def test_big_k(self): x = np.random.random((300, 3)) reader = DataInMemory(x) k = 151 c = api.cluster_uniform_time(k=k) c.estimate(reader)
def test_discretizer(self): reader_gen = DataInMemory(data=self.generated_data) # check if exception safe api.discretizer(reader_gen)._chain[-1].get_output() api.discretizer(reader_gen, transform=api.tica())._chain[-1].get_output() api.discretizer(reader_gen, cluster=api.cluster_uniform_time())._chain[-1].get_output() api.discretizer(reader_gen, transform=api.pca(), cluster=api.cluster_regspace(dmin=10))._chain[-1].get_output()
def test_big_k(self): x = np.random.random((300, 3)) reader = DataInMemory(x) k=151 c = api.cluster_uniform_time(k=k) c.data_producer = reader c.parametrize()
def test_2d_skip(self): x = np.random.random((300, 3)) reader = DataInMemory(x) k = 2 c = api.cluster_uniform_time(k=k, skip=100) c.estimate(reader)
def test_no_transform(self): reader_xtc = api.source(self.traj_files, top=self.pdb_file) api.pipeline([reader_xtc, api.cluster_kmeans(k=10)])._chain[-1].get_output() api.pipeline([reader_xtc, api.cluster_regspace(dmin=10)])._chain[-1].get_output() api.pipeline([reader_xtc, api.cluster_uniform_time()])._chain[-1].get_output()
def test_2d_skip(self): x = np.random.random((300, 3)) reader = DataInMemory(x) k = 2 c = api.cluster_uniform_time(k=k, skip=100) c.data_producer = reader c.parametrize()
def test_1d(self): x = np.random.random(1000) reader = DataInMemory(x) k = 2 c = api.cluster_uniform_time(k=k) c.data_producer = reader c.parametrize()
def test_2d(self): x = np.random.random((300, 3)) reader = DataInMemory(x) k = 2 c = api.cluster_uniform_time(k=k) c.data_producer = reader c.parametrize()
def test_big_k(self): # TODO: fix this (some error handling should be done in _param_init) x = np.random.random((300, 3)) reader = DataInMemory(x) k = 298 c = api.cluster_uniform_time(k=k) c.data_producer = reader c.parametrize()
def test_big_k(self): # TODO: fix this (some error handling should be done in _param_init) x = np.random.random((300, 3)) reader = DataInMemory(x) k = 298 c = api.cluster_uniform_time(k=k) c.data_producer = reader c.parametrize()
def test_is_parametrized(self): # construct pipeline with all possible transformers p = api.pipeline( [ api.source(self.traj_files, top=self.pdb_file), api.tica(), api.pca(), api.cluster_kmeans(k=50), api.cluster_regspace(dmin=50), api.cluster_uniform_time(k=20) ], run=False ) self.assertFalse(p._is_parametrized(), "If run=false, the pipeline should not be parametrized.") p.parametrize() self.assertTrue(p._is_parametrized(), "If parametrized was called, the pipeline should be parametrized.")
def test_no_transform(self): reader_xtc = api.source(self.traj_files, top=self.pdb_file) api.pipeline([reader_xtc, api.cluster_kmeans(k=10)])._chain[-1].get_output() api.pipeline([reader_xtc, api.cluster_regspace(dmin=10)])._chain[-1].get_output() api.pipeline([reader_xtc, api.cluster_uniform_time()])._chain[-1].get_output()
def test_1d(self): x = np.random.random(1000) reader = DataInMemory(x) k = 2 c = api.cluster_uniform_time(reader, k=k)