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--- 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
--- 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)
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
--- 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
--- 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())
self:copy_toh(res)
return res
end
--- The base class for all host (in-memory) matrices
-- @type nerv.MMatrix
--- A wrapper function for `copy_fromh`
function nerv.MMatrix:copy_toh(b, ...)
b:copy_fromh(self, ...)
end
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