diff --git a/src/machineLearning/rapidXMM/rapidXMM.cpp b/src/machineLearning/rapidXMM/rapidXMM.cpp
index 9d4fc5a259a2bd63958a039ef3c0b24a138f9291..a60d032d232d393e27d24b04acb3c2452bc878d4 100644
--- a/src/machineLearning/rapidXMM/rapidXMM.cpp
+++ b/src/machineLearning/rapidXMM/rapidXMM.cpp
@@ -81,25 +81,27 @@ bool xmmTool<SingleClassModel, Model>::train(const rapidmix::trainingData& newTr
template <class SingleClassModel, class Model>
Json::Value xmmTool<SingleClassModel, Model>::toJSON(/*std::string modelType*/) {
Json::Value root;
- Json::Value metadata;
- Json::Value modelSet;
- metadata["creator"] = "Rapid API C++";
- metadata["version"] = "v0.1.1"; //TODO: This should be a macro someplace
- metadata["family"] = "xmm";
- root["metadata"] = metadata;
+ root["docType"] = "rapid-mix:ml-model";
+ root["docVersion"] = RAPIDMIX_JSON_DOC_VERSION;
- modelSet.append(model.toJson());
- root["modelSet"] = modelSet;
+ Json::Value target;
+
+ target["name"] = "xmm";
+ target["version"] = "v1.0.0";
+
+ root["target"] = target;
+ root["payload"] = model.toJson();
return root;
}
template <class SingleClassModel, class Model>
bool xmmTool<SingleClassModel, Model>::fromJSON(Json::Value &jm) {
- if (jm["metadata"]["family"].asString().compare("xmm") == 0 &&
- jm["modelSet"].size() > 0) {
- model.fromJson(jm["modelSet"][0]);
+ if (jm["docType"].asString().compare("rapid-mix:ml-model") == 0 &&
+ jm["target"]["name"].asString().compare("xmm") == 0 &&
+ jm["payload"].size() > 0) {
+ model.fromJson(jm["payload"]);
model.reset();
return true;
}
diff --git a/tests/rapidMixTest.cpp b/tests/rapidMixTest.cpp
index f233517fe56fb3afcb39c3c950bdcffa1a205a4d..f372eaa81f088be5b5d39470a0402eb9ae890d6a 100644
--- a/tests/rapidMixTest.cpp
+++ b/tests/rapidMixTest.cpp
@@ -107,7 +107,7 @@ int main() {
std::vector<float> f({ 1, 2, 3 });
spHost.frames(0, 1, &f[0], 1, 1);
}
- std::cout << "pipo passed" << std::endl;
+ std::cout << "pipo passed." << std::endl;
/////////////////////////////////////Test rapidStream signal processing
diff --git a/tests/src/test_rapidPiPo.cpp b/tests/src/test_rapidPiPo.cpp
index f44467e6bea777e80236aa838be314c0d1762368..185f10895f4807e4994e1406a575a9bc06916d98 100644
--- a/tests/src/test_rapidPiPo.cpp
+++ b/tests/src/test_rapidPiPo.cpp
@@ -17,6 +17,22 @@
#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]")
@@ -24,23 +40,29 @@ 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; // -> this class is located in rapidPiPoTools
-
+ 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>);
@@ -50,10 +72,10 @@ SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
// #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");
@@ -73,7 +95,7 @@ SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
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 : ";
@@ -81,7 +103,7 @@ SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
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;
@@ -94,7 +116,7 @@ SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
//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);
@@ -104,7 +126,7 @@ SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
// set another chain :
// pipoHost.setChain("chroma");
-
+
WHEN("file is processed")
{
rapidmix::signalAttributes sa;
@@ -117,9 +139,9 @@ SCENARIO("Test rapidPiPoHost", "[signalProcessing]")
sa.hasVarSize = false;
sa.domain = 0;
sa.maxFrames = 1;
-
- host.setInputStreamAttributes(sa);
-
+
+ host.setInputSignalAttributes(sa);
+
float value;
for (unsigned int i = 0; i < buffer.length; ++i) {
value = buffer.play();
diff --git a/tests/src/test_rapidXMM.cpp b/tests/src/test_rapidXMM.cpp
index 590e78abda52f20a7ca0ea78aded95c654450abb..d80d57443829f6cdc9cebfff0a35a9ff22ef7e01 100644
--- a/tests/src/test_rapidXMM.cpp
+++ b/tests/src/test_rapidXMM.cpp
@@ -1,8 +1,3 @@
-//
-// test_rapidXmmTools.cpp
-// Unit tests for rapidXmmTools
-//
-
#ifndef CATCH_CONFIG_MAIN
#define CATCH_CONFIG_MAIN
#endif
@@ -18,37 +13,37 @@ SCENARIO("Test GMM", "[machineLearning]")
{
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]);
@@ -59,7 +54,7 @@ SCENARIO("Test GMM", "[machineLearning]")
{
rapidmix::xmmStaticClassification myGmmFromString;
myGmmFromString.putJSON(myGmm.getJSON());
-
+
THEN("compare results of original and deserialized models")
{
REQUIRE(myGmm.run(myXmmInput)[0] == myGmmFromString.run(myXmmInput)[0]);
@@ -78,39 +73,39 @@ SCENARIO("Test GMR", "[machineLearning]")
{
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;
@@ -119,12 +114,12 @@ SCENARIO("Test GMR", "[machineLearning]")
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;
@@ -148,10 +143,10 @@ SCENARIO("Test HMM", "[machineLearning]")
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 },
@@ -179,16 +174,16 @@ SCENARIO("Test HMM", "[machineLearning]")
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")
@@ -201,16 +196,16 @@ SCENARIO("Test HMM", "[machineLearning]")
}
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")
@@ -219,14 +214,14 @@ SCENARIO("Test HMM", "[machineLearning]")
}
}
}
-
+
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]));
@@ -250,9 +245,9 @@ SCENARIO("Test HMR", "[machineLearning]")
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 } },
@@ -276,54 +271,59 @@ SCENARIO("Test HMR", "[machineLearning]")
{ { 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;
@@ -333,14 +333,14 @@ SCENARIO("Test HMR", "[machineLearning]")
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 :
@@ -349,33 +349,36 @@ SCENARIO("Test HMR", "[machineLearning]")
}
}
}
-
+
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);
diff --git a/tests/src/test_trainingData.cpp b/tests/src/test_trainingData.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..dc36e8ddea5adf756cb4513f62f9ab2ac6a34bde
--- /dev/null
+++ b/tests/src/test_trainingData.cpp
@@ -0,0 +1,46 @@
+#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")
+ {
+
+ }
+ }
+ }
+}
diff --git a/tests/test_project.xcodeproj/project.pbxproj b/tests/test_project.xcodeproj/project.pbxproj
index fcfc561bd615ffc8920858cac0936da82dd3840f..f7ff94a1c3c94905fb0cf9bb59948a09dd9df169 100644
--- a/tests/test_project.xcodeproj/project.pbxproj
+++ b/tests/test_project.xcodeproj/project.pbxproj
@@ -38,6 +38,9 @@
311BA31E1EDC7ADE00244DAC /* maximilian.cpp in Sources */ = {isa = PBXBuildFile; fileRef = BE2C5A1F1ED8450C00E9FAFA /* maximilian.cpp */; };
311BA3211EDC7B2400244DAC /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 311BA3201EDC7B2400244DAC /* Accelerate.framework */; };
311BA3251EDCE80B00244DAC /* jsoncpp.cpp in Sources */ = {isa = PBXBuildFile; fileRef = BE2C5E081ED8450E00E9FAFA /* jsoncpp.cpp */; };
+ 312C61BD1FE95A680085E283 /* trainingData.cpp in Sources */ = {isa = PBXBuildFile; fileRef = BE2C5EC01ED8459300E9FAFA /* trainingData.cpp */; };
+ 312C61CD1FE95A680085E283 /* jsoncpp.cpp in Sources */ = {isa = PBXBuildFile; fileRef = BE2C5E081ED8450E00E9FAFA /* jsoncpp.cpp */; };
+ 312C61D41FE95A8F0085E283 /* test_trainingData.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 312C61B71FE958CB0085E283 /* test_trainingData.cpp */; };
319C94A11FC5BFA80055BE40 /* PiPoHost.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 319C93FD1FC49BAA0055BE40 /* PiPoHost.cpp */; };
319C94A21FC5C0C10055BE40 /* PiPoCollection.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 319C947C1FC49D490055BE40 /* PiPoCollection.cpp */; };
319C94A31FC5C0DE0055BE40 /* BayesianFilter.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 319C93ED1FC49B5C0055BE40 /* BayesianFilter.cpp */; };
@@ -148,6 +151,15 @@
);
runOnlyForDeploymentPostprocessing = 1;
};
+ 312C61CF1FE95A680085E283 /* CopyFiles */ = {
+ isa = PBXCopyFilesBuildPhase;
+ buildActionMask = 2147483647;
+ dstPath = /usr/share/man/man1/;
+ dstSubfolderSpec = 0;
+ files = (
+ );
+ runOnlyForDeploymentPostprocessing = 1;
+ };
31D7B7281E6B048100917757 /* CopyFiles */ = {
isa = PBXCopyFilesBuildPhase;
buildActionMask = 12;
@@ -216,6 +228,8 @@
311BA2FC1EDC6F1900244DAC /* xmm.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = xmm.h; path = xmm/src/xmm.h; sourceTree = "<group>"; };
311BA3201EDC7B2400244DAC /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
311BA3231EDC7BA400244DAC /* DnB-loop-175BPM.wav */ = {isa = PBXFileReference; lastKnownFileType = audio.wav; name = "DnB-loop-175BPM.wav"; path = "data/DnB-loop-175BPM.wav"; sourceTree = "<group>"; };
+ 312C61B71FE958CB0085E283 /* test_trainingData.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; path = test_trainingData.cpp; sourceTree = "<group>"; };
+ 312C61D31FE95A680085E283 /* test_trainingData */ = {isa = PBXFileReference; explicitFileType = "compiled.mach-o.executable"; includeInIndex = 0; path = test_trainingData; sourceTree = BUILT_PRODUCTS_DIR; };
318E7A361FD748D900FEEB7E /* pipo.json */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = text.json; name = pipo.json; path = data/pipo.json; sourceTree = "<group>"; };
319C93ED1FC49B5C0055BE40 /* BayesianFilter.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = BayesianFilter.cpp; path = pipo/modules/bayesfilter/src/BayesianFilter.cpp; sourceTree = "<group>"; };
319C93EE1FC49B5C0055BE40 /* BayesianFilter.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = BayesianFilter.h; path = pipo/modules/bayesfilter/src/BayesianFilter.h; sourceTree = "<group>"; };
@@ -448,6 +462,13 @@
);
runOnlyForDeploymentPostprocessing = 0;
};
+ 312C61CE1FE95A680085E283 /* Frameworks */ = {
+ isa = PBXFrameworksBuildPhase;
+ buildActionMask = 2147483647;
+ files = (
+ );
+ runOnlyForDeploymentPostprocessing = 0;
+ };
31D7B7271E6B048100917757 /* Frameworks */ = {
isa = PBXFrameworksBuildPhase;
buildActionMask = 2147483647;
@@ -1046,6 +1067,7 @@
BE2C5EE11ED8480D00E9FAFA /* test_rapidPiPo.cpp */,
BE2C5EE21ED8480D00E9FAFA /* test_rapidXMM.cpp */,
BE2C5EE31ED8480D00E9FAFA /* test_signalProcessing.cpp */,
+ 312C61B71FE958CB0085E283 /* test_trainingData.cpp */,
);
path = src;
sourceTree = "<group>";
@@ -1079,6 +1101,7 @@
0BFFEF311E56085C00EF19A5 /* test_rapidXMM */,
0BFFEF3F1E5608C000EF19A5 /* test_signalProcessing */,
31D7B72C1E6B048100917757 /* test_rapidPiPo */,
+ 312C61D31FE95A680085E283 /* test_trainingData */,
);
name = Products;
sourceTree = "<group>";
@@ -1154,6 +1177,23 @@
productReference = 0BFFEF3F1E5608C000EF19A5 /* test_signalProcessing */;
productType = "com.apple.product-type.tool";
};
+ 312C61B91FE95A680085E283 /* test_trainingData */ = {
+ isa = PBXNativeTarget;
+ buildConfigurationList = 312C61D01FE95A680085E283 /* Build configuration list for PBXNativeTarget "test_trainingData" */;
+ buildPhases = (
+ 312C61BA1FE95A680085E283 /* Sources */,
+ 312C61CE1FE95A680085E283 /* Frameworks */,
+ 312C61CF1FE95A680085E283 /* CopyFiles */,
+ );
+ buildRules = (
+ );
+ dependencies = (
+ );
+ name = test_trainingData;
+ productName = test_XMM;
+ productReference = 312C61D31FE95A680085E283 /* test_trainingData */;
+ productType = "com.apple.product-type.tool";
+ };
31D7B7121E6B048100917757 /* test_rapidPiPo */ = {
isa = PBXNativeTarget;
buildConfigurationList = 31D7B7291E6B048100917757 /* Build configuration list for PBXNativeTarget "test_rapidPiPo" */;
@@ -1211,6 +1251,7 @@
31D7B7121E6B048100917757 /* test_rapidPiPo */,
0BFFEF301E56085C00EF19A5 /* test_rapidXMM */,
0BFFEF3E1E5608C000EF19A5 /* test_signalProcessing */,
+ 312C61B91FE95A680085E283 /* test_trainingData */,
);
};
/* End PBXProject section */
@@ -1310,6 +1351,16 @@
);
runOnlyForDeploymentPostprocessing = 0;
};
+ 312C61BA1FE95A680085E283 /* Sources */ = {
+ isa = PBXSourcesBuildPhase;
+ buildActionMask = 2147483647;
+ files = (
+ 312C61D41FE95A8F0085E283 /* test_trainingData.cpp in Sources */,
+ 312C61BD1FE95A680085E283 /* trainingData.cpp in Sources */,
+ 312C61CD1FE95A680085E283 /* jsoncpp.cpp in Sources */,
+ );
+ runOnlyForDeploymentPostprocessing = 0;
+ };
31D7B7131E6B048100917757 /* Sources */ = {
isa = PBXSourcesBuildPhase;
buildActionMask = 2147483647;
@@ -1426,6 +1477,22 @@
};
name = Release;
};
+ 312C61D11FE95A680085E283 /* Debug */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ MACOSX_DEPLOYMENT_TARGET = 10.11;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ };
+ name = Debug;
+ };
+ 312C61D21FE95A680085E283 /* Release */ = {
+ isa = XCBuildConfiguration;
+ buildSettings = {
+ MACOSX_DEPLOYMENT_TARGET = 10.11;
+ PRODUCT_NAME = "$(TARGET_NAME)";
+ };
+ name = Release;
+ };
31D7B72A1E6B048100917757 /* Debug */ = {
isa = XCBuildConfiguration;
buildSettings = {
@@ -1576,6 +1643,15 @@
defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
+ 312C61D01FE95A680085E283 /* Build configuration list for PBXNativeTarget "test_trainingData" */ = {
+ isa = XCConfigurationList;
+ buildConfigurations = (
+ 312C61D11FE95A680085E283 /* Debug */,
+ 312C61D21FE95A680085E283 /* Release */,
+ );
+ defaultConfigurationIsVisible = 0;
+ defaultConfigurationName = Release;
+ };
31D7B7291E6B048100917757 /* Build configuration list for PBXNativeTarget "test_rapidPiPo" */ = {
isa = XCConfigurationList;
buildConfigurations = (