view Induction.cpp @ 23:633ec097fa56

Expose the processing parameters Simon suggests

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
  Vamp feature extraction plugin for the BeatRoot beat tracker.

  Centre for Digital Music, Queen Mary, University of London.
  This file copyright 2011 Simon Dixon, Chris Cannam and QMUL.
    
  This program is free software; you can redistribute it and/or
  modify it under the terms of the GNU General Public License as
  published by the Free Software Foundation; either version 2 of the
  License, or (at your option) any later version.  See the file
  COPYING included with this distribution for more information.
*/

#include "Induction.h"

double Induction::clusterWidth = 0.025;
double Induction::minIOI = 0.070;
double Induction::maxIOI = 2.500;
double Induction::minIBI = 0.3; 
double Induction::maxIBI = 1.0;
int Induction::topN = 10;


AgentList Induction::beatInduction(AgentParameters params, EventList events) {
    int i, j, b, bestCount;
    bool submult;
    int intervals = 0;			// number of interval clusters
    vector<int> bestn;// count of high-scoring clusters
    bestn.resize(topN);

    double ratio, err;
    int degree;
    int maxClusterCount = (int) ceil((maxIOI - minIOI) / clusterWidth);
    vector<double> clusterMean;
    clusterMean.resize(maxClusterCount);
    vector<int> clusterSize;
    clusterSize.resize(maxClusterCount);
    vector<int> clusterScore;
    clusterScore.resize(maxClusterCount);
		
    EventList::iterator ptr1, ptr2;
    Event e1, e2;
    ptr1 = events.begin();
    while (ptr1 != events.end()) {
        e1 = *ptr1;
        ++ptr1;
        ptr2 = events.begin();
        e2 = *ptr2;
        ++ptr2;
        while (e2 != e1 && ptr2 != events.end()) {
            e2 = *ptr2;
            ++ptr2;
        }
        while (ptr2 != events.end()) {
            e2 = *ptr2;
            ++ptr2;
            double ioi = e2.time - e1.time;
            if (ioi < minIOI)		// skip short intervals
                continue;
            if (ioi > maxIOI)		// ioi too long
                break;
            for (b = 0; b < intervals; b++)		// assign to nearest cluster
                if (fabs(clusterMean[b] - ioi) < clusterWidth) {
                    if ((b < intervals - 1) && (
                            fabs(clusterMean[b+1] - ioi) <
                            fabs(clusterMean[b] - ioi)))
                        b++;		// next cluster is closer
                    clusterMean[b] = (clusterMean[b] * clusterSize[b] +ioi)/
                        (clusterSize[b] + 1);
                    clusterSize[b]++;
                    break;
                }
            if (b == intervals) {	// no suitable cluster; create new one
                if (intervals == maxClusterCount) {
//			System.err.println("Warning: Too many clusters");
                    continue; // ignore this IOI
                }
                intervals++;
                for ( ; (b>0) && (clusterMean[b-1] > ioi); b--) {
                    clusterMean[b] = clusterMean[b-1];
                    clusterSize[b] = clusterSize[b-1];
                }
                clusterMean[b] = ioi;
                clusterSize[b] = 1;
            }
        }
    }
    for (b = 0; b < intervals; b++)	// merge similar intervals
        // TODO: they are now in order, so don't need the 2nd loop
        // TODO: check BOTH sides before averaging or upper gps don't work
        for (i = b+1; i < intervals; i++)
            if (fabs(clusterMean[b] - clusterMean[i]) < clusterWidth) {
                clusterMean[b] = (clusterMean[b] * clusterSize[b] +
                                  clusterMean[i] * clusterSize[i]) /
                    (clusterSize[b] + clusterSize[i]);
                clusterSize[b] = clusterSize[b] + clusterSize[i];
                --intervals;
                for (j = i+1; j <= intervals; j++) {
                    clusterMean[j-1] = clusterMean[j];
                    clusterSize[j-1] = clusterSize[j];
                }
            }
    if (intervals == 0)
        return AgentList();
    for (b = 0; b < intervals; b++)
        clusterScore[b] = 10 * clusterSize[b];
    bestn[0] = 0;
    bestCount = 1;
    for (b = 0; b < intervals; b++)
        for (i = 0; i <= bestCount; i++)
            if ((i < topN) && ((i == bestCount) ||
                               (clusterScore[b] > clusterScore[bestn[i]]))){
                if (bestCount < topN)
                    bestCount++;
                for (j = bestCount - 1; j > i; j--)
                    bestn[j] = bestn[j-1];
                bestn[i] = b;
                break;
            }
    for (b = 0; b < intervals; b++)	// score intervals
        for (i = b+1; i < intervals; i++) {
            ratio = clusterMean[b] / clusterMean[i];
            submult = ratio < 1;
            if (submult)
                degree = (int) nearbyint(1/ratio);
            else
                degree = (int) nearbyint(ratio);
            if ((degree >= 2) && (degree <= 8)) {
                if (submult)
                    err = fabs(clusterMean[b]*degree - clusterMean[i]);
                else
                    err = fabs(clusterMean[b] - clusterMean[i]*degree);
                if (err < (submult? clusterWidth : clusterWidth * degree)) {
                    if (degree >= 5)
                        degree = 1;
                    else
                        degree = 6 - degree;
                    clusterScore[b] += degree * clusterSize[i];
                    clusterScore[i] += degree * clusterSize[b];
                }
            }
        }

    AgentList a;
    for (int index = 0; index < bestCount; index++) {
        b = bestn[index];
        // Adjust it, using the size of super- and sub-intervals
        double newSum = clusterMean[b] * clusterScore[b];
        int newCount = clusterSize[b];
        int newWeight = clusterScore[b];
        for (i = 0; i < intervals; i++) {
            if (i == b)
                continue;
            ratio = clusterMean[b] / clusterMean[i];
            if (ratio < 1) {
                degree = (int) nearbyint(1 / ratio);
                if ((degree >= 2) && (degree <= 8)) {
                    err = fabs(clusterMean[b]*degree - clusterMean[i]);
                    if (err < clusterWidth) {
                        newSum += clusterMean[i] / degree * clusterScore[i];
                        newCount += clusterSize[i];
                        newWeight += clusterScore[i];
                    }
                }
            } else {
                degree = (int) nearbyint(ratio);
                if ((degree >= 2) && (degree <= 8)) {
                    err = fabs(clusterMean[b] - degree*clusterMean[i]);
                    if (err < clusterWidth * degree) {
                        newSum += clusterMean[i] * degree * clusterScore[i];
                        newCount += clusterSize[i];
                        newWeight += clusterScore[i];
                    }
                }
            }
        }
        double beat = newSum / newWeight;
        // Scale within range ... hope the grouping isn't ternary :(
        while (beat < minIBI)		// Maximum speed
            beat *= 2.0;
        while (beat > maxIBI)		// Minimum speed
            beat /= 2.0;
        if (beat >= minIBI) {
            a.push_back(new Agent(params, beat));
        }
    }
#ifdef DEBUG_BEATROOT
    std::cerr << "Induction complete, returning " << a.size() << " agent(s)" << std::endl;
#endif
    return a;
} // beatInduction()