def test_regularization(self):
X, y = data1()
absth0 = np.abs(solution.LogRegLearner(0.0)(X, y).th[1:])
absth10 = np.abs(solution.LogRegLearner(10.0)(X, y).th[1:])
absth100 = np.abs(solution.LogRegLearner(100.0)(X, y).th[1:])
self.assertTrue(np.all(absth0 > absth10))
self.assertTrue(np.all(absth10 > absth100))
ind = numpy.argsort(dists)
prob[yi, xi] = classifier(X[ind[0]])[1]
pylab.imshow(prob, extent=(minx, maxx, maxy, miny))
pylab.xlim(minx, maxx)
pylab.ylim(miny, maxy)
pylab.xlabel(at1)
pylab.ylabel(at2)
pylab.show()
X, y = solution.load('reg.data')
learner = solution.LogRegLearner(lambda_=0.)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
learner = solution.LogRegLearner(lambda_=0.01)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
learner = solution.LogRegLearner(lambda_=0.3)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
for lam in [0.0, 0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5]:
def test_logreg_noreg_learning_ca(self):
X, y = data1()
logreg = solution.LogRegLearner(lambda_=0)
pred = solution.test_learning(logreg, X, y)
ca = solution.CA(y, pred)
self.assertAlmostEqual(ca, 1.)
#
#
# s.draw_decision(i, X, y, classifier, 0, 1)
# Part 3 test_cv:
X, y = s.load('reg.data')
lambdas = [10, 1, 0.5, 0.1, 0.075, 0.05, 0.03, 0.01, 0.001, 0.0001, 0.]
lambdas_ca = {l: 0 for l in lambdas}
for i in range(1,21):
for l in lambdas:
learner = s.LogRegLearner(lambda_=l)
res = s.test_cv(learner, X, y, seed=i)
ca = s.CA(y, res)
lambdas_ca[l] += ca
for l in lambdas_ca.keys():
print('Lambda:', l)
print('Tocnost', lambdas_ca[l]/20)
# # Part 3 test_learning:
#
# X, y = s.load('reg.data')
#