def test_coef_ml(self):
blim_ml = BLIM(self.k, self.n_r, method="ML")
beta, eta, p_k = blim_ml.coef()
self.assertListEqual(list(blim_ml.beta), list(beta))
self.assertListEqual(blim_ml.beta.values.tolist(),
beta.values.tolist())
self.assertListEqual(list(blim_ml.eta), list(eta))
self.assertListEqual(blim_ml.eta.values.tolist(), eta.values.tolist())
self.assertListEqual(list(blim_ml.p_k), list(p_k))
self.assertListEqual(blim_ml.p_k.values.tolist(), p_k.values.tolist())
def test_coef_md(self):
blim_md = BLIM(self.k, self.n_r)
beta, eta, p_k = blim_md.coef()
self.assertListEqual(list(blim_md.beta), list(beta))
self.assertListEqual(blim_md.beta.values.tolist(),
beta.values.tolist())
self.assertListEqual(list(blim_md.eta), list(eta))
self.assertListEqual(blim_md.eta.values.tolist(), eta.values.tolist())
self.assertListEqual(list(blim_md.p_k), list(p_k))
self.assertListEqual(blim_md.p_k.values.tolist(), p_k.values.tolist())
def test_blim_ml(self):
blim_ml = BLIM(self.k, self.n_r, method="ML")
self.assertEqual(9, blim_ml.n_states)
self.assertEqual(32, blim_ml.n_patterns)
self.assertEqual(1000, blim_ml.n_total)
self.assertEqual("ML", blim_ml.method)
self.assertEqual(300, blim_ml.iteration)
self.assertEqual(12.622816435940905, blim_ml.goodness_of_fit['g2'])
self.assertEqual(13, blim_ml.goodness_of_fit['df'])
self.assertEqual(0.477349992130788, blim_ml.goodness_of_fit['pval'])
self.assertEqual(0.254, blim_ml.discrepancy)
self.assertEqual(0.044865390859123146, blim_ml.n_errors['lucky'])
self.assertEqual(0.44280715825096656, blim_ml.n_errors['careless'])
self.assertListEqual([
0.1648712647718087, 0.16311278151263192, 0.18883863747163213,
0.07983530446636058, 0.08864829052919883
],
blim_ml.beta.values.tolist()[0])
self.assertListEqual([
0.10306473120044671, 0.09507429143942243, 3.5426760020042067e-06,
3.157133824028973e-06, 0.019909716488346413
],
blim_ml.eta.values.tolist()[0])
def test_blim_md(self):
blim_md = BLIM(self.k, self.n_r)
self.assertEqual(9, blim_md.n_states)
self.assertEqual(32, blim_md.n_patterns)
self.assertEqual(1000, blim_md.n_total)
self.assertEqual("MD", blim_md.method)
self.assertEqual(1, blim_md.iteration)
self.assertEqual(91.28362323477515, blim_md.goodness_of_fit['g2'])
self.assertEqual(13, blim_md.goodness_of_fit['df'])
self.assertEqual(7.938094626069869e-14,
blim_md.goodness_of_fit['pval'])
self.assertEqual(0.254, blim_md.discrepancy)
self.assertEqual(0.090000000000000011, blim_md.n_errors['lucky'])
self.assertEqual(0.16399999999999998, blim_md.n_errors['careless'])
self.assertListEqual([
0.09208874005860192, 0.08871989860583017, 0.04505813953488372, 0.0,
0.0
],
blim_md.beta.values.tolist()[0])
self.assertListEqual([
0.0, 0.0, 0.04064039408866995, 0.04085801838610828,
0.05472197705207414
],
blim_md.eta.values.tolist()[0])
def test_deviance_md(self):
blim_md = BLIM(self.k, self.n_r)
self.assertEqual(blim_md.goodness_of_fit['g2'], blim_md.deviance())
def test_number_of_obs_md(self):
blim_md = BLIM(self.k, self.n_r)
self.assertEqual(blim_md.n_patterns, blim_md.number_of_observations())
def test_log_likelihood_md(self):
blim_md = BLIM(self.k, self.n_r)
self.assertEqual(blim_md.log_lik, blim_md.log_likelihood())
def test_deviance_ml(self):
blim_ml = BLIM(self.k, self.n_r, method="ML")
self.assertEqual(blim_ml.goodness_of_fit['g2'], blim_ml.deviance())
def test_number_of_obs_ml(self):
blim_ml = BLIM(self.k, self.n_r, method="ML")
self.assertEqual(blim_ml.n_patterns, blim_ml.number_of_observations())
def test_log_likelihood_ml(self):
blim_ml = BLIM(self.k, self.n_r, method="ML")
self.assertEqual(blim_ml.log_lik, blim_ml.log_likelihood())