/** All questions regarding the software should be addressed to* *       Prof. Charles A. Bouman*       Purdue University*       School of Electrical and Computer Engineering*       1285 Electrical Engineering Building*       West Lafayette, IN 47907-1285*       USA*       +1 765 494 0340*       +1 765 494 3358 (fax)*       email:  bouman@ecn.purdue.edu*       http://www.ece.purdue.edu/~bouman* * Copyright (c) 1995 The Board of Trustees of Purdue University.** Permission to use, copy, modify, and distribute this software and its* documentation for any purpose, without fee, and without written agreement is* hereby granted, provided that the above copyright notice and the following* two paragraphs appear in all copies of this software.** IN NO EVENT SHALL PURDUE UNIVERSITY BE LIABLE TO ANY PARTY FOR DIRECT,* INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE* USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF PURDUE UNIVERSITY HAS* BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.** PURDUE UNIVERSITY SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A* PARTICULAR PURPOSE.  THE SOFTWARE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS,* AND PURDUE UNIVERSITY HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT,* UPDATES, ENHANCEMENTS, OR MODIFICATIONS.*/#ifndef CLUST_DEFS_H#define CLUST_DEFS_H/*****************************************************//* This constant determines the ratio of the average *//* covariance to the minimum allowed covariance.     *//* It is used to insure that the measured covariance *//* is not singular. It may need to be adjusted for   *//* different applications.                           *//*****************************************************/#define COVAR_DYNAMIC_RANGE 1E5#define CLUSTER_FULL 1 /* Use full covariance matrix in clustering */#define CLUSTER_DIAG 0 /* Use diagonal covariance matrix in clustering */#define PI 3.141592654#include<stdio.h>#include<stdlib.h>#include <string.h>#include <math.h>#include <float.h>/* SigSet (Signature Set) data stucture used throughout package.         *//*   ClassSig (Class Signature) data stucture holds the parameters       *//*       of a single Gaussian mixture model. SigSet.nclasses is the      *//*       number of ClassSig's in a SigSet.                               *//*     SubSig (Subsignature) data stucture holds each component of a     *//*         Gaussian mixture model. SigSet.ClassSig[k].nsubclasses is the *//*         number of SubSig's in a ClassSig.                             */        struct ClassData
        {
            int npixels;
            double SummedWeights;
            double **x; /* list of pixel vectors:     x[npixels][nbands] */
            double **p; /* prob pixel is in subclass: p[npixels][subclasses] */
            double  *w; /* weight of pixel:           w[npixels] */
			int     *ml_label;				/* subclass_label[npixels] subclass label with ML */
        };

        struct SubSig		//rep. one Gaussian component
        {
            double N;       /* expected number of pixels in subcluster */
            double pi;      /* probability of component in GMM */
            double *means;  /* mean of component in GMM */
            double **R;     /* covariance of component in GMM */
            double **Rinv;  /* inverse of R */
            double cnst;    /* normalizing constant for multivariate Gaussian */
            int used;
        };

    struct ClassSig	//rep. one GMM model
    {
        long classnum;	
        char *title;
        int used;
        int type;
        int nsubclasses;	//No. of Gaussian components in one GMM
        struct SubSig		//rep. one Gaussian component
        {
            double N;       /* expected number of pixels in subcluster */
            double pi;      /* probability of component in GMM */
            double *means;  /* mean of component in GMM */
            double **R;     /* covariance of component in GMM */
            double **Rinv;  /* inverse of R */
            double cnst;    /* normalizing constant for multivariate Gaussian */
            int used;
        } *SubSig;
        struct ClassData
        {
            int npixels;
            double SummedWeights;
            double **x; /* list of pixel vectors:     x[npixels][nbands] */
            double **p; /* prob pixel is in subclass: p[npixels][subclasses] */
            double  *w; /* weight of pixel:           w[npixels] */
			int     *ml_label;				/* subclass_label[npixels] subclass label with ML */
        } ClassData;
    };		

struct SigSet{    int nbands;    int nclasses;	//No. of GMM models (No. of classes)    char *title;    struct ClassSig	//rep. one GMM model    {        long classnum;	        char *title;        int used;        int type;        int nsubclasses;	//No. of Gaussian components in one GMM        struct SubSig		//rep. one Gaussian component        {            double N;       /* expected number of pixels in subcluster */            double pi;      /* probability of component in GMM */            double *means;  /* mean of component in GMM */            double **R;     /* covariance of component in GMM */            double **Rinv;  /* inverse of R */            double cnst;    /* normalizing constant for multivariate Gaussian */            int used;        } *SubSig;        struct ClassData        {            int npixels;            double SummedWeights;            double **x; /* list of pixel vectors:     x[npixels][nbands] */            double **p; /* prob pixel is in subclass: p[npixels][subclasses] */            double  *w; /* weight of pixel:           w[npixels] */
			int     *ml_label;				/* subclass_label[npixels] subclass label with ML */        } ClassData;    } *ClassSig;};#define SIGNATURE_TYPE_MIXED 1#endif /* CLUST_DEFS_H */