import "github.com/alonsovidales/go_matrix_cuda"
cuda_kernel_add_bias.go cuda_kernel_matrix_add.go cuda_kernel_matrix_add_bias_top.go cuda_kernel_matrix_log.go cuda_kernel_matrix_mult.go cuda_kernel_matrix_mult_all.go cuda_kernel_matrix_mult_by.go cuda_kernel_matrix_mult_trans.go cuda_kernel_matrix_neg.go cuda_kernel_matrix_one_minus.go cuda_kernel_matrix_pow_two.go cuda_kernel_matrix_set_bias_to_zero.go cuda_kernel_matrix_sigmoid.go cuda_kernel_matrix_sigmoid_gradient.go cuda_kernel_matrix_sub.go cuda_kernel_matrix_trans.go cuda_kernel_remove_bias.go cuda_kernel_remove_bias_top.go cuda_kernel_sum_all.go matrix.go
Set the ptx files to be used from the cuda_modules directory if true, or the go kernels if false
const KER_MATRIX_ADD = "" /* 1505 byte string literal not displayed */
const KER_MATRIX_ADD_BIAS = "" /* 1605 byte string literal not displayed */
const KER_MATRIX_ADD_BIAS_TOP = "" /* 1591 byte string literal not displayed */
const KER_MATRIX_LOG = "" /* 4341 byte string literal not displayed */
const KER_MATRIX_MULT = "" /* 2021 byte string literal not displayed */
const KER_MATRIX_MULT_ALL = "" /* 1565 byte string literal not displayed */
const KER_MATRIX_MULT_BY = "" /* 1315 byte string literal not displayed */
const KER_MATRIX_MULT_TRANS = "" /* 2192 byte string literal not displayed */
const KER_MATRIX_NEG = "" /* 1193 byte string literal not displayed */
const KER_MATRIX_ONE_MINUS = "" /* 1293 byte string literal not displayed */
const KER_MATRIX_POW_TWO = "" /* 1235 byte string literal not displayed */
const KER_MATRIX_REMOVE_BIAS = "" /* 1482 byte string literal not displayed */
const KER_MATRIX_REMOVE_BIAS_TOP = "" /* 1492 byte string literal not displayed */
const KER_MATRIX_SET_BIAS_TO_ZERO = "" /* 986 byte string literal not displayed */
const KER_MATRIX_SIGMOID = "" /* 10832 byte string literal not displayed */
const KER_MATRIX_SIGMOID_GRADIENT = "" /* 10981 byte string literal not displayed */
const KER_MATRIX_SUB = "" /* 1505 byte string literal not displayed */
const KER_MATRIX_SUM_ALL = "" /* 3536 byte string literal not displayed */
const KER_MATRIX_TRANS = "" /* 1475 byte string literal not displayed */
AddToBuff Adds a memory allocated by cu.MemAlloc to the memory used on this memory space
Apply a function to all the elements of a matrix, the function will receive a float64 as param and returns a float64 too
Cofactors Returns the cofactors matrix TODO Implement using CUDA
Concat Concatenates two matrix elements, ex: m1 = (M111, M112, M113)
(M121, M122, M123) (M131, M132, M133)
m2 = (M211, M212, M213)
(M221, M222, M223) (M231, M232, M233)
rm = (M111, M112, M113, M221, M222, M223)
(M121, M122, M123, M221, M222, M223) (M131, M132, M133, M231, M232, M233)
CudaMemAlloc Allocated memory in the CUDA device and returns a pointer to the allocated memory space
func CudaSync()
CudaSync Blocks until the CUDA device has completed all preceding requested tasks
Det Calculates the determinant of the matrix TODO Implement using CUDA
Div Divide the first matrix by the second one TODO Implement using CUDA
func FreeAllMem()
FreeAllMem Releases all the memory allocated in all the namespaces
func FreeMem()
FreeMem Releases the memory allocated in the current namespace
func InitCuda()
InitCuda Initialize the CUDA driver, and loads all the CUDA kernels on the graphic card
Inv Calculates the inverse matrix TODO Implement using CUDA
Minors Returns the minors matrix TODO Implement using CUDA
func MoveToCuda(m [][]float64, p *CudaMatrix)
MoveToCuda Moves a two dimensional array from the memory of the machine to the memory of the CUDA device on a previously initialized CudaMatrix struct
func SetDefaultBuff()
SetDefaultBuff Sets the default buffer to be used
SetDevice Specify the CUDA device to be used, 0 by default
StartBufferingMem Specifies a namespace to be used in order to define a space in memory, this namespace will be used by the garbage collector in order to release memory. @see FreeAllMem, FreeMem methods
SumAll all the elements in a matrix
type CudaMatrix struct {
// contains filtered or unexported fields
}CudaMatrix This struct represents a matrix allocated in the memory of the CUDA device
func CudaSub(m1 *CudaMatrix, m2 *CudaMatrix) (rm *CudaMatrix)
CudaSub Matrix subtraction, returns the result in a new CudaMatrix allocating memory on the CUDA device to store it
func CudaSubTo(m1 *CudaMatrix, m2 *CudaMatrix, rm *CudaMatrix) *CudaMatrix
CudaSubTo Matrix subtraction, returns the result in the provided Cuda Matrix without need to allocate new memory to store it
func CudaSum(m1 *CudaMatrix, m2 *CudaMatrix) (rm *CudaMatrix)
CudaSum Sums two matrices, returns the result in a new CudaMatrix allocating memory on the CUDA device to store it
func CudaSumTo(m1 *CudaMatrix, m2 *CudaMatrix, rm *CudaMatrix) *CudaMatrix
CudaSumTo Sums two matrices and returns the result in the provided Cuda Matrix without need to allocate new memory to store it
func GetCudaMatrix(m [][]float64) (p *CudaMatrix)
GetCudaMatrix Initilizes and returns a CudaMatrix struct allocating the necessary memory on the cuda device, and copying from the machine memory to the CUDA device memory the especified matrix
func InitCudaMatrix(w int, h int) (m *CudaMatrix)
InitCudaMatrix Initializes a CUDA matrix struct allocating all the necesary memory on the CUDA device
func Mult(m1 *CudaMatrix, m2 *CudaMatrix) (rm *CudaMatrix)
Mult Multiplies two matrices, and returns the result in a new CudaMatrix allocating memory to store the result on the CUDA device memory
func MultAllElems(m1 *CudaMatrix, m2 *CudaMatrix) (rm *CudaMatrix)
MultAllElems Multiplies one by one all the elements of two matrices, and returns the result in a new allocated matrix on the CUDA device memory ex:
1 2 3 4 5 * 1 2 3 4 5 --> 1 4 9 16 25 1 2 3 4 5 * 1 2 3 4 5 --> 1 4 9 16 25 1 2 3 4 5 * 1 2 3 4 5 --> 1 4 9 16 25
func MultAllElemsTo(m1 *CudaMatrix, m2 *CudaMatrix, rm *CudaMatrix) *CudaMatrix
MultAllElemsTo Multiplies one by one all the elements of two matrices, and uses the allocated memory on the third specified matrix to store the result, this method is much more faster than allocate new memory for the result, and can reutilize previously allocated memory on the CUDA device ex:
1 2 3 4 5 * 1 2 3 4 5 --> 1 4 9 16 25 1 2 3 4 5 * 1 2 3 4 5 --> 1 4 9 16 25 1 2 3 4 5 * 1 2 3 4 5 --> 1 4 9 16 25
func MultTo(m1 *CudaMatrix, m2 *CudaMatrix, rm *CudaMatrix) *CudaMatrix
MultTo Multiplies two matrices, and returns the result in the given CudaMatrix without allocate memory on the CUDA device
func MultTrans(m1 *CudaMatrix, m2 *CudaMatrix) (rm *CudaMatrix)
MultTrans Multiply a CudaMatrix by the transpose of another CudaMatrix and returns the result in a new CudaMatrix allocating memory on the CUDA device to store it
func MultTransTo(m1 *CudaMatrix, m2 *CudaMatrix, rm *CudaMatrix) *CudaMatrix
MultTransTo Multiply a CudaMatrix by the transpose of another CudaMatrix and returns the result in the provided CudaMatrix without need to allocate new memory to store it
func (m *CudaMatrix) AddBias() (rm *CudaMatrix)
AddBias Method for machine learning: Adds a bias column setted to one to a given CudaMatrix and returns a new CudaMatrix allocating memory on the CUDA device to store the resulting matrix ex:
1 2 3 4 5 --> 1 2 3 4 5 1 1 2 3 4 5 --> 1 2 3 4 5 1 1 2 3 4 5 --> 1 2 3 4 5 1
func (m *CudaMatrix) AddBiasTo(rm *CudaMatrix) *CudaMatrix
AddBiasTo Method for machine learning: Adds a bias column setted to one to a given CudaMatrix and stores the result in the given matrix withouth allocate new memory on the CUDA device ex:
1 2 3 4 5 --> 1 2 3 4 5 1 1 2 3 4 5 --> 1 2 3 4 5 1 1 2 3 4 5 --> 1 2 3 4 5 1
func (m *CudaMatrix) AddBiasTop() (rm *CudaMatrix)
AddBiasTop Method for machine learning: Adds a new row at the top of the matrix filled by 1's, and returns a new CudaMatrix allocating the necessary memory on the CUDA device ex:
--> 1 1 1 1 1 1 2 3 4 5 --> 1 2 3 4 5 1 2 3 4 5 --> 1 2 3 4 5
func (m *CudaMatrix) AddBiasTopTo(rm *CudaMatrix) *CudaMatrix
AddBiasTopTo Method for machine learning: Adds a new row at the top of the matrix filled by 1's, stores the result in the given CudaMatrix without allocate memory on the CUDA device if is not necessary ex:
--> 1 1 1 1 1 1 2 3 4 5 --> 1 2 3 4 5 1 2 3 4 5 --> 1 2 3 4 5
func (m *CudaMatrix) Copy() (r *CudaMatrix)
Copy Returns a copy of the cuda matrix in a new memory allocated space inside the same device. This operation is performed internally in the CUDA device
func (m *CudaMatrix) CopyTo(t *CudaMatrix) *CudaMatrix
CopyTo Copy a cuda matrix to a previously memory allocated cuda matrix, this copy operation is performed internally in the CUDA device
func (m *CudaMatrix) Free()
Free Removes all the memory used to allocate a CudaMatrix
func (m *CudaMatrix) GetMatrixFromCuda() (r [][]float64)
GetMatrixFromCuda Returns a two domensional array that represents a matrix allocated on the machine memory from a CudaMatrix. Downloads a matrix from the CUDA device
func (m *CudaMatrix) H() int
H Returns the number of rows on a cuda matrix
func (m *CudaMatrix) Log() *CudaMatrix
Log Apply the log to all the elements of the CudaMatrix
func (m *CudaMatrix) MultBy(by float64) *CudaMatrix
MultBy Multiply all the elements of a matrix by a given float and returns a new CudaMatrix allocating memory on the CUDA device to store the result
func (m *CudaMatrix) Neg() *CudaMatrix
Neg Negates all the elements of the CudaMatrix ex:
1 2 3 4 5 --> -1 -2 -3 -4 -5 1 2 3 4 5 --> -1 -2 -3 -4 -5
func (m *CudaMatrix) OneMinus() *CudaMatrix
OneMinus Applies 1 - n where n is each element of the matrix on the same given CudaMatrix ex:
1 2 3 4 5 --> 0 -1 -2 -3 -4 1 2 3 4 5 --> 0 -1 -2 -3 -4
func (m *CudaMatrix) PowTwo() *CudaMatrix
PowTwo Multiplies all the elements of a CudaMatrix by themselves
func (m *CudaMatrix) RemoveBias() (rm *CudaMatrix)
RemoveBias Method for machine learning: Removes the last column of a matrix and returns a new CudaMatrix performing the memory allocation in the CUDA device ex:
1 2 3 4 5 1 --> 1 2 3 4 5 1 2 3 4 5 1 --> 1 2 3 4 5 1 2 3 4 5 1 --> 1 2 3 4 5
func (m *CudaMatrix) RemoveBiasTo(rm *CudaMatrix) *CudaMatrix
RemoveBiasTo Method for machine learning: Removes the last column of a matrix and uses the given CudaMatrix to allocate the result, if the CudaMatrix CUDA memory was previously allocates, reuses the memory without need to perform an alocation ex:
1 2 3 4 5 1 --> 1 2 3 4 5 1 2 3 4 5 1 --> 1 2 3 4 5 1 2 3 4 5 1 --> 1 2 3 4 5
func (m *CudaMatrix) RemoveBiasTop() (rm *CudaMatrix)
RemoveBiasTop Method for machine learning: Removes a bias row from the top of the given matrix, and stores the result on the given CudaMatrix without allocate new memory on the CUDA device ex:
1 1 1 1 1 --> 1 2 3 4 5 --> 1 2 3 4 5 1 2 3 4 5 --> 1 2 3 4 5
func (m *CudaMatrix) RemoveBiasTopTo(rm *CudaMatrix) *CudaMatrix
RemoveBiasTopTo Method for machine learning: Removes a bias row from the top of the given matrix, and stores the result on the given CudaMatrix without allocate new memory on the CUDA device ex:
1 1 1 1 1 1 2 3 4 5 --> 1 2 3 4 5 1 2 3 4 5 --> 1 2 3 4 5
func (m *CudaMatrix) SetBiasToZero() *CudaMatrix
SetBiasToZero Method for machine learning: Sets the right column to zero, this transformation is applied directly in the same CudaMatrix ex:
1 2 3 4 5 1 --> 1 2 3 4 5 0 1 2 3 4 5 1 --> 1 2 3 4 5 0 1 2 3 4 5 1 --> 1 2 3 4 5 0
func (m *CudaMatrix) SetPosTo(val float64, x int, y int) *CudaMatrix
SetPosTo Modifies the value on a position given by the x and y coordinates by the given value on a cuda matrix
func (m *CudaMatrix) Sigmoid() *CudaMatrix
Sigmoid Applies the sigmoid function to all the elements of the CudaMatrix with z as element: 1.0 / (1.0 + exp(-z));
func (m *CudaMatrix) SigmoidGradient() *CudaMatrix
SigmoidGradient Applies the sigmoid gradient to all the elements of the CudaMatrix with z as element: sigmoid(z) * (1 - sigmoid(z));
func (m *CudaMatrix) SumAll() float64
SumAll Returns the sum of all the elements in a CudaMatrix ex: 1 2 3 4 5 6 7 8 9 10 --> 55
func (m *CudaMatrix) Trans() (rm *CudaMatrix)
Trans Transposes a CudaMatrix and stores the result in a new CudaMatrix allocating the necessary memory on the CUDA device to it
func (m *CudaMatrix) TransOneDimMatrix() *CudaMatrix
TransOneDimMatrix Transposes a one dimensional CudaMatrix, this method doesn't performs any operation in the CUDA device, and is recomended to transform 1D matrices ex:
1 2 3 --> 1
--> 2
--> 3
func (m *CudaMatrix) TransTo(rm *CudaMatrix) *CudaMatrix
TransTo Transposes a CudaMatrix and stores the result in the provided CudaMatrix, if the CudaMatrix points to a previously allocated memory on the CUDA device, doesn't allocate new memory for the result
func (m *CudaMatrix) W() int
W Returns the number of columns on a cuda matrix
Package mt imports 3 packages (graph). Updated 2017-11-23. Refresh now. Tools for package owners.