre> </div> </div> <p class="rubric">方法<gydF4y2Ba/p> <table class="longtable table autosummary"> <colgroup> <col style="width: 10%"> <col style="width: 90%"> </colgroup> <tbody> <tr class="row-odd"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.call" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">調用<gydF4y2Ba/span></code></a>(名字,*一個)<gydF4y2Ba/p></td> <td><p>調用的方法java_model<gydF4y2Ba/p></td> </tr> <tr class="row-even"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.load" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">負載<gydF4y2Ba/span></code></a>(sc路徑)<gydF4y2Ba/p></td> <td><p>從磁盤加載GaussianMixtureModel。<gydF4y2Ba/p></td> </tr> <tr class="row-odd"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.predict" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">預測<gydF4y2Ba/span></code></a>(x)<gydF4y2Ba/p></td> <td><p>找到的集群每個抽樣點上點“x”或“x”在這個模型最大成員。<gydF4y2Ba/p></td> </tr> <tr class="row-even"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.predictSoft" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictSoft<gydF4y2Ba/span></code></a>(x)<gydF4y2Ba/p></td> <td><p>找到點“x”的會員或每個抽樣點上“x”所有混合組件。<gydF4y2Ba/p></td> </tr> <tr class="row-odd"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.save" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">保存<gydF4y2Ba/span></code></a>(sc路徑)<gydF4y2Ba/p></td> <td><p>這個模型保存到給定的路徑。<gydF4y2Ba/p></td> </tr> </tbody> </table> <p class="rubric">屬性<gydF4y2Ba/p> <table class="longtable table autosummary"> <colgroup> <col style="width: 10%"> <col style="width: 90%"> </colgroup> <tbody> <tr class="row-odd"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.gaussians" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">高斯函數<gydF4y2Ba/span></code></a></p></td> <td><p>數組的MultivariateGaussian高斯函數[我]代表多元高斯分布為高斯(正常)。<gydF4y2Ba/p></td> </tr> <tr class="row-even"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.k" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">k<gydF4y2Ba/span></code></a></p></td> <td><p>混合高斯模型的數量。<gydF4y2Ba/p></td> </tr> <tr class="row-odd"> <td><p><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.weights" title=""><code class="xref py py-obj docutils literal notranslate"><span class="pre">權重<gydF4y2Ba/span></code></a></p></td> <td><p>混合物中的每個高斯分布權重,權重[我]是高斯的重量,我和權重。和= = 1。<gydF4y2Ba/p></td> </tr> </tbody> </table> <p class="rubric">方法的文檔<gydF4y2Ba/p> <dl class="py method"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.call"> <code class="sig-name descname">調用<gydF4y2Ba/code> <span class="sig-paren">(<gydF4y2Ba/span> <em class="sig-param"><span class="n">的名字<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">str<gydF4y2Ba/span></em>,<gydF4y2Baem class="sig-param"><span class="o">*<gydF4y2Ba/span><span class="n">一個<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">任何<gydF4y2Ba/span></em> <span class="sig-paren">)<gydF4y2Ba/span>→任何<一個類="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.call" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>調用的方法java_model<gydF4y2Ba/p> </dd> </dl> <dl class="py method"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.load"> <em class="property">classmethod<gydF4y2Ba/em> <code class="sig-name descname">負載<gydF4y2Ba/code> <span class="sig-paren">(<gydF4y2Ba/span> <em class="sig-param"><span class="n">sc<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">pyspark.context.SparkContext<gydF4y2Ba/span></em>,<gydF4y2Baem class="sig-param"><span class="n">路徑<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">str<gydF4y2Ba/span></em> <span class="sig-paren">)<gydF4y2Ba/span>→<一個類="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel" title="pyspark.mllib.clustering.GaussianMixtureModelgydF4y2Ba">pyspark.mllib.clustering.GaussianMixtureModel<gydF4y2Ba/a> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.load" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>從磁盤加載GaussianMixtureModel。<gydF4y2Ba/p> <dl class="field-list"> <dt class="field-odd"> 參數<gydF4y2Ba/dt> <dd class="field-odd"> <dl> <dt> <strong>sc<gydF4y2Ba/strong> <span class="classifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparkContext<gydF4y2Ba/span></code></span> </dt> <dd></dd> <dt> <strong>路徑<gydF4y2Ba/strong> <span class="classifier">str<gydF4y2Ba/span> </dt> <dd> <p>存儲路徑的模型。<gydF4y2Ba/p> </dd> </dl> </dd> </dl> </dd> </dl> <dl class="py method"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.predict"> <code class="sig-name descname">預測<gydF4y2Ba/code> <span class="sig-paren">(<gydF4y2Ba/span> <em class="sig-param"><span class="n">x<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">聯盟<gydF4y2Baspan class="p">(<gydF4y2Ba/span>VectorLike<gydF4y2Baspan class="p">,<gydF4y2Ba/span>pyspark.rdd.RDD<gydF4y2Baspan class="p">(<gydF4y2Ba/span>VectorLike<gydF4y2Baspan class="p">]<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span></span></em> <span class="sig-paren">)<gydF4y2Ba/span>→聯盟<gydF4y2Baspan class="p">(<gydF4y2Ba/span>numpy.int64<gydF4y2Baspan class="p">,<gydF4y2Ba/span>pyspark.rdd.RDD<gydF4y2Baspan class="p">(<gydF4y2Ba/span>int<gydF4y2Baspan class="p">]<gydF4y2Ba/span> <span class="p">]<gydF4y2Ba/span> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.predict" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>找到的集群每個抽樣點上點“x”或“x”在這個模型最大成員。<gydF4y2Ba/p> <dl class="field-list"> <dt class="field-odd"> 參數<gydF4y2Ba/dt> <dd class="field-odd"> <dl> <dt> <strong>x<gydF4y2Ba/strong> <span class="classifier"><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.mllib.linalg.Vector.html" title=""><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.mllib.linalg.Vector<gydF4y2Ba/span></code></a>或<一個類="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.RDD.html" title=""><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD<gydF4y2Ba/span></code></a></span> </dt> <dd> <p>一個特征向量表示數據點或一個向量的抽樣。<gydF4y2Ba/p> </dd> </dl> </dd> <dt class="field-even"> 返回<gydF4y2Ba/dt> <dd class="field-even"> <dl class="simple"> <dt> numpy。float64或<一個類="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.RDD.html" title=""><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD<gydF4y2Ba/span></code></a>的整數<gydF4y2Ba/dt> <dd> <p>預測集群標簽或預測的抽樣集群標簽如果輸入是一個抽樣。<gydF4y2Ba/p> </dd> </dl> </dd> </dl> </dd> </dl> <dl class="py method"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.predictSoft"> <code class="sig-name descname">predictSoft<gydF4y2Ba/code> <span class="sig-paren">(<gydF4y2Ba/span> <em class="sig-param"><span class="n">x<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">聯盟<gydF4y2Baspan class="p">(<gydF4y2Ba/span>VectorLike<gydF4y2Baspan class="p">,<gydF4y2Ba/span>pyspark.rdd.RDD<gydF4y2Baspan class="p">(<gydF4y2Ba/span>VectorLike<gydF4y2Baspan class="p">]<gydF4y2Ba/span><span class="p">]<gydF4y2Ba/span></span></em> <span class="sig-paren">)<gydF4y2Ba/span>→聯盟<gydF4y2Baspan class="p">(<gydF4y2Ba/span>numpy.ndarray<gydF4y2Baspan class="p">,<gydF4y2Ba/span>pyspark.rdd.RDD<gydF4y2Baspan class="p">(<gydF4y2Ba/span>array.array<gydF4y2Baspan class="p">]<gydF4y2Ba/span> <span class="p">]<gydF4y2Ba/span> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.predictSoft" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>找到點“x”的會員或每個抽樣點上“x”所有混合組件。<gydF4y2Ba/p> <dl class="field-list"> <dt class="field-odd"> 參數<gydF4y2Ba/dt> <dd class="field-odd"> <dl> <dt> <strong>x<gydF4y2Ba/strong> <span class="classifier"><a class="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.mllib.linalg.Vector.html" title=""><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.mllib.linalg.Vector<gydF4y2Ba/span></code></a>或<一個類="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.RDD.html" title=""><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD<gydF4y2Ba/span></code></a></span> </dt> <dd> <p>一個特征向量表示數據點或一個向量的抽樣。<gydF4y2Ba/p> </dd> </dl> </dd> <dt class="field-even"> 返回<gydF4y2Ba/dt> <dd class="field-even"> <dl class="simple"> <dt> numpy.ndarray或<一個類="reference internal" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.RDD.html" title=""><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD<gydF4y2Ba/span></code></a> </dt> <dd> <p>所有混合組件的成員值向量“x”或每個向量在抽樣“x”。<gydF4y2Ba/p> </dd> </dl> </dd> </dl> </dd> </dl> <dl class="py method"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.save"> <code class="sig-name descname">保存<gydF4y2Ba/code> <span class="sig-paren">(<gydF4y2Ba/span> <em class="sig-param"><span class="n">sc<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">pyspark.context.SparkContext<gydF4y2Ba/span></em>,<gydF4y2Baem class="sig-param"><span class="n">路徑<gydF4y2Ba/span><span class="p">:<gydF4y2Ba/span><span class="n">str<gydF4y2Ba/span></em> <span class="sig-paren">)<gydF4y2Ba/span>→沒有<一個類="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.save" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>這個模型保存到給定的路徑。<gydF4y2Ba/p> </dd> </dl> <p class="rubric">屬性的文檔<gydF4y2Ba/p> <dl class="py attribute"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.gaussians"> <code class="sig-name descname">高斯函數<gydF4y2Ba/code> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.gaussians" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>數組的MultivariateGaussian高斯函數[我]代表多元高斯分布為高斯(正常)。<gydF4y2Ba/p> </dd> </dl> <dl class="py attribute"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.k"> <code class="sig-name descname">k<gydF4y2Ba/code> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.k" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>混合高斯模型的數量。<gydF4y2Ba/p> </dd> </dl> <dl class="py attribute"> <dt id="pyspark.mllib.clustering.GaussianMixtureModel.weights"> <code class="sig-name descname">權重<gydF4y2Ba/code> <a class="headerlink" href="//m.eheci.com/api-docs/python/pyspark/latest/api/#pyspark.mllib.clustering.GaussianMixtureModel.weights" title="¶gydF4y2Ba">¶<gydF4y2Ba/a> </dt> <dd> <p>混合物中的每個高斯分布權重,權重[我]是高斯的重量,我和權重。和= = 1。<gydF4y2Ba/p> </dd> </dl> </dd> </dl> </section> </div>  <div class="prev-next-area"> <a class="left-prev" id="prev-link" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.mllib.clustering.KMeans.html" title=""><i class="fas fa-angle-left"></i> <div class="prev-next-info"> <p class="prev-next-subtitle">以前的<gydF4y2Ba/p> <p class="prev-next-title">KMeans<gydF4y2Ba/p> </div></a> <a class="right-next" id="next-link" href="//m.eheci.com/api-docs/python/pyspark/latest/api/pyspark.mllib.clustering.GaussianMixture.html" title=""> <div class="prev-next-info"> <p class="prev-next-subtitle">下一個<gydF4y2Ba/p> <p 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