walk = shortest_path(all_pair, node, exit)[1:]
cost = len(path) * d_cost + len(walk) * w_cost
if cost < best_cost:
best_node = node
best_cost = cost
prev_path = path
return best_node, prev_path, 0
if __name__ == "__main__":
# row, column, parking_row, parking_column
row, column, parking_row, parking_column = 4, 4, 10, 20
print("-- generate graph --", datetime.datetime.now())
nodeset, d_size, p_size, n_size = generate_graph(row, column, parking_row,
parking_column)
total_spots = d_size + p_size + n_size
# nodeset = load_spot_graph("spot_graph")
# total_spots = 179
enter_node = nodeset[0]
# center
total_row = (1 + parking_column + 2) * row + 1
total_col = (1 + parking_row + 2) * column + 1
exit_node = nodeset[int(total_row / 2) * total_col + int(total_col / 2)]
# drive cost of one spot distance
d_cost = 1.63
# walk cost of one spot distance
for time, p_list in items:
p_equal_to_time = reduce(lambda x, y: x * y, p_list)
p_less_than_time = (1 - saving_time_pr_cdf[-1][1]) if len(saving_time_pr_cdf) > 0 else 1
# saving_time = best_cost - time
saving_time_pr_cdf.append((best_cost - time, 1 - (p_equal_to_time * p_less_than_time)))
t = [saving_time_pr_cdf[0]] + [(y[0], y[1] - x[1]) for x, y in zip(saving_time_pr_cdf, saving_time_pr_cdf[1:])]
exp[next_node] = sum(list(x*y for x, y in t))
return exp
if __name__ == "__main__":
# nodeset = load_spot_graph("spot_graph")
# row, column, parking_row, parking_column
row, column, parking_row, parking_column = 2, 2, 5, 10
nodeset, d_size, p_size, n_size = generate_graph(2, 2, 2, 3)
enter_node = nodeset[0]
exit_node = nodeset[len(nodeset) - 1]
p_available = 0.5
x = 0.2
# drive cost of one spot distance
d_cost = 1
# walk cost of one spot distance
w_cost = 4
# u turn cost of one spot distance
u_cost = 8