def count_satisfiable(arg):
N, i, p_occ = arg
expanded = 0
completed = 0
for _ in range(100):
count_satisfiable.q.put(i)
problem = get_random_grid_problem(p_occ, N, N)
path, num_nodes_expanded, _ = a_star_search(problem)
expanded += num_nodes_expanded
if path is not None:
completed += 1
return completed, expanded
Simply fill in the prob. of occupancy values for the 'phase transition' and peak nodes expanded within 0.05. You do
NOT need to submit your code that determines the values here: that should be computed on your own machine. Simply
fill in the values!
:return: tuple containing (transition_start_probability, transition_end_probability, peak_probability)
"""
####
# REPLACE THESE VALUES
####
transition_start_probability = -1.0
transition_end_probability = -1.0
peak_nodes_expanded_probability = -1.0
return transition_start_probability, transition_end_probability, peak_nodes_expanded_probability
if __name__ == '__main__':
# Test your code here!
# Create a random instance of GridSearchProblem
p_occ = 0.25
M = 50
N = 50
problem = get_random_grid_problem(p_occ, M, N)
# Solve it
path, num_nodes_expanded, max_frontier_size = a_star_search(problem)
# Check the result
correct = problem.check_solution(path)
print("Solution is correct: {:}".format(correct))
# Plot the result
problem.plot_solution(path)
# Experiment and compare with BFS
# problem = get_random_grid_problem(p_occ, M, N)
# # Solve it
# path, num_nodes_expanded, max_frontier_size = a_star_search(problem)
#
# # Check the result
# correct = problem.check_solution(path)
# print("Solution is correct: {:}".format(correct))
# # Plot the result
# problem.plot_solution(path)
# Experiment and compare with BFS
x = []
solvability = []
effort = []
for i in np.arange(0.1, 0.95, 0.05):
x.append(i)
node_count = 0
solved = 0
for j in range(100):
problem = get_random_grid_problem(i, M, N)
path, num_nodes_expanded, max_frontier_size = a_star_search(problem)
node_count += num_nodes_expanded
if path != []:
solved = solved + 1
effort.append(node_count / 100)
solvability.append(solved / 100)
fig, ax = plt.subplots(2, 1)
ax[0].plot(x, solvability)
ax[1].plot(x, effort)
plt.show()
