local Recurrent = nerv.class('nerv.AffineRecurrentLayer', 'nerv.Layer')

--id: string
--global_conf: table
--layer_conf: table
--Get Parameters
function Recurrent:__init(id, global_conf, layer_conf)
    self.id = id
    self.dim_in = layer_conf.dim_in
    self.dim_out = layer_conf.dim_out
    self.gconf = global_conf
    self.log_pre = self.id .. "[LOG]"

    self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]}) --layer_conf.bp
    self.ltp_hh = self:find_param("ltphh", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp_hh --from hidden to hidden
    self.ltp_ih = self:find_param("ltpih", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[1], self.dim_out[1]}) --layer_conf.ltp_hh --from hidden to hidden
    
    self:check_dim_len(2, 1)
    self.direct_update = layer_conf.direct_update

    self.clip = layer_conf.clip --clip error in back_propagate
    if self.clip ~= nil then
        nerv.info("%s creating, will clip the error by %f", self.log_pre, self.clip)
    end
end

--Check parameter 
function Recurrent:init(batch_size)
    if self.ltp_hh.trans:ncol() ~= self.bp.trans:ncol() or
       self.ltp_ih.trans:ncol() ~= self.bp.trans:ncol() then
        nerv.error("mismatching dimensions of ltp and bp")
    end
    if self.dim_in[1] ~= self.ltp_ih.trans:nrow() or
        self.dim_in[2] ~= self.ltp_hh.trans:nrow() then
        nerv.error("mismatching dimensions of ltp and input")
    end
    if (self.dim_out[1] ~= self.bp.trans:ncol()) then
        nerv.error("mismatching dimensions of bp and output")
    end
    
    self.ltp_hh:train_init()
    self.ltp_ih:train_init()
    self.bp:train_init()
end

function Recurrent:batch_resize(batch_size)
    -- do nothing
end

function Recurrent:update(bp_err, input, output)
    self.ltp_ih:update_by_err_input(bp_err[1], input[1])       
    self.ltp_hh:update_by_err_input(bp_err[1], input[2])       
    self.bp:update_by_gradient(bp_err[1]:colsum())
end

function Recurrent:propagate(input, output)
    output[1]:mul(input[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'N')
    output[1]:mul(input[2], self.ltp_hh.trans, 1.0, 1.0, 'N', 'N')
    output[1]:add_row(self.bp.trans, 1.0)
end

function Recurrent:back_propagate(bp_err, next_bp_err, input, output)
    next_bp_err[1]:mul(bp_err[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'T')
    next_bp_err[2]:mul(bp_err[1], self.ltp_hh.trans, 1.0, 0.0, 'N', 'T')
    --[[
    for i = 0, next_bp_err[2]:nrow() - 1 do
        for j = 0, next_bp_err[2]:ncol() - 1 do
            if (next_bp_err[2][i][j] > 10) then next_bp_err[2][i][j] = 10 end
            if (next_bp_err[2][i][j] < -10) then next_bp_err[2][i][j] = -10 end
        end
    end
    ]]--
    if self.clip ~= nil then
        next_bp_err[2]:clip(-self.clip, self.clip)
    end
end

function Recurrent:get_params()
    return nerv.ParamRepo({self.ltp_ih, self.ltp_hh, self.bp})
end