def testEdge(self):
y = np.array([-1]*10)
stump = Stump.trained(self.x, y, self.w)
assert_approx_equal(stump.err, 0.0)
self.assertTrue(all(stump.predict(np.array([3.5, 5.0, 8.0]))
== np.array([-1, -1, -1])))
y = np.array([1]*10)
stump = Stump.trained(self.x, y, self.w)
assert_approx_equal(stump.err, 0.0)
self.assertTrue(all(stump.predict(np.array([3.5, 5.0, 8.0]))
== np.array([1, 1, 1])))
def testWeighted(self):
our_w = np.ones(10) / 10
our_w[0] = 0.05
our_w[9] = 0.15
y = np.array([-1] + [1]*4 + [-1]*5)
stump = Stump.trained(self.x, y, our_w)
self.assertTrue(all(stump.predict(np.array([3.5, 5.0, 8.0]))
== np.array([1, -1, -1])))
assert_approx_equal(stump.err, 0.05)
def testTrivial(self):
y = np.array([1]*5 + [-1]*5)
stump = Stump.trained(self.x, y, self.w)
assert_approx_equal(stump.err, 0.0)
self.assertTrue(all(stump.predict(np.array([3.5, 5.0]))
== np.array([1, -1])))
y = np.array([-1]*5 + [1]*5)
stump = Stump.trained(self.x, y, self.w)
assert_approx_equal(stump.err, 0.0)
self.assertTrue(all(stump.predict(np.array([3.5, 5.0]))
== np.array([-1, +1])))
y = np.array([-1]*7 + [1]*3)
stump = Stump.trained(self.x, y, self.w)
assert_approx_equal(stump.err, 0.0)
self.assertTrue(all(stump.predict(np.array([3.5, 5.0, 8.0]))
== np.array([-1, -1, +1])))
def train(self, samples, weights=None):
print('----------------start training-----------------')
if weights is None:
weights = self.initWeights(samples)
stump0 = Stump()
stump0.train(samples, weights)
self.stumps.append(stump0)
for _ in range(1, self.n_weakClassifier):
weights = self.stumps[-1].updateWeights
stump_i = Stump()
stump_i.train(samples, weights)
self.stumps.append(stump_i)
print("----------adaboost training finished.-----------")
