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/* -*- 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"
#include "Agent.h"
#include "AgentList.h"
#include <vector>
#include <cmath>
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(const AgentParameters ¶ms,
const EventList &events)
{
int i, j, b, bestCount;
bool submult;
int intervals = 0; // number of interval clusters
std::vector<int> bestn; // count of high-scoring clusters
bestn.resize(topN);
double ratio, err;
int degree;
int maxClusterCount = (int) ceil((maxIOI - minIOI) / clusterWidth);
std::vector<double> clusterMean;
clusterMean.resize(maxClusterCount);
std::vector<int> clusterSize;
clusterSize.resize(maxClusterCount);
std::vector<int> clusterScore;
clusterScore.resize(maxClusterCount);
EventList::const_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 (std::fabs(clusterMean[b] - ioi) < clusterWidth) {
if ((b < intervals - 1) && (std::fabs(clusterMean[b + 1] - ioi)
< std::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 (std::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 = std::fabs(clusterMean[b] * degree - clusterMean[i]);
else
err = std::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 = std::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 = std::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));
}
}
return a;
}
int Induction::top(int low)
{
return low + 25; // low/10;
}
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