added probguess

numpredict.py

@@ -142,6 +142,26 @@ def costf(scale):
   return costf
 
 
+def probguess(data, vec1, low, high, k=5, weightfun=gaussianweight):
+  """Returns the probability that the result for input vec1 is in the
+  interval [low, hight], based on the trainingdata data."""
+  dlist = getdistances(data, vec1)
+  nweight = 0.0  # weight of neighbors in interval
+  tweight = 0.0  # weight of all neighbors ("total weight")
+
+  for i in range(k):
+    dist = dlist[i][0]
+    idx = dlist[i][1]
+    weight = weightfun(dist)
+    v = data[idx]['result']
+
+    if low <= v <= high:
+      nweight += weight
+    tweight += weight
+  if tweight == 0.0: return 0.0
+  return nweight / tweight
+
+
 if __name__ == '__main__':
   s = wineset1(50)
 
@@ -157,6 +177,7 @@ def costf(scale):
   print crossvalidate(lambda d, v: knnestimate(d, v, k=7), s)
   print crossvalidate(lambda d, v: weightedknn(d, v, k=5), s)
 
+  # Use optimization to automatically rescale different dimensions
   print
   print 'set 2, not-to-scale parameters (XXX buggy, broken, incomplete)'
   s = wineset2(50)
@@ -170,6 +191,15 @@ def costf(scale):
   #print optimization.annealingoptimize([(0, 20)] * 4,
       #createcostfunction(knnestimate, s), step=2)
 
+  # This shows that tracking distributions is worthwile
   print
   print 'set 3, uneven distribution'
   s = wineset3()
+  print probguess(s, [99, 20], 20, 120)
+  print probguess(s, [99, 20], 120, 1000)
+  print probguess(s, [99, 20], 40, 80)
+  print probguess(s, [99, 20], 80, 120)
+  print 'real price:', wineprice(99.0, 20.0)
+  print 'estimated price:', weightedknn(s, [99.0, 20.0])
+  print 'crossvalidation error:', crossvalidate(weightedknn, s)
+