@@ -0,0 +1,1464 @@
+package edu.neu.ccs.pyramid.classification.logistic_regression;%0A%0Aimport edu.neu.ccs.pyramid.configuration.Config;%0Aimport edu.neu.ccs.pyramid.dataset.ClfDataSet;%0Aimport edu.neu.ccs.pyramid.dataset.DataSetType;%0Aimport edu.neu.ccs.pyramid.dataset.TRECFormat;%0Aimport edu.neu.ccs.pyramid.eval.Accuracy;%0Aimport edu.neu.ccs.pyramid.optimization.GradientDescent;%0A%0Aimport java.io.File;%0A%0Aimport static org.junit.Assert.*;%0A%0Apublic class LogisticRegressionTest %7B%0A    private static final Config config = new Config(%22configs/local.config%22);%0A    private static final String DATASETS = config.getString(%22input.datasets%22);%0A    private static final String TMP = config.getString(%22output.tmp%22);%0A%0A    public static void main(String%5B%5D args) throws Exception%7B%0A        test1();%0A%0A    %7D%0A%0A    private static void test1() throws Exception%7B%0A%0A        ClfDataSet dataSet = TRECFormat.loadClfDataSet(new File(DATASETS, %22/imdb/train.trec%22),%0A                DataSetType.CLF_SPARSE, true);%0A        System.out.println(dataSet.getMetaInfo());%0A%0A        LogisticRegression logisticRegression = new LogisticRegression(dataSet.getNumClasses(),dataSet.getNumFeatures());%0A        LogisticLoss function = new LogisticLoss(logisticRegression,dataSet,1000);%0A        GradientDescent gradientDescent = new GradientDescent(function,0.00001);%0A        for (int i=0;i%3C500;i++)%7B%0A            gradientDescent.update();%0A            System.out.println(Accuracy.accuracy(logisticRegression,dataSet));%0A        %7D%0A%0A    %7D%0A%0A%7D

add test