1 files changed, 0 insertions, 103 deletions
diff --git a/contrib/torch/optim/rprop.lua b/contrib/torch/optim/rprop.lua
deleted file mode 100644
index d7af16429..000000000
--- a/contrib/torch/optim/rprop.lua
+++ /dev/null
@@ -1,103 +0,0 @@
---[[ A plain implementation of RPROP
-
-ARGS:
-- `opfunc` : a function that takes a single input (X), the point of
-             evaluation, and returns f(X) and df/dX
-- `x`      : the initial point
-- `state`  : a table describing the state of the optimizer; after each
-             call the state is modified
-- `state.stepsize`    : initial step size, common to all components
-- `state.etaplus`     : multiplicative increase factor, > 1 (default 1.2)
-- `state.etaminus`    : multiplicative decrease factor, < 1 (default 0.5)
-- `state.stepsizemax` : maximum stepsize allowed (default 50)
-- `state.stepsizemin` : minimum stepsize allowed (default 1e-6)
-- `state.niter`       : number of iterations (default 1)
-
-RETURN:
-- `x`     : the new x vector
-- `f(x)`  : the function, evaluated before the update
-
-(Martin Riedmiller, Koray Kavukcuoglu 2013)
---]]
-function optim.rprop(opfunc, x, config, state)
-   if config == nil and state == nil then
-      print('no state table RPROP initializing')
-   end
-   -- (0) get/update state
-   local config = config or {}
-   local state = state or config
-   local stepsize = config.stepsize or 0.1
-   local etaplus = config.etaplus or 1.2
-   local etaminus = config.etaminus or 0.5
-   local stepsizemax = config.stepsizemax or 50.0
-   local stepsizemin = config.stepsizemin or 1E-06
-   local niter = config.niter or 1
-
-   local hfx = {}
-
-   for i=1,niter do
-
-      -- (1) evaluate f(x) and df/dx
-      local fx,dfdx = opfunc(x)
-
-      -- init temp storage
-      if not state.delta then
-         state.delta    = dfdx.new(dfdx:size()):zero()
-         state.stepsize = dfdx.new(dfdx:size()):fill(stepsize)
-         state.sign     = dfdx.new(dfdx:size())
-         state.psign    = torch.ByteTensor(dfdx:size())
-         state.nsign    = torch.ByteTensor(dfdx:size())
-         state.zsign    = torch.ByteTensor(dfdx:size())
-         state.dminmax  = torch.ByteTensor(dfdx:size())
-         if torch.type(x)=='torch.CudaTensor' then
-            -- Push to GPU
-            state.psign    = state.psign:cuda()
-            state.nsign    = state.nsign:cuda()
-            state.zsign    = state.zsign:cuda()
-            state.dminmax  = state.dminmax:cuda()
-         end
-      end
-
-      -- sign of derivative from last step to this one
-      torch.cmul(state.sign, dfdx, state.delta)
-      torch.sign(state.sign, state.sign)
-
-      -- get indices of >0, <0 and ==0 entries
-      state.sign.gt(state.psign, state.sign, 0)
-      state.sign.lt(state.nsign, state.sign, 0)
-      state.sign.eq(state.zsign, state.sign, 0)
-
-      -- get step size updates
-      state.sign[state.psign] = etaplus
-      state.sign[state.nsign] = etaminus
-      state.sign[state.zsign] = 1
-
-      -- update stepsizes with step size updates
-      state.stepsize:cmul(state.sign)
-
-      -- threshold step sizes
-      -- >50 => 50
-      state.stepsize.gt(state.dminmax, state.stepsize, stepsizemax)
-      state.stepsize[state.dminmax] = stepsizemax
-      -- <1e-6 ==> 1e-6
-      state.stepsize.lt(state.dminmax, state.stepsize, stepsizemin)
-      state.stepsize[state.dminmax] = stepsizemin
-
-      -- for dir<0, dfdx=0
-      -- for dir>=0 dfdx=dfdx
-      dfdx[state.nsign] = 0
-      -- state.sign = sign(dfdx)
-      torch.sign(state.sign,dfdx)
-
-      -- update weights
-      x:addcmul(-1,state.sign,state.stepsize)
-
-      -- update state.dfdx with current dfdx
-      state.delta:copy(dfdx)
-
-      table.insert(hfx,fx)
-   end
-
-   -- return x*, f(x) before optimization
-   return x,hfx
-end