local ProjectionLayer = nerv.class('nerv.ProjectionLayer', 'nerv.Layer')

--- The constructor.
function ProjectionLayer:__init(id, global_conf, layer_conf)
    nerv.Layer.__init(self, id, global_conf, layer_conf)
    self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs
    self:bind_params()
end

function ProjectionLayer:bind_params()
    local lconf = self.lconf
    lconf.no_update_ltp1 = lconf.no_update_ltp1 or lconf.no_update_ltp
    for i = 1, #self.dim_in do
        local pid = "ltp" .. i
        local pid_list = i == 1 and {pid, "ltp"} or pid
        self["ltp" .. i] = self:find_param(pid_list, lconf, self.gconf,
                                            nerv.LinearTransParam,
                                            {self.dim_in[i], self.dim_out[1]})
        local no_update = lconf["no_update_ltp" .. i]
        if (no_update ~= nil) and no_update or lconf.no_update_all then
            self["ltp" .. i].no_update = true
        end
    end
    self.ltp = self.ltp1 -- alias of ltp1
end

function ProjectionLayer:init(batch_size)
    for i = 1, #self.dim_in do
        if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then
            nerv.error("mismatching dimensions of linear transform parameter and input")
        end
        if self.dim_out[1] ~= self["ltp" .. i].trans:ncol() then
            nerv.error("mismatching dimensions of linear transform parameter and output")
        end
        self["ltp" .. i]:train_init()
    end
end

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

function ProjectionLayer:update()
    for i = 1, #self.dim_in do
        self["ltp" .. i]:update_by_err_input()
    end
end

function ProjectionLayer:propagate(input, output)
    -- apply linear transform
    output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N')
    for i = 2, #self.dim_in do
        output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N')
    end
end

function ProjectionLayer:back_propagate(bp_err, next_bp_err, input, output)
    for i = 1, #self.dim_in do
        next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T')
        self["ltp" .. i]:back_propagate_by_err_input(bp_err[1], input[i])
    end
end

function ProjectionLayer:get_params()
    local pr = nerv.ParamRepo({self.ltp1}, self.loc_type)
    for i = 2, #self.dim_in do
        pr:add(self["ltp" .. i])
    end
    return pr
end