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ppa.cpp
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ppa.cpp
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#include "ppa.h"
int main(int argc, char* argv[])
{
CClppa command(argc, argv);
command.setFlags(true);
command.setVerbosity(2);
command.setMode("ppa");
try {
while(command.isFlags())
{
string argument = command.getCurrentArgument();
if(argv[command.getCurrentArgumentNo()][0]=='-')
{
int j=1;
if(argv[command.getCurrentArgumentNo()][j]=='-')
j++;
else if(command.getCurrentArgumentLength()!=2)
command.unrecognisedFlag();
switch (argv[command.getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
command.confirmCurrentArg("--?");
command.helpInfo();
exit(0);
case 'h':
if(j==2)
command.confirmCurrentArg("--help");
command.helpInfo();
exit(0);
case 'v':
if(j==2)
command.confirmCurrentArg("--verbosity");
command.incrementArgument();
command.setVerbosity(command.getIntFromCurrentArgument());
break;
default:
command.unrecognisedFlag();
}
}
else if(argument=="learn") // learning a model.
command.learn();
else if(argument=="relearn") // initialise with an old model.
command.relearn();
else if(argument=="test") // test with a model.
command.test();
else if(argument=="log-likelihood") // compute model log likelihood.
command.logLikelihood();
else if(argument=="predict")
command.predict();
else if(argument=="class-one-probabilities")
command.classOneProbabilities();
else if(argument=="display")
command.display();
else if(argument=="gnuplot")
command.gnuplot();
else
command.exitError("Invalid ppa command provided.");
command.incrementArgument();
}
command.exitError("No ppa command provided.");
}
catch(ndlexceptions::FileFormatError err)
{
command.exitError(err.getMessage());
}
catch(ndlexceptions::FileReadError err)
{
command.exitError(err.getMessage());
}
catch(ndlexceptions::FileWriteError err)
{
command.exitError(err.getMessage());
}
catch(ndlexceptions::FileError err)
{
command.exitError(err.getMessage());
}
catch(ndlexceptions::Error err)
{
command.exitError(err.getMessage());
}
catch(std::bad_alloc err)
{
command.exitError("Out of memory.");
}
catch(std::exception err)
{
command.exitError("Unhandled exception.");
}
}
CClppa::CClppa(int arc, char** arv) : CClctrl(arc, arv)
{
}
void CClppa::relearn()
{
incrementArgument();
setMode("relearn");
string labelledIndicesFile="";
bool labelledIndicesFlag = false;
int kernIters=100;
int noiseIters=20;
int extIters=4;
string modelFileName="ppa_model";
string newModelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
case 'l':
if(j==2)
confirmCurrentArg("--labelled-indices-file");
incrementArgument();
labelledIndicesFile=getCurrentArgument();
labelledIndicesFlag = true;
break;
case '#':
if(j==2)
confirmCurrentArg("--#kernel-iterations");
incrementArgument();
kernIters=getIntFromCurrentArgument();
break;
case 'n':
if(j==2)
confirmCurrentArg("--noise-iterations");
incrementArgument();
noiseIters=getIntFromCurrentArgument();
break;
case 'e':
if(j==2)
confirmCurrentArg("--external-iterations");
incrementArgument();
extIters=getIntFromCurrentArgument();
break;
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string trainDataFileName=getCurrentArgument();
if((getCurrentArgumentNo()+1)<argc)
modelFileName=argv[getCurrentArgumentNo()+1];
if((getCurrentArgumentNo()+2)<argc)
newModelFileName=argv[getCurrentArgumentNo()+2];
CMatrix X;
CMatrix y;
readData(X, y, trainDataFileName);
vector<int> labelledIndices;
if(labelledIndicesFlag)
{
ifstream in(labelledIndicesFile.c_str());
string line;
if(!in) throw ndlexceptions::FileReadError(labelledIndicesFile);
while(getline(in, line))
{
int index = atol(line.c_str())-1;
if(index<0 || index>y.getRows()) throw ndlexceptions::FileFormatError(labelledIndicesFile);
labelledIndices.push_back(index);
}
in.close();
}
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
if(pmodel->getNumInputs()!=X.getCols())
throw ndlexceptions::Error(trainDataFileName + ": input data is not of correct dimension");
if(labelledIndicesFlag)
if(pmodel->noise.getType()!="ncnm")
{
CMatrix newy(labelledIndices.size(), y.getCols());
CMatrix newX(labelledIndices.size(), X.getCols());
for(int i=0; i<labelledIndices.size(); i++)
{
newy.copyRowRow(i, y, labelledIndices[i]);
newX.copyRowRow(i, X, labelledIndices[i]);
}
y = newy;
X = newX;
if(getVerbosity()>0)
cout << "Reduced data set ... contains " << y.getRows() << " points." << endl;
}
else
{
int count=0;
for(int i=0; i<y.getRows(); i++)
{
vector<int>::iterator pos1 = find(labelledIndices.begin(), labelledIndices.end(), i);
if(pos1==labelledIndices.end())
{
count++;
for(int j=0; j<y.getCols(); j++)
y.setVal(0, i, j);
}
}
if(getVerbosity()>0)
cout << "Removed labels from " << count << " points that weren't indexed." << endl;
}
int numNoiseParams = pmodel->noise.getNumParams();
CMatrix noiseParams(1, numNoiseParams);
pmodel->noise.getParams(noiseParams);
pmodel->noise.setTarget(y);
pmodel->noise.setParams(noiseParams);
CPpa model(X, y, pmodel->kern, pmodel->noise, getVerbosity());
model.optimise(extIters, kernIters, noiseIters);
string comment="";
switch(getFileFormat())
{
case 0: /// PPA file format.
comment = "Run as:";
for(int i=0; i<argc; i++)
{
comment+=" ";
comment+=argv[i];
}
comment += " with seed " + ndlstrutil::itoa(getSeed()) + ".";
writePpaToFile(model, newModelFileName, comment);
break;
case 1: /// Matlab file format.
#ifdef _NDLMATLAB
// Write matlab output.
model.writeMatlabFile(newModelFileName, "ppaInfo");
model.kern.updateMatlabFile(newModelFileName, "kern");
model.noise.updateMatlabFile(newModelFileName, "noise");
X.updateMatlabFile(newModelFileName, "X");
y.updateMatlabFile(newModelFileName, "y");
#else
exitError("Error MATLAB not incorporated at compile time.");
#endif
break;
default:
exitError("Unrecognised file format number.");
}
exit(0);
}
void CClppa::learn()
{
incrementArgument();
setMode("learn");
string type="classification";
string labelledIndicesFile="";
bool labelledIndicesFlag = false;
double tol=1e-6;
vector<string> kernelTypes;
vector<double> rbfInvWidths;
vector<double> weightVariances;
vector<double> biasVariances;
vector<double> variances;
vector<double> degrees;
vector<bool> selectInputs;
char noiseType;
int kernIters=100;
int noiseIters=20;
int extIters=4;
string modelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
case 'l':
if(j==2)
confirmCurrentArg("--labelled-indices-file");
incrementArgument();
labelledIndicesFile=getCurrentArgument();
labelledIndicesFlag = true;
break;
case 'o':
if(j==2)
confirmCurrentArg("--output-type");
incrementArgument();
type = getCurrentArgument();
break;
case 'k':
if(j==2)
confirmCurrentArg("--kernel");
incrementArgument();
kernelTypes.push_back(getCurrentArgument());
rbfInvWidths.push_back(-1.0);
weightVariances.push_back(-1.0);
biasVariances.push_back(-1.0);
variances.push_back(-1.0);
degrees.push_back(-1.0);
selectInputs.push_back(false);
break;
case 'g':
if(j==2)
confirmCurrentArg("--gamma");
incrementArgument();
if(kernelTypes.size()==0)
exitError("Inverse width specification must come after kernel type is specified.");
if(kernelTypes[kernelTypes.size()-1]!="rbf")
exitError("Inverse width parameter only valid for RBF kernel.");
rbfInvWidths[rbfInvWidths.size()-1]=2*getDoubleFromCurrentArgument();
break;
case 'd':
if(j==2)
confirmCurrentArg("--degree");
incrementArgument();
if(kernelTypes.size()==0)
exitError("Polynomial degree specification must come after kernel type is specified.");
if(kernelTypes[kernelTypes.size()-1]!="poly")
exitError("Polynomial degree parameter only valid for poly kernel.");
degrees[degrees.size()-1]=getDoubleFromCurrentArgument();
break;
case 'w':
if(j==2)
confirmCurrentArg("--weight");
incrementArgument();
if(kernelTypes.size()==0)
exitError("`Weight variance' parameter specification must come after kernel type is specified.");
if(kernelTypes[kernelTypes.size()-1]!="poly"
&& kernelTypes[kernelTypes.size()-1]!="mlp")
exitError("`Weight variance' parameter only valid for polynomial and MLP kernel.");
weightVariances[weightVariances.size()-1]=getDoubleFromCurrentArgument();
break;
case 'b':
if(j==2)
confirmCurrentArg("--bias");
incrementArgument();
if(kernelTypes.size()==0)
exitError("`Bias variance' parameter specification must come after kernel type is specified.");
if(kernelTypes[kernelTypes.size()-1]!="poly"
&& kernelTypes[kernelTypes.size()-1]!="mlp")
exitError("`Bias variance' parameter only valid for polynomial and MLP kernel.");
biasVariances[biasVariances.size()-1]=getDoubleFromCurrentArgument();
break;
case 'v':
if(j==2)
confirmCurrentArg("--variance");
incrementArgument();
if(kernelTypes.size()==0)
exitError("Variance parameter specification must come after kernel type is specified.");
variances[variances.size()-1]=getDoubleFromCurrentArgument();
break;
case 'i':
if(j==2)
confirmCurrentArg("--input-select");
incrementArgument();
if(kernelTypes.size()==0)
exitError("Input selection flag must come after kernel type is specified.");
try
{
selectInputs[selectInputs.size()-1]=getBoolFromCurrentArgument();
}
catch(ndlexceptions::CommandLineError err)
{
exitError("Input selection flag must be either 1, 0, true or false.");
}
break;
case '#':
if(j==2)
confirmCurrentArg("--#kernel-iterations");
incrementArgument();
kernIters=getIntFromCurrentArgument();
break;
case 'n':
if(j==2)
confirmCurrentArg("--noise-iterations");
incrementArgument();
noiseIters=getIntFromCurrentArgument();
break;
case 'e':
if(j==2)
confirmCurrentArg("--external-iterations");
incrementArgument();
extIters=getIntFromCurrentArgument();
break;
case 'f':
if(j==2)
confirmCurrentArg("--file-format");
incrementArgument();
setFileFormat(getIntFromCurrentArgument());
break;
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string trainDataFileName=getCurrentArgument();
if((getCurrentArgumentNo()+1)<argc)
modelFileName=argv[getCurrentArgumentNo()+1];
if(type=="classification")
noiseType = 'c';
else if(type=="regression")
noiseType = 'r';
else if(type=="ncnm")
noiseType = 'n';
else
exitError("Unknown output type, valid types are 'classification', 'regression' and 'ncnm' (null category noise model).");
string dataFile="";
CMatrix X;
CMatrix y;
readData(X, y, trainDataFileName);
vector<int> labelledIndices;
if(labelledIndicesFlag)
{
ifstream in(labelledIndicesFile.c_str());
string line;
if(!in) throw ndlexceptions::FileReadError(labelledIndicesFile);
while(getline(in, line))
{
int index = atol(line.c_str())-1;
if(index<0 || index>y.getRows()) throw ndlexceptions::FileFormatError(labelledIndicesFile);
labelledIndices.push_back(index);
}
in.close();
}
// prior for use with ncnm.
CDist* prior = new CGammaDist();
prior->setParam(1.0, 0);
prior->setParam(1.0, 1);
// create noise model.
CNoise* noise;
bool missingData=false;
double yVal=0.0;
switch(noiseType)
{
case 'n': /// set up null category noise model.
missingData=true;
for(int i=0; i<y.getRows(); i++)
{
yVal=y.getVal(i);
if(yVal!=1.0 && yVal!=-1.0 && yVal!=0.0 && !isnan(yVal))
exitError("Input data is not a classification data set. Labels must either be -1.0, 1.0 or (for unlabelled) 0.0");
}
noise = new CNcnmNoise;
break;
case 'c': /// Set up a classification model.
for(int i=0; i<y.getRows(); i++)
{
yVal=y.getVal(i);
if(yVal!=1.0 && yVal!=-1.0)
{
if(yVal==0.0 || isnan(yVal))
{
if(missingData)
continue;
else
{
if(getVerbosity()>0)
cout << "Some data are missing labels, using null category noise model." << endl;
missingData=true;
}
}
else
exitError("Input data is not a classification data set. Labels must either be -1.0, 1.0 or (for unlabelled) 0.0");
}
}
if(missingData)
{
// set up ncnm noise model.
noise = new CNcnmNoise;
noiseType='n';
}
else
{
// set up probit noise model.
noise = new CProbitNoise;
}
break;
case 'r':
noise = new CGaussianNoise;
// set up a Gaussian noise model.
break;
otherwise:
// we should never get here ... exit with error.
exitError("Critical failure");
}
if(labelledIndicesFlag)
if(noiseType=='c' || noiseType=='r')
{
CMatrix newy(labelledIndices.size(), y.getCols());
CMatrix newX(labelledIndices.size(), X.getCols());
for(int i=0; i<labelledIndices.size(); i++)
{
newy.copyRowRow(i, y, labelledIndices[i]);
newX.copyRowRow(i, X, labelledIndices[i]);
}
y = newy;
X = newX;
if(getVerbosity()>0)
cout << "Reduced data set ... contains " << y.getRows() << " points." << endl;
}
else if(noiseType=='n')
{
int count=0;
for(int i=0; i<y.getRows(); i++)
{
vector<int>::iterator pos1 = find(labelledIndices.begin(), labelledIndices.end(), i);
if(pos1==labelledIndices.end())
{
count++;
for(int j=0; j<y.getCols(); j++)
y.setVal(0, i, j);
}
}
if(getVerbosity()>0)
cout << "Removed labels from " << count << " points that weren't indexed." << endl;
}
else
// shouldn't be here
exitError("Critical failure.");
// create kernel.
CCmpndKern kern(X);
vector<CKern*> kernels;
for(int i=0; i<kernelTypes.size(); i++)
{
if(kernelTypes[i]=="lin")
{
if(selectInputs[i])
kernels.push_back(new CLinardKern(X));
else
kernels.push_back(new CLinKern(X));
if(variances[i]!=-1.0)
kernels[i]->setParam(variances[i], 0); // set variance parameter as specified.
if(missingData)
kernels[i]->addPrior(prior, 0); // place L1 regulariser on variance for ncnm model.
}
else if(kernelTypes[i]=="poly")
{
if(selectInputs[i])
{
kernels.push_back(new CPolyardKern(X));
if(degrees[i]!=-1.0)
((CPolyardKern*)kernels[i])->setDegree(degrees[i]);
}
else
{
kernels.push_back(new CPolyKern(X));
if(degrees[i]!=-1.0)
((CPolyKern*)kernels[i])->setDegree(degrees[i]);
}
if(weightVariances[i]!=-1.0)
kernels[i]->setParam(weightVariances[i], 0); // set `weight variance' parameter as specified.
if(biasVariances[i]!=-1.0)
kernels[i]->setParam(biasVariances[i], 1); // set `bias variance' parameter as specified.
if(variances[i]!=-1.0)
kernels[i]->setParam(variances[i], 2); // set variance parameter as specified.
if(missingData)
kernels[i]->addPrior(prior, 2); // place L1 regulariser on variance for ncnm model.
}
else if(kernelTypes[i]=="rbf")
{
if(selectInputs[i])
kernels.push_back(new CRbfardKern(X));
else
kernels.push_back(new CRbfKern(X));
if(rbfInvWidths[i]!=-1.0)
kernels[i]->setParam(rbfInvWidths[i], 0); /// set rbf inverse width as specified.
if(variances[i]!=-1.0)
kernels[i]->setParam(variances[i], 1); /// set variance parameter as specified.
if(missingData)
kernels[i]->addPrior(prior, 1);// place L1 regulariser on variance for ncnm model.
}
else if(kernelTypes[i] == "mlp")
{
if(selectInputs[i])
kernels.push_back(new CMlpardKern(X));
else
kernels.push_back(new CMlpKern(X));
if(weightVariances[i]!=-1.0)
kernels[i]->setParam(weightVariances[i], 0); // set `weight variance' parameter as specified.
if(biasVariances[i]!=-1.0)
kernels[i]->setParam(biasVariances[i], 1); // set `bias variance' parameter as specified.
if(variances[i]!=-1.0)
kernels[i]->setParam(variances[i], 2); // set variance parameter as specified.
if(missingData)
kernels[i]->addPrior(prior, 2);// place L1 regulariser on variance for ncnm model.
}
else
{
exitError("Unknown kernel type: " + kernelTypes[i]);
}
kern.addKern(kernels[i]);
}
// if no kernel was specified, add a linear one.
if(kernels.size()==0)
{
CKern* defaultKern = new CLinKern(X);
if(missingData)
defaultKern->addPrior(prior, 0); // place L1 regulariser on variance for ncnm model.
kern.addKern(defaultKern);
}
CKern* biasKern = new CBiasKern(X);
if(missingData)
biasKern->addPrior(prior, 0); // place L1 regulariser on variance for ncnm model.
//biasKern->setParam(biasKernVariance, 0);
CKern* whiteKern = new CWhiteKern(X);
if(missingData)
whiteKern->addPrior(prior, 0); // place L1 regulariser on variance for ncnm model.
//biasKern->setParam(whiteKernVariance, 0);
kern.addKern(biasKern);
kern.addKern(whiteKern);
noise->setTarget(y);
noise->initParams();
CPpa model(X, y, kern, *noise, getVerbosity());
model.optimise(extIters, kernIters, noiseIters);
string comment="";
switch(getFileFormat())
{
case 0: /// PPA file format.
comment = "Run as: ";
for(int i=0; i<argc; i++)
{
comment+=argv[i];
comment+=" ";
}
writePpaToFile(model, modelFileName, comment);
break;
case 1: /// Matlab file format.
#ifdef _NDLMATLAB
// Write matlab output.
model.writeMatlabFile(modelFileName, "ppaInfo");
model.kern.updateMatlabFile(modelFileName, "kern");
model.noise.updateMatlabFile(modelFileName, "noise");
X.updateMatlabFile(modelFileName, "X");
y.updateMatlabFile(modelFileName, "y");
#else
exitError("Error MATLAB not incorporated at compile time.");
#endif
break;
default:
exitError("Unrecognised file format number.");
}
exit(0);
}
void CClppa::test()
{
incrementArgument();
setMode("test");
string modelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string testDataFileName=getCurrentArgument();
if((getCurrentArgumentNo()+1)<argc)
modelFileName=argv[getCurrentArgumentNo()+1];
string dataFile="";
CMatrix X;
CMatrix y;
readData(X, y, testDataFileName);
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
if(getVerbosity()>2)
pmodel->display(cout);
if(pmodel->getNumProcesses()!=y.getCols())
throw ndlexceptions::Error(testDataFileName + ": input targets are not of correct dimension");
if(pmodel->getNumInputs()!=X.getCols())
throw ndlexceptions::Error(testDataFileName + ": input data is not of correct dimension");
pmodel->test(y, X);
exit(0);
}
void CClppa::trainLogLikelihood()
{
incrementArgument();
setMode("train-log-likelihood");
string modelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()<argc)
modelFileName=getCurrentArgument();
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
double ll = pmodel->approxLogLikelihood();
if(getVerbosity()>0)
cout << "Model log likelihood: ";
cout << ll << endl;
exit(0);
}
void CClppa::logLikelihood()
{
incrementArgument();
setMode("log-likelihood");
string modelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string testDataFileName=getCurrentArgument();
if((getCurrentArgumentNo()+1)<argc)
modelFileName=argv[getCurrentArgumentNo()+1];
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
CMatrix X;
CMatrix y;
readData(X, y, testDataFileName);
double ll = pmodel->logLikelihood(y, X);
if(getVerbosity()>0)
cout << "Model log likelihood: ";
cout << ll << endl;
exit(0);
}
void CClppa::predict()
{
incrementArgument();
setMode("predict");
string modelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string testDataFileName=getCurrentArgument();
if((getCurrentArgumentNo()+1)<argc)
modelFileName=argv[getCurrentArgumentNo()+1];
string predictionFile="ppa_predictions";
if((getCurrentArgumentNo()+2)<argc)
predictionFile=argv[getCurrentArgumentNo()+2];
CMatrix X;
CMatrix yPred;
readData(X, yPred, testDataFileName);
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
yPred.resize(X.getRows(), pmodel->getNumProcesses());
if(pmodel->getNumInputs()!=X.getCols())
throw ndlexceptions::Error(testDataFileName + ": input data is not of correct dimension");
pmodel->out(yPred, X);
yPred.toFile(predictionFile);
exit(0);
}
void CClppa::classOneProbabilities()
{
incrementArgument();
setMode("class-one-probabilities");
string modelFileName="ppa_model";
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string testDataFileName=getCurrentArgument();
if((getCurrentArgumentNo()+1)<argc)
modelFileName=argv[getCurrentArgumentNo()+1];
string probabilityFile="ppa_probabilities";
if((getCurrentArgumentNo()+2)<argc)
probabilityFile=argv[getCurrentArgumentNo()+2];
CMatrix X;
CMatrix yTest;
readData(X, yTest, testDataFileName);
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
if(pmodel->noise.getType()!="probit" && pmodel->noise.getType()!="ncnm")
{
exitError("Model file is not a classification model.");
}
yTest.resize(X.getRows(), pmodel->getNumProcesses());
yTest.ones();
if(pmodel->getNumInputs()!=X.getCols())
throw ndlexceptions::Error(testDataFileName + ": input data is not of correct dimension");
CMatrix probs(yTest.getRows(), yTest.getCols());
pmodel->likelihoods(probs, yTest, X);
probs.toFile(probabilityFile);
exit(0);
}
void CClppa::display()
{
incrementArgument();
setMode("display");
while(isFlags())
{
if(isCurrentArgumentFlag())
{
int j=1;
if(argv[getCurrentArgumentNo()][j]=='-')
j++;
else if(getCurrentArgumentLength()!=2)
unrecognisedFlag();
switch(argv[getCurrentArgumentNo()][j])
{
case '?':
if(j==2)
confirmCurrentArg("--?");
helpInfo();
exit(0);
case 'h':
if(j==2)
confirmCurrentArg("--help");
helpInfo();
exit(0);
default:
unrecognisedFlag();
}
incrementArgument();
}
else
setFlags(false);
}
if(getCurrentArgumentNo()>=argc)
exitError("There are not enough input parameters.");
string modelFileName=getCurrentArgument();
CPpa* pmodel=readPpaFromFile(modelFileName, getVerbosity());
pmodel->display(cout);
exit(0);
}
void CClppa::gnuplot()