/*
 *  cluster_segmenter.c
 *  soundbite
 *
 *  Created by Mark Levy on 06/04/2006.
 *  Copyright 2006 Centre for Digital Music, Queen Mary, University of London. All rights reserved.
 *
 */

#include "cluster_segmenter.h"

extern int readmatarray_size(const char *filepath, int n_array, int* t, int* d);
extern int readmatarray(const char *filepath, int n_array, int t, int d, double** arr);

/* converts constant-Q features to normalised chroma */
void cq2chroma(double** cq, int nframes, int ncoeff, int bins, double** chroma)
{
	int noct = ncoeff / bins;	/* number of complete octaves in constant-Q */
	int t, b, oct, ix;
	//double maxchroma;	/* max chroma value at each time, for normalisation */
	//double sum;		/* for normalisation */
	
	for (t = 0; t < nframes; t++)
	{
		for (b = 0; b < bins; b++)
			chroma[t][b] = 0;
		for (oct = 0; oct < noct; oct++)
		{
			ix = oct * bins;
			for (b = 0; b < bins; b++)
				chroma[t][b] += fabs(cq[t][ix+b]);
		}
		/* normalise to unit sum
		sum = 0;
		for (b = 0; b < bins; b++)
			sum += chroma[t][b];
		for (b = 0; b < bins; b++)
			chroma[t][b] /= sum;
		*/
		/* normalise to unit max - NO this made results much worse!
		maxchroma = 0;
		for (b = 0; b < bins; b++)
			if (chroma[t][b] > maxchroma)
				maxchroma = chroma[t][b];
		if (maxchroma > 0)
			for (b = 0; b < bins; b++)
				chroma[t][b] /= maxchroma;	
		*/
	}
}

/* applies MPEG-7 normalisation to constant-Q features, storing normalised envelope (norm) in last feature dimension */
void mpeg7_constq(double** features, int nframes, int ncoeff)
{
	int i, j;
	double ss;
	double env;
	double maxenv = 0;
	
	/* convert const-Q features to dB scale */
	for (i = 0; i < nframes; i++)
		for (j = 0; j < ncoeff; j++)
			features[i][j] = 10.0 * log10(features[i][j]+DBL_EPSILON);
	
	/* normalise each feature vector and add the norm as an extra feature dimension */	
	for (i = 0; i < nframes; i++)
	{
		ss = 0;
		for (j = 0; j < ncoeff; j++)
			ss += features[i][j] * features[i][j];
		env = sqrt(ss);
		for (j = 0; j < ncoeff; j++)
			features[i][j] /= env;
		features[i][ncoeff] = env;
		if (env > maxenv)
			maxenv = env;
	} 
	/* normalise the envelopes */
	for (i = 0; i < nframes; i++)
		features[i][ncoeff] /= maxenv;	
}

/* return histograms h[nx*m] of data x[nx] into m bins using a sliding window of length h_len (MUST BE ODD) */
/* NB h is a vector in row major order, as required by cluster_melt() */
/* for historical reasons we normalise the histograms by their norm (not to sum to one) */
void create_histograms(int* x, int nx, int m, int hlen, double* h)
{
	int i, j, t;
	double norm;

	for (i = 0; i < nx*m; i++) 
	        h[i] = 0;

	for (i = hlen/2; i < nx-hlen/2; i++)
	{
		for (j = 0; j < m; j++)
			h[i*m+j] = 0;
		for (t = i-hlen/2; t <= i+hlen/2; t++)
			++h[i*m+x[t]];
		norm = 0;
		for (j = 0; j < m; j++)
			norm += h[i*m+j] * h[i*m+j];
		for (j = 0; j < m; j++)
			h[i*m+j] /= norm;
	}
	
	/* duplicate histograms at beginning and end to create one histogram for each data value supplied */
	for (i = 0; i < hlen/2; i++)
		for (j = 0; j < m; j++)
			h[i*m+j] = h[hlen/2*m+j];
	for (i = nx-hlen/2; i < nx; i++)
		for (j = 0; j < m; j++)
			h[i*m+j] = h[(nx-hlen/2-1)*m+j];
}

/* segment using HMM and then histogram clustering */
void cluster_segment(int* q, double** features, int frames_read, int feature_length, int nHMM_states, 
					 int histogram_length, int nclusters, int neighbour_limit)
{
	int i, j;
	
	/*****************************/
	if (0) {
	/* try just using the predominant bin number as a 'decoded state' */
	nHMM_states = feature_length + 1;	/* allow a 'zero' state */
	double chroma_thresh = 0.05;
	double maxval;
	int maxbin;
	for (i = 0; i < frames_read; i++)
	{
		maxval = 0;
		for (j = 0; j < feature_length; j++)
		{
			if (features[i][j] > maxval) 
			{
				maxval = features[i][j];
				maxbin = j;
			}				
		}
		if (maxval > chroma_thresh)
			q[i] = maxbin;
		else
			q[i] = feature_length;
	}
	
	}
	if (1) {
	/*****************************/
		
	
	/* scale all the features to 'balance covariances' during HMM training */
	double scale = 10;
	for (i = 0; i < frames_read; i++)
		for (j = 0; j < feature_length; j++)
			features[i][j] *= scale;
	
	/* train an HMM on the features */
	
	/* create a model */
	model_t* model = hmm_init(features, frames_read, feature_length, nHMM_states);
	
	/* train the model */
	hmm_train(features, frames_read, model);
	
	printf("\n\nafter training:\n");
	hmm_print(model);
	
	/* decode the hidden state sequence */
	viterbi_decode(features, frames_read, model, q);  
	hmm_close(model);
	
	/*****************************/
	}
	/*****************************/
	
    
	fprintf(stderr, "HMM state sequence:\n");
	for (i = 0; i < frames_read; i++)
		fprintf(stderr, "%d ", q[i]);
	fprintf(stderr, "\n\n");
	
	/* create histograms of states */
	double* h = (double*) malloc(frames_read*nHMM_states*sizeof(double));	/* vector in row major order */
	create_histograms(q, frames_read, nHMM_states, histogram_length, h);
	
	/* cluster the histograms */
	int nbsched = 20;	/* length of inverse temperature schedule */
	double* bsched = (double*) malloc(nbsched*sizeof(double));	/* inverse temperature schedule */
	double b0 = 100;
	double alpha = 0.7;
	bsched[0] = b0;
	for (i = 1; i < nbsched; i++)
		bsched[i] = alpha * bsched[i-1];
	cluster_melt(h, nHMM_states, frames_read, bsched, nbsched, nclusters, neighbour_limit, q);
	
	/* now q holds a sequence of cluster assignments */
	
	free(h);  
	free(bsched);
}

/* segment constant-Q or chroma features */
void constq_segment(int* q, double** features, int frames_read, int bins, int ncoeff, int feature_type, 
			 int nHMM_states, int histogram_length, int nclusters, int neighbour_limit)
{
	int feature_length;
	double** chroma;
	int i;
	
	if (feature_type == FEATURE_TYPE_CONSTQ)
	{
		fprintf(stderr, "Converting to dB and normalising...\n");
		
		mpeg7_constq(features, frames_read, ncoeff);
		
		fprintf(stderr, "Running PCA...\n");
		
		/* do PCA on the features (but not the envelope) */
		int ncomponents = 20;
		pca_project(features, frames_read, ncoeff, ncomponents);
		
		/* copy the envelope so that it immediatly follows the chosen components */
		for (i = 0; i < frames_read; i++)
			features[i][ncomponents] = features[i][ncoeff];	
		
		feature_length = ncomponents + 1;
		
		/**************************************
		//TEST
		// feature file name
		char* dir = "/Users/mark/documents/semma/audio/";
		char* file_name = (char*) malloc((strlen(dir) + strlen(trackname) + strlen("_features_c20r8h0.2f0.6.mat") + 1)*sizeof(char));
		strcpy(file_name, dir);
		strcat(file_name, trackname);
		strcat(file_name, "_features_c20r8h0.2f0.6.mat");
		
		// get the features from Matlab from mat-file
		int frames_in_file;
		readmatarray_size(file_name, 2, &frames_in_file, &feature_length);
		readmatarray(file_name, 2, frames_in_file, feature_length, features);
		// copy final frame to ensure that we get as many as we expected
		int missing_frames = frames_read - frames_in_file;
		while (missing_frames > 0)
		{
			for (i = 0; i < feature_length; i++)
				features[frames_read-missing_frames][i] = features[frames_read-missing_frames-1][i];
			--missing_frames;
		}
		
		free(file_name);
		******************************************/
	
		cluster_segment(q, features, frames_read, feature_length, nHMM_states, histogram_length, nclusters, neighbour_limit);
	}
	
	if (feature_type == FEATURE_TYPE_CHROMA)
	{
		fprintf(stderr, "Converting to chroma features...\n");
		
		/* convert constant-Q to normalised chroma features */
		chroma = (double**) malloc(frames_read*sizeof(double*));
		for (i = 0; i < frames_read; i++)
			chroma[i] = (double*) malloc(bins*sizeof(double));
		cq2chroma(features, frames_read, ncoeff, bins, chroma);
		feature_length = bins;
		
		cluster_segment(q, chroma, frames_read, feature_length, nHMM_states, histogram_length, nclusters, neighbour_limit);
	
		for (i = 0; i < frames_read; i++)
			free(chroma[i]);
		free(chroma);
	}
}



