path: root/nerv/matrix/init.lua

--- Implements a fraction of matrix operations (methods) in Lua, while
-- others are implemented in C extension.
-- @author Ted Yin <ted.sybil@gmail.com>


--- The base class for all matrices.
-- @type nerv.Matrix

--- Convert the matrix object to a string.
function nerv.Matrix:__tostring__()
    local ncol = self:ncol()
    local nrow = self:nrow()
    local dim = self:dim()
    local strt = {}
    local fmt
    if self.fmt then
        fmt = self.fmt
    else
        fmt = "%.8f "
    end
    if (dim == 2) then
        for row = 0, nrow - 1 do
            local rp = self[row]
            for col = 0, ncol - 1 do
                table.insert(strt, string.format(fmt, rp[col]))
            end
            table.insert(strt, "\n")
        end
    else
        for col = 0, ncol - 1 do
            table.insert(strt, string.format(fmt, self[col]))
        end
        table.insert(strt, "\n")
    end
    table.insert(strt, string.format(
        "[%s %d x %d]", self.__typename, nrow, ncol))
    return table.concat(strt)
end

function nerv.Matrix:_generate(gen)
    if (self:dim() == 2) then
        for i = 0, self:nrow() - 1 do
            local row = self[i]
            for j = 0, self:ncol() - 1 do
                row[j] = gen(i, j)
            end
        end
    else
        for j = 0, self:ncol() - 1 do
            self[j] = gen(j)
        end
    end
end

--- Assign each element in a matrix using the value returned by a callback `gen`.
-- @param gen the callback used to generated the values in the matrix, to which
-- the indices of row and column will be passed (e.g., `gen(i, j)`)
function nerv.Matrix:generate(gen)
    local tmp
    if nerv.is_type(self, 'nerv.CuMatrixFloat') then
        tmp = nerv.MMatrixFloat(self:nrow(), self:ncol())
    elseif nerv.is_type(self, 'nerv.CuMatrixDouble') then
        tmp = nerv.MMatrixDouble(self:nrow(), self:ncol())
    else
        tmp = self
    end
    tmp:_generate(gen)
    if nerv.is_type(self, 'nerv.CuMatrix') then
        self:copy_fromh(tmp)
    end
end

--- Create a fresh new matrix of the same matrix type (as `self`).
-- @param nrow optional, the number of rows in the created matrix if specified,
-- otherwise `self:nrow()` will be used
-- @param ncol optional, the number of columns in the created matrix if specified,
-- otherwise `self:ncol()` will be used
function nerv.Matrix:create(nrow, ncol)
    return self.__constructor(nrow or self:nrow(), ncol or self:ncol())
end

nerv.MMatrixInt.fmt = "%d "

--- Operator overloading of `+`.
function nerv.Matrix:__add__(b)
    c = self:create()
    c:add(self, b, 1.0, 1.0)
    return c
end

--- Operator overloading of `-`.
function nerv.Matrix:__sub__(b)
    c = self:create()
    c:add(self, b, 1.0, -1.0)
    return c
end

--- Operator overloading of `*`.
function nerv.Matrix:__mul__(b)
    c = nerv.get_type(self.__typename)(self:nrow(), b:ncol())
    c:mul(self, b, 1.0, 0.0, 'N', 'N')
    return c
end

--- A wrapper function for `copy_from`.
function nerv.Matrix:copy_to(b, ...)
    b:copy_from(self, ...)
end

--- The base class for all device (in-GPU) matrices.
-- @type nerv.CuMatrix

--- A wrapper function for `copy_fromd`.
nerv.CuMatrix.copy_tod = nerv.Matrix.copy_to

--- CUDA float matrices.
-- @type nerv.CuMatrixFloat

--- Create a CUDA matrix copy of the host matrix (in memory).
-- @param mat the host matrix
function nerv.CuMatrixFloat.new_from_host(mat)
    local res = nerv.CuMatrixFloat(mat:nrow(), mat:ncol())
    res:copy_fromh(mat)
    return res
end

--- Create a host matrix copy of the CUDA matrix.
function nerv.CuMatrixFloat:new_to_host()
    local res = nerv.MMatrixFloat(self:nrow(), self:ncol())
    self:copy_toh(res)
    return res
end

--- CUDA double matrices.
-- @type nerv.CuMatrixDouble

--- Create a CUDA matrix copy of the host matrix (in memory).
-- @param mat the host matrix
function nerv.CuMatrixDouble.new_from_host(mat)
    local res = nerv.CuMatrixDouble(mat:nrow(), mat:ncol())
    res:copy_fromh(mat)
    return res
end

--- Create a host matrix copy of the CUDA matrix.
function nerv.CuMatrixDouble:new_to_host()
    local res = nerv.MMatrixDouble(self:nrow(), self:ncol())