def f(TrainIn, TrainOut, TestIn): print "init......" x = numpy.array(TrainIn) y = numpy.array(TrainOut) t = numpy.array(TestIn) print "learn......" ldac = mlpy.LDAC() ldac.learn(x, y) print "out......" re = ldac.pred(t) return re
def metric(self): totalTimer = Timer() with totalTimer: model = mlpy.LDAC() model.learn(self.data_split[0], self.data_split[1]) if len(self.data) >= 2: predictions = model.pred(self.data[1]) metric = {} metric["runtime"] = totalTimer.ElapsedTime() if len(self.data) == 3: confusionMatrix = Metrics.ConfusionMatrix(self.data[2], predictions) metric['ACC'] = Metrics.AverageAccuracy(confusionMatrix) metric['MCC'] = Metrics.MCCMultiClass(confusionMatrix) metric['Precision'] = Metrics.AvgPrecision(confusionMatrix) metric['Recall'] = Metrics.AvgRecall(confusionMatrix) metric['MSE'] = Metrics.SimpleMeanSquaredError( self.data[2], predictions) return metric
def BuildModel(self, data, labels): # Create and train the classifier. lda = mlpy.LDAC() lda.learn(data, labels) return lda
'knn': [], 'tree': [] } for i in xrange(1): train, control = split_samples(d.alist) print 'training sample: ', len(train) x, y, fnames = prepareData(train) print 'control sample: ', len(control) xcontrol, ycontrol, fnames = prepareData(control) print '\ntest algorithms:' ld = mlpy.LDAC() ld.learn(x, y) test = ld.pred(xcontrol) # test points print 'LDAC: %.1f percent predicted' % (100 * len(test[test == ycontrol]) / float(len(test))) dic['ld'].append(100 * len(test[test == ycontrol]) / float(len(test))) perc = mlpy.Perceptron() perc.learn(x, y) test = perc.pred(xcontrol) # test points print 'Perceptron: %.1f percent predicted' % ( 100 * len(test[test == ycontrol]) / len(test)) dic['perc'].append(100 * len(test[test == ycontrol]) / len(test)) elnet = mlpy.ElasticNetC(lmb=0.01, eps=0.001) elnet.learn(x, y)
sys.exit(1) for d in range(len(datasets)): for k in range(K_FOLD_NUMBER): basic = BasicClassifiers(debug=DEBUG) filename = 'datasets/%s' % datasets[d][0] if basic.read_data(filepath=filename, label_is_last=(bool)(datasets[d][1])) == False: print "Error with opening the file. Probably you have given wrong path" sys.exit(1) basic.prepare_data(k_fold_number=K_FOLD_NUMBER) basic.k_fold_cross_validation(k=k) size = len(basic.testing_label) ldac = mlpy.LDAC() ldac.learn(basic.training_data, basic.training_label) classified = 0 for i in range(len(basic.testing_label)): if (int)(basic.testing_label[i]) == (int)(ldac.pred( basic.testing_data[i])): classified += 1 fd.write("%s,%s,%d,%d,%d\n" % (datasets[d][0], "LDAC", k, size, classified)) knn = mlpy.KNN(k=3) knn.learn(basic.training_data, basic.training_label) classified = 0 for i in range(len(basic.testing_label)): if (int)(basic.testing_label[i]) == (int)(knn.pred( basic.testing_data[i])):