org.apache.spark.mllib.linalg

Class BLAS

Object

org.apache.spark.mllib.linalg.BLAS

All Implemented Interfaces:

java.io.Serializable, Logging

public class BLAS
extends Object
implements scala.Serializable, Logging

BLAS routines for MLlib's vectors and matrices.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
BLAS()

Method Summary

Methods

Modifier and Type	Method and Description
static void	axpy(double a, Vector x, Vector y) y += a * x
static void	copy(Vector x, Vector y) y = x
static double	dot(Vector x, Vector y) dot(x, y)
static void	gemm(boolean transA, boolean transB, double alpha, Matrix A, DenseMatrix B, double beta, DenseMatrix C) C := alpha * A * B + beta * C
static void	gemm(double alpha, Matrix A, DenseMatrix B, double beta, DenseMatrix C) C := alpha * A * B + beta * C
static void	gemv(boolean trans, double alpha, Matrix A, DenseVector x, double beta, DenseVector y) y := alpha * A * x + beta * y
static void	gemv(double alpha, Matrix A, DenseVector x, double beta, DenseVector y) y := alpha * A * x + beta * y
static void	scal(double a, Vector x) x = a * x

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

BLAS

public BLAS()

Method Detail

axpy

public static void axpy(double a,
        Vector x,
        Vector y)

y += a * x

dot

public static double dot(Vector x,
         Vector y)

dot(x, y)

copy

public static void copy(Vector x,
        Vector y)

y = x

scal

public static void scal(double a,
        Vector x)

x = a * x

gemm

public static void gemm(boolean transA,
        boolean transB,
        double alpha,
        Matrix A,
        DenseMatrix B,
        double beta,
        DenseMatrix C)

C := alpha * A * B + beta * C

Parameters:

transA - whether to use the transpose of matrix A (true), or A itself (false).

transB - whether to use the transpose of matrix B (true), or B itself (false).

alpha - a scalar to scale the multiplication A * B.

A - the matrix A that will be left multiplied to B. Size of m x k.

B - the matrix B that will be left multiplied by A. Size of k x n.

beta - a scalar that can be used to scale matrix C.

C - the resulting matrix C. Size of m x n.

gemm

public static void gemm(double alpha,
        Matrix A,
        DenseMatrix B,
        double beta,
        DenseMatrix C)

C := alpha * A * B + beta * C

Parameters:

alpha - a scalar to scale the multiplication A * B.

A - the matrix A that will be left multiplied to B. Size of m x k.

B - the matrix B that will be left multiplied by A. Size of k x n.

beta - a scalar that can be used to scale matrix C.

C - the resulting matrix C. Size of m x n.

gemv

public static void gemv(boolean trans,
        double alpha,
        Matrix A,
        DenseVector x,
        double beta,
        DenseVector y)

y := alpha * A * x + beta * y

Parameters:

trans - whether to use the transpose of matrix A (true), or A itself (false).

alpha - a scalar to scale the multiplication A * x.

A - the matrix A that will be left multiplied to x. Size of m x n.

x - the vector x that will be left multiplied by A. Size of n x 1.

beta - a scalar that can be used to scale vector y.

y - the resulting vector y. Size of m x 1.

gemv

public static void gemv(double alpha,
        Matrix A,
        DenseVector x,
        double beta,
        DenseVector y)

y := alpha * A * x + beta * y

Parameters:

alpha - a scalar to scale the multiplication A * x.

A - the matrix A that will be left multiplied to x. Size of m x n.

x - the vector x that will be left multiplied by A. Size of n x 1.

beta - a scalar that can be used to scale vector y.

y - the resulting vector y. Size of m x 1.