#include <math.h>
#include <stdlib.h>
#include <grass/gis.h>
#include <grass/raster.h>
#include <grass/imagery.h>
#include <grass/glocale.h>
#include <grass/gmath.h>
#include "local_proto.h"

#define ZERO 1e-10
#define SMALLEST_SUBCLUST 1

static void seed();
static double refine_clusters();
static int reestimate();
static double regroup();
static void reduce_order();
static double loglike();
static double distance();
static int compute_constants();
static void add_SubSigs();
static void copy_ClassSig();
static void copy_SubSig();

static int total_nulls, *n_nulls;

void
subcluster(struct SigSet *S, int Class_Index, int *Max_num, int maxsubclasses)
{
    int nparams_clust;
    int ndata_points;
    int min_i, min_j;
    int nbands;
    double rissanen;
    double min_riss;
    struct ClassSig *Sig;

    static int first = 1;
    static struct SigSet min_S;
    static struct ClassSig *min_Sig;

    /* set class pointer */
    Sig = &(S->ClassSig[Class_Index]);

    /* set number of bands */
    nbands = S->nbands;

    /* allocate scratch class first time subroutine is called */
    if (first) {
	int i;

	I_InitSigSet(&min_S, nbands);
	min_Sig = I_NewClassSig(&min_S);

	/* allocate enough subsignatures in scratch space */
	for (i = 0; i < maxsubclasses; i++)
	    I_NewSubSig(&min_S, min_Sig);

	first = 0;
    }

    /* compute number of parameters per cluster */
    nparams_clust = 1 + nbands + 0.5 * (nbands + 1) * nbands;

    /* compute number of data points */
    ndata_points = Sig->ClassData.npixels * nbands - total_nulls;
    if (ndata_points <= 1)
	G_fatal_error("Not enough data points");

    /* Check for too few pixels */
    *Max_num = (ndata_points + 1) / nparams_clust - 1;
    if (maxsubclasses > *Max_num / 2)
	maxsubclasses = *Max_num / 2;
    if (maxsubclasses < 1) {
	G_warning(_("Not enough pixels in class %d"),
		  Class_Index + 1);
	Sig->nsubclasses = 0;
	Sig->used = 0;
	return;
    }

    /* check for too many subclasses */
    if (Sig->nsubclasses > maxsubclasses) {
	Sig->nsubclasses = maxsubclasses;
	G_warning(_("Too many subclasses for class index %d"),
		  Class_Index + 1);
	G_message(_("Number of subclasses set to %d"),
		  Sig->nsubclasses);
    }


    /* initialize clustering */
    seed(Sig, nbands);

    /* EM algorithm */
    min_riss = refine_clusters(Sig, nbands);
    G_debug(1, "Subclasses = %d Rissanen = %f", Sig->nsubclasses,
	      min_riss);
    copy_ClassSig(Sig, min_Sig, nbands);

    G_debug(1, "combine classes");
    while (Sig->nsubclasses > 1) {
	min_i = min_j = 0;
	reduce_order(Sig, nbands, &min_i, &min_j);
	G_verbose_message(_("Combining subclasses (%d,%d)..."), min_i + 1,
			  min_j + 1);
	
	rissanen = refine_clusters(Sig, nbands);
	G_debug(1, "Subclasses = %d; Rissanen = %f", Sig->nsubclasses,
		rissanen);
	if (rissanen < min_riss) {
	    min_riss = rissanen;
	    copy_ClassSig(Sig, min_Sig, nbands);
	}
    }

    copy_ClassSig(min_Sig, Sig, nbands);
}


static void seed(struct ClassSig *Sig, int nbands)
{
    int i, b1, b2;
    double period;
    double *mean, **R;

    G_debug(1, "seed()");

    /* Compute the mean of variance for each band */
    mean = G_alloc_vector(nbands);
    R = G_alloc_matrix(nbands, nbands);
    n_nulls = (int *)G_calloc(nbands, sizeof(int));

    total_nulls = 0;
    for (b1 = 0; b1 < nbands; b1++) {
	n_nulls[b1] = 0;
	mean[b1] = 0.0;
	for (i = 0; i < Sig->ClassData.npixels; i++) {
	    if (Rast_is_d_null_value(&Sig->ClassData.x[i][b1])) {
		n_nulls[b1]++;
		total_nulls++;
	    }
	    else
		mean[b1] += Sig->ClassData.x[i][b1];
	}
	mean[b1] /= (double)(Sig->ClassData.npixels - n_nulls[b1]);
    }

    for (b1 = 0; b1 < nbands; b1++)
	for (b2 = 0; b2 < nbands; b2++) {
	    R[b1][b2] = 0.0;
	    for (i = 0; i < Sig->ClassData.npixels; i++) {
		if (!Rast_is_d_null_value(&Sig->ClassData.x[i][b1]) &&
		    !Rast_is_d_null_value(&Sig->ClassData.x[i][b2]))
		    R[b1][b2] +=
			(Sig->ClassData.x[i][b1]) * (Sig->ClassData.x[i][b2]);
	    }
	    R[b1][b2] /= (double)(Sig->ClassData.npixels - n_nulls[b1] -
				  n_nulls[b2]);
	    R[b1][b2] -= mean[b1] * mean[b2];
	}

    /* Compute the sampling period for seeding */
    if (Sig->nsubclasses > 1) {
	period = (Sig->ClassData.npixels - 1) / (Sig->nsubclasses - 1.0);
    }
    else
	period = 0;


    /* Seed the means and set the diagonal covariance components */
    for (i = 0; i < Sig->nsubclasses; i++) {
	for (b1 = 0; b1 < nbands; b1++) {
	    if (Rast_is_d_null_value(&Sig->ClassData.x[(int)(i * period)][b1]))
		Rast_set_d_null_value(&Sig->SubSig[i].means[b1], 1);
	    else
		Sig->SubSig[i].means[b1] =
		    Sig->ClassData.x[(int)(i * period)][b1];

	    for (b2 = 0; b2 < nbands; b2++) {
		Sig->SubSig[i].R[b1][b2] = R[b1][b2];
	    }
	}

	Sig->SubSig[i].pi = 1.0 / Sig->nsubclasses;
    }

    G_free_vector(mean);
    G_free_matrix(R);

    compute_constants(Sig, nbands);
}


static double refine_clusters(
				 /* Computes ML clustering of data using Gaussian Mixture model.  */
				 /* Returns the values of the Rissen constant for the clustering. */
				 /* If all clusters are singular, the Sig data structure is       */
				 /* returned with Sig->nsubclasses==0 .                           */
				 struct ClassSig *Sig, int nbands)
{
    int nparams_clust;
    int num_params;
    int ndata_points;
    int singular;
    int repeat = 0;
    double rissanen_const;
    double change, ll_new, ll_old;
    double epsilon;

    G_debug(1, "refine_clusters()");

    /* compute number of parameters per cluster */
    nparams_clust = 1 + nbands + 0.5 * (nbands + 1) * nbands;

    /* compute number of data points */
    ndata_points = Sig->ClassData.npixels * nbands - total_nulls;

    /* compute epsilon */
    epsilon = nparams_clust * log((double)ndata_points);
    epsilon *= 0.01;

    /* Perform initial regrouping */
    ll_new = regroup(Sig, nbands);

    /* Perform EM algorithm */
    change = 2 * epsilon;
    do {
	ll_old = ll_new;
	singular = reestimate(Sig, nbands);

	if (singular == 0) {
	    ll_new = regroup(Sig, nbands);
	    change = ll_new - ll_old;
	    repeat = change > epsilon;
	}
	if (singular == 1) {
	    ll_new = regroup(Sig, nbands);
	    repeat = 1;
	}
	if (singular == 2) {
	    repeat = 0;
	}

    } while (repeat);

    /* compute Rissanens expression */
    if (Sig->nsubclasses > 0) {
	num_params = Sig->nsubclasses * nparams_clust - 1;
	rissanen_const =
	    -ll_new + 0.5 * num_params * log((double)ndata_points);
	rissanen_const /= ndata_points;
	return (rissanen_const);
    }
    else {
	return ((double)0);
    }
}


static int reestimate(struct ClassSig *Sig, int nbands)
{
    int i;
    int s;
    int b1, b2;
    int singular;
    double pi_sum;
    double diff1, diff2;
    struct ClassData *Data;

    G_debug(2, "reestimate()");

    /* set data pointer */
    Data = &(Sig->ClassData);

    pi_sum = 0;
    for (i = 0; i < Sig->nsubclasses; i++) {
	/* Compute N */
	Sig->SubSig[i].N = 0;
	for (s = 0; s < Data->npixels; s++)
	    Sig->SubSig[i].N += Data->p[s][i];
	Sig->SubSig[i].pi = Sig->SubSig[i].N;

	/* Compute means and variances for each subcluster, */
	/* and remove small clusters.                       */

	/* For large subclusters */
	if (Sig->SubSig[i].N > SMALLEST_SUBCLUST) {
	    /* Compute mean */
	    for (b1 = 0; b1 < nbands; b1++) {
		Sig->SubSig[i].means[b1] = 0;
		for (s = 0; s < Data->npixels; s++)
		    if (!Rast_is_d_null_value(&Data->x[s][b1]))
			Sig->SubSig[i].means[b1] +=
			    Data->p[s][i] * Data->x[s][b1];
		Sig->SubSig[i].means[b1] /= (Sig->SubSig[i].N);

		/* Compute R */
		for (b2 = 0; b2 <= b1; b2++) {
		    Sig->SubSig[i].R[b1][b2] = 0;
		    for (s = 0; s < Data->npixels; s++) {
			if (!Rast_is_d_null_value(&Data->x[s][b1])
			    && !Rast_is_d_null_value(&Data->x[s][b2])) {
			    diff1 = Data->x[s][b1] - Sig->SubSig[i].means[b1];
			    diff2 = Data->x[s][b2] - Sig->SubSig[i].means[b2];
			    Sig->SubSig[i].R[b1][b2] +=
				Data->p[s][i] * diff1 * diff2;
			}
		    }
		    Sig->SubSig[i].R[b1][b2] /= (Sig->SubSig[i].N);
		    Sig->SubSig[i].R[b2][b1] = Sig->SubSig[i].R[b1][b2];
		}
	    }
	}
	/* For small subclusters */
	else {
	    G_warning(_("Subsignature %d only contains %.0f pixels"),
		      i, Sig->SubSig[i].N);
	    
	    Sig->SubSig[i].pi = 0;

	    for (b1 = 0; b1 < nbands; b1++) {
		Sig->SubSig[i].means[b1] = 0;

		for (b2 = 0; b2 < nbands; b2++)
		    Sig->SubSig[i].R[b1][b2] = 0;
	    }
	}
	pi_sum += Sig->SubSig[i].pi;
    }


    /* Normalize probabilities for subclusters */
    if (pi_sum > 0) {
	for (i = 0; i < Sig->nsubclasses; i++)
	    Sig->SubSig[i].pi /= pi_sum;
    }
    else {
	for (i = 0; i < Sig->nsubclasses; i++)
	    Sig->SubSig[i].pi = 0;
    }


    /* Compute constants and reestimate if any singular subclusters occur */
    singular = compute_constants(Sig, nbands);
    return (singular);
}


static double regroup(struct ClassSig *Sig, int nbands)
{
    int s;
    int i;
    double tmp;
    double maxlike = 0.0;
    double likelihood;
    double subsum;
    struct ClassData *Data;

    /* set data pointer */
    Data = &(Sig->ClassData);

    /* compute likelihoods */
    likelihood = 0;
    for (s = 0; s < Data->npixels; s++) {

	for (i = 0; i < Sig->nsubclasses; i++) {
	    tmp = loglike(Data->x[s], &(Sig->SubSig[i]), nbands);
	    Data->p[s][i] = tmp;
	    if (i == 0)
		maxlike = tmp;
	    if (tmp > maxlike)
		maxlike = tmp;
	}

	subsum = 0;
	for (i = 0; i < Sig->nsubclasses; i++) {
	    tmp = exp(Data->p[s][i] - maxlike) * Sig->SubSig[i].pi;
	    subsum += tmp;
	    Data->p[s][i] = tmp;
	}
	likelihood += log(subsum) + maxlike;

	for (i = 0; i < Sig->nsubclasses; i++)
	    Data->p[s][i] /= subsum;
    }

    return (likelihood);
}


static void
reduce_order(struct ClassSig *Sig, int nbands, int *min_ii, int *min_jj)
{
    int i, j;
    int min_i = 0, min_j = 0;
    double dist;
    double min_dist = 0;
    struct SubSig *SubSig1, *SubSig2;

    static int first = 1;
    struct SigSet S;
    static struct ClassSig *Sig3;
    static struct SubSig *SubSig3;

    /* allocate scratch space first time subroutine is called */
    if (first) {
	I_InitSigSet(&S, nbands);
	Sig3 = I_NewClassSig(&S);
	I_NewSubSig(&S, Sig3);
	SubSig3 = Sig3->SubSig;
	first = 0;
    }

    if (Sig->nsubclasses > 1) {
	/* find the closest subclasses */
	for (i = 0; i < Sig->nsubclasses - 1; i++)
	    for (j = i + 1; j < Sig->nsubclasses; j++) {
		dist = distance(&(Sig->SubSig[i]), &(Sig->SubSig[j]), nbands);
		if ((i == 0) && (j == 1)) {
		    min_dist = dist;
		    min_i = i;
		    min_j = j;
		}
		if (dist < min_dist) {
		    min_dist = dist;
		    min_i = i;
		    min_j = j;
		}
	    }

	*min_ii = min_i;
	*min_jj = min_j;

	/* Combine Subclasses */
	SubSig1 = &(Sig->SubSig[min_i]);
	SubSig2 = &(Sig->SubSig[min_j]);
	add_SubSigs(SubSig1, SubSig2, SubSig3, nbands);
	compute_constants(Sig3, nbands);
	copy_SubSig(SubSig3, SubSig1, nbands);

	/* remove extra subclass */
	for (i = min_j; i < Sig->nsubclasses - 1; i++)
	    copy_SubSig(&(Sig->SubSig[i + 1]), &(Sig->SubSig[i]), nbands);

	/* decrement number of Subclasses */
	Sig->nsubclasses--;
    }
}


static double loglike(DCELL * x, struct SubSig *SubSig, int nbands)
{
    int b1, b2;
    double diff1, diff2;
    double sum;

    sum = 0;
    for (b1 = 0; b1 < nbands; b1++)
	for (b2 = 0; b2 < nbands; b2++) {
	    if (Rast_is_d_null_value(&x[b1])
		|| Rast_is_d_null_value(&x[b2]))
		continue;
	    diff1 = x[b1] - SubSig->means[b1];
	    diff2 = x[b2] - SubSig->means[b2];
	    sum += diff1 * diff2 * SubSig->Rinv[b1][b2];
	}

    sum = -0.5 * sum + SubSig->cnst;
    return (sum);
}


static double
distance(struct SubSig *SubSig1, struct SubSig *SubSig2, int nbands)
{
    double dist;

    static int first = 1;
    struct SigSet S;
    static struct ClassSig *Sig3;
    static struct SubSig *SubSig3;


    /* allocate scratch space first time subroutine is called */
    if (first) {
	I_InitSigSet(&S, nbands);
	Sig3 = I_NewClassSig(&S);
	I_NewSubSig(&S, Sig3);
	SubSig3 = Sig3->SubSig;
	first = 0;
    }

    /* form SubSig3 by adding SubSig1 and SubSig2 */
    add_SubSigs(SubSig1, SubSig2, SubSig3, nbands);

    /* compute constant for SubSig3 */
    compute_constants(Sig3, nbands);

    /* compute distance */
    dist = SubSig1->N * SubSig1->cnst + SubSig2->N * SubSig2->cnst
	- SubSig3->N * SubSig3->cnst;

    return (dist);
}


static int compute_constants(
				/* invert matrix and compute Sig->SubSig[i].cnst          */
				/* Returns singular=1 if a singular subcluster was found. */
				/* Returns singular=2 if all subclusters were singular.   */
				/* When singular=2 then nsubclasses=0.                    */
				struct ClassSig *Sig, int nbands)
{
    int i, j;
    int b1, b2;
    int singular;
    double det;
    double pi_sum;

    static int first = 1;
    static int *indx;
    static double **y;
    static double *col;


    /* allocate memory first time subroutine is called */
    if (first) {
	indx = G_alloc_ivector(nbands);
	y = G_alloc_matrix(nbands, nbands);
	col = G_alloc_vector(nbands);
	first = 0;
    }

    G_debug(2, "compute_constants()");
    /* invert matrix and compute constant for each subclass */
    i = 0;
    singular = 0;
    do {
	for (b1 = 0; b1 < nbands; b1++)
	    for (b2 = 0; b2 < nbands; b2++)
		Sig->SubSig[i].Rinv[b1][b2] = Sig->SubSig[i].R[b1][b2];

	invert(Sig->SubSig[i].Rinv, nbands, &det, indx, y, col);
	if (det <= ZERO) {
	    if (Sig->nsubclasses == 1) {
		Sig->nsubclasses--;
		singular = 2;
		G_warning(_("Unreliable clustering. "
			    "Try a smaller initial number of clusters"));
	    }
	    else {
		for (j = i; j < Sig->nsubclasses - 1; j++)
		    copy_SubSig(&(Sig->SubSig[j + 1]), &(Sig->SubSig[j]),
				nbands);
		Sig->nsubclasses--;
		singular = 1;
		G_warning(_("Removed a singular subsignature number %d (%d remain)"),
			  i + 1, Sig->nsubclasses);
		if (Sig->nsubclasses < 0)	/* MN added 12/2001: to avoid endless loop */
		    Sig->nsubclasses = 1;
	    }
	}
	else {
	    Sig->SubSig[i].cnst =
		(-nbands / 2.0) * log(2 * M_PI) - 0.5 * log(det);
	    i++;
	}
    } while (i < Sig->nsubclasses);

    /* renormalize pi */
    pi_sum = 0;
    for (i = 0; i < Sig->nsubclasses; i++)
	pi_sum += Sig->SubSig[i].pi;
    for (i = 0; i < Sig->nsubclasses; i++)
	Sig->SubSig[i].pi /= pi_sum;

    return (singular);
}


static void add_SubSigs(
			   /* add SubSig1 and SubSig2 to form SubSig3 */
			   struct SubSig *SubSig1,
			   struct SubSig *SubSig2,
			   struct SubSig *SubSig3, int nbands)
{
    int b1, b2;
    double wt1, wt2;
    double tmp;

    wt1 = SubSig1->N / (SubSig1->N + SubSig2->N);
    wt2 = 1 - wt1;

    for (b1 = 0; b1 < nbands; b1++) {
	/* compute means */
	SubSig3->means[b1] =
	    wt1 * SubSig1->means[b1] + wt2 * SubSig2->means[b1];

	/* compute covariance */
	for (b2 = 0; b2 <= b1; b2++) {
	    tmp = (SubSig3->means[b1] - SubSig1->means[b1])
		* (SubSig3->means[b2] - SubSig1->means[b2]);
	    SubSig3->R[b1][b2] = wt1 * (SubSig1->R[b1][b2] + tmp);
	    tmp = (SubSig3->means[b1] - SubSig2->means[b1])
		* (SubSig3->means[b2] - SubSig2->means[b2]);
	    SubSig3->R[b1][b2] += wt2 * (SubSig2->R[b1][b2] + tmp);
	    SubSig3->R[b2][b1] = SubSig3->R[b1][b2];
	}
    }

    /* compute pi and N */
    SubSig3->pi = SubSig1->pi + SubSig2->pi;
    SubSig3->N = SubSig1->N + SubSig2->N;
}

static void copy_ClassSig(
			     /* copy Sig1 to Sig2 */
			     struct ClassSig *Sig1,
			     struct ClassSig *Sig2, int nbands)
{
    int i;

    Sig2->classnum = Sig1->classnum;
    Sig2->title = Sig1->title;
    Sig2->used = Sig1->used;
    Sig2->type = Sig1->type;
    Sig2->nsubclasses = Sig1->nsubclasses;
    for (i = 0; i < Sig1->nsubclasses; i++)
	copy_SubSig(&(Sig1->SubSig[i]), &(Sig2->SubSig[i]), nbands);
}


static void copy_SubSig(
			   /* copy SubSig1 to SubSig2 */
			   struct SubSig *SubSig1,
			   struct SubSig *SubSig2, int nbands)
{
    int b1, b2;

    SubSig2->N = SubSig1->N;
    SubSig2->pi = SubSig1->pi;
    SubSig2->cnst = SubSig1->cnst;
    SubSig2->used = SubSig1->used;

    for (b1 = 0; b1 < nbands; b1++) {
	SubSig2->means[b1] = SubSig1->means[b1];
	for (b2 = 0; b2 < nbands; b2++) {
	    SubSig2->R[b1][b2] = SubSig1->R[b1][b2];
	    SubSig2->Rinv[b1][b2] = SubSig1->Rinv[b1][b2];
	}
    }
}

