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Some code clean-up.
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@skytreader
skytreader committed Dec 26, 2012
1 parent 9a651b6 commit 4f32123
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212 changes: 106 additions & 106 deletions python/2_Stochastic_Algorithms/iterated_local_search.py
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"""

def euc_2d(c1, c2):
return math.sqrt(((c1[0] - c2[0]) ** 2) + ((c1[1] - c2[1]) ** 2))
return math.sqrt(((c1[0] - c2[0]) ** 2) + ((c1[1] - c2[1]) ** 2))

def cost(permutation, cities):
distance = 0
limit = len(permutation)
for i in range(limit):
if i == (limit - 1):
c2 = permutation[0]
else:
c2 = permutation[i + 1]
distance += euc_2d(cities[permutation[i]], cities[c2])
return distance
distance = 0
limit = len(permutation)
for i in range(limit):
if i == (limit - 1):
c2 = permutation[0]
else:
c2 = permutation[i + 1]
distance += euc_2d(cities[permutation[i]], cities[c2])
return distance

def random_permutation(cities):
perm = [i for i in range(len(cities))]
for i in perm:
r = random.randint(0, len(perm) - 1 - i) + i
perm[r], perm[i] = perm[i], perm[r]
return perm
perm = [i for i in range(len(cities))]
for i in perm:
r = random.randint(0, len(perm) - 1 - i) + i
perm[r], perm[i] = perm[i], perm[r]
return perm

def stochastic_two_opt(permutation):
perm = [permutation[i] for i in range(len(permutation))]
upper_bound = len(perm) - 1
c1, c2 = random.randint(0, upper_bound), random.randint(0, upper_bound)
exclude = [c1]
if c1 == 0:
exclude.append(upper_bound)
else:
exclude.append(c1 - 1)
if c1 == upper_bound:
exclude.append(0)
else:
exclude.append(c1 + 1)
while c2 in exclude:
c2 = random.randint(0, upper_bound)
if c2 < c1:
c1, c2 = c2, c1
perm_range = perm[c1:c2]
perm_range.reverse()
perm[c1:c2] = perm_range
return perm
perm = [permutation[i] for i in range(len(permutation))]
upper_bound = len(perm) - 1
c1, c2 = random.randint(0, upper_bound), random.randint(0, upper_bound)
exclude = [c1]
if c1 == 0:
exclude.append(upper_bound)
else:
exclude.append(c1 - 1)
if c1 == upper_bound:
exclude.append(0)
else:
exclude.append(c1 + 1)
while c2 in exclude:
c2 = random.randint(0, upper_bound)
if c2 < c1:
c1, c2 = c2, c1
perm_range = perm[c1:c2]
perm_range.reverse()
perm[c1:c2] = perm_range
return perm

#FIXME modifying and returning an argument?
def local_search(best, cities, max_no_improv):
count = 0
while count < max_no_improv:
candidate = {}
candidate["vector"] = stochastic_two_opt(best["vector"])
candidate["cost"] = cost(candidate["vector"], cities)
if candidate["cost"] < best["cost"]:
count = 0
else:
count += 1
if candidate["cost"] < best["cost"]:
best = candidate
return best
count = 0
while count < max_no_improv:
candidate = {}
candidate["vector"] = stochastic_two_opt(best["vector"])
candidate["cost"] = cost(candidate["vector"], cities)
if candidate["cost"] < best["cost"]:
count = 0
else:
count += 1
if candidate["cost"] < best["cost"]:
best = candidate
return best

def double_bridge_move(perm):
pos1 = 1 + random.randint(0, math.floor(len(perm) / 4))
pos2 = pos1 + 1 + random.randint(0, math.floor(len(perm) / 4))
pos3 = pos2 + 1 + random.randint(0, math.floor(len(perm) / 4))
p1 = perm[0:pos1] + perm[pos3:len(perm)]
p2 = perm[pos2:pos3] + perm[pos1:pos2]
return p1 + p2
def double_bridge_move(perm):
pos1 = 1 + random.randint(0, math.floor(len(perm) / 4))
pos2 = pos1 + 1 + random.randint(0, math.floor(len(perm) / 4))
pos3 = pos2 + 1 + random.randint(0, math.floor(len(perm) / 4))
p1 = perm[0:pos1] + perm[pos3:len(perm)]
p2 = perm[pos2:pos3] + perm[pos1:pos2]
return p1 + p2

def perturbation(cities, best):
candidate = {}
candidate["vector"] = double_bridge_move(best["vector"])
candidate["cost"] = cost(candidate["vector"], cities)
return candidate
candidate = {}
candidate["vector"] = double_bridge_move(best["vector"])
candidate["cost"] = cost(candidate["vector"], cities)
return candidate

def search(cities, max_iterations, max_no_improv):
best = {}
best["vector"] = random_permutation(cities)
best["cost"] = cost(best["vector"], cities)
best = local_search(best, cities, max_no_improv)
for i in range(max_iterations):
candidate = perturbation(cities, best)
candidate = local_search(candidate, cities, max_no_improv)
if candidate["cost"] < best["cost"]:
best = candidate
print("Iteration #" + str(i) + " best = " + str(best["cost"]))
return best
best = {}
best["vector"] = random_permutation(cities)
best["cost"] = cost(best["vector"], cities)
best = local_search(best, cities, max_no_improv)
for i in range(max_iterations):
candidate = perturbation(cities, best)
candidate = local_search(candidate, cities, max_no_improv)
if candidate["cost"] < best["cost"]:
best = candidate
print("Iteration #" + str(i) + " best = " + str(best["cost"]))
return best

if __name__ == "__main__":
# Problem configuration
berlin52 = [[565,575],[25,185],[345,750],[945,685],[845,655],
[880,660],[25,230],[525,1000],[580,1175],[650,1130],[1605,620],
[1220,580],[1465,200],[1530,5],[845,680],[725,370],[145,665],
[415,635],[510,875],[560,365],[300,465],[520,585],[480,415],
[835,625],[975,580],[1215,245],[1320,315],[1250,400],[660,180],
[410,250],[420,555],[575,665],[1150,1160],[700,580],[685,595],
[685,610],[770,610],[795,645],[720,635],[760,650],[475,960],
[95,260],[875,920],[700,500],[555,815],[830,485],[1170,65],
[830,610],[605,625],[595,360],[1340,725],[1740,245]]
# Algorithm configuration
max_iterations = 100
max_no_improv = 50
# Execute the algorithm
best = search(berlin52, max_iterations, max_no_improv)
print("Done. Best solution: c = " + str(best["cost"]) +", v = " + str(best["vector"]))
# Problem configuration
berlin52 = [[565,575],[25,185],[345,750],[945,685],[845,655],
[880,660],[25,230],[525,1000],[580,1175],[650,1130],[1605,620],
[1220,580],[1465,200],[1530,5],[845,680],[725,370],[145,665],
[415,635],[510,875],[560,365],[300,465],[520,585],[480,415],
[835,625],[975,580],[1215,245],[1320,315],[1250,400],[660,180],
[410,250],[420,555],[575,665],[1150,1160],[700,580],[685,595],
[685,610],[770,610],[795,645],[720,635],[760,650],[475,960],
[95,260],[875,920],[700,500],[555,815],[830,485],[1170,65],
[830,610],[605,625],[595,360],[1340,725],[1740,245]]
# Algorithm configuration
max_iterations = 100
max_no_improv = 50
# Execute the algorithm
best = search(berlin52, max_iterations, max_no_improv)
print("Done. Best solution: c = " + str(best["cost"]) +", v = " + str(best["vector"]))
56 changes: 28 additions & 28 deletions python/2_Stochastic_Algorithms/random_search_tests.py
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import unittest

class RandomSearchTests(unittest.TestCase):
"""
Unit tests for functions in random_search.py .
"""
def test_objective_function(self):
#integer
self.assertEqual(99**2, rs.objective_function([99]))
#float
self.assertEqual(0.1**2.0, rs.objective_function([0.1]))
#vector
self.assertEqual((1**2) + (2**2) + (3**2), rs.objective_function([1,2,3]))
def test_random_vector(self):
bounds, trials, size = [-3, 3], 300, 20
minmax = [bounds for i in range(size)]
for i in range(trials):
vector, total = rs.random_vector(minmax), 0.0
self.assertEqual(size, len(vector))
for v in vector:
self.assertTrue(v >= bounds[0])
self.assertTrue(v < bounds[1])
total += v
#TODO: test with total
"""
Unit tests for functions in random_search.py .
"""
def test_objective_function(self):
#integer
self.assertEqual(99**2, rs.objective_function([99]))
#float
self.assertEqual(0.1**2.0, rs.objective_function([0.1]))
#vector
self.assertEqual((1**2) + (2**2) + (3**2), rs.objective_function([1,2,3]))
def test_random_vector(self):
bounds, trials, size = [-3, 3], 300, 20
minmax = [bounds for i in range(size)]
for i in range(trials):
vector, total = rs.random_vector(minmax), 0.0
self.assertEqual(size, len(vector))
for v in vector:
self.assertTrue(v >= bounds[0])
self.assertTrue(v < bounds[1])
total += v
#TODO: test with total

if __name__ == "__main__":
tests = unittest.TestLoader().loadTestsFromTestCase(RandomSearchTests)
unittest.TextTestRunner(verbosity=2).run(tests)
tests = unittest.TestLoader().loadTestsFromTestCase(RandomSearchTests)
unittest.TextTestRunner(verbosity=2).run(tests)
13 changes: 13 additions & 0 deletions python/2_Stochastic_Algorithms/stochastic.py
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#! /usr/bin/env python3

import math

"""
Not in the original code listing. This factors out the common functions
used in Chapter 2.
"""

def euc_2d(p1, p2):
dx = p1[0] - p2[0]
dy = p1[1] - p2[1]
return math.sqrt((dx ** 2) + (dy ** 2))
19 changes: 19 additions & 0 deletions python/2_Stochastic_Algorithms/stochastic_tests.py
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#! /usr/bin/env python3

import stochastic
import unittest

"""
Tests for the base class of Chapter 2: Stochastic Algorithms
"""

class StochasticTests(unittest.TestCase):

def test_euc_2d(self):
self.assertEqual(0, stochastic.euc_2d([0, 0], [0, 0]))
self.assertEqual(0, stochastic.euc_2d([1.1, 1.1], [1.1, 1.1]))
self.assertEqual(1, stochastic.euc_2d([1, 1], [2, 2]))
self.assertEqual(3, stochastic.euc_2d([-1, -1], [1, 1]))

if __name__ == "__main__":
unittest.main()

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