void m_bundleopen(int argc,const t_atom *argv)
{
double t = 0;
if(argc--) t += GetAFloat(*argv++);
if(argc--) t += GetAFloat(*argv++);
osc::uint64 timetag = GetTimetag(t);
FLEXT_ASSERT(packet);
++bundle;
if(timetag <= 1)
// here immediate timetag can also be 0... but do OSC a favor
*packet << osc::BeginBundleImmediate;
else
*packet << osc::BeginBundle(timetag);
}
void regression::map(int argc, const t_atom *argv)
{
GRT::UINT numSamples = regression_data.getNumSamples();
GRT::Regressifier ®ressifier = get_Regressifier_instance();
if (numSamples == 0)
{
error("no observations added, use 'add' to add training data");
return;
}
if (regressifier.getTrained() == false)
{
error("data_typel has not been trained, use 'train' to train the data_typel");
return;
}
GRT::UINT numInputNeurons = regressifier.getNumInputFeatures();
GRT::VectorDouble query(numInputNeurons);
if (argc < 0 || (unsigned)argc != numInputNeurons)
{
error("invalid input length, expected " + std::to_string(numInputNeurons) + " got " + std::to_string(argc));
}
for (uint32_t index = 0; index < (uint32_t)argc; ++index)
{
double value = GetAFloat(argv[index]);
query[index] = value;
}
bool success = regressifier.predict(query);
if (success == false)
{
error("unable to map input");
return;
}
GRT::VectorDouble regression_data = regressifier.getRegressionData();
GRT::VectorDouble::size_type numOutputDimensions = regression_data.size();
if (numOutputDimensions != regressifier.getNumOutputDimensions())
{
error("invalid output dimensions: " + std::to_string(numOutputDimensions));
return;
}
AtomList result;
for (uint32_t index = 0; index < numOutputDimensions; ++index)
{
t_atom value_a;
double value = regression_data[index];
SetFloat(value_a, value);
result.Append(value_a);
}
ToOutList(0, result);
}
void feature_extraction::map(int argc, const t_atom *argv)
{
GRT::VectorDouble input(argc);
GRT::FeatureExtraction &feature_extractor = get_FeatureExtraction_instance();
if (argc <= 0 || (GRT::UINT)argc != feature_extractor.getNumInputDimensions())
{
std::stringstream ss;
ss << "invalid input length: " << argc << ", expected: " << feature_extractor.getNumInputDimensions();
error(ss.str());
return;
}
for (uint32_t index = 0; index < (uint32_t)argc; ++index)
{
double value = GetAFloat(argv[index]);
input[index] = value;
}
bool success = feature_extractor.computeFeatures(input);
if (success == false)
{
error("unable to map input");
return;
}
GRT::VectorDouble features = feature_extractor.getFeatureVector();
if (features.size() == 0 || features.size() != feature_extractor.getNumOutputDimensions())
{
std::stringstream ss;
ss << "unexpected output length: " << features.size() << ", expected: " << feature_extractor.getNumOutputDimensions();
error(ss.str());
return;
}
AtomList features_l;
GRT::VectorDouble::iterator iterator;
for (iterator = features.begin(); iterator != features.end(); iterator++)
{
t_atom feature_a;
SetDouble(&feature_a, *iterator);
features_l.Append(feature_a);
}
ToOutList(0, features_l);
}
void ann::map(int argc, const t_atom *argv)
{
const data_type data_type = get_data_type();
GRT::UINT numSamples = data_type == LABELLED_CLASSIFICATION ? classification_data.getNumSamples() : regression_data.getNumSamples();
if (numSamples == 0)
{
flext::error("no observations added, use 'add' to add training data");
return;
}
if (grt_ann.getTrained() == false)
{
flext::error("model has not been trained, use 'train' to train the model");
return;
}
GRT::UINT numInputNeurons = grt_ann.getNumInputNeurons();
GRT::VectorDouble query(numInputNeurons);
if (argc < 0 || (unsigned)argc != numInputNeurons)
{
flext::error("invalid input length, expected %d, got %d", numInputNeurons, argc);
}
for (uint32_t index = 0; index < (uint32_t)argc; ++index)
{
double value = GetAFloat(argv[index]);
query[index] = value;
}
bool success = grt_ann.predict(query);
if (success == false)
{
flext::error("unable to map input");
return;
}
if (grt_ann.getClassificationModeActive())
{
const GRT::VectorDouble likelihoods = grt_ann.getClassLikelihoods();
const GRT::Vector<GRT::UINT> labels = classification_data.getClassLabels();
const GRT::UINT predicted = grt_ann.getPredictedClassLabel();
const GRT::UINT classification = predicted == 0 ? 0 : get_class_id_for_index(predicted);
if (likelihoods.size() != labels.size())
{
flext::error("labels / likelihoods size mismatch");
}
else if (probs)
{
AtomList probs_list;
for (unsigned count = 0; count < labels.size(); ++count)
{
t_atom label_a;
t_atom likelihood_a;
SetFloat(likelihood_a, static_cast<float>(likelihoods[count]));
SetInt(label_a, get_class_id_for_index(labels[count]));
probs_list.Append(label_a);
probs_list.Append(likelihood_a);
}
ToOutAnything(1, get_s_probs(), probs_list);
}
ToOutInt(0, classification);
}
else if (grt_ann.getRegressionModeActive())
{
GRT::VectorDouble regression_data = grt_ann.getRegressionData();
GRT::VectorDouble::size_type numOutputDimensions = regression_data.size();
if (numOutputDimensions != grt_ann.getNumOutputNeurons())
{
flext::error("invalid output dimensions: %d", numOutputDimensions);
return;
}
AtomList result;
for (uint32_t index = 0; index < numOutputDimensions; ++index)
{
t_atom value_a;
double value = regression_data[index];
SetFloat(value_a, value);
result.Append(value_a);
}
ToOutList(0, result);
}
}
void ann::add(int argc, const t_atom *argv)
{
if (get_data_type() != data_type::LABELLED_CLASSIFICATION)
{
ml::add(argc, argv);
return;
}
// work around a bug in GRT where class labels must be contigious
if (argc < 2)
{
flext::error("invalid input length, must contain at least 2 values");
return;
}
GRT::UINT numInputDimensions = classification_data.getNumDimensions();
GRT::UINT numOutputDimensions = 1;
GRT::UINT combinedVectorSize = numInputDimensions + numOutputDimensions;
if ((unsigned)argc != combinedVectorSize)
{
numInputDimensions = argc - numOutputDimensions;
if (numInputDimensions < 1)
{
flext::error(std::string("invalid input length, expected at least " + std::to_string(numOutputDimensions + 1)).c_str());
return;
}
post("new input vector size, adjusting num_inputs to " + std::to_string(numInputDimensions));
set_num_inputs(numInputDimensions);
}
GRT::VectorDouble inputVector(numInputDimensions);
GRT::VectorDouble targetVector(numOutputDimensions);
for (uint32_t index = 0; index < (unsigned)argc; ++index)
{
float value = GetAFloat(argv[index]);
if (index < numOutputDimensions)
{
targetVector[index] = value;
}
else
{
inputVector[index - numOutputDimensions] = value;
}
}
GRT::UINT label = get_index_for_class((GRT::UINT)targetVector[0]);
assert(label > 0);
// if ((double)label != targetVector[0])
// {
// flext::error("class label must be a positive integer");
// return;
// }
//
// if (label == 0)
// {
// flext::error("class label must be non-zero");
// return;
// }
classification_data.addSample(label, inputVector);
}
virtual bool CbMethodResort(int inlet,const t_symbol *sym,int argc,const t_atom *argv)
{
const char *dst = GetString(sym);
if(*dst != '/')
return false;
FLEXT_ASSERT(packet);
{
// treat destination string
const char *var = strchr(dst,'%');
*packet << osc::BeginMessage(var?Convert(dst,var,argc,argv).c_str():dst);
}
while(argc) {
if(IsSymbol(*argv)) {
const char *hdr = GetString(*argv++); --argc;
const char *var = strchr(hdr,'%');
if(var) {
// variable found in string
char typetag = var[1];
if(!typetag)
// % is only character
*packet << "%";
else if(hdr != var || var[2])
// variable not in front, or string more than 2 chars long -> substitute variables
*packet << Convert(hdr,var,argc,argv).c_str();
else {
// standalone
switch(typetag) {
case osc::TRUE_TYPE_TAG: *packet << true; break;
case osc::FALSE_TYPE_TAG: *packet << false; break;
case osc::NIL_TYPE_TAG: *packet << osc::Nil; break;
case osc::INFINITUM_TYPE_TAG: *packet << osc::Infinitum; break;
case osc::INT32_TYPE_TAG: {
osc::int32 z = (argc--)?GetAInt(*argv++):0;
*packet << z;
break;
}
case osc::FLOAT_TYPE_TAG: {
float z = (argc--)?GetAFloat(*argv++):0;
*packet << z;
break;
}
case osc::CHAR_TYPE_TAG: {
Symbol s = (argc--)?GetASymbol(*argv++):NULL;
*packet << (s?*GetString(s):'\0');
break;
}
case osc::RGBA_COLOR_TYPE_TAG: {
osc::uint32 r = (argc--)?(GetAInt(*argv++)&0xff):0;
osc::uint32 g = (argc--)?(GetAInt(*argv++)&0xff):0;
osc::uint32 b = (argc--)?(GetAInt(*argv++)&0xff):0;
osc::uint32 a = (argc--)?(GetAInt(*argv++)&0xff):0;
*packet << osc::RgbaColor((r<<24)+(g<<16)+(b<<8)+a);
break;
}
case osc::MIDI_MESSAGE_TYPE_TAG: {
osc::uint32 channel = (argc--)?(GetAInt(*argv++)&0xff):0;
osc::uint32 status = (argc--)?(GetAInt(*argv++)&0xff):0;
osc::uint32 data1 = (argc--)?(GetAInt(*argv++)&0xff):0;
osc::uint32 data2 = (argc--)?(GetAInt(*argv++)&0xff):0;
*packet << osc::MidiMessage((channel<<24)+(status<<16)+(data1<<8)+data2);
break;
}
case osc::INT64_TYPE_TAG: {
osc::int64 z = 0;
if(argc--) z += GetAInt(*argv++);
if(argc--) z += GetAInt(*argv++);
*packet << z;
break;
}
case osc::TIME_TAG_TYPE_TAG: {
double z = 0;
if(argc--) z += GetAFloat(*argv++);
if(argc--) z += GetAFloat(*argv++);
*packet << osc::TimeTag(GetTimetag(z));
break;
}
case osc::DOUBLE_TYPE_TAG: {
double z = 0;
if(argc--) z += GetAFloat(*argv++);
if(argc--) z += GetAFloat(*argv++);
*packet << z;
break;
}
case osc::STRING_TYPE_TAG: {
Symbol s = (argc--)?GetASymbol(*argv++):NULL;
*packet << (s?GetString(s):"");
break;
}
case osc::SYMBOL_TYPE_TAG: {
Symbol s = (argc--)?GetASymbol(*argv++):NULL;
*packet << osc::Symbol(s?GetString(s):"");
break;
}
case osc::BLOB_TYPE_TAG:
post("%s %s - Blob type not supported",thisName(),GetString(thisTag()));
break;
default:
post("%s %s - Unknown type tag %s",thisName(),GetString(thisTag()),typetag);
}
}
}
else
*packet << osc::Symbol(hdr);
}
else if(CanbeFloat(*argv))
*packet << GetAFloat(*argv++),--argc;
else {
post("%s %s - Invalid atom type",thisName(),GetString(thisTag()));
++argv,--argc;
}
}
*packet << osc::EndMessage;
if(!bundle && autosend) Send(true);
return true;
}
