def test_str(self):
p = JointNormal(labels=['x', 'y'], mu=[0, 0], cov=[[1e-19, 1e-19], [1e-19, 1e-19]])
p.ingest(self.df)
s = str(p)
self.assertTrue('N' in s)
self.assertTrue('x' in s)
self.assertTrue('x' in s)
def test_cov(self):
p = JointNormal(labels=['x', 'y'])
p.ingest(self.df)
self.assertAlmostEqual(p.cov[0, 0], self.cov[0, 0])
self.assertAlmostEqual(p.cov[1, 1], self.cov[1, 1])
self.assertAlmostEqual(p.cov[1, 0], self.cov[1, 0])
self.assertAlmostEqual(p.cov[0, 1], self.cov[0, 1])
self.assertAlmostEqual(self.N, p.N)
def test_list_input(self):
p = JointNormal(labels=['x', 'y'], mu=[0, 0], cov=[[1e-19, 1e-19], [1e-19, 1e-19]])
df = self.df[['y', 'x']]
p.ingest(df)
mu, cov, N = p.mu_cov_n
self.assertAlmostEqual(p.mu[0, 0], self.mu[0])
self.assertAlmostEqual(p.mu[1, 0], self.mu[1])
self.assertEqual(tuple(cov.shape), (2, 2))
self.assertEqual(N, len(self.df))
def test_array_input(self):
p = JointNormal(labels=['x', 'y'], mu=np.array([0, 0]), cov=np.array([[1e-19, 1e-19], [1e-19, 1e-19]]))
p.ingest(self.df)
self.assertAlmostEqual(p.mu[0, 0], self.mu[0])
self.assertAlmostEqual(p.mu[1, 0], self.mu[1])
def test_list_input_bad_frame(self):
p = JointNormal(labels=['a', 'b'], mu=[0, 0], cov=[[1e-19, 1e-19], [1e-19, 1e-19]])
with self.assertRaises(ValueError):
p.ingest(self.df)
def test_n_limit(self):
p = JointNormal(labels=['x', 'y'], n_max=5)
p.ingest(self.df.values)
self.assertAlmostEqual(5, p.N)
def test_numpy_bad_input(self):
p = JointNormal(labels=['x', 'y'])
self.df['z'] = self.df.x
with self.assertRaises(ValueError):
p.ingest(self.df.values)
def test_numpy_input(self):
p = JointNormal(labels=['x', 'y'])
p.ingest(self.df.values)
self.assertAlmostEqual(p.mu[0, 0], self.mu[0])
self.assertAlmostEqual(p.mu[1, 0], self.mu[1])
self.assertAlmostEqual(self.N, p.N)
def test_mean(self):
p = JointNormal(labels=['x', 'y'])
p.ingest(self.df)
self.assertAlmostEqual(p.mu[0, 0], self.mu[0])
self.assertAlmostEqual(p.mu[1, 0], self.mu[1])
self.assertAlmostEqual(self.N, p.N)