Differences

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--- python:intvectorrange [2012/08/30 17:24] – tkbletsc
+++ python:intvectorrange [2012/09/25 13:04] (current) – tkbletsc
@@ Line 12: / Line 12: @@
 # if only one argument is given to __init__, then the range goes from [0,0,...,0] to the vector given
 class IntVectorRange(object):
-	def __init__(self,start,end=None):
+    def __init__(self,start,end=None):
-		if end is None:
+        if end is None:
-			end=start
+            end=start
-			start=[0] * len(end)
+            start=[0] * len(end)
-		self.start=start
+        self.start=start
-		self.end=end
+        self.end=end
-		self.size = len(start)
+        self.size = len(start)
-		self.delta = [end[i]-start[i]+1  for  i in xrange(self.size)]
+        self.delta = [end[i]-start[i]+1  for  i in xrange(self.size)]
-		self.len = reduce(lambda x,y: x*y, self.delta, 1)
+        self.len = reduce(lambda x,y: x*y, self.delta, 1)
-	def __getitem__(self,index):
+    def __getitem__(self,index):
-		if index >= self.len: raise IndexError
+        if index >= self.len: raise IndexError
-		r = [0] * self.size
+        r = [0] * self.size
-		for i in xrange(self.size):
+        for i in xrange(self.size):
-			r[i] = index % self.delta[i] + self.start[i]
+            r[i] = index % self.delta[i] + self.start[i]
-			index /= self.delta[i]
+            index /= self.delta[i]
-		return r
+        return r
-	def __len__(self): return self.len
+    def __len__(self): return self.len
-# provides a list with all possible subsets of the given list
+# Provides a list with all possible subsets of the given list
 # (based on a binary IntVectorRange object)
 #
-# Example: AllCombinations(['alpha','beta','gamma']) yields:
+# Example: AllSubsets(['alpha','beta','gamma']) yields:
 #   []
 #   ['alpha']
@@ Line 45: / Line 45: @@
 #   ['beta', 'gamma']
 #   ['alpha', 'beta', 'gamma']
-class AllCombinations:
+class AllSubsets:
-	def __init__(self,items):
+    def __init__(self,items):
-		self.vector_range = IntVectorRange([1] * len(items))
+        self.vector_range = IntVectorRange([1] * len(items))
-		self.items = items
+        self.items = items
-	def __getitem__(self,index):
+    def __getitem__(self,index):
-		v = self.vector_range[index]
+        v = self.vector_range[index]
-		return [self.items[i] for i in xrange(len(v)) if v[i]]
+        return [self.items[i] for i in xrange(len(v)) if v[i]]
-	def __len__(self): return len(self.vector_range)
+    def __len__(self): return len(self.vector_range)
 # for academic purposes only, here's the same class based on inheritence instead of composition
-class AllCombinations2(IntVectorRange):
+class AllSubsets2(IntVectorRange):
 	def __init__(self,items):
 		super(AllCombinations2,self).__init__([1] * len(items))
@@ Line 66: / Line 66: @@
 		return [self.items[i] for i in xrange(len(v)) if v[i]]
+# Provides a list with all possible "choices" of the sublists of the input list
+#
+# Example: AllCombinations(["abc","123"]) yields:
+#   ['a', '1']
+#   ['b', '1']
+#   ['c', '1']
+#   ['a', '2']
+#   ['b', '2']
+#   ['c', '2']
+#   ['a', '3']
+#   ['b', '3']
+#   ['c', '3']
+class AllCombinations:
+    def __init__(self,choices):
+        self.vector_range = IntVectorRange([len(choice)-1 for choice in choices])
+        self.choices = choices
+    def __getitem__(self,index):
+        c_indices = self.vector_range[index]
+        return [self.choices[i][c_indices[i]] for i in xrange(len(self.choices))]
+    def __len__(self): return len(self.vector_range)
 </code>
@@ Line 86: / Line 109: @@
 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))
+    choices = {}
-	fixed_values = dict((k,v) for k,v in vargs.items() if not isinstance(v,Choice))
+    fixed_values = {}
-	c_keys = choices.keys()
+    for k,v in vargs.items():
+        if isinstance(v,Choice): choices[k] = v
-	choice_max_indices = [len(choices[k])-1 for k in c_keys]
+        else:                    fixed_values[k] = v
+    c_keys = choices.keys()
-	best_value = None
+    c_value_choices = [choices[k] for k in c_keys]
-	best_inputs = None
-	for c_indices in IntVectorRange(choice_max_indices):
+    best_value = None
-		#print c_indices
+    best_inputs = None
-		c_values = dict((x,choices[x][c_indices[i]]) for i,x in enumerate(c_keys))
+    for c_values in AllCombinations(c_value_choices):
-		all_values = c_values.copy()
+        chosen = dict((k,c_values[i]) for i,k in enumerate(c_keys))
-		all_values.update(fixed_values)
+        all_values = chosen.copy()
-		value = func(**all_values)
+        all_values.update(fixed_values)
-		if best_value is None or is_this_better(value,best_value):
+        value = func(**all_values)
-			best_inputs = c_values if return_choices_only else all_values
+        if best_value is None or is_this_better(value,best_value):
-			best_value = value
+            best_inputs = chosen if return_choices_only else all_values
-			if print_on_better: print best_inputs,best_value
+            best_value = value
-		#print c_values
+            if print_on_better: print best_inputs,best_value
-	return (best_inputs,best_value)
+    return (best_inputs,best_value)
 </code>