def generateLearningCurve(X, y, degree, regLambda):
'''
computing learning curve via leave one out CV
'''
n = len(X)
errorTrains = np.zeros((n, n - 1))
errorTests = np.zeros((n, n - 1))
loo = LeaveOneOut()
itrial = 0
for train_index, test_index in loo.split(X):
#print("TRAIN indices:", train_index, "TEST indices:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
(errTrain, errTest) = learningCurve(X_train, y_train, X_test, y_test,
regLambda, degree)
errorTrains[itrial, :] = errTrain
errorTests[itrial, :] = errTest
itrial = itrial + 1
errorTrain = errorTrains.mean(axis=0)
errorTest = errorTests.mean(axis=0)
plotLearningCurve(errorTrain, errorTest, regLambda, degree)
def generateLearningCurve(X, y, degree, regLambda):
'''
computing learning curve via leave one out CV
'''
n = len(X);
errorTrains = np.zeros((n, n-1));
errorTests = np.zeros((n, n-1));
loo = cross_validation.LeaveOneOut(n)
itrial = 0
for train_index, test_index in loo:
#print("TRAIN indices:", train_index, "TEST indices:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
(errTrain, errTest) = learningCurve(X_train, y_train, X_test, y_test, regLambda, degree)
errorTrains[itrial, :] = errTrain
errorTests[itrial, :] = errTest
itrial = itrial + 1
errorTrain = errorTrains.mean(axis = 0)
errorTest = errorTests.mean(axis = 0)
plotLearningCurve(errorTrain, errorTest, regLambda, degree)