baseCode - 0.2.5

Clover coverage report - baseCode - 0.2.5

Coverage timestamp: Tue Apr 12 2005 11:31:58 EDT

FRAMES NO FRAMES

file stats:	LOC:	159		Methods:	2
	NCLOC:	43		Classes:	1

30 day Evaluation Version distributed via the Maven Jar Repository. Clover is not free. You have 30 days to evaluate it. Please visit http://www.thecortex.net/clover to obtain a licensed version of Clover

Source file

Conditionals

Statements

Methods

TOTAL

Wishart.java

70%

86.4%

100%

82.4%

 package baseCode.math.distribution;
 
 import cern.colt.matrix.DoubleMatrix2D;
 import cern.colt.matrix.impl.DenseDoubleMatrix2D;
 import cern.colt.matrix.linalg.Algebra;
 import cern.colt.matrix.linalg.CholeskyDecomposition;
 import cern.jet.random.Gamma;
 import cern.jet.random.Normal;
 import cern.jet.random.engine.RandomEngine;
 
 /**
  * Wishart distribution, used to simulate covariance matrices.
  * <p>
  * Based on method in Odell and Feiveson JASA 1966 p.199-203
  * <p>
  * The interface is modeled after ContinuousDistribution from colt, which unfortunately is designed only for univariate
  * distributions.
  * <hr>
  * <p>
  * Copyright (c) 2004 Columbia University
  * 
  * @author pavlidis
  * @version $Id: Wishart.java,v 1.1 2005/01/16 03:51:11 pavlidis Exp $
  */
 public class Wishart {
 
    private Gamma rgamma;
    int s; // dimension of matrix
    double df; // degrees of freedom
    DoubleMatrix2D cov; // input covariance matrix
    DoubleMatrix2D chol; // cholesky decomposition of the covariance matrix.
    private DoubleMatrix2D mat;
    private Normal rnorm;
    private Algebra a = new Algebra();
    private RandomEngine r;
 
    /**
     * @param s
     * @param df
     * @param covariance
     * @param randomGenerator
     */
    public Wishart( double df, DoubleMatrix2D covariance, RandomEngine randomGenerator ) {
       this.s = covariance.columns();
       if ( s != covariance.rows() ) throw new IllegalArgumentException( "Covariance matrix must be square" );
       if ( df <= s - 1 ) throw new IllegalArgumentException( "df must be greater than s - 1" );
       if ( randomGenerator == null ) throw new IllegalArgumentException( "Null random number generator" );
 
       this.r = randomGenerator;
       this.df = df;
       this.cov = covariance;
 
       rgamma = new Gamma( 1, 1, r );
       rnorm = new Normal( s * ( s - 1.0 ) / 2.0, 1.0, r );
 
       CholeskyDecomposition c = new CholeskyDecomposition( covariance );
       chol = a.transpose( c.getL() ); // returns lower triangle so we transpose to make upper triangular.
       mat = new DenseDoubleMatrix2D( this.s, this.s );
    }
 
    /**
     * Based on R code from Francesca Dominici, <a
     * href="http://www.biostat.jhsph.edu/~fdominic/teaching/BM/bm.html">http://www.biostat.jhsph.edu/~fdominic/teaching/BM/bm.html
     * </a>
     * <p>
     * Returns
     * 
     * <pre>
     * 
     *  
     *   
     *    
     *     
     *            
     *                 w=(RU)'RU
     *       
     *      
     *     
     *    
     *   
     *  
     * </pre>
     * 
     * where
     * 
     * <pre>
     * 
     *  
     *   
     *    
     *     
     *      
     *       
     *        
     *         Cov=U'U (U is upper triang) 
     *    
     *   
     *  
     * </pre>
     * 
     * and where upper-tri R is
     * 
     * <pre>
     * 
     *  
     *                                         R_ij&tilde;N(0,1), i&lt;j ; (R_ii)&circ;2&tilde;Chisq(nu-s+i)
     *    
     *   
     *  
     * </pre>
     * 
     * @param s
     * @param nu
     * @param covariance
     * @return
     */
    public DoubleMatrix2D nextDoubleMatrix() {
       mat.assign( 0.0 );
 
       // fill in diagonal with random gamma deviates, upper triangle with random normal deviates.
       for ( int i = 0; i < s; i++ ) {
          mat.setQuick( i, i, Math.sqrt( 2 * rgamma.nextDouble( s, ( df + 1.0 - i ) / 2.0 ) ) );
          for ( int j = i + 1; j < s; j++ ) {
             mat.setQuick( i, j, rnorm.nextDouble() );
          }
       }
 
       mat = a.mult( mat, chol );
       return a.mult( a.transpose( mat ).copy(), mat );
 
    }
 
    //   #GENERATE WISHART------------------------------------------------------------
    //   "rwish" <- function(s,nu,Cov)
    //   {
    //   #sxs Wishart matrix, nu degree of freedom, var/covar Cov based on
    //   #P.L.Odell & A.H. Feiveson(JASA 1966 p.199-203). Returns w=(RU)'RU
    //   #where Cov=U'U (U is upper triang) and where upper-tri R is
    //   # R_ij~N(0,1), i<j ; (R_ii)^2~Chisq(nu-s+i)
    //   if (nu<=s-1) stop ("Wishart algorithm requires nu>s-1")
    //   R<- diag(sqrt(2*rgamma(s,(nu+1 - 1:s)/2)))
    //   R[outer(1:s, 1:s, "<")] <- rnorm (s*(s-1)/2)
    //   R <- R%*% chol(Cov)
    //   return(t(R)%*%R)
    //   }
    //   #GENERATE INVERSE WISHART----------------------------------------------------
    //   "riwish" <- function(s,df,Prec)
    //   {
    //   #sxs Inverse Wishart matrix, df degree of freedom, precision matrix
    //   #Prec. Distribution of W^{-1} for Wishart W with nu=df+s-1 degree of
    //   # freedoom, covar martix Prec^{-1}.
    //   # NOTE mean of riwish is proportional to Prec
    //   if (df<=0) stop ("Inverse Wishart algorithm requires df>0")
    //   R <- diag(sqrt(2*rgamma(s,(df + s - 1:s)/2)))
    //   R[outer(1:s, 1:s, "<")] <- rnorm (s*(s-1)/2)
    //   S <- t(solve(R))%*% chol(Prec)
    //   return(t(S)%*%S)
 
 }

1		package baseCode.math.distribution;
2
3		import cern.colt.matrix.DoubleMatrix2D;
4		import cern.colt.matrix.impl.DenseDoubleMatrix2D;
5		import cern.colt.matrix.linalg.Algebra;
6		import cern.colt.matrix.linalg.CholeskyDecomposition;
7		import cern.jet.random.Gamma;
8		import cern.jet.random.Normal;
9		import cern.jet.random.engine.RandomEngine;
10
11		/**
12		* Wishart distribution, used to simulate covariance matrices.
13		* <p>
14		* Based on method in Odell and Feiveson JASA 1966 p.199-203
15		* <p>
16		* The interface is modeled after ContinuousDistribution from colt, which unfortunately is designed only for univariate
17		* distributions.
18		* <hr>
19		* <p>
20		* Copyright (c) 2004 Columbia University
21		*
22		* @author pavlidis
23		* @version $Id: Wishart.java,v 1.1 2005/01/16 03:51:11 pavlidis Exp $
24		*/
25		public class Wishart {
26
27		private Gamma rgamma;
28		int s; // dimension of matrix
29		double df; // degrees of freedom
30		DoubleMatrix2D cov; // input covariance matrix
31		DoubleMatrix2D chol; // cholesky decomposition of the covariance matrix.
32		private DoubleMatrix2D mat;
33		private Normal rnorm;
34		private Algebra a = new Algebra();
35		private RandomEngine r;
36
37		/**
38		* @param s
39		* @param df
40		* @param covariance
41		* @param randomGenerator
42		*/
43	1	public Wishart( double df, DoubleMatrix2D covariance, RandomEngine randomGenerator ) {
44	1	this.s = covariance.columns();
45	0	if ( s != covariance.rows() ) throw new IllegalArgumentException( "Covariance matrix must be square" );
46	0	if ( df <= s - 1 ) throw new IllegalArgumentException( "df must be greater than s - 1" );
47	0	if ( randomGenerator == null ) throw new IllegalArgumentException( "Null random number generator" );
48
49	1	this.r = randomGenerator;
50	1	this.df = df;
51	1	this.cov = covariance;
52
53	1	rgamma = new Gamma( 1, 1, r );
54	1	rnorm = new Normal( s * ( s - 1.0 ) / 2.0, 1.0, r );
55
56	1	CholeskyDecomposition c = new CholeskyDecomposition( covariance );
57	1	chol = a.transpose( c.getL() ); // returns lower triangle so we transpose to make upper triangular.
58	1	mat = new DenseDoubleMatrix2D( this.s, this.s );
59		}
60
61		/**
62		* Based on R code from Francesca Dominici, <a
63		* href="http://www.biostat.jhsph.edu/~fdominic/teaching/BM/bm.html">http://www.biostat.jhsph.edu/~fdominic/teaching/BM/bm.html
64		* </a>
65		* <p>
66		* Returns
67		*
68		* <pre>
69		*
70		*
71		*
72		*
73		*
74		*
75		* w=(RU)'RU
76		*
77		*
78		*
79		*
80		*
81		*
82		* </pre>
83		*
84		* where
85		*
86		* <pre>
87		*
88		*
89		*
90		*
91		*
92		*
93		*
94		*
95		* Cov=U'U (U is upper triang)
96		*
97		*
98		*
99		* </pre>
100		*
101		* and where upper-tri R is
102		*
103		* <pre>
104		*
105		*
106		* R_ij&tilde;N(0,1), i<j ; (R_ii)&circ;2&tilde;Chisq(nu-s+i)
107		*
108		*
109		*
110		* </pre>
111		*
112		* @param s
113		* @param nu
114		* @param covariance
115		* @return
116		*/
117	1	public DoubleMatrix2D nextDoubleMatrix() {
118	1	mat.assign( 0.0 );
119
120		// fill in diagonal with random gamma deviates, upper triangle with random normal deviates.
121	1	for ( int i = 0; i < s; i++ ) {
122	2	mat.setQuick( i, i, Math.sqrt( 2 * rgamma.nextDouble( s, ( df + 1.0 - i ) / 2.0 ) ) );
123	2	for ( int j = i + 1; j < s; j++ ) {
124	1	mat.setQuick( i, j, rnorm.nextDouble() );
125		}
126		}
127
128	1	mat = a.mult( mat, chol );
129	1	return a.mult( a.transpose( mat ).copy(), mat );
130
131		}
132
133		// #GENERATE WISHART------------------------------------------------------------
134		// "rwish" <- function(s,nu,Cov)
135		// {
136		// #sxs Wishart matrix, nu degree of freedom, var/covar Cov based on
137		// #P.L.Odell & A.H. Feiveson(JASA 1966 p.199-203). Returns w=(RU)'RU
138		// #where Cov=U'U (U is upper triang) and where upper-tri R is
139		// # R_ij~N(0,1), i<j ; (R_ii)^2~Chisq(nu-s+i)
140		// if (nu<=s-1) stop ("Wishart algorithm requires nu>s-1")
141		// R<- diag(sqrt(2*rgamma(s,(nu+1 - 1:s)/2)))
142		// R[outer(1:s, 1:s, "<")] <- rnorm (s*(s-1)/2)
143		// R <- R%*% chol(Cov)
144		// return(t(R)%*%R)
145		// }
146		// #GENERATE INVERSE WISHART----------------------------------------------------
147		// "riwish" <- function(s,df,Prec)
148		// {
149		// #sxs Inverse Wishart matrix, df degree of freedom, precision matrix
150		// #Prec. Distribution of W^{-1} for Wishart W with nu=df+s-1 degree of
151		// # freedoom, covar martix Prec^{-1}.
152		// # NOTE mean of riwish is proportional to Prec
153		// if (df<=0) stop ("Inverse Wishart algorithm requires df>0")
154		// R <- diag(sqrt(2*rgamma(s,(df + s - 1:s)/2)))
155		// R[outer(1:s, 1:s, "<")] <- rnorm (s*(s-1)/2)
156		// S <- t(solve(R))%*% chol(Prec)
157		// return(t(S)%*%S)
158
159		}