[Minor] Move lua contrib libraries to lua- prefix

1 files changed, 55 insertions, 0 deletions

diff --git a/contrib/lua-torch/optim/adadelta.lua b/contrib/lua-torch/optim/adadelta.lua
new file mode 100644
index 000000000..7cc058d29
--- /dev/null
+++ b/contrib/lua-torch/optim/adadelta.lua
@@ -0,0 +1,55 @@
+--[[ ADADELTA implementation for SGD http://arxiv.org/abs/1212.5701
+
+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
+- `config` : a table of hyper-parameters
+- `config.rho` : interpolation parameter
+- `config.eps` : for numerical stability
+- `config.weightDecay` : weight decay
+- `state` : a table describing the state of the optimizer; after each
+         call the state is modified
+- `state.paramVariance` : vector of temporal variances of parameters
+- `state.accDelta` : vector of accummulated delta of gradients
+RETURN:
+- `x` : the new x vector
+- `f(x)` : the function, evaluated before the update
+]]
+function optim.adadelta(opfunc, x, config, state)
+    -- (0) get/update state
+    if config == nil and state == nil then
+        print('no state table, ADADELTA initializing')
+    end
+    local config = config or {}
+    local state = state or config
+    local rho = config.rho or 0.9
+    local eps = config.eps or 1e-6
+    local wd = config.weightDecay or 0
+    state.evalCounter = state.evalCounter or 0
+    -- (1) evaluate f(x) and df/dx
+    local fx,dfdx = opfunc(x)
+
+    -- (2) weight decay
+    if wd ~= 0 then
+      dfdx:add(wd, x)
+    end
+
+    -- (3) parameter update
+    if not state.paramVariance then
+        state.paramVariance = torch.Tensor():typeAs(x):resizeAs(dfdx):zero()
+        state.paramStd = torch.Tensor():typeAs(x):resizeAs(dfdx):zero()
+        state.delta = torch.Tensor():typeAs(x):resizeAs(dfdx):zero()
+        state.accDelta = torch.Tensor():typeAs(x):resizeAs(dfdx):zero()
+    end
+    state.paramVariance:mul(rho):addcmul(1-rho,dfdx,dfdx)
+    state.paramStd:resizeAs(state.paramVariance):copy(state.paramVariance):add(eps):sqrt()
+    state.delta:resizeAs(state.paramVariance):copy(state.accDelta):add(eps):sqrt():cdiv(state.paramStd):cmul(dfdx)
+    x:add(-1, state.delta)
+    state.accDelta:mul(rho):addcmul(1-rho, state.delta, state.delta)
+    -- (4) update evaluation counter
+    state.evalCounter = state.evalCounter + 1
+
+    -- return x*, f(x) before optimization
+    return x,{fx}
+end