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)
