def test_two_views_column_partition_normal__ci_(lovecat): D = retrieve_normal_dataset() engine = Engine(D.T, outputs=[5, 0, 1, 2, 3, 4], cctypes=['normal'] * len(D), rng=gu.gen_rng(12), num_states=64) if lovecat: engine.transition_lovecat(N=200) else: engine.transition(N=200) P = engine.dependence_probability_pairwise() R1 = engine.row_similarity_pairwise(cols=[5, 0, 1]) R2 = engine.row_similarity_pairwise(cols=[2, 3, 4]) pu.plot_clustermap(P) pu.plot_clustermap(R1) pu.plot_clustermap(R2) P_THEORY = [ [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], ] return engine
def test_two_views_row_partition_bernoulli__ci_(lovecat): D = retrieve_bernoulli_dataset() if lovecat: engine = Engine(D.T, cctypes=['categorical'] * len(D), distargs=[{ 'k': 2 }] * len(D), Zv={ 0: 0, 1: 0, 2: 1, 3: 1 }, rng=gu.gen_rng(12), num_states=64) engine.transition_lovecat(N=100, kernels=[ 'row_partition_assignments', 'row_partition_hyperparameters', 'column_hyperparameters', ]) else: engine = Engine(D.T, cctypes=['bernoulli'] * len(D), Zv={ 0: 0, 1: 0, 2: 1, 3: 1 }, rng=gu.gen_rng(12), num_states=64) engine.transition(N=100, kernels=[ 'view_alphas', 'rows', 'column_hypers', ]) R1 = engine.row_similarity_pairwise(cols=[0, 1]) R2 = engine.row_similarity_pairwise(cols=[2, 3]) pu.plot_clustermap(R1) pu.plot_clustermap(R2) return engine
def test_two_views_row_partition_normal__ci_(lovecat): D = retrieve_normal_dataset() engine = Engine(D.T, cctypes=['normal'] * len(D), Zv={ 0: 0, 1: 0, 2: 0, 3: 1, 4: 1, 5: 1 }, rng=gu.gen_rng(12), num_states=64) if lovecat: engine.transition_lovecat(N=100, kernels=[ 'row_partition_assignments', 'row_partition_hyperparameters', 'column_hyperparameters', ]) else: engine.transition(N=100, kernels=[ 'view_alphas', 'rows', 'column_hypers', ]) R1 = engine.row_similarity_pairwise(cols=[0, 1, 2]) R2 = engine.row_similarity_pairwise(cols=[3, 4, 5]) pu.plot_clustermap(R1) pu.plot_clustermap(R2) return engine
def test_two_views_column_partition_bernoulli__ci_(lovecat): D = retrieve_bernoulli_dataset() engine = Engine(D.T, cctypes=['categorical'] * len(D), distargs=[{ 'k': 2 }] * len(D), rng=gu.gen_rng(12), num_states=64) if lovecat: engine.transition_lovecat(N=200) else: # engine = Engine( # D.T, # cctypes=['bernoulli']*len(D), # rng=gu.gen_rng(12), # num_states=64) engine.transition(N=200) P = engine.dependence_probability_pairwise() R1 = engine.row_similarity_pairwise(cols=[0, 1]) R2 = engine.row_similarity_pairwise(cols=[2, 3]) pu.plot_clustermap(P) pu.plot_clustermap(R1) pu.plot_clustermap(R2) P_THEORY = [ [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], ] return engine