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)