Beispiel #1
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def test_graph_str_multi_attr():
    nx.set_node_attributes(graph, attr_str, attr_dict)
    cluster_object = AZPBasicTabu(random_state=0)
    cluster_object.fit_from_networkx(graph, double_attr_str, n_regions=2)
    result = region_list_from_array(cluster_object.labels_)
    compare_region_lists(result, optimal_clustering)
Beispiel #2
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def test_geodataframe_multi_attr():
    cluster_object = AZPBasicTabu(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)
Beispiel #3
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def test_dict_multi_attr():
    cluster_object = AZPBasicTabu(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)
Beispiel #4
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def test_scipy_sparse_matrix_multi_attr():
    cluster_object = AZPBasicTabu(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)
Beispiel #5
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def test_w_basic():
    cluster_object = AZPBasicTabu(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)
Beispiel #6
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def test_graph_dict_basic():
    cluster_object = AZPBasicTabu(random_state=0)
    cluster_object.fit_from_networkx(graph, attr_dict, n_regions=2)
    result = region_list_from_array(cluster_object.labels_)
    compare_region_lists(result, optimal_clustering)
Beispiel #7
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def test_dict():
    value_dict = dataframe_to_dict(gdf, attr_str)
    cluster_object = AZPBasicTabu(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)
Beispiel #8
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def test_geodataframe():
    cluster_object = AZPBasicTabu(random_state=0)
    cluster_object.fit_from_geodataframe(gdf, attr_str, n_regions=2)
    result = region_list_from_array(cluster_object.labels_)
    compare_region_lists(result, optimal_clustering)