changed anneal.py

scripts/chunker_scripts/feature_selection/anneal.py

@@ -7,13 +7,15 @@ Created on 08-04-2013
 '''
 import random
 import os
+import math
 
 class AnnealOptimalizer():
     
-    def __init__(self, func, a_vector, args, T0, Tf=1e-12, maxiter=400, temp_reducing_rate=0.95, changing_features=2, opt="min", verbose=False):
+    def __init__(self, func, a_vector, args, seed, T0, Tf=1e-12, maxiter=400, temp_reducing_rate=0.95, changing_features=2, opt="min", verbose=False):
         self.func = func
         self.a_vector = a_vector
         self.args = args
+        self.out_dir = args['out_dir']
         if T0 == None:
             raise IOError('You have to set some T0')
         self.T0 = T0
@@ -23,7 +25,9 @@ class AnnealOptimalizer():
         self.changing_features = changing_features
         self.opt = opt
         self.verbose = verbose
-
+        
+        random.seed( seed )
+        
     def P(self, e, en, temp):
         print "e: ", e, " en: ", en
         if self.opt == "min":
@@ -63,15 +67,17 @@ class AnnealOptimalizer():
         iterations = 100           #ilosc iteracji symulacji
         sum = 0
         results = {}                 #histogram wyników
+         
         for i in range(iterations):
-            
+            if 'out_dir' in self.args:
+                self.args['out_dir'] = os.path.join(self.out_dir, "tempest", str(i))
             result = self.func(vector, self.args)
             sum += result
             if result not in results.keys():
                 results[result] = 1
             else:
                 results[result] += 1
-            vector = neightbour(vector)
+            vector = self.neightbour(vector)
         avg = sum / float(iterations)#obliczenie średniego wyniku
         k = 0
         deviation = 0
@@ -85,18 +91,16 @@ class AnnealOptimalizer():
     def anneal(self):
         temperature = self.T0
         i = 0
-        out_dir = self.args['out_dir']
-        if 'out_dir' in self.args:
-            self.args['out_dir'] = os.path.join(out_dir, str(i)) 
             
         a_vector = self.a_vector
+        self.args['out_dir'] = os.path.join(self.out_dir, "selection", str(i))
         a_value = self.func(a_vector, self.args)
         b_value = None
         
         i += 1
         while temperature > self.Tf and i < self.maxiter:
-            if 'out_dir' in self.args.keys():
-                self.args['out_dir'] = os.path.join(out_dir, i) 
+            if 'out_dir' in self.args:
+                self.args['out_dir'] = os.path.join(self.out_dir, "selection", str(i))
             b_vector = self.neightbour(a_vector)
             b_value = self.func(b_vector, self.args)
             p = self.P(a_value, b_value, temperature)
@@ -113,10 +117,32 @@ class AnnealOptimalizer():
             
             i += 1
         return (a_vector, a_value)
+    
+    def calculate_temp(self):
+        sum = 0
+        results = []
+        for (path, dirs, files) in os.walk(os.path.join(self.out_dir, "tempest")):
+            for file in files:
+                if os.path.basename(file) == "result.csv":
+                    f = open(os.path.join(path, file), 'r')
+                    line = f.readline()
+                    line = f.readline()
+                    fmeasure = float(line.split(":")[4][:-1].strip())
+                    results.append(fmeasure)
+                    sum += fmeasure
+                    f.close()
+        avg = sum / float(len(results))
+        k = 0
+        deviation = 0
+        for result in results:
+            deviation += result * ((k - avg) ** 2)
+            k += 1
+        self.T0 = math.sqrt(deviation / len(results))
+        return self.T0
         
 def main(func, a_vector, args={}, T0=None, Tf=1e-12, maxiter=400, temp_reducing_rate=0.95, changing_features=2, opt="min"):
     ao = AnnealOptimalizer(func, a_vector, args, T0, Tf, maxiter, temp_reducing_rate, changing_features, opt)
-    print ao.anneal()
+    ao.anneal()
     
 if __name__ == '__main__':
     def func(v, args):
@@ -130,4 +156,5 @@ if __name__ == '__main__':
     
     a = [False, False, False, False, False, False, False]
     
-    main(func, a, args={}, T0=0.1, Tf=1e-12, maxiter=200, temp_reducing_rate=0.95, changing_features=2, opt="max")
+    main(func, a, args={}, T0=0.1, Tf=1e-12, maxiter=200, temp_reducing_rate=0.95, changing_features=2, opt="max")
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+    
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