y_pred = clf.predict(X_[1000:])
score = accuracy_score(y[1000:], y_pred)
#print("%i - %.3f" % (i, score))
accuracy_vector[i - 1] = score
print(accuracy_vector)
plt.plot(range(1, 41), accuracy_vector)
plt.ylim(0.5, 1)
plt.savefig("foo.png")
############################################
np.random.seed(1410)
n, d, n_classes = (2000, 80, 2)
X, y = make_classification(n, d, int(d / 2), int(d / 2), 0, n_classes)
clf = KNeighborsClassifier()
clf.fit(X[:1000], y[:1000])
y_pred = clf.predict(X[1000:])
print(y_pred[:5], y_pred.shape)
pp = clf.predict_proba(X[1000:])
print(pp[:5], pp.shape)
