def createMLPsP(self, H1, H2, nu, batchsize, k): for j in range(4,8) : data = Data(k, 0, 0) data.importDataFromMat() data.normalize() train = TrainerValidator(k, 50, H1, H2, nu, j/10.0, batchsize, data) train.trainAndClassify() train.plotResults()
def createMLPsH(self, H1, nu, mu, batchsize, k): for j in range(10) : data = Data(k, 0, 0) data.importDataFromMat() data.normalize() train = TrainerValidator(k, 5, H1, (j+1)*10, nu, mu, batchsize, data) train.trainAndClassify() train.plotResults()
def testBinary(): k = 2 data = Data(k, 0, 0) data.importDataFromMat() data.normalize() train = TrainerValidator(k, 70, 100, 10, 0.1, 0.2, 1, data) train.trainAndClassify() train.plotResults() test = Test(train.getMLP(), data, k) test.classify() test.examples() test.plot_confusion_matrix()