pooled_subjects = np.asarray(pooled_subjects)
scores = {}
for kind in kinds:
scores[kind] = []
for train, test in cv.split(pooled_subjects, classes):
# *ConnectivityMeasure* can output the estimated subjects coefficients
# as a 1D arrays through the parameter *vectorize*.
connectivity = ConnectivityMeasure(kind=kind, vectorize=True)
# build vectorized connectomes for subjects in the train set
connectomes = connectivity.fit_transform(pooled_subjects[train])
# fit the classifier
classifier = LinearSVC().fit(connectomes, classes[train])
# make predictions for the left-out test subjects
predictions = classifier.predict(
connectivity.transform(pooled_subjects[test]))
# store the accuracy for this cross-validation fold
scores[kind].append(accuracy_score(classes[test], predictions))
######################################################################
# display the results
mean_scores = [np.mean(scores[kind]) for kind in kinds]
scores_std = [np.std(scores[kind]) for kind in kinds]
plt.figure(figsize=(6, 4))
positions = np.arange(len(kinds)) * .1 + .1
plt.barh(positions, mean_scores, align='center', height=.05, xerr=scores_std)
yticks = [k.replace(' ', '\n') for k in kinds]
plt.yticks(positions, yticks)
plt.gca().grid(True)
