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Class weka.classifiers.m5.Matrix 

java.lang.Object
    |
    +----weka.classifiers.m5.Matrix
public final class Matrix
extends java.lang.Object
Class for handling a matrix

Version:
$Revision: 1.4 $
Author:
Yong Wang (yongwang@cs.waikato.ac.nz)

Constructor Index

o Matrix(int, int)
Constructs a matrix

Method Index

o lubksb(int, int[], double[])
LU backward substitution
o ludcmp(int, int[])
LU decomposition
o multiply(Matrix, int, int, int)
Reurns the multiplication of two matrices
o regression(Matrix, int, int)
Linear regression
o toString(int, int, int, int)
Converts a matrix to a string
o transpose(int, int)
Returns the transpose of a matrix [0:n-1][0:m-1]

Constructor Detail

o Matrix
public Matrix(int nr,
              int nc)
          Constructs a matrix
Parameters:
nr - the number of the rows
nc - the number of the columns

Method Detail

o toString
public final java.lang.String toString(int nrl,
                             int nrh,
                             int ncl,
                             int nch)
          Converts a matrix to a string
Parameters:
nrl - the smallest index of the rows
nrh - the largest index of the rows
ncl - the smallest index of the column
ncl - the largest index of the column
Returns:
the converted string
o transpose
public final Matrix transpose(int n,
                              int m)
          Returns the transpose of a matrix [0:n-1][0:m-1]
Parameters:
n - the number of rows
m - the number of columns
Returns:
the transposed matrix
o multiply
public final Matrix multiply(Matrix b,
                             int l,
                             int m,
                             int n)
          Reurns the multiplication of two matrices
Parameters:
b - the multiplication matrix
l - the number of the rows of the instance matrix
m - the number of the columns of the instance matrix, and the number of the rows of matrix b
n - the number of the columns of matrix b
Returns:
the product matrix
o regression
public final double[] regression(Matrix y,
                                 int n,
                                 int m)
          Linear regression
Parameters:
y - the dependent variable vector
n - the number of the observations
m - the number of the coefficients
Returns:
the coefficients
o lubksb
public final void lubksb(int n,
                         int indx[],
                         double b[])
          LU backward substitution
Parameters:
n - the number of the coefficients
indx - the index
b - the double vector, storing constant terms in the equation sets; it later stores the computed coefficients' values
o ludcmp
public final int[] ludcmp(int n,
                          int indx[])
          LU decomposition
Parameters:
n - the number of coefficients
indx - the index
Returns:
the integer vector of the attributes's singularities
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