2019-09-01 21:12:22 -04:00
|
|
|
/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
|
|
|
|
|
|
|
|
/*
|
|
|
|
pYIN - A fundamental frequency estimator for monophonic audio
|
|
|
|
Centre for Digital Music, Queen Mary, University of London.
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
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 "LocalCandidatePYIN.h"
|
|
|
|
#include "MonoPitch.h"
|
|
|
|
#include "YinUtil.h"
|
|
|
|
|
|
|
|
#include "vamp-sdk/FFT.h"
|
|
|
|
|
|
|
|
#include <vector>
|
|
|
|
#include <algorithm>
|
|
|
|
|
|
|
|
#include <cstdio>
|
|
|
|
#include <sstream>
|
|
|
|
// #include <iostream>
|
|
|
|
#include <cmath>
|
|
|
|
#include <complex>
|
|
|
|
#include <map>
|
|
|
|
|
|
|
|
using std::string;
|
|
|
|
using std::vector;
|
|
|
|
using std::map;
|
|
|
|
using Vamp::RealTime;
|
|
|
|
|
|
|
|
|
|
|
|
LocalCandidatePYIN::LocalCandidatePYIN(float inputSampleRate) :
|
|
|
|
Plugin(inputSampleRate),
|
|
|
|
m_channels(0),
|
|
|
|
m_stepSize(256),
|
|
|
|
m_blockSize(2048),
|
|
|
|
m_fmin(40),
|
|
|
|
m_fmax(700),
|
|
|
|
m_oPitchTrackCandidates(0),
|
|
|
|
m_threshDistr(2.0f),
|
|
|
|
m_outputUnvoiced(0.0f),
|
|
|
|
m_preciseTime(0.0f),
|
|
|
|
m_pitchProb(0),
|
|
|
|
m_timestamp(0),
|
|
|
|
m_nCandidate(13)
|
|
|
|
{
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::~LocalCandidatePYIN()
|
|
|
|
{
|
|
|
|
}
|
|
|
|
|
|
|
|
string
|
|
|
|
LocalCandidatePYIN::getIdentifier() const
|
|
|
|
{
|
|
|
|
return "localcandidatepyin";
|
|
|
|
}
|
|
|
|
|
|
|
|
string
|
|
|
|
LocalCandidatePYIN::getName() const
|
|
|
|
{
|
|
|
|
return "Local Candidate PYIN";
|
|
|
|
}
|
|
|
|
|
|
|
|
string
|
|
|
|
LocalCandidatePYIN::getDescription() const
|
|
|
|
{
|
|
|
|
return "Monophonic pitch and note tracking based on a probabilistic Yin extension.";
|
|
|
|
}
|
|
|
|
|
|
|
|
string
|
|
|
|
LocalCandidatePYIN::getMaker() const
|
|
|
|
{
|
|
|
|
return "Matthias Mauch";
|
|
|
|
}
|
|
|
|
|
|
|
|
int
|
|
|
|
LocalCandidatePYIN::getPluginVersion() const
|
|
|
|
{
|
|
|
|
// Increment this each time you release a version that behaves
|
|
|
|
// differently from the previous one
|
|
|
|
return 2;
|
|
|
|
}
|
|
|
|
|
|
|
|
string
|
|
|
|
LocalCandidatePYIN::getCopyright() const
|
|
|
|
{
|
|
|
|
return "GPL";
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::InputDomain
|
|
|
|
LocalCandidatePYIN::getInputDomain() const
|
|
|
|
{
|
|
|
|
return TimeDomain;
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t
|
|
|
|
LocalCandidatePYIN::getPreferredBlockSize() const
|
|
|
|
{
|
|
|
|
return 2048;
|
|
|
|
}
|
|
|
|
|
2019-09-02 22:52:01 -04:00
|
|
|
size_t
|
2019-09-01 21:12:22 -04:00
|
|
|
LocalCandidatePYIN::getPreferredStepSize() const
|
|
|
|
{
|
|
|
|
return 256;
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t
|
|
|
|
LocalCandidatePYIN::getMinChannelCount() const
|
|
|
|
{
|
|
|
|
return 1;
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t
|
|
|
|
LocalCandidatePYIN::getMaxChannelCount() const
|
|
|
|
{
|
|
|
|
return 1;
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::ParameterList
|
|
|
|
LocalCandidatePYIN::getParameterDescriptors() const
|
|
|
|
{
|
|
|
|
ParameterList list;
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
ParameterDescriptor d;
|
|
|
|
|
|
|
|
d.identifier = "threshdistr";
|
|
|
|
d.name = "Yin threshold distribution";
|
|
|
|
d.description = ".";
|
|
|
|
d.unit = "";
|
|
|
|
d.minValue = 0.0f;
|
|
|
|
d.maxValue = 7.0f;
|
|
|
|
d.defaultValue = 2.0f;
|
|
|
|
d.isQuantized = true;
|
|
|
|
d.quantizeStep = 1.0f;
|
|
|
|
d.valueNames.push_back("Uniform");
|
|
|
|
d.valueNames.push_back("Beta (mean 0.10)");
|
|
|
|
d.valueNames.push_back("Beta (mean 0.15)");
|
|
|
|
d.valueNames.push_back("Beta (mean 0.20)");
|
|
|
|
d.valueNames.push_back("Beta (mean 0.30)");
|
|
|
|
d.valueNames.push_back("Single Value 0.10");
|
|
|
|
d.valueNames.push_back("Single Value 0.15");
|
|
|
|
d.valueNames.push_back("Single Value 0.20");
|
|
|
|
list.push_back(d);
|
|
|
|
|
|
|
|
d.identifier = "outputunvoiced";
|
|
|
|
d.valueNames.clear();
|
|
|
|
d.name = "Output estimates classified as unvoiced?";
|
|
|
|
d.description = ".";
|
|
|
|
d.unit = "";
|
|
|
|
d.minValue = 0.0f;
|
|
|
|
d.maxValue = 2.0f;
|
|
|
|
d.defaultValue = 0.0f;
|
|
|
|
d.isQuantized = true;
|
|
|
|
d.quantizeStep = 1.0f;
|
|
|
|
d.valueNames.push_back("No");
|
|
|
|
d.valueNames.push_back("Yes");
|
|
|
|
d.valueNames.push_back("Yes, as negative frequencies");
|
|
|
|
list.push_back(d);
|
|
|
|
|
|
|
|
d.identifier = "precisetime";
|
|
|
|
d.valueNames.clear();
|
|
|
|
d.name = "Use non-standard precise YIN timing (slow).";
|
|
|
|
d.description = ".";
|
|
|
|
d.unit = "";
|
|
|
|
d.minValue = 0.0f;
|
|
|
|
d.maxValue = 1.0f;
|
|
|
|
d.defaultValue = 0.0f;
|
|
|
|
d.isQuantized = true;
|
|
|
|
d.quantizeStep = 1.0f;
|
|
|
|
list.push_back(d);
|
|
|
|
|
|
|
|
return list;
|
|
|
|
}
|
|
|
|
|
|
|
|
float
|
|
|
|
LocalCandidatePYIN::getParameter(string identifier) const
|
|
|
|
{
|
|
|
|
if (identifier == "threshdistr") {
|
|
|
|
return m_threshDistr;
|
|
|
|
}
|
|
|
|
if (identifier == "outputunvoiced") {
|
|
|
|
return m_outputUnvoiced;
|
|
|
|
}
|
|
|
|
if (identifier == "precisetime") {
|
|
|
|
return m_preciseTime;
|
|
|
|
}
|
|
|
|
return 0.f;
|
|
|
|
}
|
|
|
|
|
|
|
|
void
|
2019-09-02 22:52:01 -04:00
|
|
|
LocalCandidatePYIN::setParameter(string identifier, float value)
|
2019-09-01 21:12:22 -04:00
|
|
|
{
|
|
|
|
if (identifier == "threshdistr")
|
|
|
|
{
|
|
|
|
m_threshDistr = value;
|
|
|
|
}
|
|
|
|
if (identifier == "outputunvoiced")
|
|
|
|
{
|
|
|
|
m_outputUnvoiced = value;
|
|
|
|
}
|
|
|
|
if (identifier == "precisetime")
|
|
|
|
{
|
|
|
|
m_preciseTime = value;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::ProgramList
|
|
|
|
LocalCandidatePYIN::getPrograms() const
|
|
|
|
{
|
|
|
|
ProgramList list;
|
|
|
|
return list;
|
|
|
|
}
|
|
|
|
|
|
|
|
string
|
|
|
|
LocalCandidatePYIN::getCurrentProgram() const
|
|
|
|
{
|
|
|
|
return ""; // no programs
|
|
|
|
}
|
|
|
|
|
|
|
|
void
|
|
|
|
LocalCandidatePYIN::selectProgram(string name)
|
|
|
|
{
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::OutputList
|
|
|
|
LocalCandidatePYIN::getOutputDescriptors() const
|
|
|
|
{
|
|
|
|
OutputList outputs;
|
|
|
|
|
|
|
|
OutputDescriptor d;
|
|
|
|
|
|
|
|
d.identifier = "pitchtrackcandidates";
|
|
|
|
d.name = "Pitch track candidates";
|
|
|
|
d.description = "Multiple candidate pitch tracks.";
|
|
|
|
d.unit = "Hz";
|
|
|
|
d.hasFixedBinCount = false;
|
|
|
|
d.hasKnownExtents = true;
|
|
|
|
d.minValue = m_fmin;
|
|
|
|
d.maxValue = 500; //!!!???
|
|
|
|
d.isQuantized = false;
|
|
|
|
d.sampleType = OutputDescriptor::FixedSampleRate;
|
|
|
|
d.sampleRate = (m_inputSampleRate / m_stepSize);
|
|
|
|
d.hasDuration = false;
|
|
|
|
outputs.push_back(d);
|
|
|
|
|
|
|
|
return outputs;
|
|
|
|
}
|
|
|
|
|
|
|
|
bool
|
|
|
|
LocalCandidatePYIN::initialise(size_t channels, size_t stepSize, size_t blockSize)
|
|
|
|
{
|
|
|
|
if (channels < getMinChannelCount() ||
|
|
|
|
channels > getMaxChannelCount()) return false;
|
|
|
|
|
|
|
|
/*
|
|
|
|
std::cerr << "LocalCandidatePYIN::initialise: channels = " << channels
|
|
|
|
<< ", stepSize = " << stepSize << ", blockSize = " << blockSize
|
|
|
|
<< std::endl;
|
|
|
|
*/
|
|
|
|
m_channels = channels;
|
|
|
|
m_stepSize = stepSize;
|
|
|
|
m_blockSize = blockSize;
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
reset();
|
|
|
|
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
void
|
|
|
|
LocalCandidatePYIN::reset()
|
2019-09-02 22:52:01 -04:00
|
|
|
{
|
2019-09-01 21:12:22 -04:00
|
|
|
m_pitchProb.clear();
|
|
|
|
m_timestamp.clear();
|
2019-09-02 22:52:01 -04:00
|
|
|
/*
|
2019-09-01 21:12:22 -04:00
|
|
|
std::cerr << "LocalCandidatePYIN::reset"
|
|
|
|
<< ", blockSize = " << m_blockSize
|
|
|
|
<< std::endl;
|
|
|
|
*/
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::FeatureSet
|
|
|
|
LocalCandidatePYIN::process(const float *const *inputBuffers, RealTime timestamp)
|
|
|
|
{
|
|
|
|
int offset = m_preciseTime == 1.0 ? m_blockSize/2 : m_blockSize/4;
|
|
|
|
timestamp = timestamp + Vamp::RealTime::frame2RealTime(offset, lrintf(m_inputSampleRate));
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
double *dInputBuffers = new double[m_blockSize];
|
|
|
|
for (size_t i = 0; i < m_blockSize; ++i) dInputBuffers[i] = inputBuffers[0][i];
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
size_t yinBufferSize = m_blockSize/2;
|
|
|
|
double* yinBuffer = new double[yinBufferSize];
|
|
|
|
if (!m_preciseTime) YinUtil::fastDifference(dInputBuffers, yinBuffer, yinBufferSize);
|
2019-09-02 22:52:01 -04:00
|
|
|
else YinUtil::slowDifference(dInputBuffers, yinBuffer, yinBufferSize);
|
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
delete [] dInputBuffers;
|
|
|
|
|
|
|
|
YinUtil::cumulativeDifference(yinBuffer, yinBufferSize);
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
float minFrequency = 60;
|
|
|
|
float maxFrequency = 900;
|
2019-09-02 22:52:01 -04:00
|
|
|
vector<double> peakProbability = YinUtil::yinProb(yinBuffer,
|
|
|
|
m_threshDistr,
|
|
|
|
yinBufferSize,
|
|
|
|
m_inputSampleRate/maxFrequency,
|
2019-09-01 21:12:22 -04:00
|
|
|
m_inputSampleRate/minFrequency);
|
|
|
|
|
|
|
|
vector<pair<double, double> > tempPitchProb;
|
|
|
|
for (size_t iBuf = 0; iBuf < yinBufferSize; ++iBuf)
|
|
|
|
{
|
|
|
|
if (peakProbability[iBuf] > 0)
|
|
|
|
{
|
2019-09-02 22:52:01 -04:00
|
|
|
double currentF0 =
|
2019-09-01 21:12:22 -04:00
|
|
|
m_inputSampleRate * (1.0 /
|
|
|
|
YinUtil::parabolicInterpolation(yinBuffer, iBuf, yinBufferSize));
|
|
|
|
double tempPitch = 12 * std::log(currentF0/440)/std::log(2.) + 69;
|
|
|
|
tempPitchProb.push_back(pair<double, double>(tempPitch, peakProbability[iBuf]));
|
|
|
|
}
|
|
|
|
}
|
|
|
|
m_pitchProb.push_back(tempPitchProb);
|
|
|
|
m_timestamp.push_back(timestamp);
|
|
|
|
|
|
|
|
delete[] yinBuffer;
|
|
|
|
|
|
|
|
return FeatureSet();
|
|
|
|
}
|
|
|
|
|
|
|
|
LocalCandidatePYIN::FeatureSet
|
|
|
|
LocalCandidatePYIN::getRemainingFeatures()
|
|
|
|
{
|
|
|
|
// timestamp -> candidate number -> value
|
|
|
|
map<RealTime, map<int, float> > featureValues;
|
|
|
|
|
|
|
|
// std::cerr << "in remaining features" << std::endl;
|
|
|
|
|
|
|
|
if (m_pitchProb.empty()) {
|
|
|
|
return FeatureSet();
|
|
|
|
}
|
|
|
|
|
|
|
|
// MONO-PITCH STUFF
|
|
|
|
MonoPitch mp;
|
|
|
|
size_t nFrame = m_timestamp.size();
|
|
|
|
vector<vector<float> > pitchTracks;
|
|
|
|
vector<float> freqSum = vector<float>(m_nCandidate);
|
|
|
|
vector<float> freqNumber = vector<float>(m_nCandidate);
|
|
|
|
vector<float> freqMean = vector<float>(m_nCandidate);
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
boost::math::normal normalDist(0, 8); // semitones sd
|
|
|
|
float maxNormalDist = boost::math::pdf(normalDist, 0);
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
// Viterbi-decode multiple times with different frequencies emphasised
|
|
|
|
for (size_t iCandidate = 0; iCandidate < m_nCandidate; ++iCandidate)
|
|
|
|
{
|
|
|
|
pitchTracks.push_back(vector<float>(nFrame));
|
|
|
|
vector<vector<pair<double,double> > > tempPitchProb;
|
|
|
|
float centrePitch = 45 + 3 * iCandidate;
|
|
|
|
|
|
|
|
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame) {
|
|
|
|
tempPitchProb.push_back(vector<pair<double,double> >());
|
|
|
|
float sumProb = 0;
|
|
|
|
float pitch = 0;
|
|
|
|
float prob = 0;
|
|
|
|
for (size_t iProb = 0; iProb < m_pitchProb[iFrame].size(); ++iProb)
|
|
|
|
{
|
2019-09-02 22:52:01 -04:00
|
|
|
pitch = m_pitchProb[iFrame][iProb].first;
|
|
|
|
prob = m_pitchProb[iFrame][iProb].second *
|
2019-09-01 21:12:22 -04:00
|
|
|
boost::math::pdf(normalDist, pitch-centrePitch) /
|
|
|
|
maxNormalDist * 2;
|
|
|
|
sumProb += prob;
|
|
|
|
tempPitchProb[iFrame].push_back(
|
|
|
|
pair<double,double>(pitch,prob));
|
|
|
|
}
|
|
|
|
for (size_t iProb = 0; iProb < m_pitchProb[iFrame].size(); ++iProb)
|
|
|
|
{
|
|
|
|
tempPitchProb[iFrame][iProb].second /= sumProb;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
vector<float> mpOut = mp.process(tempPitchProb);
|
2019-09-02 23:01:59 -04:00
|
|
|
//float prevFreq = 0;
|
2019-09-01 21:12:22 -04:00
|
|
|
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame)
|
|
|
|
{
|
|
|
|
if (mpOut[iFrame] > 0) {
|
|
|
|
|
|
|
|
pitchTracks[iCandidate][iFrame] = mpOut[iFrame];
|
|
|
|
freqSum[iCandidate] += mpOut[iFrame];
|
|
|
|
freqNumber[iCandidate]++;
|
2019-09-02 23:01:59 -04:00
|
|
|
//prevFreq = mpOut[iFrame];
|
2019-09-01 21:12:22 -04:00
|
|
|
|
|
|
|
}
|
|
|
|
}
|
|
|
|
freqMean[iCandidate] = freqSum[iCandidate]*1.0/freqNumber[iCandidate];
|
|
|
|
}
|
|
|
|
|
|
|
|
// find near duplicate pitch tracks
|
|
|
|
vector<size_t> duplicates;
|
|
|
|
for (size_t iCandidate = 0; iCandidate < m_nCandidate; ++iCandidate) {
|
|
|
|
for (size_t jCandidate = iCandidate+1; jCandidate < m_nCandidate; ++jCandidate) {
|
|
|
|
size_t countEqual = 0;
|
2019-09-02 22:52:01 -04:00
|
|
|
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame)
|
2019-09-01 21:12:22 -04:00
|
|
|
{
|
|
|
|
if ((pitchTracks[jCandidate][iFrame] == 0 && pitchTracks[iCandidate][iFrame] == 0) ||
|
|
|
|
fabs(pitchTracks[iCandidate][iFrame]/pitchTracks[jCandidate][iFrame]-1)<0.01)
|
|
|
|
countEqual++;
|
|
|
|
}
|
2019-09-02 22:52:01 -04:00
|
|
|
// std::cerr << "proportion equal: " << (countEqual * 1.0 / nFrame) << std::endl;
|
2019-09-01 21:12:22 -04:00
|
|
|
if (countEqual * 1.0 / nFrame > 0.8) {
|
|
|
|
if (freqNumber[iCandidate] > freqNumber[jCandidate]) {
|
|
|
|
duplicates.push_back(jCandidate);
|
|
|
|
} else if (iCandidate < jCandidate) {
|
|
|
|
duplicates.push_back(iCandidate);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// now find non-duplicate pitch tracks
|
|
|
|
map<int, int> candidateActuals;
|
|
|
|
map<int, std::string> candidateLabels;
|
|
|
|
|
|
|
|
vector<vector<float> > outputFrequencies;
|
|
|
|
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame) outputFrequencies.push_back(vector<float>());
|
|
|
|
|
|
|
|
int actualCandidateNumber = 0;
|
|
|
|
for (size_t iCandidate = 0; iCandidate < m_nCandidate; ++iCandidate)
|
|
|
|
{
|
|
|
|
bool isDuplicate = false;
|
|
|
|
for (size_t i = 0; i < duplicates.size(); ++i) {
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
if (duplicates[i] == iCandidate) {
|
|
|
|
isDuplicate = true;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (!isDuplicate && freqNumber[iCandidate] > 0.5*nFrame)
|
|
|
|
{
|
|
|
|
std::ostringstream convert;
|
|
|
|
convert << actualCandidateNumber++;
|
|
|
|
candidateLabels[iCandidate] = convert.str();
|
|
|
|
candidateActuals[iCandidate] = actualCandidateNumber;
|
|
|
|
// std::cerr << iCandidate << " " << actualCandidateNumber << " " << freqNumber[iCandidate] << " " << freqMean[iCandidate] << std::endl;
|
2019-09-02 22:52:01 -04:00
|
|
|
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame)
|
2019-09-01 21:12:22 -04:00
|
|
|
{
|
|
|
|
if (pitchTracks[iCandidate][iFrame] > 0)
|
|
|
|
{
|
2019-09-02 22:52:01 -04:00
|
|
|
// featureValues[m_timestamp[iFrame]][iCandidate] =
|
2019-09-01 21:12:22 -04:00
|
|
|
// pitchTracks[iCandidate][iFrame];
|
|
|
|
outputFrequencies[iFrame].push_back(pitchTracks[iCandidate][iFrame]);
|
|
|
|
} else {
|
|
|
|
outputFrequencies[iFrame].push_back(0);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
// fs[m_oPitchTrackCandidates].push_back(f);
|
|
|
|
}
|
|
|
|
|
|
|
|
// adapt our features so as to return a stack of candidate values
|
|
|
|
// per frame
|
|
|
|
|
|
|
|
FeatureSet fs;
|
|
|
|
|
|
|
|
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame){
|
|
|
|
Feature f;
|
|
|
|
f.hasTimestamp = true;
|
|
|
|
f.timestamp = m_timestamp[iFrame];
|
|
|
|
f.values = outputFrequencies[iFrame];
|
|
|
|
fs[0].push_back(f);
|
|
|
|
}
|
2019-09-02 22:52:01 -04:00
|
|
|
|
2019-09-01 21:12:22 -04:00
|
|
|
// I stopped using Chris's map stuff below because I couldn't get my head around it
|
|
|
|
//
|
|
|
|
// for (map<RealTime, map<int, float> >::const_iterator i =
|
|
|
|
// featureValues.begin(); i != featureValues.end(); ++i) {
|
|
|
|
// Feature f;
|
|
|
|
// f.hasTimestamp = true;
|
|
|
|
// f.timestamp = i->first;
|
|
|
|
// int nextCandidate = candidateActuals.begin()->second;
|
2019-09-02 22:52:01 -04:00
|
|
|
// for (map<int, float>::const_iterator j =
|
2019-09-01 21:12:22 -04:00
|
|
|
// i->second.begin(); j != i->second.end(); ++j) {
|
|
|
|
// while (candidateActuals[j->first] > nextCandidate) {
|
|
|
|
// f.values.push_back(0);
|
|
|
|
// ++nextCandidate;
|
|
|
|
// }
|
|
|
|
// f.values.push_back(j->second);
|
|
|
|
// nextCandidate = j->first + 1;
|
|
|
|
// }
|
|
|
|
// //!!! can't use labels?
|
|
|
|
// fs[0].push_back(f);
|
|
|
|
// }
|
|
|
|
|
|
|
|
return fs;
|
|
|
|
}
|