def test_yes():
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-2).np_like_firstn()
tw = TemporalWeight('yes', 2)
y = tw.fit_predict(D)
for i in xrange(D.shape[0]):
val = D[i][-1]
assert_equal(val, y[i])
def test_yes():
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-2).np_like_firstn()
tw = TemporalWeight('yes', 2)
y = tw.fit_predict(D)
for i in xrange(D.shape[0]):
val = D[i][-1]
assert_equal(val, y[i])
def test_pow():
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-2).np_like_firstn()
tw = TemporalWeight('pow', 2)
y = tw.fit_predict(D)
for i in xrange(D.shape[0]):
div = sum((np.arange(D.shape[1]) + 1) ** 2)
val = sum(D[i] * ((np.arange(D.shape[1]) + 1) ** 2) / div)
assert_equal(val, y[i])
def test_pow():
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-2).np_like_firstn()
tw = TemporalWeight('pow', 2)
y = tw.fit_predict(D)
for i in xrange(D.shape[0]):
div = sum((np.arange(D.shape[1]) + 1)**2)
val = sum(D[i] * ((np.arange(D.shape[1]) + 1)**2) / div)
assert_equal(val, y[i])
def test_avg():
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-2)
tw = TemporalWeight('avg')
y = tw.fit_predict(D)
assert_array_equal(D.np_like_firstn().mean(axis=1), y)
def test_avg():
D = tsio.from_id_row_mat(YOUTUBE_1K, add_eps=1e-2)
tw = TemporalWeight('avg')
y = tw.fit_predict(D)
assert_array_equal(D.np_like_firstn().mean(axis=1), y)
