Some code clean-up.

Author: skytreader

python/2_Stochastic_Algorithms/iterated_local_search.py

@@ -23,125 +23,125 @@
 """
 
 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"]))

python/2_Stochastic_Algorithms/random_search_tests.py

@@ -5,33 +5,33 @@
 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)

python/2_Stochastic_Algorithms/stochastic.py

@@ -0,0 +1,13 @@
+#! /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))

python/2_Stochastic_Algorithms/stochastic_tests.py

@@ -0,0 +1,19 @@
+#! /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()