def crossvalidation(self, fold, v, k):
error = 0
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
n = data.train_cat.shape[1]
all_indices = sp.arange(n)
data.shuffleData()
sq = SquaredErrorLinearClassifier(v,k)
dataset_indices = sp.split(all_indices, fold)
for i in range(fold):
set_without_D_i_indices = sp.concatenate(dataset_indices[0:i]+dataset_indices[i+1:fold])
#print "-"*30+"train"+"-"*30
sq.train(data.train_left[:,set_without_D_i_indices], data.train_right[:,set_without_D_i_indices], data.train_cat[:,set_without_D_i_indices])
#print "-"*30+"classify"+"-"*30
results, classes = sq.classify(data.train_left[:,dataset_indices[i]], data.train_right[:,dataset_indices[i]])
#print "-"*30+"error"+"-"*30
err, _ = sq.error(results, data.train_cat[:,dataset_indices[i]].T)
error += fold/(n*1.0)*err
error = error / fold
with open("results/crossvalidation.txt", "a") as myfile:
toWrite = "v="+str(v)+" error="+str(error)
myfile.write(toWrite)
def findNuMuLinearClass(self, batchsize, k):
k=5
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
error_fig = plt.figure()
ax1 = error_fig.add_subplot(111)
for nu in [0.05, 0.1] :
for mu in [0.05, 0.1] :
lg = LogisticLinearClassifier(nu, mu, 576, k, data)
err_train, miss_train, err_val, miss_val = lg.train(30)
label1 = "validation error mu="+str(mu)+" nu="+str(nu)
label2 = "training error mu="+str(mu)+" nu="+str(nu)
ax1.plot(err_train, label=label1)
ax1.plot(err_val, label=label2)
ax1.set_ylabel('error')
ax1.set_xlabel('epoch')
title = "Validation and training errors"
error_fig.suptitle(title)
plt.legend()
def testLogisticError():
k = 5
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
lg = LogisticLinearClassifier(0.03, 0.03, 576, k, data)
err_train, miss_train, err_val, miss_val = lg.train(30)
mis_fig = plt.figure()
ax2 = mis_fig.add_subplot(111)
ax2.plot(err_val, label="error (validation)")
ax2.plot(err_train, label="error (training)")
title = "std(val)=%f std(err)=%f" % (sp.std(err_val), sp.std(err_train))
mis_fig.suptitle(title)
ax2.set_ylabel("error")
ax2.set_xlabel("epoch")
plt.legend()
mis_fig = plt.figure()
ax2 = mis_fig.add_subplot(111)
ax2.plot(miss_val, label="misclassification ratio (validation)")
ax2.plot(miss_train, label="misclassification ratio (training)")
mis_fig.suptitle(title)
ax2.set_ylabel("misclassification ratio")
ax2.set_xlabel("epoch")
plt.legend()
results, cat = lg.classify(data.test_left, data.test_right)
lg.confusion_matrix(cat, data.test_cat.argmax(axis=0))
err = Error()
err, misclass = err.norm_total_error(results.T, data.test_cat, k)
print "Error on the test set " + str(err)
print "Misclassification ratio on the test set " + str(misclass)
def compareParameters():
k = 5
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
train = TrainerValidator(k, 40, 80, 60, 0.001, 0.1, 1, data)
train.trainAndClassify()
train2 = TrainerValidator(k, 40, 80, 60, 0.04, 0.1, 1, data)
train2.trainAndClassify()
train3 = TrainerValidator(k, 40, 80, 60, 0.1, 0.1, 1, data)
train3.trainAndClassify()
error_fig = plt.figure()
ax1 = error_fig.add_subplot(111)
ax1.plot(train.validation_error, label="validation error mu=0.1 nu=0.001")
ax1.plot(train.training_error, label="training error mu=0.1 nu=0.001")
ax1.plot(train2.validation_error, label="validation error mu=0.1 nu=0.04")
ax1.plot(train2.training_error, label="training error mu=0.1 nu=0.04")
ax1.plot(train3.validation_error, label="validation error mu=0.1 nu=0.1")
ax1.plot(train3.training_error, label="training error mu=0.1 nu=0.1")
ax1.set_ylabel("error")
ax1.set_xlabel("epoch")
title = "Validation and training errors k=5 H1=80 H2=60 batchsize=1"
error_fig.suptitle(title)
plt.legend()
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 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 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()
def testSquaredError():
k = 5
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
sq = SquaredErrorLinearClassifier(2 ** 10, k)
sq.train(data.train_left, data.train_right, data.train_cat)
results, cat = sq.classify(data.test_left, data.test_right)
sq.confusion_matrix(cat, data.test_cat.argmax(axis=0))
err = Error()
err, misclass = err.norm_total_error(results.T, data.test_cat, k)
print "Error on the test set " + str(err)
print "Misclassification ratio on the test set " + str(misclass)