lbl= "SelfLearning QDA:"
model = SelfLearningModel(WQDA())
if i == 2:
lbl= "CPLE(pessimistic) QDA:"
model = CPLELearningModel(WQDA(), predict_from_probabilities=True)
elif i == 3:
lbl= "CPLE(optimistic) QDA:"
CPLELearningModel.pessimistic = False
model = CPLELearningModel(WQDA(), predict_from_probabilities=True)
model.fit(Xs, ys)
print ""
print lbl
print "Model training time: ", round(time.time()-t1, 3)
# predict, and evaluate
pred = model.predict(Xs)
acc = np.mean(pred==ytrue)
print "accuracy:", round(acc, 3)
# plot probabilities
[minx, maxx] = [np.min(Xs[:, 0]), np.max(Xs[:, 0])]
[miny, maxy] = [np.min(Xs[:, 1]), np.max(Xs[:, 1])]
gridsize = 100
xx = np.linspace(minx, maxx, gridsize)
yy = np.linspace(miny, maxy, gridsize).T
xx, yy = np.meshgrid(xx, yy)
Xfull = np.c_[xx.ravel(), yy.ravel()]
probas = model.predict_proba(Xfull)
plt.imshow(probas[:, 1].reshape((gridsize, gridsize)), extent=(minx, maxx, miny, maxy), origin='lower')
lbl = "SelfLearning QDA:"
model = SelfLearningModel(WQDA())
if i == 2:
lbl = "CPLE(pessimistic) QDA:"
model = CPLELearningModel(WQDA())
elif i == 3:
lbl = "CPLE(optimistic) QDA:"
CPLELearningModel.pessimistic = False
model = CPLELearningModel(WQDA())
model.fit(Xs, ys)
print ""
print lbl
print "Model training time: ", round(time.time() - t1, 3)
# predict, and evaluate
pred = model.predict(Xs)
acc = np.mean(pred == ytrue)
print "accuracy:", round(acc, 3)
# plot probabilities
[minx, maxx] = [np.min(Xs[:, 0]), np.max(Xs[:, 0])]
[miny, maxy] = [np.min(Xs[:, 1]), np.max(Xs[:, 1])]
gridsize = 100
xx = np.linspace(minx, maxx, gridsize)
yy = np.linspace(miny, maxy, gridsize).T
xx, yy = np.meshgrid(xx, yy)
Xfull = np.c_[xx.ravel(), yy.ravel()]
probas = model.predict_proba(Xfull)
plt.imshow(probas[:, 1].reshape((gridsize, gridsize)), extent=(minx, maxx, miny, maxy), origin="lower")
