angular_clf = AngularRegression(clf=clf)
bag_clf = BaggingRegressor(angular_clf, warm_start=True)
clf_angle3 = make_pipeline(StandardScaler(), bag_clf)
gat_angle3 = GeneralizationAcrossTime(clf=clf_angle3, scorer=scorer_angle,
n_jobs=-1)
clf = RidgeCV()
angular_clf = AngularClassifier(clf=LogisticRegression())
bag_clf = BaggingRegressor(angular_clf, warm_start=True)
clf_angle4 = make_pipeline(StandardScaler(), bag_clf)
gat_angle4 = GeneralizationAcrossTime(clf=clf_angle3, scorer=scorer_angle,
n_jobs=-1)
for subject in subjects:
report = OnlineReport()
# target ------------------------------------------------------------------
epochs, events = load(subject, 'Target')
# Decode angles -----------------------------------------------------------
gat = gat_angle
angles = np.array(events['left_angle'].as_matrix(), float)
gat.fit(epochs, y=angles)
gat.score(epochs, y=angles)
fig, ax = plt.subplots(1)
gat.plot(ax=ax, show=True)
report.add_figs_to_section(fig, 'left_SVR', subject + 'Target')
print('left_SVR', 'Target')
gat = gat_angle
angles = np.array(events['right_angle'].as_matrix(), float)
elif decodAngle_parameter == 2:
angle_bin = np.linspace(0+np.pi/6, np.pi*2+np.pi/6, 6)
clf_angle = make_pipeline(StandardScaler(), AngularClassifier(clf=LogisticRegression(),
bins=angle_bin))
gat_angle = GeneralizationAcrossTime(clf=clf_angle, scorer=scorer_angle,
n_jobs=-1)
clf_regress = make_pipeline(StandardScaler(), LinearSVR())
gat_regress = GeneralizationAcrossTime(clf=clf_regress, scorer=scorer_spearman,
n_jobs=-1)
gat_class = GeneralizationAcrossTime(n_jobs=-1)
for subject in subjects:
report = OnlineReport()
# target ------------------------------------------------------------------
epochs, events = load(subject, 'Target')
epochs.resample(64)
# Decode angles -----------------------------------------------------------
gat = gat_angle
angles = np.array(events['left_angle'].as_matrix(), float)
gat.fit(epochs, y=angles)
score = gat.score(epochs, y=angles)
fig, ax = plt.subplots(1)
gat.plot(ax=ax, show=False)
report.add_figs_to_section(fig, 'left_angle', subject + 'Target')
all_left_angle = list()
all_left_angle.append(score)
print('left_angle', 'Target')