diff --git a/f1.py b/f1.py
index 52e2554..3f62fe7 100644
--- a/f1.py
+++ b/f1.py
@@ -1,43 +1,58 @@
 import torch
+import sys
 
 def reverse(t):
     idx = [i for i in range(t.size(0)-1, -1, -1)]
     idx = torch.LongTensor(idx)
     it  = t.index_select(0, idx)
     return it
 
 def PRFromHistograms(hPos,hNeg):
-    print("hPos",hPos,"hNeg",hNeg)
+    #print("hPos",hPos,"hNeg",hNeg)
     positives=hPos.sum()
     negatives=hNeg.sum()
-    print("positives, negatives", positives, negatives)
+    #print("positives, negatives", positives, negatives)
     truepositives =reverse(hPos.clone()).long().cumsum(dim=0)
     falsepositives=reverse(hNeg.clone()).long().cumsum(dim=0)
     predpositives=torch.add(truepositives,falsepositives)
     #protect against zero division
     predpositives[predpositives==0]=1
     precision=torch.Tensor.div_(truepositives.float(),predpositives.float())
     recall   =torch.Tensor.div  (truepositives.float(),positives.float())
-    precision[precision<=0]=1e-12
-    recall   [recall   <=0]=1e-12
-    print("precision,recall",precision,recall)
+    precision[precision<=0]=sys.float_info.epsilon
+    recall   [recall   <=0]=sys.float_info.epsilon
+    #print("precision,recall",precision,recall)
+    return precision,recall
+
+def PRFromDiscrete(nTruePos,nPredPos,nGtPos):
+    truepositives =nTruePos
+    falsepositives=nPredPos-nTruePos
+    positives     =nGtPos
+    predpositives =nPredPos
+    if predpositives==0: predpositives =1 #protect against zero division
+    precision=truepositives/float(predpositives)
+    recall   =truepositives/float(positives)
+    if precision<=0: precision=sys.float_info.epsilon
+    if recall   <=0: recall   =sys.float_info.epsilon
     return precision,recall
 
 def PRFromOutGt(outps,targs,nbins=10000):
     hPos=torch.zeros(nbins)
     hNeg=torch.zeros(nbins)
     for o,t in zip(outps,targs):
         pos=o[lbl==1]
         neg=t[lbl==0]
         hPos+=torch.from_numpy(pos.astype(np.float32)).histc(nbins,0,1)
         hNeg+=torch.from_numpy(neg.astype(np.float32)).histc(nbins,0,1)
     precision,recall=f1.PRFromHistograms(hPos,hNeg)
     f1s=f1.F1FromPR(precision,recall)
     f=f1s.max()
     return f
 
 def F1FromPR(p,r):
     suminv=torch.pow(p,-1)+torch.pow(r,-1)
     f1s=torch.pow(suminv,-1).mul(2)
-    print("f1s",f1s)
+    #print("f1s",f1s)
     return f1s
+
+