def test_bow(): print 'BagOfWords Check:\n----------------------' try: d = ['this is the final cme193 assignment', 'i hope you learn some skills you can apply elsewhere'] bow = BagOfWords(top_n=8) bow.fit(d) X = bow.transform(d) ref = [[ 0., 1., 0., 1., 1., 1., 0., 0.], [ 2., 0., 1., 0., 0., 0., 1., 1.]] if not np.allclose(X, ref): print '[FAILED], incorrect representation' return False else: print '[PASSED]' return True except Exception: print '[FAILED], error in calculation' return False
TEST(test_regression_theta())
TEST(test_regression_pred())
yhat = False
with open('labels.txt') as f:
y = [int(l.strip()) for l in f.readlines()]
with open('example_text.txt') as f:
texts = [l.strip().lower() for l in f.readlines()]
bow = BagOfWords(top_n=1500)
bow.fit(texts)
X = bow.transform(texts)
#-CME193-START-------------------
# !!!!!! PLEASE READ !!!!!!
# you will need to create a new instance of LinearProbabilityModel, you will need
# to call your .fit on X and y, and you will need to put your preductions
# (after calling .predict) in a variable called yhat.
#-CME193-END---------------------
if not yhat:
yhat = np.random.normal(0, 1, len(y))
