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)