C = 0.5 classifier = trainLogisticRegressionModel( trainingData[0][subset,:], trainingData[1][subset], C, classifierBaseFilename, \ scaleData=True, \ requireAllClasses=False ) elif 0: # Neural Network # Construct nn dataset datmat = trainingData[0][subset, :] labvec = trainingData[1][subset] nbFeatures = datmat.shape[1] nbClasses = pomio.getNumClasses() nbHidden = 100 maxIter = 200 classifier = NeuralNet.NNet(nbFeatures, nbClasses, nbHidden) nnds = classifier.createTrainingSetFromMatrix(datmat, labvec) classifier.trainNetworkBackprop(nnds, maxIter) else: #classifier = None # Random forest datmat = trainingData[0][subset, :] labvec = trainingData[1][subset] print '**Training a random forest on %d examples...' % len(labvec) print 'Labels represented: ', np.unique(labvec) classifier = sklearn.ensemble.RandomForestClassifier(\ n_estimators=100) classifier = classifier.fit(datmat, labvec) pickleObject(classifier, classifierBaseFilename) print "Rand forest classifier saved to " + str( classifierBaseFilename)