function SGD() { this._maxWinnings = 0; return this; } Object.defineProperty(SGD, 'maxWinnings', { get : function () { return this._maxWinnings; }, set : function (val) { this._maxWinnings = val; } }); var sgd = new SGD(); sgd.maxWinnings = 100; alert(sgd.maxWinnings.toString());
/** * Constructor */ public MultiClassClassifierUpdateable() { m_Classifier = new weka.classifiers.functions.SGD(); }
/** * Constructor */ public MultiClassClassifierUpdateable() { m_Classifier = new weka.classifiers.functions.SGD(); }
/** * Main method for testing this class. */ public static void main(String[] args) { runClassifier(new SGD(), args); } }
/** * Main method for testing this class. */ public static void main(String[] args) { runClassifier(new SGD(), args); } }
/** Creates a default MultiClassClassifierUpdateable */ public Classifier getClassifier() { MultiClassClassifierUpdateable m = new MultiClassClassifierUpdateable(); m.setClassifier(new weka.classifiers.functions.SGD()); return m; }
protected void initializeSVMProbs(Instances data) throws Exception { m_svmProbs = new SGD(); m_svmProbs.setLossFunction(new SelectedTag(SGD.LOGLOSS, TAGS_SELECTION)); m_svmProbs.setLearningRate(m_learningRate); m_svmProbs.setLambda(m_lambda); m_svmProbs.setEpochs(m_epochs); ArrayList<Attribute> atts = new ArrayList<Attribute>(2); atts.add(new Attribute("pred")); ArrayList<String> attVals = new ArrayList<String>(2); attVals.add(data.classAttribute().value(0)); attVals.add(data.classAttribute().value(1)); atts.add(new Attribute("class", attVals)); m_fitLogisticStructure = new Instances("data", atts, 0); m_fitLogisticStructure.setClassIndex(1); m_svmProbs.buildClassifier(m_fitLogisticStructure); }
/** Creates a default MultiClassClassifierUpdateable */ public Classifier getClassifier() { MultiClassClassifierUpdateable m = new MultiClassClassifierUpdateable(); m.setClassifier(new weka.classifiers.functions.SGD()); return m; }
protected void initializeSVMProbs(Instances data) throws Exception { m_svmProbs = new SGD(); m_svmProbs.setLossFunction(new SelectedTag(SGD.LOGLOSS, TAGS_SELECTION)); m_svmProbs.setLearningRate(m_learningRate); m_svmProbs.setLambda(m_lambda); m_svmProbs.setEpochs(m_epochs); ArrayList<Attribute> atts = new ArrayList<Attribute>(2); atts.add(new Attribute("pred")); ArrayList<String> attVals = new ArrayList<String>(2); attVals.add(data.classAttribute().value(0)); attVals.add(data.classAttribute().value(1)); atts.add(new Attribute("class", attVals)); m_fitLogisticStructure = new Instances("data", atts, 0); m_fitLogisticStructure.setClassIndex(1); m_svmProbs.buildClassifier(m_fitLogisticStructure); }
protected WekaClassifierMapTask setupIncrementalRegressor() { WekaClassifierMapTask task = new WekaClassifierMapTask(); weka.classifiers.functions.SGD sgd = new weka.classifiers.functions.SGD(); try { sgd.setOptions(Utils.splitOptions("-F 2")); task.setClassifier(sgd); } catch (Exception e) { e.printStackTrace(); } return task; }
/** Creates a default SGD */ public Classifier getClassifier() { SGD p = new SGD(); p.setDontNormalize(true); p.setDontReplaceMissing(true); p.setEpochs(1); p.setLearningRate(0.001); return p; }
/** Creates a default SGD */ public Classifier getClassifier() { SGD p = new SGD(); p.setDontNormalize(true); p.setDontReplaceMissing(true); p.setEpochs(1); p.setLearningRate(0.001); return p; }