Ejemplo n.º 1
0
 def compute_statistics(self):
     """
     Compute max, min, average, median, standard deviation and
     number of elements.
     """
     if not self.statistics:
         self.data_sorted = self.data[:]
         self.data_sorted.sort()
         self._min      = self.data_sorted[0]
         self._max      = self.data_sorted[-1]
         self._median   = self.data_sorted[int(len(self.data)/2)]
         self._mean     = pylib_basics.mean(self.data)
         self._sd       = pylib_basics.standard_deviation(self.data)
         self.statistics = True
Ejemplo n.º 2
0
 def compute_statistics(self):
     """
     Compute max, min, average, median, standard deviation and
     number of elements.
     """
     if not self.statistics:
         self.data_sorted = self.data[:]
         self.data_sorted.sort()
         self._min = self.data_sorted[0]
         self._max = self.data_sorted[-1]
         self._median = self.data_sorted[int(len(self.data) / 2)]
         self._mean = pylib_basics.mean(self.data)
         self._sd = pylib_basics.standard_deviation(self.data)
         self.statistics = True
Ejemplo n.º 3
0
        (succ, count) = tree.classify_set(i[1],pylib_basics.verbose())
        succ_percent = float(succ)/count*100
        print "Test: %4d out of %4d, %7.3f%%" % (succ, count, succ_percent)
        te_results.append((succ, count,succ_percent)) 

    tr_percent = (map(lambda x:x[2],tr_results))
    te_percent = (map(lambda x:x[2],te_results))
    rig        = (map(lambda x:x[3],tr_results))
    depths     = (map(lambda x:x[4],tr_results))
    size       = (map(lambda x:x[5],tr_results))
    leaves     = (map(lambda x:x[6],tr_results))
    print "%s Splits %2d RIGL %5.3f RIG %5.3f+/-%5.3f (%5.2f+/-%4.2f, %7.2f+/-%4.2f, %7.2f+/-%4.2f) Train: %7.3f+/-%7.3f%%  Test:  %7.3f+/-%7.3f%%" %\
          (feature_selection,
           max_split,
           relgain_limit,
           pylib_basics.mean(rig),
           pylib_basics.standard_deviation(rig),
           pylib_basics.mean(depths),
           pylib_basics.standard_deviation(depths),
           pylib_basics.mean(size),
           pylib_basics.standard_deviation(size),
           pylib_basics.mean(leaves),
           pylib_basics.standard_deviation(leaves),
           pylib_basics.mean(tr_percent),
           pylib_basics.standard_deviation(tr_percent),
           pylib_basics.mean(te_percent),
           pylib_basics.standard_deviation(te_percent))
           
else:
    tree = dectree_constructor(set,
                               entropy_compare_fun,
Ejemplo n.º 4
0
        (succ, count) = tree.classify_set(i[1],pylib_basics.verbose())
        succ_percent = float(succ)/count*100
        print "Test: %4d out of %4d, %7.3f%%" % (succ, count, succ_percent)
        te_results.append((succ, count,succ_percent)) 

    tr_percent = (map(lambda x:x[2],tr_results))
    te_percent = (map(lambda x:x[2],te_results))
    rig        = (map(lambda x:x[3],tr_results))
    depths     = (map(lambda x:x[4],tr_results))
    size       = (map(lambda x:x[5],tr_results))
    leaves     = (map(lambda x:x[6],tr_results))
    print "%s Splits %2d RIGL %5.3f RIG %5.3f+/-%5.3f (%5.2f+/-%4.2f, %7.2f+/-%4.2f, %7.2f+/-%4.2f) Train: %7.3f+/-%7.3f%%  Test:  %7.3f+/-%7.3f%%" %\
          (feature_selection,
           max_split,
           relgain_limit,
           pylib_basics.mean(rig),
           pylib_basics.standard_deviation(rig),
           pylib_basics.mean(depths),
           pylib_basics.standard_deviation(depths),
           pylib_basics.mean(size),
           pylib_basics.standard_deviation(size),
           pylib_basics.mean(leaves),
           pylib_basics.standard_deviation(leaves),
           pylib_basics.mean(tr_percent),
           pylib_basics.standard_deviation(tr_percent),
           pylib_basics.mean(te_percent),
           pylib_basics.standard_deviation(te_percent))
           
else:
    tree = dectree_constructor(set,
                               entropy_compare_fun,