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--- python:intvectorrange [2010/10/29 11:52] – tkbletsc
+++ python:intvectorrange [2012/08/30 17:24] – tkbletsc
@@ Line 67: / Line 67: @@
+</code>
+This can be used to build a simple combinatorial optimizer:
+<code=python>
+import operator
+# Optimize the return value of the given function func(), comparing return values with the is_this_better() function (which defaults to greater-than).
+# All parameters must be named parameters, and come after all other arguments. Options:
+# - Print out the latest best answer if print_on_better is True
+# - If return_choices_only is True, then the best_inputs part of the return will only have the choices, not the fixed values
+# Returns the tuple: (best_inputs,best_value)
+#
+# Example:
+#   def f(x,y,z): return x**2+x+5-y**3+z
+#   print optimize(f, x=Choice(range(-3,5)), y=Choice(range(-3,5)), z=2)
+#
+# This finds the best x and y to maximize f(x,y,z) with the given choices, with z fixed at 2.
+class Choice(list): pass
+def optimize(func, is_this_better=operator.gt, print_on_better=False, return_choices_only=False, **vargs):
+	choices = dict((k,v) for k,v in vargs.items() if isinstance(v,Choice))
+	fixed_values = dict((k,v) for k,v in vargs.items() if not isinstance(v,Choice))
+	c_keys = choices.keys()
+	choice_max_indices = [len(choices[k])-1 for k in c_keys]
+	best_value = None
+	best_inputs = None
+	for c_indices in IntVectorRange(choice_max_indices):
+		#print c_indices
+		c_values = dict((x,choices[x][c_indices[i]]) for i,x in enumerate(c_keys))
+		all_values = c_values.copy()
+		all_values.update(fixed_values)
+		value = func(**all_values)
+		if best_value is None or is_this_better(value,best_value):
+			best_inputs = c_values if return_choices_only else all_values
+			best_value = value
+			if print_on_better: print best_inputs,best_value
+		#print c_values
+	return (best_inputs,best_value)
 </code>