Let's get started gathering stats.

Author: skytreader

.gitignore

@@ -14,3 +14,4 @@ src/*
 *.bak
 dist/
 build/
+stats/

python/stochastic/random_search.py

@@ -1,6 +1,9 @@
 #! usr/bin/env python3
 
+from ..switch import decide
+
 import random
+import sys
 
 """
 2.2
@@ -12,52 +15,55 @@
 The example problem (solved by the code below) is an instance of a
 continuous function optimization that seeks min f(x) where
 f = \sum_{i=1}^{n} x_i^2, -5 <= x_i <= 5 and n = 2.
-
-@author Chad Estioco
 """
 
 def objective_function(vector):
-	sum = 0
-	
-	for val in vector:
-		sum += val ** 2
-	
-	return sum
+    sum = 0
+    
+    for val in vector:
+        sum += val ** 2
+    
+    return sum
 
 def random_vector(minmax):
-	i = 0
-	limit = len(minmax)
-	random_vector = [0 for i in range(limit)]
-	
-	for i in range(limit):
-		spam = minmax[i][0]
-		random_vector[i] = spam + ((minmax[i][1] - spam) * random.random())
-	
-	return random_vector
-
-def search(search_space, max_iter):
-	best = None
-	
-	for i in range(max_iter):
-		candidate = {}
-		candidate['vector'] = random_vector(search_space)
-		candidate['cost'] = objective_function(candidate['vector'])
-		
-		if best is None or candidate['cost'] < best['cost']:
-			best = candidate
-		
-		print("Iteration " + str(i) + ": best = " + str(best['cost']))
-	
-	return best
+    i = 0
+    limit = len(minmax)
+    random_vector = [0 for i in range(limit)]
+    
+    for i in range(limit):
+        spam = minmax[i][0]
+        random_vector[i] = spam + ((minmax[i][1] - spam) * random.random())
+    
+    return random_vector
+
+def search(search_space, max_iter, show_log=True):
+    best = None
+    
+    for i in range(max_iter):
+        candidate = {}
+        candidate['vector'] = random_vector(search_space)
+        candidate['cost'] = objective_function(candidate['vector'])
+        
+        if best is None or candidate['cost'] < best['cost']:
+            best = candidate
+        
+        if show_log:
+            print("Iteration " + str(i) + ": best = " + str(best['cost']))
+    
+    return best
 
 if __name__ == "__main__":
-	# problem configuration
-	problem_size = 2
-	search_space = [[-5, 5] for i in range(problem_size)]
-	
-	# algorithm configuration
-	max_iter = 100
-	
-	# execute the algorithm
-	best = search(search_space ,max_iter)
-	print("Done. Best Solution: cost = " + str(best['cost']) + ", v = " + str(best['vector']))
+    # problem configuration
+    problem_size = 2
+    search_space = [[-5, 5] for i in range(problem_size)]
+    
+    # algorithm configuration
+    max_iter = 100
+    
+    # execute the algorithm
+    if decide():
+        best = search(search_space ,max_iter)
+        print("Done. Best Solution: cost = " + str(best['cost']) + ", v = " + str(best['vector']))
+    else:
+        best = search(search_space, max_iter, False)
+        print(best["cost"])

python/switch.py

@@ -0,0 +1,12 @@
+import sys
+
+def decide():
+    if len(sys.argv) == 2:
+        flag = sys.argv[1].lower()
+
+        if flag == "true":
+            return True
+        elif flag != "false":
+            print("Usage: python3 %s <true|false>" % sys.argv[0])
+            exit(1)
+    return False