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])):