def heur(puzzle, item_total_util, total_util):
"""
heuristic template that provides the current and target position for each number and the
total function.
parameters:
puzzle - the puzzle
item_total_util - takes 4 parameters: current row, target row, current col, target col. returns int.
total_util - takes 1 parameter, the sum of item_total_util over all entries, and returns int.
"""
t = 0
utilit = util.utility()
for row in range(3):
for col in range(3):
val = utilit.peek(
puzzle, row, col
) - 1 # value is -1 if the peeked index is 0. triggers if target_row < 0 below.
target_col = val % 3
target_row = val / 3
# account for 0 as blank
if target_row < 0:
target_row = 2
t += item_total_util(row, target_row, col, target_col)
return total_util(t)
def graph_expected_utility_vs_alpha(numpy_regular, numpy_margin, outdir_name):
"""alpha is as in utility(wealth) = wealth^alpha"""
MIN_ALPHA = 0
MAX_ALPHA = 1
NUM_POINTS = 1000
alpha_values = numpy.linspace(MIN_ALPHA,MAX_ALPHA,NUM_POINTS)
ratio_of_expected_utilities = map(lambda alpha: \
numpy.mean(map(lambda wealth: util.utility(wealth, alpha), numpy_margin)) / \
numpy.mean(map(lambda wealth: util.utility(wealth, alpha), numpy_regular)),
alpha_values)
pyplot.plot(alpha_values,ratio_of_expected_utilities)
pyplot.title("Ratio of margin/regular expected utility vs. risk tolerance, alpha")
pyplot.xlabel("alpha: measure of risk tolerance, as in utility(wealth) = (wealth)^alpha")
pyplot.ylabel("expected_utility(margin) / expected_utility(regular)")
pyplot.savefig("%s_%s" % (outdir_name, EXPECTED_UTILITY_GRAPH_PREFIX))
pyplot.close()
def graph_expected_utility_vs_alpha(numpy_regular, numpy_margin, outdir_name):
"""alpha is as in utility(wealth) = wealth^alpha"""
MIN_ALPHA = 0
MAX_ALPHA = 1
NUM_POINTS = 1000
alpha_values = numpy.linspace(MIN_ALPHA, MAX_ALPHA, NUM_POINTS)
ratio_of_expected_utilities = map(lambda alpha: \
numpy.mean(map(lambda wealth: util.utility(wealth, alpha), numpy_margin)) / \
numpy.mean(map(lambda wealth: util.utility(wealth, alpha), numpy_regular)),
alpha_values)
pyplot.plot(alpha_values, ratio_of_expected_utilities)
pyplot.title(
"Ratio of margin/regular expected utility vs. risk tolerance, alpha")
pyplot.xlabel(
"alpha: measure of risk tolerance, as in utility(wealth) = (wealth)^alpha"
)
pyplot.ylabel("expected_utility(margin) / expected_utility(regular)")
pyplot.savefig("%s_%s" % (outdir_name, EXPECTED_UTILITY_GRAPH_PREFIX))
pyplot.close()
def _generate_moves(self):
utilit = util.utility()
free = utilit._get_legal_moves(self)
zero = utilit.find(self, 0)
def swap_and_clone(a, b):
p = self._clone()
p = utilit.swap(p, a, b)
p._depth = self._depth + 1
p._parent = self
return p
return map(lambda pair: swap_and_clone(zero, pair), free)
def main():
utility = util.utility()
p = EightPuzzle()
shuffle = utility.shuffle(p, 20)
print "Starting puzzle:"
print shuffle
selection = raw_input(
"Which algorithm would you like to use to solve this matrix? (DFS, BFS, UCS, A*, or All for all four) \n"
)
if selection == "DFS":
path, count = shuffle.solve_DFS()
path.reverse()
print "Path to goal state:"
for i in path:
print i
print "Solved with DFS search in", count, "node expansions"
elif selection == "BFS":
path, count = shuffle.solve(h_default, 'BFS')
path.reverse()
print "Path to goal state:"
for i in path:
print i
print "Solved with BFS search in", count, "node expansions"
elif selection == "UCS":
path, count = shuffle.solve(h_default, 'UCS')
path.reverse()
print "Path to goal state:"
for i in path:
print i
print "Solved with UCS search in", count, "node expansions"
elif selection == "A*":
path, count = shuffle.solve(h_manhattan, 'A*')
path.reverse()
print "Path to goal state:"
for i in path:
print i
print "Solved with A* search utilizing Manhattan distance hueuristic in", count, "node expansions"
elif selection == "All" or "all":
path, count = shuffle.solve(h_manhattan, 'A*')
path.reverse()
print "Path to goal state:"
for i in path:
print i
print "Solved with A* search utilizing Manhattan distance hueuristic in", count, "node expansions"
path, count = shuffle.solve(h_default, 'UCS')
print "Solved with UCS search in", count, "node expansions"
path, count = shuffle.solve(h_default, 'BFS')
print "Solved with BFS search in", count, "node expansions"
path, count = shuffle.solve_DFS()
print "Solved with DFS search in", count, "node expansions"
class color:
PURPLE = "\033[95m"
CYAN = "\033[96m"
DARKCYAN = "\033[36m"
BLUE = "\033[94m"
GREEN = "\033[92m"
YELLOW = "\033[93m"
RED = "\033[91m"
BOLD = "\033[1m"
UNDERLINE = "\033[4m"
END = "\033[0m"
utility()
boys = open('./boys.csv')
girls = open('./girls.csv')
getboy = csv.reader(boys, delimiter=',')
getgirl = csv.reader(girls, delimiter=',')
boy_list = []
girl_list = []
for i in getboy:
boy_list += [Boy(i[0], int(i[1]), int(i[2]), int(i[3]), int(i[4]), i[5])]
for j in getgirl:
girl_list += [Girl(j[0], int(j[1]), int(j[2]), int(j[3]), j[4])]
for g in girl_list:
for b in boy_list: