def test_dataset4():
''' (2 points) test dataset4'''
n= 400
X, Y = RF.load_dataset()
assert X.shape == (16,400)
assert Y.shape == (400,)
d = DT()
# train over half of the dataset
t = d.train(X[:,::2],Y[::2])
# test on the other half
Y_predict = DT.predict(t,X[:,1::2])
accuracy0 = sum(Y[1::2]==Y_predict)/float(n)*2.
print('test accuracy of a decision tree:', accuracy0)
b = Bag()
# train over half of the dataset
T = b.train(X[:,::2],Y[::2],21)
# test on the other half
Y_predict = Bag.predict(T,X[:,1::2])
accuracy1 = sum(Y[1::2]==Y_predict)/float(n)*2.
print('test accuracy of a bagging of 21 trees:', accuracy1)
r = RF()
# train over half of the dataset
T = r.train(X[:,::2],Y[::2],21)
# test on the other half
Y_predict = RF.predict(T,X[:,1::2])
accuracy2 = sum(Y[1::2]==Y_predict)/float(n)*2.
print('test accuracy of a random forest of 21 trees:', accuracy2)
assert accuracy1 >= accuracy0
assert accuracy2 >= accuracy0
assert accuracy2 >= accuracy1-.05
def step(X, Y, D):
'''
Compute one step of Boosting.
Input:
X: the feature matrix, a numpy matrix of shape p by n.
Each element can be int/float/string.
Here n is the number data instances in the node, p is the number of attributes.
Y: the class labels, a numpy array of length n. Each element can be int/float/string.
D: the current weights of instances, a numpy float vector of length n
Output:
t: the root node of a decision stump trained in this step
a: (alpha) the weight of the decision stump, a float scalar.
D: the new weights of instances, a numpy float vector of length n
'''
#########################################
## INSERT YOUR CODE HERE
t = DS().build_tree(X, Y, D)
Y_ = DT.predict(t, X)
e = AB.weighted_error_rate(Y, Y_, D)
a = AB.compute_alpha(e)
D = AB.update_D(D, a, Y, Y_)
#########################################
return t, a, D