Moved some stable classes from nnx to optim.

They make more sense here.

1 files changed, 115 insertions, 0 deletions

diff --git a/ConfusionMatrix.lua b/ConfusionMatrix.lua
new file mode 100644
index 0000000..a4d44e5
--- /dev/null
+++ b/ConfusionMatrix.lua
@@ -0,0 +1,115 @@
+----------------------------------------------------------------------
+-- A Confusion Matrix class
+--
+-- Example:
+-- conf = optim.ConfusionMatrix( {'cat','dog','person'} )   -- new matrix
+-- conf:zero()                                              -- reset matrix
+-- for i = 1,N do
+--    conf:add( neuralnet:forward(sample), label )          -- accumulate errors
+-- end
+-- print(conf)                                              -- print matrix
+--
+local ConfusionMatrix = torch.class('optim.ConfusionMatrix')
+
+function ConfusionMatrix:__init(nclasses, classes)
+   if type(nclasses) == 'table' then
+      classes = nclasses
+      nclasses = #classes
+   end
+   self.mat = torch.FloatTensor(nclasses,nclasses):zero()
+   self.valids = torch.FloatTensor(nclasses):zero()
+   self.unionvalids = torch.FloatTensor(nclasses):zero()
+   self.nclasses = nclasses
+   self.totalValid = 0
+   self.averageValid = 0
+   self.classes = classes or {}
+end
+
+function ConfusionMatrix:add(prediction, target)
+   if type(prediction) == 'number' then
+      -- comparing numbers
+      self.mat[target][prediction] = self.mat[target][prediction] + 1
+   elseif type(target) == 'number' then
+      -- prediction is a vector, then target assumed to be an index
+      local prediction_1d = torch.FloatTensor(self.nclasses):copy(prediction)
+      local _,prediction = prediction_1d:max(1)
+      self.mat[target][prediction[1]] = self.mat[target][prediction[1]] + 1
+   else
+      -- both prediction and target are vectors
+      local prediction_1d = torch.FloatTensor(self.nclasses):copy(prediction)
+      local target_1d = torch.FloatTensor(self.nclasses):copy(target)
+      local _,prediction = prediction_1d:max(1)
+      local _,target = target_1d:max(1)
+      self.mat[target[1]][prediction[1]] = self.mat[target[1]][prediction[1]] + 1
+   end
+end
+
+function ConfusionMatrix:zero()
+   self.mat:zero()
+   self.valids:zero()
+   self.unionvalids:zero()
+   self.totalValid = 0
+   self.averageValid = 0
+end
+
+function ConfusionMatrix:updateValids()
+   local total = 0
+   for t = 1,self.nclasses do
+      self.valids[t] = self.mat[t][t] / self.mat:select(1,t):sum()
+      self.unionvalids[t] = self.mat[t][t] / (self.mat:select(1,t):sum()+self.mat:select(2,t):sum()-self.mat[t][t])
+      total = total + self.mat[t][t]
+   end
+   self.totalValid = total / self.mat:sum()
+   self.averageValid = 0
+   self.averageUnionValid = 0
+   local nvalids = 0
+   local nunionvalids = 0
+   for t = 1,self.nclasses do
+      if not sys.isNaN(self.valids[t]) then
+         self.averageValid = self.averageValid + self.valids[t]
+         nvalids = nvalids + 1
+      end
+      if not sys.isNaN(self.valids[t]) and not sys.isNaN(self.unionvalids[t]) then
+         self.averageUnionValid = self.averageUnionValid + self.unionvalids[t]
+         nunionvalids = nunionvalids + 1
+      end
+   end
+   self.averageValid = self.averageValid / nvalids
+   self.averageUnionValid = self.averageUnionValid / nunionvalids
+end
+
+function ConfusionMatrix:__tostring__()
+   self:updateValids()
+   local str = 'ConfusionMatrix:\n'
+   local nclasses = self.nclasses
+   str = str .. '['
+   for t = 1,nclasses do
+      local pclass = self.valids[t] * 100
+      pclass = string.format('%2.3f', pclass)
+      if t == 1 then
+         str = str .. '['
+      else
+         str = str .. ' ['
+      end
+      for p = 1,nclasses do
+         str = str .. '' .. string.format('%8d', self.mat[t][p])
+      end
+      if self.classes and self.classes[1] then
+         if t == nclasses then
+            str = str .. ']]  ' .. pclass .. '% \t[class: ' .. (self.classes[t] or '') .. ']\n'
+         else
+            str = str .. ']   ' .. pclass .. '% \t[class: ' .. (self.classes[t] or '') .. ']\n'
+         end
+      else
+         if t == nclasses then
+            str = str .. ']]  ' .. pclass .. '% \n'
+         else
+            str = str .. ']   ' .. pclass .. '% \n'
+         end
+      end
+   end
+   str = str .. ' + average row correct: ' .. (self.averageValid*100) .. '% \n'
+   str = str .. ' + average rowUcol correct (VOC measure): ' .. (self.averageUnionValid*100) .. '% \n'
+   str = str .. ' + global correct: ' .. (self.totalValid*100) .. '%'
+   return str
+end