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src/machineLearning/trainingData.cpp
View file @ a0bfd689
/*
* trainingData.cpp
* Created by Michael Zbyszynski on 2 Feb 2017
* Copyright © 2017 Goldsmiths. All rights reserved.
/**
* @file trainingData.cpp
* @author Michael Zbyszynski
* @date 2 Feb 2017
* @copyright Copyright © 2017 Goldsmiths. All rights reserved.
*/
#include <fstream>
#include "trainingData.h"
RAPIDMIX_BEGIN_NAMESPACE
trainingData::trainingData () {
phrase defaultPhrase = {0, "default"};
trainingSet.push_back(defaultPhrase);
currentId = 1;
targetPhrase = 0;
};
uint32_t trainingData::assignCurrentId() {
uint32_t returnVal = currentId;
++currentId;
return returnVal;
}
uint32_t trainingData::startRecording() {
phrase tempPhrase = { assignCurrentId(), std::to_string(tempPhrase.uniqueId) }; //TODO: Is this label helpful? -MZ
trainingSet.push_back(tempPhrase);
targetPhrase = int(trainingSet.size() - 1);
return tempPhrase.uniqueId;
};
uint32_t trainingData::startRecording(std::string label) {
phrase tempPhrase = { assignCurrentId(), label };
labels.insert(std::make_pair(label, labels.size()));
trainingSet.push_back(tempPhrase);
targetPhrase = int(trainingSet.size() - 1);
return tempPhrase.uniqueId;
};
uint32_t trainingData::addElement(std::vector<double>input, std::vector<double> output) {
element newElement;
newElement.uniqueId = assignCurrentId();
newElement.input = input;
newElement.output = output;
newElement.timeStamp = NULL;
trainingSet[targetPhrase].elements.push_back(newElement);
return newElement.uniqueId;
}
void trainingData::stopRecording() {
targetPhrase = 0; //direct elements to default phrase
}
std::string trainingData::getLabel(int value) {
std::string theLabel = "not found";
for (const auto& element : labels) {
if (element.second == value) {
theLabel = element.first;
}
namespace rapidmix {
trainingData::trainingData () {
currentId = 0;
targetPhrase = 0;
};
uint32_t trainingData::assignCurrentId() {
uint32_t returnVal = currentId;
++currentId;
return returnVal;
}
return theLabel;
}
std::vector<std::string> trainingData::getColumnNames() {
return trainingSet[targetPhrase].columnNames;
}
void trainingData::setColumnNames(std::vector<std::string> column_names) {
trainingSet[targetPhrase].columnNames = column_names;
}
Json::Value trainingData::parse2json() {
Json::Value root;
Json::Value metadata;
Json::Value trainingSetJSON;
metadata["creator"] = "RAPID-MIX API C++";
metadata["version"] = RAPIDMIX_VERSION;
//Go through all the phrases
for (int i = 0; i < trainingSet.size(); ++i) {
Json::Value thisPhrase;
thisPhrase.append(trainingSet[i].uniqueId);
thisPhrase.append(trainingSet[i].label);
Json::Value column_names;
for (int j = 0; j < trainingSet[i].columnNames.size(); ++j) {
column_names.append(trainingSet[i].columnNames[j]);
}
thisPhrase.append(column_names);
std::vector<trainingData::phrase>::iterator trainingData::createNewPhrase(std::string label) {
phrase tempPhrase = { assignCurrentId(), label };
trainingSet.push_back(tempPhrase);
std::vector<trainingData::phrase>::iterator it = --trainingSet.end();
return it;
};
uint32_t trainingData::startRecording() {
phrase tempPhrase = { assignCurrentId(), std::to_string(tempPhrase.uniqueId) }; //TODO: Is this label helpful? -MZ
trainingSet.push_back(tempPhrase);
targetPhrase = int(trainingSet.size() - 1);
return tempPhrase.uniqueId;
};
uint32_t trainingData::startRecording(const std::string &label) {
phrase tempPhrase = { assignCurrentId(), label };
trainingSet.push_back(tempPhrase);
targetPhrase = int(trainingSet.size() - 1);
return tempPhrase.uniqueId;
Json::Value elements;
for (int j = 0; j < trainingSet[i].elements.size(); ++j) {
Json::Value singleElement;
};
uint32_t trainingData::addElement(const std::vector<double> &input, const std::vector<double> &output) {
element newElement;
newElement.uniqueId = assignCurrentId();
newElement.input = input;
newElement.output = output;
newElement.timeStamp = 0.0;
trainingSet[targetPhrase].elements.push_back(newElement);
return newElement.uniqueId;
}
uint32_t trainingData::addElement(const std::vector<double> &input) {
element newElement;
newElement.uniqueId = assignCurrentId();
newElement.input = input;
newElement.timeStamp = 0.0;
trainingSet[targetPhrase].elements.push_back(newElement);
return newElement.uniqueId;
}
void trainingData::stopRecording() {
//TODO: This doesn't do much. -MZ
}
uint32_t trainingData::recordSingleElement(const std::string &label, const std::vector<double> &input) {
startRecording(label);
int returnId = addElement(input);
stopRecording();
return returnId;
};
uint32_t trainingData::recordSingleElement(const std::vector<double> &input, const std::vector<double> &output) {
startRecording();
int returnId = addElement(input, output);
stopRecording();
return returnId;
};
uint32_t trainingData::recordSingleElement(const std::string &label, const std::vector<double> &input, const std::vector<double> &output) {
startRecording(label);
int returnId = addElement(input, output);
stopRecording();
return returnId;
};
std::vector<std::string> trainingData::getColumnNames() {
return trainingSet[targetPhrase].columnNames;
}
void trainingData::setColumnNames(const std::vector<std::string> &column_names) {
trainingSet[targetPhrase].columnNames = column_names;
}
Json::Value trainingData::parse2json() {
Json::Value root;
Json::Value metadata;
Json::Value trainingSetJSON;
metadata["creator"] = "RAPID-MIX API C++";
metadata["version"] = RAPIDMIX_VERSION;
//Go through all the phrases
for (int i = 0; i < trainingSet.size(); ++i) {
Json::Value thisPhrase;
thisPhrase.append(trainingSet[i].uniqueId);
thisPhrase.append(trainingSet[i].label);
Json::Value elementInput;
for (int k = 0; k < trainingSet[i].elements[j].input.size(); ++k) {
elementInput.append(trainingSet[i].elements[j].input[k]);
Json::Value column_names;
for (int j = 0; j < trainingSet[i].columnNames.size(); ++j) {
column_names.append(trainingSet[i].columnNames[j]);
}
singleElement.append(elementInput);
thisPhrase.append(column_names);
Json::Value elementOutput;
for (int k = 0; k < trainingSet[i].elements[j].output.size(); ++k) {
elementOutput.append(trainingSet[i].elements[j].output[k]);
Json::Value elements;
for (int j = 0; j < trainingSet[i].elements.size(); ++j) {
Json::Value singleElement;
Json::Value elementInput;
for (int k = 0; k < trainingSet[i].elements[j].input.size(); ++k) {
elementInput.append(trainingSet[i].elements[j].input[k]);
}
singleElement.append(elementInput);
Json::Value elementOutput;
for (int k = 0; k < trainingSet[i].elements[j].output.size(); ++k) {
elementOutput.append(trainingSet[i].elements[j].output[k]);
}
singleElement.append(elementOutput);
singleElement.append(trainingSet[i].elements[j].timeStamp);
elements.append(singleElement);
}
singleElement.append(elementOutput);
thisPhrase.append(elements);
singleElement.append(trainingSet[i].elements[j].timeStamp);
elements.append(singleElement);
trainingSetJSON.append(thisPhrase);
}
thisPhrase.append(elements);
trainingSetJSON.append(thisPhrase);
root["metadata"] = metadata;
root["trainingSet"] = trainingSetJSON;
return root;
}
root["metadata"] = metadata;
root["trainingSet"] = trainingSetJSON;
return root;
}
std::string trainingData::getJSON() {
Json::Value root = parse2json();
return root.toStyledString();
}
void trainingData::writeJSON(const std::string &filepath) {
Json::Value root = parse2json();
std::ofstream jsonOut;
jsonOut.open (filepath);
Json::StyledStreamWriter writer;
writer.write(jsonOut, root);
jsonOut.close();
}
void trainingData::json2trainingSet(const Json::Value &root) {
trainingSet = {};
for (const Json::Value& jsonPhrase : root["trainingSet"]) {
std::string trainingData::getJSON() {
Json::Value root = parse2json();
return root.toStyledString();
}
phrase tempPhrase = { jsonPhrase[0].asUInt(), jsonPhrase[1].asString() };
for (int i = 0; i < jsonPhrase[2].size(); ++i) {
tempPhrase.columnNames.push_back(jsonPhrase[2][i].asString());
}
void trainingData::writeJSON(const std::string &filepath) {
Json::Value root = parse2json();
std::ofstream jsonOut;
jsonOut.open (filepath);
Json::StyledStreamWriter writer;
writer.write(jsonOut, root);
jsonOut.close();
for (int i = 0; i < jsonPhrase[3].size(); ++i) {
element tempElement;
for (int j = 0; j < jsonPhrase[3][i][0].size(); ++j) {
tempElement.input.push_back(jsonPhrase[3][i][0][j].asDouble());
}
for (int j = 0; j < jsonPhrase[3][i][1].size(); ++j) {
tempElement.output.push_back(jsonPhrase[3][i][1][j].asDouble());
}
void trainingData::json2trainingSet(const Json::Value &root) {
trainingSet = {};
for (const Json::Value& jsonPhrase : root["trainingSet"]) {
phrase tempPhrase = { jsonPhrase[0].asUInt(), jsonPhrase[1].asString() };
for (int i = 0; i < jsonPhrase[2].size(); ++i) {
tempPhrase.columnNames.push_back(jsonPhrase[2][i].asString());
}
tempElement.timeStamp = jsonPhrase[3][i][2].asDouble();
tempPhrase.elements.push_back(tempElement);
for (int i = 0; i < jsonPhrase[3].size(); ++i) {
element tempElement;
for (int j = 0; j < jsonPhrase[3][i][0].size(); ++j) {
tempElement.input.push_back(jsonPhrase[3][i][0][j].asDouble());
}
for (int j = 0; j < jsonPhrase[3][i][1].size(); ++j) {
tempElement.output.push_back(jsonPhrase[3][i][1][j].asDouble());
}
tempElement.timeStamp = jsonPhrase[3][i][2].asDouble();
tempPhrase.elements.push_back(tempElement);
}
trainingSet.push_back(tempPhrase);
}
trainingSet.push_back(tempPhrase);
}
}
bool trainingData::putJSON(const std::string &jsonMessage) {
Json::Value parsedFromString;
Json::Reader reader;
bool parsingSuccessful = reader.parse(jsonMessage, parsedFromString);
if (parsingSuccessful)
{
json2trainingSet(parsedFromString);
bool trainingData::putJSON(const std::string &jsonMessage) {
Json::Value parsedFromString;
Json::Reader reader;
bool parsingSuccessful = reader.parse(jsonMessage, parsedFromString);
if (parsingSuccessful)
{
json2trainingSet(parsedFromString);
}
return parsingSuccessful;
}
return parsingSuccessful;
}
bool trainingData::readJSON(const std::string &filepath) {
Json::Value root;
std::ifstream file(filepath);
file >> root;
json2trainingSet(root);
return true; //TODO: check something first
bool trainingData::readJSON(const std::string &filepath) {
Json::Value root;
std::ifstream file(filepath);
file >> root;
json2trainingSet(root);
return true; //TODO: check something first
}
}
RAPIDMIX_END_NAMESPACE
src/machineLearning/trainingData.h
View file @ a0bfd689
/*
* trainingData.h
* Created by Michael Zbyszynski on 2 Feb 2017
/**
* @file trainingData.h
* @author Michael Zbyszynski
* @date 2 Feb 2017
* @copyright
* Copyright © 2017 Goldsmiths. All rights reserved.
*
* @ingroup machinelearning
*/
#ifndef trainingData_h
......@@ -11,75 +15,100 @@
#include <string>
#include <unordered_map>
#include "rapidMix.h"
#include "../rapidmix.h"
#include "json.h"
RAPIDMIX_BEGIN_NAMESPACE
/** This is used by both NN and KNN models for training */
class trainingData {
public:
trainingData();
namespace rapidmix {
struct element{
uint32_t uniqueId; //MZ: Does this scope of this id need to extend beyond this instantiation?
std::vector<double> input;
std::vector<double> output;
double timeStamp;
/** This is used by both NN and KNN models for training */
class trainingData {
public:
trainingData();
struct element{
uint32_t uniqueId; //MZ: Does this scope of this id need to extend beyond this instantiation?
std::vector<double> input;
std::vector<double> output;
double timeStamp;
};
struct phrase {
uint32_t uniqueId;
std::string label; //TODO: Need to work this with templates
std::vector<std::string> columnNames; //equal to the number of inputs
std::vector<element> elements;
//TODO: This is just a design idea right now.
void addElement (const std::vector<double> &input, const std::vector<double> &output)
{
element tempElement;
// tempElement.uniqueId = assignCurrentId(); //TODO: how to do this? Do we need this?
tempElement.input = input;
tempElement.output = output;
//tempElement.timeStamp = NULL;
this->elements.push_back(tempElement);
}
};
std::vector<phrase> trainingSet;
//TODO: Deleting phrases (last or by label)
//Design ideas to make phrase building stateless:
std::vector<phrase>::iterator createNewPhrase(); //??? Do we need this?
std::vector<phrase>::iterator createNewPhrase(std::string label);
/** Create a new phrase that can be recorded into. Returns phrase id */
uint32_t startRecording(); //FIXME: this should go away. -MZ
/** Create new phrase, with a label, that can be recorded into. Returns phrase id */
uint32_t startRecording(const std::string &label);
/** Add an element with input and output to the phrase that is recording,
or to the default phrase if recording is stopped. Returns phrase id. */
uint32_t addElement(const std::vector<double> &input, const std::vector<double> &output);
/** Add an element with just input to the phrase that is recording,
or to the default phrase if recording is stopped. Returns phrase id. */
uint32_t addElement(const std::vector<double> &input);
void stopRecording();
/** Create a phrase with a single element that has a label and input. Returns phrase id. */
uint32_t recordSingleElement(const std::string &label, const std::vector<double> &input);
/** Create a phrase with a single element that has input, and output. Returns phrase id. */
uint32_t recordSingleElement(const std::vector<double> &input, const std::vector<double> &output);
/** Create a phrase with a single element that has a label, input, and output. Returns phrase id. */
uint32_t recordSingleElement(const std::string &label, const std::vector<double> &input, const std::vector<double> &output);
std::vector<std::string> getColumnNames();
void setColumnNames(const std::vector<std::string> &columnNames);
/** Get a JSON representation of the data set in the form of a styled string */
std::string getJSON();
/** Write a JSON version of the training set to specified file path */
void writeJSON(const std::string &filepath);
/** populate a data set with string. See getJSON() */
bool putJSON(const std::string &jsonMessage);
/** read a JSON file at file path and build a training set from it */
bool readJSON(const std::string &filepath);
private:
int targetPhrase;
uint32_t currentId;
//* Returns and increments current id */
uint32_t assignCurrentId();
Json::Value parse2json();
void json2trainingSet(const Json::Value &newTrainingData);
};
struct phrase {
uint32_t uniqueId;
std::string label; //TODO: Need to work this with templates
std::vector<std::string> columnNames;
std::vector<element> elements;
};
std::vector<phrase> trainingSet;
//* Create a new phrase that can be recorded into. Returns phrase id */
uint32_t startRecording();
//* Create new phrase, with a label, that can be recorded into. Returns phrase id */
uint32_t startRecording(std::string label);
//* Add an element to the phrase that is recording, or to the default phrase if recording is stopped. Returns phrase id. */
uint32_t addElement(std::vector<double>input, std::vector<double> output);
void stopRecording();
std::vector<std::string> getColumnNames();
void setColumnNames(std::vector<std::string> columnNames);
/** Get a JSON representation of the data set in the form of a styled string */
std::string getJSON();
/** Write a JSON version of the training set to specified file path */
void writeJSON(const std::string &filepath);
/** populate a data set with string. See getJSON() */
bool putJSON(const std::string &jsonMessage);
/** read a JSON file at file path and build a training set from it */
bool readJSON(const std::string &filepath);
//this holds string labels
std::unordered_map<std::string, int> labels;
std::string getLabel(int value);
private:
int targetPhrase;
uint32_t currentId;
//* Returns and increments current id */
uint32_t assignCurrentId();
Json::Value parse2json();
void json2trainingSet(const Json::Value &newTrainingData);
};
RAPIDMIX_END_NAMESPACE
}
#endif
src/rapidmix.h
View file @ a0bfd689
/*
* rapidmix.h
* Created by Michael Zbyszynski on 12 Jan 2017
/**
* @file rapidmix.h
* @author Michael Zbyszynski
* @date 12 Jan 2017
*
* @copyright
* Copyright © 2017 Goldsmiths. All rights reserved.
*/
/**
* @mainpage RAPID-MIX API
*
* @section Introduction
*
* Hello, API ! <br>
* All the classes, functions and structs documented here belong to the
* <code>rapidmix</code> namespace.
*
* @section Examples
* @subsection Simple classification
* TODO
*/
/////////////////////////////////////////////////////////////////////////////
// ______ _ _ ___ ____ ___ ______ _____ //
// | ___ \ (_) | | | \/ (_) / _ \ | ___ \_ _| //
......@@ -19,16 +36,14 @@
#define rapidmix_h
#define RAPIDMIX_VERSION_MAJOR 2
#define RAPIDMIX_VERSION_MINOR 0
#define RAPIDMIX_VERSION_MINOR 3
#define RAPIDMIX_VERSION_PATCH 0
#define RAPIDMIX_VERSION "2.0.0"
#define RAPIDMIX_REVISION "31-May-2017"
#define RAPIDMIX_BEGIN_NAMESPACE namespace rapidmix {
#define RAPIDMIX_END_NAMESPACE }
#define RAPIDMIX_VERSION "2.3.1"
#define RAPIDMIX_JSON_DOC_VERSION "1.0.0"
#define RAPIDMIX_REVISION "2-MAY-2018"
#include "machineLearning.h"
#include "trainingData.h"
#include "signalProcessing.h"
#include "machineLearning/machineLearning.h"
#include "machineLearning/trainingData.h"
#include "signalProcessing/signalProcessing.h"
#endif
src/signalProcessing/rapidPiPoTools/rapidPiPoHost.cpp 0 → 100644
View file @ a0bfd689
#include "rapidmix.h"
#include "rapidPiPoHost.h"
#include <iostream>
#include <fstream>
//=========================== H O S T === U T I L S ==========================//
static void fromPiPoStreamAttributes(PiPoStreamAttributes &src,
RapidPiPoStreamAttributes &dst)
{
unsigned int numCols = src.dims[0];
unsigned int numLabels = src.numLabels;
if (numLabels > PIPO_MAX_LABELS) {
numLabels = PIPO_MAX_LABELS;
}
if (numLabels > numCols) {
numLabels = numCols;
}
dst.hasTimeTags = src.hasTimeTags;
if (src.rate <= MIN_PIPO_SAMPLERATE) {
dst.rate = MIN_PIPO_SAMPLERATE;
} else if (src.rate >= MAX_PIPO_SAMPLERATE) {
dst.rate = MAX_PIPO_SAMPLERATE;
} else {
dst.rate = src.rate;
}
dst.rate = src.rate;
dst.offset = src.offset;
dst.width = src.dims[0];
dst.height = src.dims[1];
dst.labels = std::vector<std::string>();
for (unsigned int i = 0; i < numLabels; ++i)
{
dst.labels.push_back(std::string(src.labels[i]));
}
dst.hasVarSize = src.hasVarSize;
dst.domain = src.domain;
dst.maxFrames = src.maxFrames;
}
static void toPiPoStreamAttributes(RapidPiPoStreamAttributes &src,
PiPoStreamAttributes &dst)
{
const char *labs[src.labels.size()];
for (unsigned int i = 0; i < src.labels.size(); ++i)
{
labs[i] = src.labels[i].c_str();
}
dst = PiPoStreamAttributes(
src.hasTimeTags,
src.rate,
src.offset,
src.width,
src.height,
&labs[0],
src.hasVarSize,
src.domain,
src.maxFrames
);
}
//========================= H O S T === M E T H O D S ========================//
int
RapidPiPoHost::setInputStreamAttributes(RapidPiPoStreamAttributes &rpsa)
{
PiPoStreamAttributes psa;
toPiPoStreamAttributes(rpsa, psa);
return PiPoHost::setInputStreamAttributes(psa);
}
void
RapidPiPoHost::onNewFrame(double time, double weight, PiPoValue *values, unsigned int size)
{
std::cout << "I received " << size << " new frame(s)" << std::endl;
}
//----------------------------- JSON FORMATTING ------------------------------//
std::string
RapidPiPoHost::getJSON()
{
Json::Value result = toJSON();
return result.toStyledString();
}
void
RapidPiPoHost::writeJSON(const std::string &filepath)
{
Json::Value root = toJSON();
std::ofstream jsonOut;
jsonOut.open (filepath);
Json::StyledStreamWriter writer;
writer.write(jsonOut, root);
jsonOut.close();
}
bool
RapidPiPoHost::putJSON(const std::string &jsonMessage)
{
Json::Value parsedFromString;
Json::Reader reader;
bool parsingSuccessful = reader.parse(jsonMessage, parsedFromString);
return (parsingSuccessful && fromJSON(parsedFromString));
}
bool
RapidPiPoHost::readJSON(const std::string &filepath)
{
Json::Value root;
std::ifstream file(filepath);
file >> root;
return fromJSON(root);
}
//-------------------------- PRIVATE HOST METHODS ----------------------------//
Json::Value
RapidPiPoHost::toJSON()
{
Json::Value root;
//*
root["docType"] = "rapid-mix:signal-processing";
root["docVersion"] = RAPIDMIX_JSON_DOC_VERSION;
Json::Value target;
target["name"] = "pipo";
target["version"] = PIPO_SDK_VERSION;
root["target"] = target;
Json::Value pipodata;
pipodata["description"] = this->graphName;
Json::Value inputStream;
inputStream["hasTimeTags"] = inputStreamAttrs.hasTimeTags;
inputStream["rate"] = inputStreamAttrs.rate;
inputStream["offset"] = inputStreamAttrs.offset;
inputStream["width"] = inputStreamAttrs.dims[0];
inputStream["height"] = inputStreamAttrs.dims[1];
inputStream["labels"] = inputStreamAttrs.labels;
inputStream["hasVarSize"] = inputStreamAttrs.hasVarSize;
inputStream["domain"] = inputStreamAttrs.domain;
inputStream["maxFrames"] = inputStreamAttrs.maxFrames;
Json::Value outputStream;
outputStream["hasTimeTags"] = outputStreamAttrs.hasTimeTags;
outputStream["rate"] = outputStreamAttrs.rate;
outputStream["offset"] = outputStreamAttrs.offset;
outputStream["width"] = outputStreamAttrs.dims[0];
outputStream["height"] = outputStreamAttrs.dims[1];
outputStream["labels"] = outputStreamAttrs.labels;
outputStream["hasVarSize"] = outputStreamAttrs.hasVarSize;
outputStream["domain"] = outputStreamAttrs.domain;
outputStream["maxFrames"] = outputStreamAttrs.maxFrames;
Json::Value streams;
streams["input"] = inputStream;
streams["output"] = outputStream;
pipodata["streamAttributes"] = streams;
Json::Value params;
int n = this->graph->getNumAttrs();
params.resize(static_cast<Json::ArrayIndex>(n));
for (unsigned int i = 0; i < n; ++i)
{
Json::Value param;
PiPo::Attr *a = this->graph->getAttr(i);
param["name"] = a->getName();
switch (a->getType())
{
case PiPo::Bool:
param["type"] = "Bool";
param["value"] = a->getInt(0);
break;
case PiPo::Enum:
param["type"] = "Enum";
param["value"] = std::string(a->getStr(0));
break;
case PiPo::String:
param["type"] = "String";
param["value"] = std::string(a->getStr(0));
break;
case PiPo::Int:
param["type"] = "Int";
param["value"] = Json::Value(Json::arrayValue);
for (int i = 0; i < a->getSize(); ++i)
{
param["value"].append(a->getInt(i));
}
break;
case PiPo::Double:
param["type"] = "Double";
param["value"] = Json::Value(Json::arrayValue);
for (int i = 0; i < a->getSize(); ++i)
{
param["value"].append(a->getDbl(i));
}
break;
default:
std::cout << a->getType() << std::endl;
break;
}
params[i] = param;
}
pipodata["parameters"] = params;
root["payload"] = pipodata;
//*/
return root;
}
bool
RapidPiPoHost::fromJSON(Json::Value &jv)
{
//*
if (jv["docType"].asString().compare("rapid-mix:signal-processing") == 0 &&
//jv["docVersion"].asString().compare(RAPIDMIX_JSON_DOC_VERSION) == 0 &&
jv["target"]["name"].asString().compare("pipo") == 0 &&
jv["payload"].size() > 0) {
this->setGraph(jv["payload"]["description"].asString());
Json::Value params = jv["payload"]["parameters"];
unsigned int nAttrs = params.size();
for (unsigned int i = 0; i < nAttrs; ++i)
{
std::string type = params[i]["type"].asString();
const char *name = params[i]["name"].asString().c_str();
if (type.compare("Bool") == 0)
{
this->setAttr(name, params[i]["value"].asInt());
}
else if (type.compare("Enum") == 0 || type.compare("String") == 0)
{
this->setAttr(name, params[i]["value"].asString());
}
else if (type.compare("Int") == 0)
{
std::vector<int> values;
for (int j = 0; j < params[i]["value"].size(); ++j)
{
values.push_back(params[i]["value"][j].asInt());
}
this->setAttr(name, values);
}
else if (type.compare("Double") == 0)
{
std::vector<double> values;
for (int j = 0; j < params[i]["value"].size(); ++j)
{
values.push_back(params[i]["value"][j].asDouble());
}
this->setAttr(name, values);
}
}
Json::Value inputStream = jv["pipodata"]["streamAttributes"]["input"];
// setInputStreamAttributes(
// inputStream["hasTimeTags"].getDbl()//,
// //...
// );
return true;
}
//*/
return false;
}
src/signalProcessing/rapidPiPoTools/rapidPiPoHost.h 0 → 100644
View file @ a0bfd689
#ifndef _RAPID_PIPO_HOST_H_
#define _RAPID_PIPO_HOST_H_
#include "PiPo.h"
#include "PiPoHost.h"
//#ifdef EXTERNAL_JSONCPP_PATH
#define EXTERNAL_JSONCPP_PATH "json.h"
#include EXTERNAL_JSONCPP_PATH
//#endif /* EXTERNAL_JSONCPP_PATH */
#define MIN_PIPO_SAMPLERATE (1.0 / 31536000000.0) /* once a year */
#define MAX_PIPO_SAMPLERATE (96000000000.0)
#define DEFAULT_PIPO_SAMPLERATE 1000.0
struct RapidPiPoStreamAttributes {
RapidPiPoStreamAttributes() : // default parameters suited for audio
hasTimeTags(false),
rate(DEFAULT_PIPO_SAMPLERATE),
offset(0),
width(1),
height(1),
labels({ "" }),
hasVarSize(false),
domain(0),
maxFrames(1) {}
bool hasTimeTags;
double rate;
double offset;
unsigned int width;
unsigned int height;
std::vector<std::string> labels;
bool hasVarSize;
double domain;
unsigned int maxFrames;
};
//================================ H O S T ===================================//
class RapidPiPoHost : public PiPoHost {
public:
virtual int setInputStreamAttributes(RapidPiPoStreamAttributes &a);
virtual void onNewFrame(double time, double weight, PiPoValue *values, unsigned int size);
/** Get a JSON representation of the model in the form of a styled string */
virtual std::string getJSON();
/** Write a JSON model description to specified file path */
virtual void writeJSON(const std::string &filepath);
/** configure empty model with string. See getJSON() */
virtual bool putJSON(const std::string &jsonMessage);
/** read a JSON file at file path and build a modelSet from it */
virtual bool readJSON(const std::string &filepath);
private:
Json::Value toJSON();
bool fromJSON(Json::Value &jv);
};
#endif /* _RAPID_PIPO_HOST_H_ */
src/signalProcessing/rapidPiPoTools/rapidPiPoTools.h 0 → 100644
View file @ a0bfd689
#ifndef _RAPID_PIPO_TOOLS_H_
#define _RAPID_PIPO_TOOLS_H_
#include "rapidPiPoHost.h"
typedef RapidPiPoStreamAttributes signalAttributes;
typedef RapidPiPoHost signalProcessingHost;
#endif /* _RAPID_PIPO_TOOLS_H_ */
src/signalProcessing/rapidStream/rapidStream.cpp deleted 100644 → 0
View file @ 6fddbff2
/*
* rapidStream.cpp
* Created by Michael Zbyszynski on 6 Feb 2017
* Copyright © 2017 Goldsmiths. All rights reserved.
*/
#include "rapidStream.h"
#include <iostream>
#include <cmath>
rapidStream::rapidStream(int window_size) {
windowSize = window_size;
windowIndex = 0;
circularWindow = new double[window_size];
for (int i = 0; i < windowSize; ++i) {
circularWindow[i] = 0;
}
}
rapidStream::rapidStream() {
windowSize = 3;
windowIndex = 0;
circularWindow = new double[windowSize];
for (int i = 0; i < windowSize; ++i) {
circularWindow[i] = 0;
}
}
rapidStream::~rapidStream() {
delete []circularWindow;
}
void rapidStream::clear() {
windowIndex = 0;
circularWindow = new double[windowSize];
for (int i = 0; i < windowSize; ++i) {
circularWindow[i] = 0;
}
}
void rapidStream::pushToWindow(double input) {
circularWindow[windowIndex] = input;
windowIndex = (windowIndex + 1) % windowSize;
}
inline double rapidStream::calcCurrentVel(int i) {
return circularWindow[(i + windowIndex) % windowSize] - circularWindow[(i + windowIndex - 1) % windowSize];
}
double rapidStream::velocity() {
return calcCurrentVel(-1);
};
double rapidStream::acceleration() {
return calcCurrentVel(-2) - calcCurrentVel(-3);
};
double rapidStream::minimum() {
double minimum = std::numeric_limits<double>::infinity();
for (int i = 0; i < windowSize; ++i) {
if (circularWindow[i] < minimum) {
minimum = circularWindow[i];
}
}
return minimum;
}
double rapidStream::maximum() {
double maximum = std::numeric_limits<double>::min();
for (int i = 0; i < windowSize; ++i) {
if (circularWindow[i] > maximum) {
maximum = circularWindow[i];
}
}
return maximum;
}
double rapidStream::sum() {
double newSum = 0;
for(int i = 0; i < windowSize; ++i)
{
newSum += circularWindow[i];
}
return newSum;
}
double rapidStream::mean() {
return sum()/windowSize;
}
double rapidStream::standardDeviation() {
double newMean = mean();
double standardDeviation = 0.;
for(int i = 0; i < windowSize; ++i) {
standardDeviation += pow(circularWindow[i] - newMean, 2);
}
return sqrt(standardDeviation / windowSize);
}
double rapidStream::minVelocity() {
double minVel = std::numeric_limits<double>::infinity();
for (int i = 0; i < windowSize; ++i) {
double currentVel = calcCurrentVel(i);
if ( currentVel < minVel) {
minVel = currentVel;
}
}
return minVel;
}
double rapidStream::maxVelocity() {
double maxVel = std::numeric_limits<double>::lowest();
for (int i = 0; i < windowSize; ++i) {
double currentVel = calcCurrentVel(i);
if (currentVel > maxVel) {
maxVel = currentVel;
}
}
return maxVel;
}
double rapidStream::minAcceleration() {
double minAccel = std::numeric_limits<double>::infinity();
double lastVel = calcCurrentVel(1);
for (int i = 2; i < windowSize; ++i) {
double currentVel = calcCurrentVel(i);
double currentAccel = currentVel - lastVel;
lastVel = currentVel;
if (currentAccel < minAccel) {
minAccel = currentAccel;
}
}
return minAccel;
}
double rapidStream::maxAcceleration() {
double maxAccel = std::numeric_limits<double>::lowest();
double lastVel = calcCurrentVel(1);
for (int i = 2; i < windowSize; ++i) {
double currentVel = calcCurrentVel(i);
double currentAccel = currentVel - lastVel;
lastVel = currentVel;
if (currentAccel > maxAccel) {
maxAccel = currentAccel;
}
}
return maxAccel;
}
\ No newline at end of file
src/signalProcessing/rapidStream/rapidStream.h deleted 100644 → 0
View file @ 6fddbff2
/*
* rapidStream.h
* Created by Michael Zbyszynski on 6 Feb 2017
* Copyright © 2017 Goldsmiths. All rights reserved.
*/
#ifndef rapidStream_h
#define rapidStream_h
#include <stdint.h>
class rapidStream {
public:
/**
* Create a circular buffer with 3 elements.
*/
rapidStream();
/**
* Create a circular buffer with an arbitrary number of elements.
* @param int: number of elements to hold in the buffer
*/
rapidStream(int windowSize);
~rapidStream();
/**
* Resets all the values in the buffer to zero.
*/
void clear();
/** Add a value to a circular buffer whose size is defined at creation.
* @param double: value to be pushed into circular buffer.
*/
void pushToWindow(double input);
/** Calculate the first-order difference (aka velocity) between the last two inputs.
* @return double: difference between last two inputs.
*/
double velocity();
/** Calculate the second-order difference (aka acceleration) over the last three inputs.
* @return double: acceleration over the last three inputs.
*/
double acceleration();
/** Find the minimum value in the buffer.
* @return double: minimum.
*/
double minimum();
/** Find the maximum value in the buffer.
* @return double: maximum.
*/
double maximum();
/** Calculate the sum of all values in the buffer.
* @return double: sum.
*/
double sum();
/** Calculate the mean of all values in the buffer.
* @return double: mean.
*/
double mean();
/** Calculate the standard deviation of all values in the buffer.
* @return double: standard deviation.
*/
double standardDeviation();
/** Calculate the minimum first-order difference over consecutive inputs in the buffer.
* @return double: minimum velocity.
*/
double minVelocity();
/** Calculate the maximum first-order difference over consecutive inputs in the buffer.
* @return double: maximum velocity.
*/
double maxVelocity();
/** Calculate the minimum second-order difference over consecutive inputs in the buffer.
* @return double: minimum acceleration.
*/
double minAcceleration();
/** Calculate the maximum second-order difference over consecutive inputs in the buffer.
* @return double: maximum acceleration.
*/
double maxAcceleration();
private:
uint32_t windowSize;
uint32_t windowIndex;
double *circularWindow;
double calcCurrentVel(int i);
};
#endif
\ No newline at end of file
src/signalProcessing/signalProcessing.h
View file @ a0bfd689
/*
* signalProcessing.h
* Created by Michael Zbyszynski on 6 Feb 2017
/**
* @file signalProcessing.h
* @author Michael Zbyszynski
* @date 6 Feb 2017
* @copyright
* Copyright © 2017 Goldsmiths. All rights reserved.
*
* @ingroup signalprocessing
*/
#ifndef signalProcessing_h
#define signalProcessing_h
#include "rapidMix.h"
#include "rapidmix.h"
#include "maximilian.h"
#include "maxim.h"
#include "rapidStream.h"
//#include "rapidPiPoTools.h"
#include "./rapidPiPoTools/rapidPiPoTools.h"
RAPIDMIX_BEGIN_NAMESPACE
namespace rapidmix {
/*
* Wrapper for signalProcessing modules, currently a collection of typedefs
*/
typedef maxiFFT FFT;
typedef maxiMFCC MFCC;
typedef rapidStream rapidStream; //MZ: Best... typedef... EVER!
/*
* Wrapper for signalProcessing modules, currently a collection of typedefs
*/
typedef maxiFFT FFT;
typedef maxiMFCC MFCC;
typedef rapidStream<double> rapidStream;
RAPIDMIX_END_NAMESPACE
typedef signalProcessingHost signalProcessingHost;
typedef signalAttributes signalAttributes;
}
#endif
tests/data/DnB-loop-175BPM.wav 0 → 100644
View file @ a0bfd689
File added
tests/data/pipo.json 0 → 100644
View file @ a0bfd689
{
"docType" : "rapid-mix:signal-processing",
"docVersion" : "1.0.0",
"payload" :
{
"description" : "slice:fft:sum:scale:onseg",
"parameters" :
[
{
"name" : "slice.size",
"type" : "Int",
"value" : [ 1024 ]
},
{
"name" : "slice.hop",
"type" : "Int",
"value" : [ 256 ]
},
{
"name" : "slice.wind",
"type" : "Enum",
"value" : "hann"
},
{
"name" : "slice.norm",
"type" : "Enum",
"value" : "power"
},
{
"name" : "fft.size",
"type" : "Int",
"value" : [ 0 ]
},
{
"name" : "fft.mode",
"type" : "Enum",
"value" : "power"
},
{
"name" : "fft.norm",
"type" : "Bool",
"value" : 1
},
{
"name" : "fft.weighting",
"type" : "Enum",
"value" : "itur468"
},
{
"name" : "sum.norm",
"type" : "Bool",
"value" : 0
},
{
"name" : "sum.colname",
"type" : "String",
"value" : ""
},
{
"name" : "scale.inmin",
"type" : "Double",
"value" : [ 1 ]
},
{
"name" : "scale.inmax",
"type" : "Double",
"value" : [ 10 ]
},
{
"name" : "scale.outmin",
"type" : "Double",
"value" : [ 0 ]
},
{
"name" : "scale.outmax",
"type" : "Double",
"value" : [ 10 ]
},
{
"name" : "scale.clip",
"type" : "Bool",
"value" : 0
},
{
"name" : "scale.func",
"type" : "Enum",
"value" : "log"
},
{
"name" : "scale.base",
"type" : "Double",
"value" : [ 10 ]
},
{
"name" : "scale.minlog",
"type" : "Double",
"value" : [ 1.0000000195414814e-24 ]
},
{
"name" : "scale.complete",
"type" : "Enum",
"value" : "repeatlast"
},
{
"name" : "scale.colindex",
"type" : "Int",
"value" : [ 0 ]
},
{
"name" : "scale.numcols",
"type" : "Int",
"value" : [ 0 ]
},
{
"name" : "onseg.colindex",
"type" : "Int",
"value" : [ 0 ]
},
{
"name" : "onseg.numcols",
"type" : "Int",
"value" : [ -1 ]
},
{
"name" : "onseg.filtersize",
"type" : "Int",
"value" : [ 3 ]
},
{
"name" : "onseg.threshold",
"type" : "Double",
"value" : [ 9 ]
},
{
"name" : "onseg.odfmode",
"type" : "Enum",
"value" : "mean"
},
{
"name" : "onseg.mininter",
"type" : "Double",
"value" : [ 50 ]
},
{
"name" : "onseg.startisonset",
"type" : "Bool",
"value" : 1
},
{
"name" : "onseg.duration",
"type" : "Bool",
"value" : 1
},
{
"name" : "onseg.durthresh",
"type" : "Double",
"value" : [ 0 ]
},
{
"name" : "onseg.offthresh",
"type" : "Double",
"value" : [ -120 ]
},
{
"name" : "onseg.maxsize",
"type" : "Double",
"value" : [ 0 ]
},
{
"name" : "onseg.min",
"type" : "Bool",
"value" : 0
},
{
"name" : "onseg.max",
"type" : "Bool",
"value" : 1
},
{
"name" : "onseg.mean",
"type" : "Bool",
"value" : 0
},
{
"name" : "onseg.stddev",
"type" : "Bool",
"value" : 0
},
{
"name" : "onseg.odfoutput",
"type" : "Bool",
"value" : 0
}
],
"streamAttributes" :
{
"input" :
{
"domain" : 0,
"hasTimeTags" : 0,
"hasVarSize" : false,
"height" : 1,
"labels" : true,
"maxFrames" : 1,
"offset" : 0,
"rate" : 1000,
"width" : 1
},
"output" :
{
"domain" : 0,
"hasTimeTags" : 1,
"hasVarSize" : false,
"height" : 1,
"labels" : true,
"maxFrames" : 1,
"offset" : 0,
"rate" : 3.90625,
"width" : 2
}
}
},
"target" :
{
"name" : "pipo",
"version" : 0.20000000000000001
}
}
tests/rapidMixTest.cpp 0 → 100644
View file @ a0bfd689
#include <cassert>
#include <vector>
#include <iostream>
#include "rapidmix.h"
int main() {
///////////////////////////////////Test GVF
rapidmix::gvfTemporalVariation gvf;
rapidmix::trainingData myGVFData;
// Record first gesture
myGVFData.startRecording();
myGVFData.addElement({ 4.0, 0.7 });
myGVFData.addElement({ 3.0, 0.8 });
myGVFData.addElement({ 2.0, 0.9 });
myGVFData.addElement({ 1.0, 1.0 });
myGVFData.addElement({ 0.4, 1.2 });
myGVFData.addElement({ 0.2, 1.4 });
myGVFData.stopRecording();
// Record reverse of first gesture
myGVFData.startRecording();
myGVFData.addElement({ 0.2, 1.4 });
myGVFData.addElement({ 0.4, 1.2 });
myGVFData.addElement({ 1.0, 1.0 });
myGVFData.addElement({ 2.0, 0.9 });
myGVFData.addElement({ 3.0, 0.8 });
myGVFData.addElement({ 4.0, 0.7 });
myGVFData.stopRecording();
// Train
std::cout << "gvf train = " << gvf.train(myGVFData) << std::endl;
std::cout << "gvf passed." << std::endl;
//////////////////////////////////////Test RapidLib
rapidmix::staticRegression myNN;
rapidmix::trainingData myRLData;
std::vector<double> input = { 0.2, 0.7 };
std::vector<double> output = { 3.0 };
myRLData.recordSingleElement("label", input, output);
input = { 2.0, 44.2 };
output = { 20.14 };
myRLData.recordSingleElement("label", input, output);
std::cout << "staticRegression train = " << myNN.train(myRLData) << std::endl;
std::vector<double> inputVec = { 1.618, 18.9 };
assert(myNN.run(inputVec)[0] == 12.596715279688549);
std::cout << "staticRegression passed." << std::endl;
//////////////////////////////////////Test XMM
rapidmix::xmmConfig xcfg;
xcfg.relativeRegularization = 0.1;
rapidmix::trainingData myXmmData;
std::vector<double> myXmmInput;
std::vector<double> myXmmOutput;
myXmmData.startRecording("lab1");
myXmmInput = { 0.2, 0.7 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.startRecording("lab2");
myXmmInput = { 0.8, 0.1 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.writeJSON("/var/tmp/testTrainingData.json");
rapidmix::xmmStaticClassification myGmm(xcfg);
myGmm.train(myXmmData);
std::string filepath = "/var/tmp/modelSetDescription";
myGmm.writeJSON(filepath);
myXmmInput = { 0.2, 0.7 };
rapidmix::xmmStaticClassification myGmmFromFile;
myGmmFromFile.readJSON(filepath);
assert(myGmm.run(myXmmInput)[0] == myGmmFromFile.run(myXmmInput)[0]);
std::cout << "xmm passed." << std::endl;
/////////////////////////////////////Test pipo signal processing
rapidmix::signalProcessingHost spHost;
spHost.setGraph("slice<fft:moments,moments>");
spHost.setAttr("slice.size", 128);
spHost.setAttr("slice.hop", 64);
rapidmix::signalAttributes sa;
spHost.setInputStreamAttributes(sa);
for (unsigned int i = 0; i < 256; ++i)
{
std::vector<float> f({ 1, 2, 3 });
spHost.frames(0, 1, &f[0], 1, 1);
}
std::cout << "pipo passed." << std::endl;
/////////////////////////////////////Test rapidStream signal processing
rapidmix::rapidStream myProcessor(5); //create a processor with a window size of 5
myProcessor.pushToWindow(0.246);
myProcessor.pushToWindow(0.44);
myProcessor.pushToWindow(-0.228);
myProcessor.pushToWindow(0.402);
myProcessor.pushToWindow(-0.382);
assert(myProcessor.maximum() == 0.44);
assert(myProcessor.minimum() == -0.382);
assert(myProcessor.sum() == 0.478);
assert(myProcessor.mean() == 0.09559999999999999);
assert(myProcessor.standardDeviation() == 0.33702557766436664);
assert(myProcessor.rms() == 0.3503221374677884);
std::cout << "rapidStream passed." << std::endl;
/////////////////////////////////////Test Maxi signal processing
rapidmix::FFT myFFT;
myFFT.setup(1024, 512, 265);
for (int i = 0; i < 1024; ++i) {
float input = (i % 256/256.0);
myFFT.process(input);
}
assert(myFFT.spectralCentroid() == 3520.84277f);
std::cout << "maxi fft passed." << std::endl;
return 0;
}
tests/src/test_RapidLib.cpp 0 → 100644
View file @ a0bfd689
//
// test_RapidLib.cpp
// Unit tests for RapidLib
//
// Created by Francisco Bernardo on 17/02/2017.
// Copyright © 2017 Goldsmiths. All rights reserved.
//
#define CATCH_CONFIG_MAIN
#include "catch.hpp"
#include "rapidMix.h"
#include <iostream>
SCENARIO("Test NN Regression", "[machineLearning]")
{
GIVEN("NN Regression object and training dataset")
{
rapidmix::staticRegression myNN;
rapidmix::trainingData myData;
std::vector<double> input = { 0.2, 0.7 };
std::vector<double> output = { 3.0 };
REQUIRE(myData.recordSingleElement("label", input, output) == 1); //FIXME: Label is stupd here. -MZ
//testing alternate API
//auto newPhrase = myData.createNewPhrase("lab1");
//newPhrase->addElement(input, output);
input = { 2.0, 44.2 };
output = { 20.14 };
REQUIRE(myData.recordSingleElement("label", input, output) == 3);
myData.writeJSON("/var/tmp/testTrainingData.json");
REQUIRE(myNN.train(myData) == true);
std::string filepath = "/var/tmp/modelSetDescription.json";
myNN.writeJSON(filepath);
std::vector<double> inputVec = { 1.618, 18.9 }; //arbitrary input
REQUIRE(myNN.run(inputVec)[0] == 12.596715279688549);
WHEN("when NN model is deserialized from file")
{
rapidmix::staticRegression myNNfromFile;
REQUIRE(myNNfromFile.readJSON(filepath) == true);
THEN("run models and in-memory model")
{
REQUIRE(myNN.run(inputVec)[0] == myNNfromFile.run(inputVec)[0]);
}
}
WHEN("when NN model is deserialized from JSON stream")
{
rapidmix::staticRegression myNNfromString;
REQUIRE(myNNfromString.putJSON(myNN.getJSON()) == true);
THEN("run models and in-memory model")
{
REQUIRE(myNN.run(inputVec)[0] == myNNfromString.run(inputVec)[0]);
}
}
}
}
SCENARIO("Test kNN classification", "[machineLearning]")
{
GIVEN("kNN object and training dataset")
{
rapidmix::staticClassification myKnn;
rapidmix::trainingData myData;
REQUIRE(myData.recordSingleElement("cat", { 0.2, 0.7 }) == 1);
REQUIRE(myData.recordSingleElement("dog", { 2.0, 44.2 }) == 3); // This is not 2, because phrases get numbers, too.
REQUIRE(myKnn.train(myData) == true);
std::string filepath2 = "/var/tmp/modelSetDescription_knn.json";
myKnn.writeJSON(filepath2);
rapidmix::staticClassification myKnnFromString;
myKnnFromString.putJSON(myKnn.getJSON());
rapidmix::staticClassification myKnnFromFile;
myKnnFromFile.readJSON(filepath2);
std::vector<double> inputVec = { 2.0, 44.2 };
REQUIRE(myKnn.run(inputVec, "label") == "dog");
WHEN("when kNN model is read from file")
{
THEN("run models and compare")
{
REQUIRE(myKnn.run(inputVec)[0] == myKnnFromFile.run(inputVec)[0]);
}
}
WHEN("when kNN model is read from JSON stream")
{
THEN("run models and compare")
{
REQUIRE(myKnn.run(inputVec)[0] == myKnnFromString.run(inputVec)[0]);
}
}
}
}
SCENARIO("Test DTW classification", "[machineLearning]")
{
GIVEN("DTW object and training dataset")
{
rapidmix::dtwTemporalClassification myDTW;
rapidmix::trainingData myData;
myData.startRecording("setOne");
std::vector<double> input = { 0.1, 0.5 };
std::vector<double> output = {};
REQUIRE(myData.addElement(input, output) == 1);
input = { 0.2, 0.4 };
REQUIRE(myData.addElement(input, output) == 2);
input = { 0.3, 0.3 };
REQUIRE(myData.addElement(input, output) == 3);
input = { 0.4, 0.2 };
REQUIRE(myData.addElement(input, output) == 4);
input = { 0.5, 0.1 };
REQUIRE(myData.addElement(input, output) == 5);
myData.stopRecording();
//alternate API
auto setTwo = myData.createNewPhrase("setTwo");
input = { 1., 4. };
setTwo->addElement(input, output);
input = { 2., -3. };
setTwo->addElement(input, output);
input = { 1., 5. };
setTwo->addElement(input, output);
input = { -2., 1. };
setTwo->addElement(input, output);
REQUIRE(myDTW.train(myData) == true);
std::vector<std::vector<double> > inputSet1;
inputSet1.push_back( {1., 4.});
inputSet1.push_back( {2., -3.});
inputSet1.push_back( {1., 5.});
inputSet1.push_back( {-2., 1.});
REQUIRE(myDTW.run(inputSet1) == "setTwo");
std::vector<std::vector<double> > inputSet0;
inputSet0.push_back( { 0.1, 0.5 });
inputSet0.push_back( { 0.2, 0.4 });
inputSet0.push_back( { 0.3, 0.3 });
inputSet0.push_back( { 0.4, 0.2 });
inputSet0.push_back( { 0.5, 0.1 });
REQUIRE(myDTW.run(inputSet0) == "setOne");
}
}
SCENARIO("Test both classes reject bad data", "[machineLearning]") {
rapidmix::staticRegression badNN;
rapidmix::staticClassification badKNN;
rapidmix::trainingData myBadData;
std::vector<double> input = { 0.1, 0.2, 0.3};
std::vector<double> output = { 1.0 };
myBadData.recordSingleElement("label", input,output); //FIXME: This label is uselsess? -MZ
input = { 1.0, 2.0, 3.0, 4.0 };
myBadData.addElement(input, output);
REQUIRE(badNN.train(myBadData) == false);
REQUIRE(badKNN.train(myBadData) == false);
//TODO: These should return false with empty data set. I think it just crashes now. -mz
}
tests/src/test_gvf.cpp 0 → 100644
View file @ a0bfd689
//
// test_GVF.cpp
// Unit tests for GFV
//
// Created by Francisco Bernardo on 17/02/2017.
// Copyright © 2017 Goldsmiths. All rights reserved.
//
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do this in one cpp file
#include "catch.hpp"
#include "rapidMix.h"
TEST_CASE( "Tests default GVF ctor.", "[GVF]" ) {
GVF * gvf = new GVF();
gvf->translate(false);
gvf->segmentation(false);
REQUIRE(gvf != NULL);
}
TEST_CASE( "Tests default GVFGesture ctor.", "[GVF]" ) {
GVF * gvf = new GVF();
GVFGesture gesture;
GVFOutcomes outcomes;
gvf->translate(false);
gvf->segmentation(false);
gvf->setNumberOfParticles(1000);
gvf->setTolerance(0.2f);
gvf->setScalingsVariance(0.00001f); //0.00001f); //0.000002f); //0.00004f);
gvf->setDynamicsVariance(0.0001f);
vector<vector<float>> trainingData;
if(gvf->getState() == GVF::STATE_LEARNING)
{
gvf->startGesture();
for (vector<vector<float>>::iterator frame = trainingData.begin(); frame != trainingData.end(); frame++)
{
// Fill the template
gesture.addObservation(*frame);
}
}
gvf->setState(GVF::STATE_FOLLOWING);
gesture.clear();
trainingData.clear();
vector<vector<float>> testData;
if (gvf->getState()==GVF::STATE_FOLLOWING)
{
for (vector<vector<float>>::iterator frame = testData.begin(); frame != testData.end(); frame++)
{
// Fill the template
gesture.addObservation(*frame);
}
gvf->update(gesture.getLastObservation());
}
// float phase = gvf->getOutcomes().estimations[0].alignment;
// float speed = gvf->getOutcomes().estimations[0].dynamics[0];
//
// getDynamicsVariance();
// getScalingsVariance();
// getRotationsVariance();
REQUIRE( gvf != NULL);
}
SCENARIO("Test GVF Regression", "[machineLearning]")
{
GIVEN("gvf object and training dataset")
{
rapidmix::gvfTemporalVariation gvf;
rapidmix::trainingData myData;
// Record first gesture
myData.startRecording();
std::vector<double> inputs = { 4.0, 0.7 };
myData.addElement(inputs);
inputs = { 3.0, 0.8 };
myData.addElement(inputs);
inputs = { 2.0, 0.9 };
myData.addElement(inputs);
inputs = { 1.0, 1.0 };
myData.addElement(inputs);
inputs = { 0.4, 1.2 };
myData.addElement(inputs);
inputs = { 0.2, 1.4 };
myData.addElement(inputs);
myData.stopRecording();
// Record reverse of first gesture
myData.startRecording();
inputs = { 0.2, 1.4 };
myData.addElement(inputs);
inputs = { 0.4, 1.2 };
myData.addElement(inputs);
inputs = { 1.0, 1.0 };
myData.addElement(inputs);
inputs = { 2.0, 0.9 };
myData.addElement(inputs);
inputs = { 3.0, 0.8 };
myData.addElement(inputs);
inputs = { 4.0, 0.7 };
myData.addElement(inputs);
myData.stopRecording();
// Train
REQUIRE(gvf.train(myData) == true);
// Set first gesture (or a fragment of it)
std::vector<double> outcomes;
WHEN("when gvf is trained with a gesture and the reverse gesture")
{
THEN("follows the the gesture (first) and confirm it is the likeliestGesture and likelihoods reasonable")
{
outcomes = gvf.run(inputs = { 3.0, 0.8 });
outcomes = gvf.run({ 2.0, 0.9 });
outcomes = gvf.run({ 1.0, 1.0 });
outcomes = gvf.run({ 0.4, 1.2 });
outcomes = gvf.run({ 0.2, 1.4 });
// The assumtion for the test is that the outcome of the last segment of the test gesture must converge
REQUIRE(outcomes[0] == 0); // outcomes[0] - likeliestGesture must be equal to first gesture '0'
REQUIRE(gvf.getLikelihoods()[0] > 0.5); // outcomes[1] - likelihood gesture '0' must be greater than 50%
REQUIRE(gvf.getLikelihoods()[1] < 0.5); // outcomes[2] - likelihood gesture '1' must be lesser than 50%
REQUIRE(gvf.getAlignments()[0] < 0.5); // outcomes[3] - alignment gesture '0' must be lesser than 50%
// REQUIRE(outcomes[4] < 0.5); // outcomes[4] - alignment gesture '1' must be lesser than 50%
}
WHEN("when gvf is trained with two gestures")
{
THEN("gvf follows the test gesture (first gesture scaled) and confirm it is the likeliestGesture and likelihoods anc reasonable ")
{
}
}
WHEN("when gvf is trained with two gestures")
{
THEN("gvf follows the test gesture (first gesture scaled) and confirm it is the likeliestGesture and likelihoods anc reasonable ")
{
}
}
}
}
}
tests/src/test_rapidPiPo.cpp 0 → 100644
View file @ a0bfd689
//
// test_rapidPiPoTools.cpp
// Unit tests for rapidPiPoTools
//
#ifndef CATCH_CONFIG_MAIN
#define CATCH_CONFIG_MAIN
#endif
#ifdef RTA_USE_VECLIB
#undef RTA_USE_VECLIB
#endif
#include <unistd.h>
#include "catch.hpp"
#include "signalProcessing.h"
#define MAX_PATH_SIZE 256
void doSomething(double time, double weight, PiPoValue *values, unsigned int size)
{
std::cout << time << std::endl;
}
class Stuff {
public:
Stuff() {}
~Stuff() {}
void doSomething(double time, double weight, PiPoValue *values, unsigned int size)
{
std::cout << time << std::endl;
}
};
//=============================== ONSEG TEST =================================//
SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
{
GIVEN("A rapidPiPoHost class with a rapidPiPo chain and an audio file")
{
maxiSample buffer;
Stuff stuff;
// in XCode this gives the path to DerivedData folder
// char pathStrBuf[MAX_PATH_SIZE];
// char *cwd = getcwd(pathStrBuf, sizeof(pathStrBuf));
// std::cout << std::string(cwd) << std::endl;
// but here we just add the file to the Copy File(s) Build Phase
buffer.load("./data/DnB-loop-175BPM.wav", 0);
// ( source : http://freesound.org/people/yewbic/sounds/40107/ )
buffer.reset(); // (no real need to do this here)
//====================================================================//
// instantiate PiPo related classes here :
rapidmix::signalProcessingHost host;
// host.setFrameCallback(doSomething);
// host.setFrameCallback(stuff.doSomething);
// host.setFrameCallback(dynamic_cast<frameCallback>(std::bind(&Stuff::doSomething, stuff)));
// host.setFrameCallback(dynamic_cast<frameCallback>(Stuff::* doSomething));
// if we want to add some custom PiPos to our collection :
// #include "myCustomPiPo.h"
// PiPoCollection::addToCollection("myCustomPiPo", new PiPoCreator<myCustomPiPo>);
// now we can write :
// pipoHost.setChain("myCustomPiPo");
// #include "PiPoMaximChroma.h"
// this one is not part of the default collection :
// PiPoCollection::addToCollection("chroma", new PiPoCreator<PiPoMaximChroma>);
//*
host.setGraph("slice:fft:sum:scale:onseg");
host.setAttr("slice.size", 1024);
host.setAttr("slice.hop", 256);
host.setAttr("slice.norm", "power");
host.setAttr("fft.mode", "power");
host.setAttr("fft.weighting", "itur468");
host.setAttr("scale.inmin", 1.);
host.setAttr("scale.inmax", 10.);
host.setAttr("scale.outmin", 0.);
host.setAttr("scale.outmax", 10.);
host.setAttr("scale.func", "log");
host.setAttr("scale.base", 10.);
host.setAttr("onseg.duration", 1.);
host.setAttr("onseg.min", 0.);
host.setAttr("onseg.max", 1.);
host.setAttr("onseg.mean", 0.);
host.setAttr("onseg.stddev", 0.);
host.setAttr("onseg.startisonset", 1.);
host.setAttr("onseg.threshold", 9.);
host.setAttr("onseg.offthresh", -120.);
std::cout << "onseg threshold : ";
std::cout << host.getDoubleAttr("onseg.threshold") << std::endl;
std::cout << "fft mode : ";
std::cout << host.getEnumAttr("fft.mode") << std::endl;
std::cout << "param names : " << std::endl;
std::vector<std::string> attrs = host.getAttrNames();
for (int i = 0; i < attrs.size(); ++i)
{
std::cout << "- " << attrs[i] << std::endl;
}
//*/
//char pathStrBuf[MAX_PATH_SIZE];
//char *cwd = getcwd(pathStrBuf, sizeof(pathStrBuf));
//std::string filePath = std::string(cwd) + "/hello.json";
//std::string filePath = ${PROJECT_DIR};
std::string filePath = "./data/pipo.json";
std::cout << filePath << std::endl;
//host.writeJSON(filePath);
host.readJSON(filePath);
//host.putJSON(jj);
//std::string jj = host.getJSON();
//std::cout << jj << std::endl;
// set another chain :
// pipoHost.setChain("chroma");
WHEN("file is processed")
{
rapidmix::signalAttributes sa;
sa.hasTimeTags = true;
sa.rate = 44100;
sa.offset = 0;
sa.width = 1;
sa.height = 1;
sa.labels = std::vector<std::string>();
sa.hasVarSize = false;
sa.domain = 0;
sa.maxFrames = 1;
host.setInputSignalAttributes(sa);
float value;
for (unsigned int i = 0; i < buffer.length; ++i) {
value = buffer.play();
host.frames((double)i, 0.0, &value, 1, 1);
}
THEN("compare results of actual processing and same file processed in another envrironment")
{
// compare the results of the processing
REQUIRE(true);
}
}
}
}
tests/src/test_rapidXMM.cpp 0 → 100644
View file @ a0bfd689
#ifndef CATCH_CONFIG_MAIN
#define CATCH_CONFIG_MAIN
#endif
#include "catch.hpp"
#include "machineLearning.h"
//================================== GMM =====================================//
SCENARIO("Test GMM", "[machineLearning]")
{
GIVEN("GMM static classification object and training dataset")
{
rapidmix::xmmConfig xcfg;
xcfg.relativeRegularization = 0.1;
rapidmix::trainingData myXmmData;
std::vector<double> myXmmInput;
std::vector<double> myXmmOutput;
myXmmData.startRecording("lab1");
myXmmInput = { 0.2, 0.7 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.startRecording("lab2");
myXmmInput = { 0.8, 0.1 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.writeJSON("/var/tmp/testTrainingData.json");
rapidmix::xmmStaticClassification myGmm(xcfg);
myGmm.train(myXmmData);
std::string filepath = "/var/tmp/modelSetDescription";
myGmm.writeJSON(filepath);
myXmmInput = { 0.2, 0.7 };
WHEN("GMM model is deserialized from file")
{
rapidmix::xmmStaticClassification myGmmFromFile;
myGmmFromFile.readJSON(filepath);
THEN("compare results of original and deserialized models")
{
REQUIRE(myGmm.run(myXmmInput)[0] == myGmmFromFile.run(myXmmInput)[0]);
}
}
WHEN("GMM model is deserialized from JSON stream")
{
rapidmix::xmmStaticClassification myGmmFromString;
myGmmFromString.putJSON(myGmm.getJSON());
THEN("compare results of original and deserialized models")
{
REQUIRE(myGmm.run(myXmmInput)[0] == myGmmFromString.run(myXmmInput)[0]);
}
}
}
}
//================================== GMR =====================================//
SCENARIO("Test GMR", "[machineLearning]")
{
GIVEN("GMR static regression object and training dataset")
{
rapidmix::xmmConfig xcfg;
xcfg.relativeRegularization = 0.1;
rapidmix::trainingData myXmmData;
std::vector<double> myXmmInput;
std::vector<double> myXmmOutput;
myXmmData.startRecording("lab1");
myXmmInput = { 0.2, 0.7 };
myXmmOutput = { 1.0 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.startRecording("lab2");
myXmmInput = { 0.8, 0.1 };
myXmmOutput = { 2.0 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.writeJSON("/var/tmp/testTrainingData.json");
rapidmix::xmmStaticRegression myGmr(xcfg);
myGmr.train(myXmmData);
std::string filepath = "/var/tmp/modelSetDescription";
myGmr.writeJSON(filepath);
myXmmInput = { 0.2, 0.7 };
WHEN("GMM model is deserialized from file")
{
rapidmix::xmmStaticClassification myGmrFromFile;
myGmrFromFile.readJSON(filepath);
THEN("compare results of original and deserialized models")
{
double epsilon = 0.001;
double origOut = myGmr.run(myXmmInput)[0];
double fileOut = myGmrFromFile.run(myXmmInput)[0];
REQUIRE(std::abs(origOut - fileOut) < epsilon);
}
}
WHEN("GMM model is deserialized from JSON stream")
{
rapidmix::xmmStaticClassification myGmrFromString;
myGmrFromString.putJSON(myGmr.getJSON());
THEN("compare results of original and deserialized models")
{
double epsilon = 0.001;
double origOut = myGmr.run(myXmmInput)[0];
double stringOut = myGmrFromString.run(myXmmInput)[0];
REQUIRE(std::abs(origOut - stringOut) < epsilon);
}
}
}
}
//================================== HMM =====================================//
SCENARIO("Test HMM", "[machineLearning]")
{
GIVEN("HMM temporal classification object and training dataset")
{
rapidmix::xmmConfig xcfg;
xcfg.relativeRegularization = 0.1;
xcfg.states = 6;
xcfg.likelihoodWindow = 10;
rapidmix::trainingData myXmmData;
std::vector<double> myXmmOutput;
myXmmData.startRecording("lab1");
std::vector<std::vector<double>> myXmmPhrase = {
{ 0.0, 0.0 },
{ 0.0, 0.0 },
{ 0.0, 0.0 },
{ 0.0, 0.0 },
{ 0.1, 1.1 },
{ 0.2, 1.2 },
{ 0.3, 1.3 },
{ 0.4, 1.4 },
{ 0.5, 1.5 },
{ 0.6, 1.6 },
{ 0.7, 1.7 },
{ 0.8, 1.8 },
{ 0.9, 1.9 },
{ 1.0, 2.0 },
{ 1.1, 2.1 },
{ 1.2, 2.2 },
{ 1.3, 2.3 },
{ 1.4, 2.4 },
{ 1.5, 2.5 },
{ 1.6, 2.6 }
};
for (int i = 0; i < myXmmPhrase.size(); ++i) {
myXmmData.addElement(myXmmPhrase[i], myXmmOutput);
}
myXmmData.stopRecording();
rapidmix::xmmTemporalClassification myHmm(xcfg);
myHmm.train(myXmmData);
myXmmData.writeJSON("/var/tmp/testTrainingData.json");
std::string filepath = "/var/tmp/modelSetDescription";
myHmm.writeJSON(filepath);
WHEN("HMM model processes the phrase it was trained with")
{
THEN("check its time progression output is constantly increasing")
{
std::vector<double> progress(myXmmPhrase.size());
for (int i = 0; i < myXmmPhrase.size(); ++i) {
// we take the second value because run() returns
// [ likelihood_1, timeProgression_1, .... likelihood_n, timeProgression_n ]
progress[i] = myHmm.run(myXmmPhrase[i])[1];
}
std::vector<double> sortedProgress = progress;
std::sort(sortedProgress.begin(), sortedProgress.end());
REQUIRE(std::equal(progress.begin(), progress.end(), sortedProgress.begin()));
}
}
WHEN("HMM model is deserialized from file")
{
rapidmix::xmmTemporalClassification myHmmFromFile;
myHmmFromFile.readJSON(filepath);
for (int i = 0; i < myXmmPhrase.size(); ++i) {
THEN("compare results of original and deserialized models")
{
REQUIRE(myHmm.run(myXmmPhrase[i]) == myHmmFromFile.run(myXmmPhrase[i]));
}
}
}
WHEN("HMM model is deserialized from JSON stream")
{
rapidmix::xmmTemporalClassification myHmmFromString;
myHmmFromString.putJSON(myHmm.getJSON());
for (int i = 0; i < myXmmPhrase.size(); ++i) {
THEN("compare results of original and deserialized models")
{
REQUIRE(myHmm.run(myXmmPhrase[i]) == myHmmFromString.run(myXmmPhrase[i]));
}
}
}
}
}
//================================== HMR =====================================//
////////// here we compute RMS error and validate if it stays under some epsilon
SCENARIO("Test HMR", "[machineLearning]")
{
GIVEN("HMR temporal regression object and training dataset")
{
rapidmix::xmmConfig xcfg;
xcfg.relativeRegularization = 0.001;
xcfg.absoluteRegularization = 0.001;
xcfg.states = 6;
xcfg.likelihoodWindow = 10;
rapidmix::trainingData myXmmData;
myXmmData.startRecording("lab1");
std::vector <std::pair <std::vector<double>, std::vector<double>>> myXmmPhrase = {
{ { 0.0, 0.0 }, { 1.0 } },
{ { 0.0, 0.0 }, { 2.0 } },
{ { 0.0, 0.0 }, { 3.0 } },
{ { 0.0, 0.0 }, { 4.0 } },
{ { 0.1, 1.1 }, { 5.0 } },
{ { 0.2, 1.2 }, { 6.0 } },
{ { 0.3, 1.3 }, { 7.0 } },
{ { 0.4, 1.4 }, { 8.0 } },
{ { 0.5, 1.5 }, { 9.0 } },
{ { 0.6, 1.6 }, { 10.0 } },
{ { 0.7, 1.7 }, { 11.0 } },
{ { 0.8, 1.8 }, { 12.0 } },
{ { 0.9, 1.9 }, { 13.0 } },
{ { 1.0, 2.0 }, { 14.0 } },
{ { 1.1, 2.1 }, { 15.0 } },
{ { 1.2, 2.2 }, { 16.0 } },
{ { 1.3, 2.3 }, { 17.0 } },
{ { 1.4, 2.4 }, { 18.0 } },
{ { 1.5, 2.5 }, { 19.0 } },
{ { 1.6, 2.6 }, { 20.0 } }
};
for (int i = 0; i < myXmmPhrase.size(); ++i) {
myXmmData.addElement(myXmmPhrase[i].first, myXmmPhrase[i].second);
}
myXmmData.stopRecording();
rapidmix::xmmTemporalRegression myHmr(xcfg);
myHmr.train(myXmmData);
myXmmData.writeJSON("/var/tmp/testTrainingData.json");
std::string filepath = "/var/tmp/modelSetDescription";
myHmr.writeJSON(filepath);
myHmr.reset();
WHEN("HMR model processes the phrase it was trained with")
{
THEN("check its regression output is the same as the output example")
{
int cnt = 0;
double sum = 0;
for (int i = 0; i < myXmmPhrase.size(); ++i) {
std::vector<double> regression;
regression = myHmr.run(myXmmPhrase[i].first);
for (int j = 0; j < regression.size(); ++j) {
double delta = regression[j] - myXmmPhrase[i].second[j];
sum += delta * delta;
cnt++;
}
}
sum = std::sqrt(sum / cnt);
// totally arbitrary epsilon value :
double epsilon = 1.0;
REQUIRE(sum <= epsilon);
}
}
WHEN("HMR model is deserialized from file")
{
rapidmix::xmmTemporalRegression myHmrFromFile;
myHmrFromFile.readJSON(filepath);
for (int i = 0; i < myXmmPhrase.size(); ++i) {
myHmr.reset();
myHmrFromFile.reset();
THEN("compare results of original and deserialized models")
{
int cnt = 0;
double sum = 0;
for (int i = 0; i < myXmmPhrase.size(); ++i) {
std::vector<double> r1, r2;
r1 = myHmr.run(myXmmPhrase[i].first);
r2 = myHmrFromFile.run(myXmmPhrase[i].first);
for (int j = 0; j < r1.size(); ++j) {
double delta = r1[j] - r2[j];
sum += delta * delta;
cnt++;
}
}
sum = std::sqrt(sum / cnt);
// totally arbitrary epsilon value :
double epsilon = 0.1;
REQUIRE(sum <= epsilon);
}
}
}
WHEN("HMR model is deserialized from JSON stream")
{
rapidmix::xmmTemporalRegression myHmrFromString;
myHmrFromString.putJSON(myHmr.getJSON());
for (int i = 0; i < myXmmPhrase.size(); ++i) {
myHmr.reset();
myHmrFromString.reset();
THEN("compare results of original and deserialized models")
{
int cnt = 0;
double sum = 0;
for (int i = 0; i < myXmmPhrase.size(); ++i) {
std::vector<double> r1, r2;
r1 = myHmr.run(myXmmPhrase[i].first);
r2 = myHmrFromString.run(myXmmPhrase[i].first);
for (int j = 0; j < r1.size(); ++j) {
double delta = r1[j] - r2[j];
sum += delta * delta;
cnt++;
}
}
sum = std::sqrt(sum / cnt);
// totally arbitrary epsilon value :
double epsilon = 0.1;
REQUIRE(sum <= epsilon);
}
}
}
}
}
tests/src/test_rapidmix-api.cpp 0 → 100644
View file @ a0bfd689
#define CATCH_CONFIG_MAIN // This tells Catch to provide a main() - only do this in one cpp file
#include "catch.hpp"
#include <vector>
#include <iostream>
#include <cassert>
#include <random>
#include <algorithm>
#include "json.h"
#include "rapidmix.h"
SCENARIO("Test facade for RapidLib classification", "[machineLearning]")
{
GIVEN("a static classification object")
{
rapidmix::staticClassification myKnn;
rapidmix::trainingData myData;
WHEN("column names are set") {
THEN("verify them") {
std::vector<std::string> columnNames = { "X", "Y" };
myData.setColumnNames(columnNames);
REQUIRE(myData.getColumnNames() == columnNames);
}
}
std::vector<double> input = { 0.2, 0.7 };
std::vector<double> output = { 3.0 };
int testVar = myData.addElement(input, output);
input = { 2.0, 44.2 };
output = { 20.14 };
int testVar2 = myData.addElement(input, output);
WHEN("data are added") {
THEN("check id") {
REQUIRE(testVar == 1);
REQUIRE(testVar2 == 2);
}
}
bool trainTest = myKnn.train(myData);
WHEN ("training happens") {
THEN("check") {
REQUIRE(trainTest == true);
}
}
REQUIRE(myKnn.getK()[0] == 1);
WHEN ("k is set") {
myKnn.setK(0, 2);
THEN("check that it has changed") {
REQUIRE(myKnn.getK()[0] == 2);
}
}
std::vector<double> inputVec = { 2.0, 44.2 };
WHEN("models are serialized and deserialized") {
std::string filepath2 = "/var/tmp/modelSetDescription_knn.json";
myKnn.writeJSON(filepath2);
rapidmix::staticClassification myKnnFromString;
myKnnFromString.putJSON(myKnn.getJSON());
rapidmix::staticClassification myKnnFromFile;
myKnnFromFile.readJSON(filepath2);
//std::cout << "knn before: " << myKnn.run(inputVec)[0] << std::endl;
//std::cout << "knn from string: " << myKnnFromString.run(inputVec)[0] << std::endl;
//std::cout << "knn from file: " << myKnnFromFile.run(inputVec)[0] << std::endl;
THEN("check that results are the same") {
REQUIRE(myKnn.run(inputVec)[0] == myKnnFromString.run(inputVec)[0]);
REQUIRE(myKnn.run(inputVec)[0] == myKnnFromFile.run(inputVec)[0]);
}
}
std::string trainingPath = "/var/tmp/testTrainingData.json";
myData.writeJSON(trainingPath);
rapidmix::trainingData myStoredData;
bool msdTest = myStoredData.readJSON(trainingPath);
rapidmix::staticClassification myStoredKnn;
bool msdTrain = myStoredKnn.train(myStoredData);
WHEN("data are saved as a JSON") {
THEN("check that the file reads and parses") {
REQUIRE(msdTest == true);
}
THEN("check that the new model is the same as the old model") {
REQUIRE(msdTrain == true);
REQUIRE(myKnn.run(inputVec)[0] == myStoredKnn.run(inputVec)[0]);
}
}
}
}
SCENARIO("Test facade for RapidLib svm classification", "[machineLearning]")
{
GIVEN("an svm classification object")
{
rapidmix::staticClassification mySVM(classification::svm);
rapidmix::trainingData myData;
std::vector<double> input = { 0.2, 0.7 };
std::vector<double> output = { 3.0 };
int testVar = myData.addElement(input, output);
input = { 2.0, 44.2 };
output = { 20.14 };
int testVar2 = myData.addElement(input, output);
mySVM.train(myData);
}
}
SCENARIO("Test facade for RapidLib regression", "[machineLearning]")
{
GIVEN("an regression object")
{
rapidmix::staticRegression myNN;
rapidmix::trainingData myData;
std::vector<double> input = { 0.2, 0.7 };
std::vector<double> output = { 3.0 };
int testVar = myData.addElement(input, output);
input = { 2.0, 44.2 };
output = { 20.14 };
int testVar2 = myData.addElement(input, output);
WHEN("data are added") {
THEN("check id") {
REQUIRE(testVar == 1);
REQUIRE(testVar2 == 2);
}
}
bool trainTest = myNN.train(myData);
WHEN ("training happens") {
THEN("check") {
REQUIRE(trainTest == true);
}
}
std::vector<double> inputVec = { 2.0, 44.2 };
WHEN("models are serialized and deserialized") {
std::string filepath = "/var/tmp/modelSetDescription.json";
myNN.writeJSON(filepath);
rapidmix::staticRegression myNNfromString;
myNNfromString.putJSON(myNN.getJSON());
rapidmix::staticRegression myNNfromFile;
myNNfromFile.readJSON(filepath);
//std::cout << "before: " << myNN.run(inputVec)[0] << std::endl;
//std::cout << "from string: " << myNNfromString.run(inputVec)[0] << std::endl;
//std::cout << myNNfromString.getJSON() << std::endl;
//std::cout << "from file: " << myNNfromFile.run(inputVec)[0] << std::endl;
THEN("check that results are the same") {
REQUIRE(myNN.run(inputVec)[0] == myNNfromString.run(inputVec)[0]);
REQUIRE(myNN.run(inputVec)[0] == myNNfromFile.run(inputVec)[0]);
}
}
}
}
SCENARIO("Test RapidLib regression with a bigger vector", "[machineLearning]")
{
GIVEN("a static regeression object")
{
rapidmix::staticRegression bigVector;
rapidmix::trainingData trainingSet2;
std::default_random_engine generator;
std::uniform_real_distribution<double> distribution(-0.5,0.5);
int vecLength = 64;
for (int j = 0; j < vecLength; ++j) {
std::vector<double> input;
std::vector<double> output;
for (int i = 0; i < vecLength; ++i) {
input.push_back(distribution(generator));
}
output = { distribution(generator) };
trainingSet2.addElement(input, output);
}
bigVector.train(trainingSet2);
std::vector<double> inputVec2;
for (int i=0; i < vecLength; ++i) {
inputVec2.push_back(distribution(generator));
}
REQUIRE(isfinite(bigVector.process(inputVec2)[0]));
}
}
SCENARIO("Test facade around XMM", "[machineLearning]")
{
GIVEN("some XMM")
{
//====================== rapidXmmTools test ==============================//
rapidmix::trainingData myXmmData;
std::vector<double> myXmmInput;
std::vector<double> myXmmOutput;
xmmToolConfig xcfg;
//////////////////// testing GMM :
myXmmData.startRecording("lab1");
myXmmInput = { 0.2, 0.7 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
myXmmData.startRecording("lab2");
myXmmInput = { 0.8, 0.1 };
myXmmData.addElement(myXmmInput, myXmmOutput);
myXmmData.stopRecording();
xcfg.relativeRegularization = 0.1;
rapidmix::xmmStaticClassification myGmm(xcfg);
if (myGmm.train(myXmmData)) {
std::cout << "GMM training successful !" << std::endl;
std::vector<double> res;
std::vector<double> input = { 0.2, 0.7 };
res = myGmm.run(input);
for (auto &elt : res) std::cout << elt << " ";
std::cout << std::endl;
input = { 0.8, 0.1 };
res = myGmm.run(input);
for (auto &elt : res) std::cout << elt << " ";
std::cout << std::endl; } else {
std::cout << "GMM training impossible !" << std::endl;
}
//mz label test
WHEN("rapidLib classifier trains on the same data") {
rapidmix::staticClassification labelKnn;
labelKnn.train(myXmmData);
THEN("verify it returns the same label") {
REQUIRE(myXmmData.getLabel(labelKnn.run({ 0.2, 0.7 })[0]) == "lab1");
}
}
//////////////////// testing HMM :
// phrases seem to need a minimum length related to the number of states
// otherwise sometimes we get stuck into NaN values
xcfg.states = 6;
// we don't really need this here as we only have 1 label ...
xcfg.likelihoodWindow = 10;
rapidmix::xmmTemporalClassification myHmm(xcfg);
myXmmData = rapidmix::trainingData(); // reset the whole set
myXmmData.startRecording("lab1");
std::vector<std::vector<double>> myXmmPhrase = {
{ 0.0, 0.0 },
{ 0.0, 0.0 },
{ 0.0, 0.0 },
{ 0.0, 0.0 },
{ 0.1, 1.1 },
{ 0.2, 1.2 },
{ 0.3, 1.3 },
{ 0.4, 1.4 },
{ 0.5, 1.5 },
{ 0.6, 1.6 },
{ 0.7, 1.7 },
{ 0.8, 1.8 },
{ 0.9, 1.9 },
{ 1.0, 2.0 },
{ 1.1, 2.1 },
{ 1.2, 2.2 },
{ 1.3, 2.3 },
{ 1.4, 2.4 },
{ 1.5, 2.5 },
{ 1.6, 2.6 }
};
for (int i = 0; i < myXmmPhrase.size(); ++i) {
myXmmData.addElement(myXmmPhrase[i], myXmmOutput);
}
myXmmData.stopRecording();
if (myHmm.train(myXmmData)) {
std::cout << "HMM training successful !" << std::endl;
std::vector<double> res;
for (int i = 0; i < myXmmPhrase.size(); ++i) {
res = myHmm.run(myXmmPhrase[i]);
for (auto &elt : res) std::cout << elt << " ";
std::cout << std::endl;
}
} else {
std::cout << "HMM training impossible !" << std::endl;
}
}
}
tests/src/test_signalProcessing.cpp 0 → 100644
View file @ a0bfd689
#define CATCH_CONFIG_MAIN
#include "catch.hpp"
#include <math.h>
#include "rapidmix.h"
SCENARIO("Test rapidStream processing", "[signalProcessing]")
{
GIVEN("rapidStream object with default window size")
{
rapidmix::rapidStream defaultProcessor; //create a processor with no argument
WHEN("feeding in positions") {
THEN("check velocity") {
defaultProcessor.pushToWindow(1);
defaultProcessor.pushToWindow(2);
REQUIRE(defaultProcessor.velocity() == 1); //aka First-order difference
}
THEN("check acceleration") {
defaultProcessor.clear();
defaultProcessor.pushToWindow(1);
defaultProcessor.pushToWindow(2);
defaultProcessor.pushToWindow(10);
REQUIRE(defaultProcessor.acceleration() == 7); //aka Second-order difference
}
}
GIVEN("rapidStream object with a window size of 5")
{
rapidmix::rapidStream myProcessor(5); //create a processor with a window size of 5
WHEN("feeding in some arbitrary numbers") {
myProcessor.pushToWindow(0.246); //append? or push?
myProcessor.pushToWindow(0.44);
myProcessor.pushToWindow(-0.228);
myProcessor.pushToWindow(0.402);
myProcessor.pushToWindow(-0.382);
THEN("check min and max") {
REQUIRE(myProcessor.maximum() == 0.44);
REQUIRE(myProcessor.minimum() == -0.382);
}
THEN("check sum, mean and std dev") {
REQUIRE(myProcessor.sum() == 0.478);
REQUIRE(myProcessor.mean() == 0.09559999999999999);
REQUIRE(myProcessor.standardDeviation() == 0.33702557766436664);
REQUIRE(myProcessor.rms() == 0.3503221374677884);
}
}
myProcessor.clear(); //forget any stored vaules
REQUIRE(myProcessor.sum() == 0);
WHEN("putting positions in a window") {
myProcessor.pushToWindow(1.);
myProcessor.pushToWindow(2.);
myProcessor.pushToWindow(2.);
myProcessor.pushToWindow(4.);
myProcessor.pushToWindow(5.);
THEN("check min and max velocity") {
REQUIRE(myProcessor.maxVelocity() == 2.0);
REQUIRE(myProcessor.minVelocity() == 0.);
}
THEN("check min and max acceleration") {
REQUIRE(myProcessor.maxAcceleration() == 2.0);
REQUIRE(myProcessor.minAcceleration() == -1.0);
}
}
}
}
}
SCENARIO("Test maximilian feature extraction", "[signalProcessing]")
{
GIVEN("an fft object") {
rapidmix::FFT myFFT;
myFFT.setup(1024, 512, 265);
for (int i = 0; i < 1024; ++i) {
float input = (i % 256/256.0);
myFFT.process(input);
}
WHEN("phasor is the input") {
THEN("check centroid") {
REQUIRE(myFFT.spectralCentroid() == 3520.84277f);
}
float foo = myFFT.spectralFlatness();
THEN("check flatness") {
//REQUIRE(myFFT.spectralFlatness() == 0.43209f); // or 0.432094097 Why doesn't this work? -mz
}
}
rapidmix::MFCC myMFCC;
int sampleRate = 44100;
myMFCC.setup(512, 42, 13, 20, 20000, sampleRate);
double *mfccs = (double*) malloc(sizeof(double) * 13);
myMFCC.mfcc(myFFT.magnitudes, mfccs); //MZ: This runs the thing.
for (int i = 2; i < 42; ++i) {
REQUIRE(myMFCC.melBands[i] < 0);
}
for (int i = 0; i < 13; ++i) {
REQUIRE(mfccs[i] > 0.);
}
}
}
\ No newline at end of file
tests/src/test_trainingData.cpp 0 → 100644
View file @ a0bfd689
#ifndef CATCH_CONFIG_MAIN
#define CATCH_CONFIG_MAIN
#endif
#include "catch.hpp"
#include "machineLearning.h"
//============================= TRAINING DATA ================================//
SCENARIO("Test training sets managements", "[machineLearning]")
{
GIVEN("A training set we fill in a variety of ways")
{
rapidmix::trainingData set;
set.createNewPhrase("test_label_01");
std::vector <std::pair <std::vector<double>, std::vector<double>>> phrase = {
{ { 0.0, 0.0 }, { 9.0 } },
{ { 1.0, 0.0 }, { 8.0 } },
{ { 2.0, 0.0 }, { 7.0 } },
{ { 3.0, 0.0 }, { 6.0 } },
{ { 4.0, 0.0 }, { 5.0 } },
{ { 5.0, 0.0 }, { 4.0 } },
{ { 6.0, 0.0 }, { 3.0 } },
{ { 7.0, 0.0 }, { 2.0 } },
{ { 8.0, 0.0 }, { 1.0 } },
{ { 9.0, 0.0 }, { 0.0 } }
};
for (int i = 0; i < phrase.size(); ++i) {
set.addElement(phrase[i].first, phrase[i].second);
}
WHEN("We serialize / deserialize a training set")
{
std::string js = set.getJSON();
std::cout << js << std::endl;
THEN("It should stay the same")
{
}
}
}
}
tests/test_project.xcodeproj/project.pbxproj 0 → 100644
View file @ a0bfd689
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