1 files changed, 85 insertions, 0 deletions
diff --git a/WeightedEuclidean.lua b/WeightedEuclidean.lua
new file mode 100644
index 0000000..2761228
--- /dev/null
+++ b/WeightedEuclidean.lua
@@ -0,0 +1,85 @@
+local WeightedEuclidean, parent = torch.class('nn.WeightedEuclidean', 'nn.Module')
+
+function WeightedEuclidean:__init(inputSize,outputSize)
+   parent.__init(self)
+
+   self.templates = torch.Tensor(inputSize,outputSize)
+   self.gradTemplates = torch.Tensor(inputSize,outputSize)
+
+   self.diagCov = torch.Tensor(inputSize,outputSize)
+   self.gradDiagCov = torch.Tensor(inputSize,outputSize)
+
+   self.gradInput:resize(inputSize)
+   self.output:resize(outputSize)
+   self.temp = torch.Tensor(inputSize)
+
+   -- for compat with Torch's modules (it's bad we have to do that)
+   do
+      self.weight = self.templates
+      self.gradWeight = self.gradTemplates
+      self.bias = self.diagCov
+      self.gradBias = self.gradDiagCov
+   end
+
+   self:reset()
+end
+
+function WeightedEuclidean:reset(stdv)
+   if stdv then
+      stdv = stdv * math.sqrt(3)
+   else
+      stdv = 1./math.sqrt(self.templates:size(1))
+   end
+
+   for i=1,self.templates:size(2) do
+      self.templates:select(2, i):apply(function()
+                                        return torch.uniform(-stdv, stdv)
+                                     end)
+   end
+
+   self.diagCov:fill(1)
+end
+
+function WeightedEuclidean:updateOutput(input)
+   self.output:zero()
+   for o = 1,self.templates:size(2) do
+      self.temp:copy(input):add(-1,self.templates:select(2,o))
+      self.temp:cmul(self.temp)
+      self.temp:cmul(self.diagCov:select(2,o)):cmul(self.diagCov:select(2,o))
+      self.output[o] = math.sqrt(self.temp:sumall())
+   end
+   return self.output
+end
+
+function WeightedEuclidean:updateGradInput(input, gradOutput)
+   self:forward(input)
+   self.gradInput:zero()
+   for o = 1,self.templates:size(2) do
+      if self.output[o] ~= 0 then
+         self.temp:copy(input):add(-1,self.templates:select(2,o))
+         self.temp:cmul(self.diagCov:select(2,o)):cmul(self.diagCov:select(2,o))
+         self.temp:mul(gradOutput[o]/self.output[o])
+         self.gradInput:add(self.temp)
+      end
+   end
+   return self.gradInput
+end
+
+function WeightedEuclidean:accGradParameters(input, gradOutput, scale)
+   self:forward(input)
+   scale = scale or 1
+   for o = 1,self.templates:size(2) do
+      if self.output[o] ~= 0 then
+         self.temp:copy(self.templates:select(2,o)):add(-1,input)
+         self.temp:cmul(self.diagCov:select(2,o)):cmul(self.diagCov:select(2,o))
+         self.temp:mul(gradOutput[o]/self.output[o])
+         self.gradTemplates:select(2,o):add(self.temp)
+
+         self.temp:copy(self.templates:select(2,o)):add(-1,input)
+         self.temp:cmul(self.temp)
+         self.temp:cmul(self.diagCov:select(2,o))
+         self.temp:mul(gradOutput[o]/self.output[o])
+         self.gradDiagCov:select(2,o):add(self.temp)
+      end
+   end
+end