def create_latency_vs_percent_found(max_latency, number_samples, max_timestep):
iterations = []
for i in trange(max_latency):
num_yes = 0
for j in range(number_samples):
m = BasicMap(15, 15)
middle = (7, 7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searcher00 = RandomWalkSearcher(m)
searcher00.init(middle)
s = Scenario(m, [lp00], [searcher00], i)
count = s.simulate(max_timestep) # Simulate for N time steps
if s.num_rescued > 0:
num_yes += 1
perc_found = num_yes / number_samples
iterations.append(perc_found * 100)
plt.plot(iterations)
plt.xlabel('Latency')
plt.ylabel('% lost persons found')
plt.savefig('latency_vs_percent_found.pdf', bbox_inches='tight')
plt.close()
def create_searchers_vs_time(max_searchers, latency, number_samples,
num_time_steps):
iterations = []
for num_searchers in trange(1, max_searchers + 1):
total = 0
for i in range(number_samples):
m = BasicMap(15, 15)
middle = (7, 7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
searchers = []
for j in range(num_searchers):
# Add some searchers to the map
searcher = RandomWalkSearcher(m)
searcher.init(middle)
searchers.append(searcher)
s = Scenario(m, [lp00], searchers)
count = s.simulate(num_time_steps) # Simulate for N time steps
total += count
avg = total / number_samples
iterations.append(avg)
plt.plot(list(range(1, max_searchers + 1)), iterations)
plt.xlabel('Number of searchers')
plt.ylabel('Average number of search time steps')
plt.savefig('searchers_vs_time.pdf', bbox_inches='tight')
plt.close()
def get_quadrantPartitionPerformance(num_searchers=15,
max_timestep=100,
latency=15,
number_samples=1000):
iterations = []
for i in range(0, num_searchers + 1):
total = 0
for j in range(number_samples):
m = BasicMap(15, 15)
partitioner = QuadrantPartitioner()
[rows_midpoint, cols_midpoint] = partitioner.partition(m)
quad01_rows = (0, rows_midpoint)
quad01_cols = (0, cols_midpoint)
quad02_rows = (0, rows_midpoint)
quad02_cols = (cols_midpoint, m.numColumns() - 1)
quad03_rows = (rows_midpoint, m.numRows() - 1)
quad03_cols = (0, cols_midpoint - 1)
quad04_rows = (rows_midpoint, m.numRows() - 1)
quad04_cols = (cols_midpoint, m.numColumns() - 1)
middle = (7, 7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searchers = []
for j in range(0, i):
if j % 4 == 0:
# Assign to quadrant 1
searcher = RandomWalkSearcher(m, quad01_rows, quad01_cols)
elif j % 4 == 1:
# Assign to quadrant 2
searcher = RandomWalkSearcher(m, quad02_rows, quad02_cols)
elif j % 4 == 2:
# Assign to quadrant 3
searcher = RandomWalkSearcher(m, quad03_rows, quad03_cols)
else:
# Assign to quadrant 4
searcher = RandomWalkSearcher(m, quad04_rows, quad04_cols)
searcher.init(middle)
searchers.append(searcher)
s = Scenario(m, [lp00], searchers, latency)
count = s.simulate(max_timestep) # Simulate for N time steps
total += count
avg = total / number_samples
iterations.append(avg)
plt.title("searchers vs time step")
plt.plot(list(range(len(iterations))), iterations)
plt.xlabel('searchers')
plt.ylabel('time step')
plt.show()
print(iterations)
return iterations
def create_latency_vs_timestep(max_latency, number_samples, max_timestep):
iterations = []
for i in trange(max_latency):
total = 0
for j in range(number_samples):
m = BasicMap(15, 15)
middle = (7, 7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searcher00 = RandomWalkSearcher(m)
searcher00.init(middle)
s = Scenario(m, [lp00], [searcher00], i)
count = s.simulate(max_timestep) # Simulate for N time steps
total += count
avg = total / number_samples
iterations.append(avg)
plt.plot(iterations)
plt.xlabel('Latency')
plt.ylabel('Average number of search time steps')
plt.savefig('latency_vs_time_steps.pdf', bbox_inches='tight')
plt.close()
def test_scenario_one_multiple_searcher(self):
m = BasicMap(15, 15)
middle = (7,7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
searchers = []
for i in range(10):
# Add some searchers to the map
searcher = RandomWalkSearcher(m)
searcher.init(middle)
searchers.append(searcher)
s = Scenario(m, [lp00], searchers)
s.simulate(100) # Simulate for N time steps
print("lost person history: \n")
print(lp00.get_history())
print("\n")
for i in range(10):
print("searcher history: " , i)
print(searchers[i].get_history())
print("\n")
def get_lanePartitionPerformance(num_searchers=15,
max_timestep=100,
max_latency=15,
number_samples=100):
# TODO - Finish this.
iterations = []
num_rows = 15
num_cols = 15
for i in range(1, num_searchers + 1):
# Get our lanes!!!
lanes = []
lane_size = (num_cols - 1) / i
for j in range(0, i):
lower = j * lane_size
upper = (j + 1) * lane_size
lanes.append((math.floor(lower) + 1, math.floor(upper)))
lanes[0] = (0, lanes[0][1])
print(lanes)
total = 0
for j in range(number_samples):
m = BasicMap(15, 15)
middle = (7, 7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searchers = []
for k in range(0, len(lanes)):
searcher = VerticalSweepSearcher(m, lanes[k])
searcher.init((0, math.floor((lanes[k][0] + lanes[k][1]) / 2)))
searchers.append(searcher)
s = Scenario(m, [lp00], searchers, max_latency)
count = s.simulate(max_timestep) # Simulate for N time steps
total += count
avg = total / number_samples
iterations.append(avg)
plt.title("latency vs time step")
plt.plot(list(range(len(iterations))), iterations)
plt.xlabel('latency')
plt.ylabel('time step')
plt.show()
print(iterations)
return iterations
def getNoPartitionStats(num_searchers, number_samples, max_timestep, latency):
num_yes = 0
avg_found = []
perc_found = 0
num_timesteps_successful = []
num_timesteps_overall = []
for i in range(number_samples):
m = BasicMap(101, 101)
middle = (50, 50)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searchers = []
for j in range(0, num_searchers):
searcher = RandomWalkSearcher(m)
searcher.init(middle)
searchers.append(searcher)
s = Scenario(m, [lp00], searchers, latency=latency)
count = s.simulate(max_timestep) # Simulate for N time steps
if count < max_timestep:
num_yes += 1
num_timesteps_successful.append(count)
num_timesteps_overall.append(count)
perc_found = num_yes / (i + 1)
perc_found = perc_found * 100
avg_found.append(perc_found)
# Graph convergence
plt.title(
"Convergence to expected probability of success, no partitioning")
plt.plot(list(range(number_samples)), avg_found)
plt.xlabel('Number of Samples')
plt.ylabel('Probability of Success')
plt.savefig('convergence_psucess.png')
print("Result: Percent Lost Person Discovered = " + str(perc_found))
print("Average Number of Time Steps (when successful): " +
str(sum(num_timesteps_successful) / len(num_timesteps_successful)))
print("Standard deviation: " +
str(statistics.stdev(num_timesteps_successful)))
print("Average Number of Time Steps (overall): " +
str(sum(num_timesteps_overall) / len(num_timesteps_overall)))
print("Standard deviation: " +
str(statistics.stdev(num_timesteps_overall)))
def test_scenario_randomWalker(self):
m = BasicMap(10, 10)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init((2, 5))
# Add some searchers to the map
searcher00 = StationarySearcher(m)
searcher00.init((4, 8))
s = Scenario(m, [lp00], [searcher00])
s.simulate(10) # Simulate for N time steps
print(lp00.get_history())
print(searcher00.get_history())
def test_quadrant_partitioner(self):
m = BasicMap(25, 25)
middle = (12, 12)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Partition map into quadrants
searchers = []
partitioner = QuadrantPartitioner()
[rows_midpoint, cols_midpoint] = partitioner.partition(m)
quad01_rows = (0, rows_midpoint)
quad01_cols = (0, cols_midpoint)
s00 = RandomWalkSearcher(m, quad01_rows, quad01_cols)
s00.init(middle)
searchers.append(s00)
quad02_rows = (0, rows_midpoint)
quad02_cols = (cols_midpoint, m.numColumns()-1)
s01 = RandomWalkSearcher(m, quad02_rows, quad02_cols)
s01.init(middle)
searchers.append(s01)
quad03_rows = (rows_midpoint, m.numRows()-1)
quad03_cols = (0, cols_midpoint-1)
s02 = RandomWalkSearcher(m, quad03_rows, quad03_cols)
s02.init(middle)
searchers.append(s02)
quad04_rows = (rows_midpoint, m.numRows()-1)
quad04_cols = (cols_midpoint, m.numColumns()-1)
s03 = RandomWalkSearcher(m, quad04_rows, quad04_cols)
s03.init(middle)
searchers.append(s03)
scenario = Scenario(m, [lp00], searchers, latency=50)
scenario.simulate(100)
print("lost person history: \n")
print(lp00.get_history())
print("\n")
for i in range(4):
print("searcher history: " , i)
print(searchers[i].get_history())
print("\n")
def test_scenario_one_random_searcher(self):
m = BasicMap(15, 15)
middle = (7,7)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searcher00 = RandomWalkSearcher(m)
searcher00.init(middle)
s = Scenario(m, [lp00], [searcher00])
s.simulate(100) # Simulate for N time steps
print("lost person history: \n")
print(lp00.get_history())
print("\n")
print("searcher history: ")
print(searcher00.get_history())
def test_vertical_sweep_searcher(self):
m = BasicMap(101, 101)
# m.print() interval: [0, 29]
middle = (50, 50)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
num_searchers = 10
lane_size = int(30 / num_searchers)
lanes = []
lower = 0
upper = lane_size - 1
lanes.append((lower, upper))
while upper < 29:
lower = upper + 1
upper = upper + lane_size
lanes.append((lower, upper))
searchers = []
for i in range(0, len(lanes)):
searcher = VerticalSweepSearcher(m, lanes[i])
searcher.init((0, math.floor((lanes[i][0] + lanes[i][1])/2)))
searchers.append(searcher)
self.assertTrue(len(searchers) == num_searchers)
scenario = Scenario(m, [lp00], searchers)
scenario.simulate(100)
print("lost person history: \n")
print(lp00.get_history())
print("\n")
for i in range(num_searchers):
print("searcher history: " , i)
print(searchers[i].get_history())
print(len(searchers[i].get_history()))
print("\n")
def getQuadrantPartitionStats(num_searchers, number_samples, max_timestep,
latency):
num_yes = 0
avg_found = []
perc_found = 0
num_timesteps_successful = []
num_timesteps_overall = []
for i in range(number_samples):
m = BasicMap(101, 101)
middle = (50, 50)
partitioner = QuadrantPartitioner()
[rows_midpoint, cols_midpoint] = partitioner.partition(m)
quad01_rows = (0, rows_midpoint)
quad01_cols = (0, cols_midpoint)
quad02_rows = (0, rows_midpoint)
quad02_cols = (cols_midpoint, m.numColumns() - 1)
quad03_rows = (rows_midpoint, m.numRows() - 1)
quad03_cols = (0, cols_midpoint - 1)
quad04_rows = (rows_midpoint, m.numRows() - 1)
quad04_cols = (cols_midpoint, m.numColumns() - 1)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searchers = []
for j in range(0, num_searchers):
if j % 4 == 0:
# Assign to quadrant 1
searcher = RandomWalkSearcher(m, quad01_rows, quad01_cols)
elif j % 4 == 1:
# Assign to quadrant 2
searcher = RandomWalkSearcher(m, quad02_rows, quad02_cols)
elif j % 4 == 2:
# Assign to quadrant 3
searcher = RandomWalkSearcher(m, quad03_rows, quad03_cols)
else:
# Assign to quadrant 4
searcher = RandomWalkSearcher(m, quad04_rows, quad04_cols)
searcher.init(middle)
searchers.append(searcher)
s = Scenario(m, [lp00], searchers, latency=latency)
count = s.simulate(max_timestep) # Simulate for N time steps
if count < max_timestep:
num_yes += 1
num_timesteps_successful.append(count)
num_timesteps_overall.append(count)
perc_found = num_yes / (i + 1)
perc_found = perc_found * 100
avg_found.append(perc_found)
# Graph convergence
plt.title(
"Convergence to expected probability of success, quadrant partitioning"
)
plt.plot(list(range(number_samples)), avg_found)
plt.xlabel('Number of Samples')
plt.ylabel('Probability of Success')
plt.savefig('convergence_psucess_quadrant.png')
print("Result: Percent Lost Person Discovered = " + str(perc_found))
print("Average Number of Time Steps (when successful): " +
str(sum(num_timesteps_successful) / len(num_timesteps_successful)))
print("Standard deviation: " +
str(statistics.stdev(num_timesteps_successful)))
print("Average Number of Time Steps (overall): " +
str(sum(num_timesteps_overall) / len(num_timesteps_overall)))
print("Standard deviation: " +
str(statistics.stdev(num_timesteps_overall)))
def getLanePartitionStats(num_searchers, number_samples, max_timestep, latency,
lane_size):
num_yes = 0
avg_found = []
perc_found = 0
num_timesteps_successful = []
num_timesteps_overall = []
# Final lane is just a bit wider than the others...
# lanes = [(0, 9), (10, 19), (20, 29), (30, 39), (40, 49),
# (50, 59), (60, 69), (70, 79), (80, 89), (90, 100)]
lanes = []
lower = 0
upper = lane_size - 1
lanes.append((lower, upper))
for i in range(0, num_searchers - 1):
# Each searcher gets a dedicated lane
lower = upper + 1
upper += lane_size
lanes.append((lower, upper))
print(lanes)
for i in range(number_samples):
m = BasicMap(101, 101)
middle = (50, 50)
# Add some lost persons to the map
lp00 = RandomWalkLostPerson(m)
lp00.init(middle)
# Add some searchers to the map
searchers = []
for j in range(0, len(lanes)):
searcher = VerticalSweepSearcher(m, lanes[j])
searcher.init((0, math.floor((lanes[j][0] + lanes[j][1]) / 2)))
searchers.append(searcher)
s = Scenario(m, [lp00], searchers, latency=latency)
count = s.simulate(max_timestep) # Simulate for N time steps
if count < max_timestep:
num_yes += 1
num_timesteps_successful.append(count)
num_timesteps_overall.append(count)
perc_found = num_yes / (i + 1)
perc_found = perc_found * 100
avg_found.append(perc_found)
# Graph convergence
plt.title(
"Convergence to expected probability of success, lanes partitioning")
plt.plot(list(range(number_samples)), avg_found)
plt.xlabel('Number of Samples')
plt.ylabel('Probability of Success')
plt.savefig('convergence_psucess_lanes.png')
print("Result: Percent Lost Person Discovered = " + str(perc_found))
print("Average Number of Time Steps (when successful): " +
str(sum(num_timesteps_successful) / len(num_timesteps_successful)))
print("Standard deviation: " +
str(statistics.stdev(num_timesteps_successful)))
print("Average Number of Time Steps (overall): " +
str(sum(num_timesteps_overall) / len(num_timesteps_overall)))
print("Standard deviation: " +
str(statistics.stdev(num_timesteps_overall)))