def compare_across_all_tasks(tasks, subject, position, sessionnum="", sq=""):
print('For subject ' + str(subject) + ':')
for i in range(len(tasks)):
taskX = feature_vector_generator(tasks[i], subject, position, sessionnum, sq)
def f():
return
yield
g = f()
for j in range(i):
# print(tasks[j])
taskA = feature_vector_generator(tasks[j], subject, position, sessionnum, sq)
g = chain(g, taskA)
for k in range(i+1, len(tasks)):
# print(tasks[k])
taskB = feature_vector_generator(tasks[k], subject, position, sessionnum, sq)
g = chain(g, taskB)
X, y = labeler.vectorsAndLabels([taskX, g])
comparison = tasks[i] + " vs. others cross-validation is: " + str(labeler.crossValidate(X, y))
print(comparison)
def make_svm_classifier(tasks, subject, position, sessionnum="", sq=""):
for i in range(len(tasks)):
print('For subject ' + str(subject) + ', task ' + str(tasks[i]) + ':')
taskX = feature_vector_generator(tasks[i], subject, position, sessionnum, sq)
def f():
return
yield
g = f()
for j in range(i):
# print(tasks[j])
taskA = feature_vector_generator(tasks[j], subject, position, sessionnum, sq)
g = chain(g, taskA)
for k in range(i+1, len(tasks)):
# print(tasks[k])
taskB = feature_vector_generator(tasks[k], subject, position, sessionnum, sq)
g = chain(g, taskB)
X, y = labeler.vectorsAndLabels([taskX, g])
'''Toggle the following for learner function or cross-validation.'''
# lin_clf = LinearSVC()
# target = lin_clf.fit(X, y)
# return target
comparison = tasks[i] + " vs. others cross-validation is: " + str(labeler.crossValidate(X, y))
print(comparison)
def compare_by_subject(tasks, subjects, position, sessionnum=""):
for i in range(len(tasks)):
print('For task ' + tasks[i] + ', position ' + str(position) + ':')
for j in range(len(subjects)):
for k in range(j+1, len(subjects)):
subA = feature_vector_generator(tasks[i], subjects[j], position, sessionnum)
subB = feature_vector_generator(tasks[i], subjects[k], position, sessionnum)
X, y = labeler.vectorsAndLabels([subA, subB])
comparison = 'subject' + str(subjects[j]) + " vs. subject" + str(subjects[k]) + " cross-validation is: " + str(labeler.crossValidate(X, y))
print(comparison)
def compare_by_task(tasks, subject1, position, sessionnum="", sq=""):
print('For subject ' + str(subject1) + ', position ' + str(position) + ':')
for i in range(len(tasks)):
for j in range(i+1, len(tasks)):
for k in range(j+1, len(tasks)):
for l in range(k+1, len(tasks)):
taskA = feature_vector_generator(tasks[i], subject1, position, sessionnum, sq)
taskB = feature_vector_generator(tasks[j], subject1, position, sessionnum, sq)
taskC = feature_vector_generator(tasks[k], subject1, position, sessionnum, sq)
taskD = feature_vector_generator(tasks[l], subject1, position, sessionnum, sq)
X, y = labeler.vectorsAndLabels([taskA, taskB, taskC, taskD])
comparison = tasks[i] + " vs. " + tasks[j] + " vs. " + tasks[k] + " vs. " + tasks[l] + " cross-validation is: " + str(labeler.crossValidate(X, y))
print(comparison)
