def test_geodataframe_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_geodataframe(gdf, double_attr_str, n_regions=2) obtained = region_list_from_array(cluster_object.labels_) compare_region_lists(obtained, optimal_clustering)
def test_w_basic(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_w(w, attr, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_scipy_sparse_matrix(adj, double_attr, n_regions=2) obtained = region_list_from_array(cluster_object.labels_) compare_region_lists(obtained, optimal_clustering)
def test_dict_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_dict(neighbors_dict, double_attr_dict, n_regions=2) obtained = region_list_from_array(cluster_object.labels_) compare_region_lists(obtained, optimal_clustering)
def test_graph_dict_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_networkx(graph, double_attr_dict, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_geodataframe_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_geodataframe(gdf, double_attr_str, n_regions=2) obtained = region_list_from_array(cluster_object.labels_) compare_region_lists(obtained, optimal_clustering)
def test_dict_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_dict(neighbors_dict, double_attr_dict, n_regions=2) obtained = region_list_from_array(cluster_object.labels_) compare_region_lists(obtained, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_scipy_sparse_matrix(adj, double_attr, n_regions=2) obtained = region_list_from_array(cluster_object.labels_) compare_region_lists(obtained, optimal_clustering)
def test_w_basic(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_w(w, attr, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_graph_dict_multi_attr(): cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_networkx(graph, double_attr_dict, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_graph_str_basic(): nx.set_node_attributes(graph, attr_dict, attr_str) cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_networkx(graph, attr_str, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_dict(): value_dict = dataframe_to_dict(gdf, attr_str) cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_dict(neighbors_dict, value_dict, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_graph_str_basic(): nx.set_node_attributes(graph, attr_str, attr_dict) cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_networkx(graph, attr_str, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)
def test_dict(): value_dict = dataframe_to_dict(gdf, attr_str) cluster_object = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2, random_state=0) cluster_object.fit_from_dict(neighbors_dict, value_dict, n_regions=2) result = region_list_from_array(cluster_object.labels_) compare_region_lists(result, optimal_clustering)