def __init__(self, graphs: Graphs, feature_pairs, split=1):
self._interval = int(graphs.number_of_graphs() / split)
self._all_features = graphs.features_matrix_by_index(for_all=True)
self._nodes_for_graph = graphs.nodes_count_list()
self._all_ftr_graph_index = []
for i in range(len(self._nodes_for_graph) + 1):
self._all_ftr_graph_index.append(np.sum(
self._nodes_for_graph[0:i]))
super(LinearContext, self).__init__(graphs, feature_pairs)
def __init__(self, graphs: Graphs, feature_pairs, split=1):
self._interval = int(graphs.number_of_graphs() / split)
self._all_features = []
for graph in graphs.graph_names():
m = graphs.features_matrix(graph)
# self._nodes_for_graph.append(m.shape[0])
# append graph features
self._all_features.append(m)
# append 0.001 for all missing nodes
self._all_features.append(np.ones((graphs.nodes_for_graph(graphs.name_to_index(graph)) - m.shape[0],
m.shape[1])) * 0.001)
# create one big matrix of everything - rows: nodes, columns: features
self._all_features = np.concatenate(self._all_features)
# all_ftr_graph_index - [ .... last_row_index_for_graph_i ... ]
self._all_ftr_graph_index = np.cumsum([0] + graphs.nodes_count_list()).tolist()
super(LinearContext, self).__init__(graphs, feature_pairs)
