from grid_digraph_generator import GridDigraphGenerator
from suitability import SuitableNodeWeightGenerator, SuitabilityGraph
if __name__ == '__main__':
generator = SuitableNodeWeightGenerator()
seed = 0
m = n = 30
gh = GridDigraphGenerator()
node_weighted = gh.generate(m,
n,
edge_weighted=True,
node_weighted=True,
node_weight_generator=generator,
seed=seed)
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(node_weighted)
weights = {i: generator.weights["VERY_SUITABLE"][0] for i in range(m * n)}
suitability_graph.update_node_weights(weights)
suitability_graph = SuitabilityGraph()
suitability_graph.append_graph(graph)
# # Compute shortest distances between every pair of nodes.
# suitability_graph.compute_dist_paths(compute_paths=False)
# As our goal is to show how Mustafizur's algorithm works, terminals can meet anywhere, thus original suitable nodes
# are reset and any node in the graph becomes a suitable node (except terminals and POIs).
# Reset suitable nodes.
hotspots = suitability_graph.get_suitable_nodes(generator)
reset_hotspots_weights = {
h: generator.weights["WARNING"][0]
for h in hotspots
}
suitability_graph.update_node_weights(reset_hotspots_weights)
# Choose random terminals.
# Terminals are chosen from a subset of nodes resulting from padding the original graph.
where = []
offset = width - 2 * padding - 1
a = (width + 1) * padding
for _ in range(0, offset + 1):
b = a + offset
where.extend(range(a, b + 1))
a = b + 2 * padding + 1
terminals = np.random.choice(a=where, size=35, replace=False)
# Choose POIs.
