//add rows of data to the table
void WekaData::Append(const realvec& in)
{
MRSASSERT(in.getRows()==cols_);
// skip feature vectors labeled with negative labels
if (in(in.getRows()-1, 0) >=0)
{
data_ = new vector<mrs_real>(cols_);
for(mrs_natural ii=0; ii<in.getRows(); ++ii)
{
data_->at(ii) = in(ii, 0);
}
Append(data_);
}
}
mrs_real
QGMMModel::deltaBIC(realvec C1, mrs_natural N1, realvec C2, mrs_natural N2, realvec C, mrs_real lambda)
{
//matrices should be square and equal sized
if(C1.getCols() != C2.getCols() && C1.getCols() != C.getCols() &&
C1.getCols()!= C1.getRows())
{
MRSERR("QGMMModel:deltaBIC: matrices should all be squared and equal sized...");
return MAXREAL; //just a way to sinalize the above error... [!]
}
mrs_real res;
mrs_real N = (mrs_real)(N1 + N2);
mrs_real d = (mrs_real)C1.getCols();
res = N * log(C.det());
res -= (mrs_real)N1 * log(C1.det());
res -= (mrs_real)N2 * log(C2.det());
res *= 0.5f;
res -= 0.5f * lambda * (d + 0.5f*d*(d+1.0f))* log(N);
return res;
}
void
PeakClusterSelect::swap(realvec& rv, mrs_natural sample1, mrs_natural sample2, mrs_bool swapColumns)
{
if( swapColumns ) // swap two columns
{
int rows = rv.getRows();
mrs_real tmp;
for( int i=0 ; i<rows ; ++i )
{
tmp = rv(i, sample1);
rv(i,sample1) = rv(i,sample2);
rv(i,sample2) = tmp;
}
}
else // swap two rows
{
int cols = rv.getCols();
mrs_real tmp;
for( int i=0 ; i<cols ; ++i )
{
tmp = rv(sample1,i);
rv(sample1,i) = rv(sample2,i);
rv(sample2,i) = tmp;
}
}
}
void Map::myProcess(realvec & in, realvec & out)
{
{
MarControlAccessor input_access(m_input_ctl);
realvec & input_data = input_access.to<realvec>();
assert(input_data.getRows() == in.getRows() &&
input_data.getCols() == in.getCols());
input_data = in;
}
const realvec & output_data = m_output_ctl->to<realvec>();
assert(output_data.getRows() == out.getRows() &&
output_data.getCols() == out.getCols());
out = output_data;
}
void Parallel::myProcess(realvec& in, realvec& out)
{
mrs_natural t,o;
mrs_natural inIndex = 0;
mrs_natural outIndex = 0;
mrs_natural localIndex = 0;
unsigned int child_count = marsystems_.size();
if (child_count == 1)
{
marsystems_[0]->process(in, out);
}
else if (child_count > 1)
{
for (mrs_natural i = 0; i < child_count; ++i)
{
localIndex = childInfos_[i].inObservations;
for (o = 0; o < localIndex; o++)
{
// avoid reading unallocated memory
if ((inIndex + o) < in.getRows()) {
for (t = 0; t < inSamples_; t++) //lmartins: was t < onSamples [!]
{
(*(slices_[2*i]))(o,t) = in(inIndex + o,t);
}
} else {
for (t = 0; t < inSamples_; t++) {
(*(slices_[2*i]))(o,t) = 0;
}
}
}
inIndex += localIndex;
marsystems_[i]->process(*(slices_[2*i]), *(slices_[2*i+1]));
localIndex = childInfos_[i].onObservations;
for (o = 0; o < localIndex; o++)
{
for (t = 0; t < onSamples_; t++)
{
out(outIndex + o,t) = (*(slices_[2*i+1]))(o,t);
}
}
outIndex += localIndex;
}
}
else if(child_count == 0) //composite has no children!
{
MRSWARN("Parallel::process: composite has no children MarSystems - passing input to output without changes.");
out = in;
}
}
void
MATLABengine::putVariable(realvec value, mrs_string MATLABname)
{
//----------------------------------
// send a realvec to a MATLAB matrix
//----------------------------------
mwSize dims[2]; //realvec is 2D
dims[0] = value.getRows();
dims[1] = value.getCols();
//realvec are by default double precision matrices => mxDOUBLE_CLASS
mxArray *mxMatrix = mxCreateNumericArray(2, dims, mxDOUBLE_CLASS, mxREAL);
mrs_real *data = value.getData();
memcpy((void *)mxGetPr(mxMatrix), (void *)(data), dims[0]*dims[1]*mxGetElementSize(mxMatrix));
engPutVariable(engine_, MATLABname.c_str(), mxMatrix);
mxDestroyArray(mxMatrix);
}
void
peakView::fromTable(const realvec& vecTable)
{
//get data from header
fs_ = vecTable(0, 1);
frameSize_ = (mrs_natural)vecTable(0, 2);
frameMaxNumPeaks_ = (mrs_natural)vecTable(0, 3);
numFrames_ = (mrs_natural)vecTable(0, 4);
//get the first frame in table (it may not be 0!!!)
mrs_natural frame = (mrs_natural)vecTable(1, pkFrame); // start frame [!]
//resize (and set to zero) vec_ for new peak data
//(should accommodate empty frames before the first frame in table!)
vec_.create(frameMaxNumPeaks_*nbPkParameters, numFrames_+frame); //[!]
mrs_natural p = 0; // peak index inside each frame
mrs_natural r = 1;//peak index in table (i.e. row index) - ignore header row
//check in case realvec has less parameters than nbPkParameters
mrs_natural actualNbPkParams = (mrs_natural)min((mrs_real)nbPkParameters, (mrs_real)vecTable.getCols());
//iterate over table rows (i.e. peaks) - excluding header row
while(r < vecTable.getRows()-1)
{
//get parameters for this peak
for(mrs_natural prm = 0; prm < actualNbPkParams; ++prm)
{
(*this)(p, pkParameter(prm), frame) = vecTable(r, prm);
}
++r; //move on to next table row (i.e. peak)
p++;
//if the next row in table is form a different frame,
//reset peak index and get new frame index
if(vecTable(r, pkFrame) != frame)
{
frame = (mrs_natural)vecTable(r, pkFrame);
p = 0;
}
}
}
void PeakConvert2::ComputePeaker (mrs_realvec in, realvec& out)
{
#ifdef ORIGINAL_VERSION
peaker_->updControl("mrs_real/peakStrength", 0.2);// to be set as a control [!]
#else
peaker_->updControl("mrs_real/peakStrength",1e-1);
peaker_->updControl("mrs_real/peakStrengthRelMax" ,1e-2);
peaker_->updControl("mrs_real/peakStrengthAbs",1e-10 );
peaker_->updControl("mrs_real/peakStrengthTreshLpParam" ,0.95);
peaker_->updControl("mrs_real/peakStrengthRelThresh" , 1.);
#endif
peaker_->updControl("mrs_real/peakSpacing", 2e-3); // 0
peaker_->updControl("mrs_natural/peakStart", downFrequency_); // 0
peaker_->updControl("mrs_natural/peakEnd", upFrequency_); // size_
peaker_->updControl("mrs_natural/inSamples", in.getCols());
peaker_->updControl("mrs_natural/inObservations", in.getRows());
peaker_->updControl("mrs_natural/onSamples", out.getCols());
peaker_->updControl("mrs_natural/onObservations", out.getRows());
peaker_->process(in, out);
}
summaryStatistics ClassificationReport::computeSummaryStatistics(const realvec& mat)
{
MRSASSERT(mat.getCols()==mat.getRows());
summaryStatistics stats;
mrs_natural size = mat.getCols();
vector<mrs_natural>rowSums(size);
for(int ii=0; ii<size; ++ii) rowSums[ii] = 0;
vector<mrs_natural>colSums(size);
for(int ii=0; ii<size; ++ii) colSums[ii] = 0;
mrs_natural diagonalSum = 0;
mrs_natural instanceCount = 0;
for(mrs_natural row=0; row<size; row++)
{
for(mrs_natural col=0; col<size; col++)
{
mrs_natural num = (mrs_natural)mat(row,col);
instanceCount += num;
rowSums[row] += num;
colSums[col] += num;
if(row==col)
diagonalSum += num;
}
}
//printf("row1 sum:%d\n",rowSums[0]);
//printf("row2 sum:%d\n",rowSums[1]);
//printf("col1 sum:%d\n",colSums[0]);
//printf("col2 sum:%d\n",colSums[1]);
//printf("diagonal sum:%d\n",diagonalSum);
//printf("instanceCount:%d\n",instanceCount);
mrs_natural N = instanceCount;
mrs_natural N2 = (N*N);
stats.instances = instanceCount;
stats.correctInstances = diagonalSum;
mrs_natural sum = 0;
for(mrs_natural ii=0; ii<size; ++ii)
{
sum += (rowSums[ii] * colSums[ii]);
}
mrs_real PE = (mrs_real)sum / (mrs_real)N2;
mrs_real PA = (mrs_real)diagonalSum / (mrs_real)N;
stats.kappa = (PA - PE) / (1.0 - PE);
mrs_natural not_diagonal_sum = instanceCount - diagonalSum;
mrs_real MeanAbsoluteError = (mrs_real)not_diagonal_sum / (mrs_real)instanceCount;
//printf("MeanAbsoluteError:%f\n",MeanAbsoluteError);
stats.meanAbsoluteError = MeanAbsoluteError;
mrs_real RootMeanSquaredError = sqrt(MeanAbsoluteError);
//printf("RootMeanSquaredError:%f\n",RootMeanSquaredError);
stats.rootMeanSquaredError = RootMeanSquaredError;
mrs_real RelativeAbsoluteError = (MeanAbsoluteError / 0.5) * 100.0;
//printf("RelativeAbsoluteError:%f%%\n",RelativeAbsoluteError);
stats.relativeAbsoluteError = RelativeAbsoluteError;
mrs_real RootRelativeSquaredError = (RootMeanSquaredError / (0.5)) * 100.0;
//printf("RootRelativeSquaredError:%f%%\n",RootRelativeSquaredError);
stats.rootRelativeSquaredError = RootRelativeSquaredError;
return stats;
}//computeSummaryStatistics
void
PeakViewMerge::myProcess(realvec& in, realvec& out)
{
peakView *In[kNumMatrices],
Out (out);
mrs_natural i, rowIdx = 0,
numPeaks[kNumMatrices],
outputIdx = 0;
const mrs_bool discNegGroups = ctrl_noNegativeGroups_->to<mrs_bool>();
out.setval(0.);
for (i = 0; i < kNumMatrices; i++)
{
mrs_natural numRows = (i==kMat1)? ctrl_frameMaxNumPeaks1_->to<mrs_natural>() : ctrl_frameMaxNumPeaks2_->to<mrs_natural>();
numRows *= peakView::nbPkParameters;
if (numRows == 0) // if the controls have not been set assume both matrixes to be of equal size
numRows = in.getRows ()/kNumMatrices;
peakViewIn_[i].stretch (numRows, in.getCols ());
in.getSubMatrix (rowIdx, 0, peakViewIn_[i]);
rowIdx += numRows;
In[i] = new peakView(peakViewIn_[i]);
numPeaks[i] = In[i]->getTotalNumPeaks ();
}
if (ctrl_mode_->to<mrs_string>() == "OR")
{
// write all entries of the second peakView to output
for (i = 0; i < numPeaks[1]; i++)
{
if (discNegGroups && (*In[1])(i,peakView::pkGroup) < 0)
continue;
WriteOutput (Out, In[1], i, outputIdx);
outputIdx++;
}
// write all entries of the first peakView to output except duplicates
for (i = 0; i < numPeaks[0]; i++)
{
mrs_natural Idx;
if (discNegGroups && (*In[0])(i,peakView::pkGroup) < 0)
continue;
for (mrs_natural k = 1; k < kNumMatrices; k++)
Idx = FindDuplicate (In[k], (*In[0])(i, peakView::pkFrequency), numPeaks[k]);
if (Idx < 0)
{
WriteOutput (Out, In[0], i, outputIdx);
outputIdx++;
}
}
}
else if (ctrl_mode_->to<mrs_string>() == "AND")
{
// find duplicates and write only them to output
for (i = 0; i < numPeaks[0]; i++)
{
mrs_natural Idx;
if (discNegGroups && (*In[0])(i,peakView::pkGroup) < 0)
continue;
for (mrs_natural k = 1; k < kNumMatrices; k++)
Idx = FindDuplicate (In[k], (*In[0])(i, peakView::pkFrequency), numPeaks[k]);
if (Idx >= 0)
{
if (discNegGroups && (*In[1])(Idx,peakView::pkGroup) < 0)
continue;
WriteOutput (Out, In[0], i, outputIdx);
outputIdx++;
}
}
}
else if (ctrl_mode_->to<mrs_string>() == "ANDOR")
{
// keep the input[0] peaks that are not in input[1]
for (i = 0; i < numPeaks[0]; i++)
{
mrs_natural Idx;
if (discNegGroups && (*In[0])(i,peakView::pkGroup) < 0)
continue;
for (mrs_natural k = 1; k < kNumMatrices; k++)
Idx = FindDuplicate (In[k], (*In[0])(i, peakView::pkFrequency), numPeaks[k]);
if (Idx < 0)
{
WriteOutput (Out, In[0], i, outputIdx);
outputIdx++;
}
}
}
else if (ctrl_mode_->to<mrs_string>() == "XOR")
{
// find duplicates and write only residual to output
for (i = 0; i < numPeaks[0]; i++)
{
if (discNegGroups && (*In[0])(i,peakView::pkGroup) < 0)
continue;
mrs_natural Idx = FindDuplicate (In[1], (*In[0])(i, peakView::pkFrequency), numPeaks[1]);
if (Idx < 0)
{
WriteOutput (Out, In[0], i, outputIdx);
outputIdx++;
}
}
// find duplicates and write only residual to output
for (i = 0; i < numPeaks[1]; i++)
{
if (discNegGroups && (*In[1])(i,peakView::pkGroup) < 0)
continue;
mrs_natural Idx= FindDuplicate (In[0], (*In[1])(i, peakView::pkFrequency), numPeaks[0]);
if (Idx < 0)
{
WriteOutput (Out, In[1], i, outputIdx);
outputIdx++;
}
}
}
else
{
MRSERR("PeakViewMerfe::myProcess() : illegal mode string: " << ctrl_mode_->to<mrs_string>());
}
for (i = 0; i < kNumMatrices; i++)
{
delete In[i];
}
ctrl_totalNumPeaks_->setValue(outputIdx);
}
void
Filter::myProcess(realvec& in, realvec& out)
{
//checkFlow(in,out);
mrs_natural i,j,c;
mrs_natural size = in.getCols();
mrs_natural stateSize = state_.getCols();
mrs_natural channels = in.getRows();
mrs_real gain = getctrl("mrs_real/fgain")->to<mrs_real>();
// State array holds the various delays for the difference equation
// of the filter. Similar implementation as described in the manual
// for MATLAB Signal Processing Toolbox. State corresponds to
// the z, num_coefs to the b and denom_coefs to the a vector respectively
// in_window is the input x(n) and out_window is the output y(n)
//dcoeffs_/=10;
// state_.setval(0);
if (norder_ == dorder_) {
for (c = 0; c < channels; ++c) {
for (i = 0; i < size; ++i) {
out(c,i) = ncoeffs_(0) * in(c,i) + state_(c,0);
for (j = 0; j < stateSize - 1; j++)
{
state_(c,j) = ncoeffs_(j+1) * in(c,i) + state_(c,j+1) - dcoeffs_(j+1) * out(c,i);
}
state_(c,stateSize - 1) = ncoeffs_(order_-1) * in(c,i) - dcoeffs_(order_-1) * out(c,i);
}
}
}
else if (norder_ < dorder_) {
for (c = 0; c < channels; ++c) {
for (i = 0; i < size; ++i) {
out(c,i) = ncoeffs_(0) * in(c,i) + state_(c,0);
for (j = 0; j < norder_ - 1; j++)
{
state_(c,j) = ncoeffs_(j+1) * in(c,i) + state_(c,j+1) - dcoeffs_(j+1) * out(c,i);
}
for (j = norder_ - 1; j < stateSize - 1; j++)
{
state_(c,j) = state_(c,j+1) - dcoeffs_(j+1) * out(c,i);
}
state_(c,stateSize - 1) = -dcoeffs_(order_ - 1) * out(c,i);
}
}
}
else {
for (c = 0; c < channels; ++c) {
for (i = 0; i < size; ++i) {
out(c,i) = ncoeffs_(0) * in(c,i) + state_(c,0);
for (j = 0; j < dorder_ - 1; j++)
{
state_(c,j) = ncoeffs_(j+1) * in(c,i) + state_(c,j+1) - dcoeffs_(j+1) * out(c,i);
}
for (j = dorder_ - 1; j < stateSize - 1; j++)
{
state_(c,j) = ncoeffs_(j+1) * in(c,i) + state_(c,j+1);
}
state_(c,stateSize - 1) = ncoeffs_(order_-1) * in(c,i);
}
}
}
out *= gain;
// MATLAB_PUT(in, "Filter_in");
// MATLAB_PUT(out, "Filter_out");
// MATLAB_PUT(ncoeffs_, "ncoeffs_");
// MATLAB_PUT(dcoeffs_, "dcoeffs_");
// MATLAB_EVAL("MAT_out = filter(ncoeffs_, dcoeffs_, Filter_in)");
//
// MATLAB_EVAL("spec_in = abs(fft(Filter_in));");
// MATLAB_EVAL("spec_out = abs(fft(Filter_out));");
// MATLAB_EVAL("spec_mat = abs(fft(MAT_out));");
//
// MATLAB_EVAL("subplot(2,1,1);plot(Filter_in);hold on; plot(Filter_out, 'r'); plot(MAT_out, 'g');hold off");
// MATLAB_EVAL("subplot(2,1,2);plot(spec_in(1:end/2));hold on; plot(spec_out(1:end/2),'r');plot(spec_mat(1:end/2),'g');hold off;");
// MATLAB_EVAL("h = abs(fft([1 -.97], length(Filter_in)));");
// MATLAB_EVAL("hold on; plot(h(1:end/2), 'k'); hold off");
// //MATLAB_GET("MAT_out", out)
//
}
void
SelfSimilarityMatrix::myProcess(realvec& in, realvec& out)
{
if(this->getctrl("mrs_natural/mode")->to<mrs_natural>() == SelfSimilarityMatrix::outputDistanceMatrix)
{
//check if there are any elements to process at the input
//(in some cases, they may not exist!) - otherwise, do nothing
//(i.e. output will be zeroed out)
if(inSamples_ > 0)
{
unsigned int child_count = marsystems_.size();
if(child_count == 1)
{
mrs_natural nfeats = in.getRows();
//normalize input features if necessary
if(ctrl_normalize_->to<mrs_string>() == "MinMax")
in.normObsMinMax(); // (x - min)/(max - min)
else if(ctrl_normalize_->to<mrs_string>() == "MeanStd")
in.normObs(); // (x - mean)/std
//calculate the Covariance Matrix from the input, if defined
if(ctrl_calcCovMatrix_->to<mrs_natural>() & SelfSimilarityMatrix::fixedStdDev)
{
//fill covMatrix diagonal with fixed value (remaining values are zero)
MarControlAccessor acc(ctrl_covMatrix_);
realvec& covMatrix = acc.to<mrs_realvec>();
covMatrix.create(inObservations_, inObservations_);
mrs_real var = ctrl_stdDev_->to<mrs_real>();
var *= var;
for(mrs_natural i=0; i< inObservations_; ++i)
{
covMatrix(i,i) = var;
}
}
else if(ctrl_calcCovMatrix_->to<mrs_natural>() & SelfSimilarityMatrix::diagCovMatrix)
{
in.varObs(vars_); //FASTER -> only get the vars for each feature
mrs_natural dim = vars_.getSize();
//fill covMatrix diagonal with var values (remaining values are zero)
MarControlAccessor acc(ctrl_covMatrix_);
realvec& covMatrix = acc.to<mrs_realvec>();
covMatrix.create(dim, dim);
for(mrs_natural i=0; i< dim; ++i)
{
covMatrix(i,i) = vars_(i);
}
}
else if(ctrl_calcCovMatrix_->to<mrs_natural>() & SelfSimilarityMatrix::fullCovMatrix)
{
MarControlAccessor acc(ctrl_covMatrix_);
realvec& covMatrix = acc.to<mrs_realvec>();
in.covariance(covMatrix); //SLOWER -> estimate the full cov matrix
}
else if(ctrl_calcCovMatrix_->to<mrs_natural>() == SelfSimilarityMatrix::noCovMatrix)
{
ctrl_covMatrix_->setValue(realvec());
}
for(mrs_natural i=0; i < in.getCols(); ++i)
{
in.getCol(i, i_featVec_);
for(mrs_natural j=0; j <= i; ++j)
{
in.getCol(j, j_featVec_);
//stack i and j feat vecs
for(mrs_natural r=0; r < nfeats; ++r)
{
stackedFeatVecs_(r, 0) = i_featVec_(r);
stackedFeatVecs_(r+nfeats, 0) = j_featVec_(r);
}
//do the metric calculation for these two feat vectors
//and store it in the similarity matrix (which is symmetric)
marsystems_[0]->process(stackedFeatVecs_, metricResult_);
out(i,j) = metricResult_(0,0);
//metric should be symmetric!
out(j, i) = out(i, j);
}
}
}
else
{
out.setval(0.0);
if(child_count == 0)
{
MRSWARN("SelfSimilarityMatrix::myProcess - no Child Metric MarSystem added - outputting zero similarity matrix!");
}
else
{
MRSWARN("SelfSimilarityMatrix::myProcess - more than one Child MarSystem exists (i.e. invalid metric) - outputting zero similarity matrix!");
}
}
}
//MATLAB_PUT(out, "simMatrix");
//MATLAB_EVAL("figure(1);imagesc(simMatrix);");
//MATLAB_PUT(out, "simMat");
//MATLAB_EVAL(name_+"=["+name_+",simMat(:)'];");
}
else if(this->getctrl("mrs_natural/mode")->to<mrs_natural>() == SelfSimilarityMatrix::outputPairDistance)
{
if(inSamples_ == 2) //we always need two column vector instances at input
{
unsigned int child_count = marsystems_.size();
if(child_count == 1)
{
MarControlAccessor acc(ctrl_instanceIndexes_);
realvec& instIdxs = acc.to<mrs_realvec>();
mrs_natural i = mrs_natural(instIdxs(0));
mrs_natural j = mrs_natural(instIdxs(1));
//check for out of bound indexes (which could have been set
//by someone outside changing the value of the ctrl_instanceIndexes control)
mrs_natural nInstances = ctrl_nInstances_->to<mrs_natural>();
if(i >= nInstances || j >= nInstances)
ctrl_done_->setValue(true);
if(!ctrl_done_->isTrue())
{
mrs_natural nfeats = in.getRows();
//COMPUTE DISTANCE between the two column vector at input
in.getCol(0, i_featVec_);
in.getCol(1, j_featVec_);
//stack i and j feat vecs
for(mrs_natural r=0; r < nfeats; ++r)
{
stackedFeatVecs_(r, 0) = i_featVec_(r);
stackedFeatVecs_(r+nfeats, 0) = j_featVec_(r);
}
//do the metric calculation for these two feat vectors
//and send it to the output
marsystems_[0]->process(stackedFeatVecs_, out);
//out(0) = metricResult_(0,0);
}
else
{
//Self Similarity has completed all pair-wise similarity computations
//so, it will just send zero valued values and a warning
out(0) = 0.0;
MRSWARN("SelfSimilarityMatrix::myProcess - no more pairwise similarity computations to be performed - outputting zero similarity value!")
}
//Select indexes for next pair of instances for distance computation
//Similarity matrix is tringular simetric, so we should just compute
//half of it (including diagonal). These indexes are to be used by some
//source MarSystem that has a control linked to ctrl_instanceIndexes (e.g. WekaSource)
if (i < j)
++i;
else
{
++j;
i = 0;
}
if (j >= nInstances)
{
ctrl_done_->setValue(true);
j = -1; //used to signal that there are no more instance pairs to compute
i = -1; //used to signal that there are no more instance pairs to compute
}
else
ctrl_done_->setValue(false);
//set indexes into the ctrl_instanceIndexes_ control
instIdxs(0) = i;
instIdxs(1) = j;
}
else
{
void
OneRClassifier::myProcess(realvec& in, realvec& out)
{
cout << "OneRClassifier::myProcess" << endl;
cout << "in.getCols() = " << in.getCols() << endl;
cout << "in.getRows() = " << in.getRows() << endl;
//get the current mode, either train of predict mode
bool trainMode = (getctrl("mrs_string/mode")->to<mrs_string>() == "train");
row_.stretch(in.getRows());
if (trainMode)
{
if(lastModePredict_ || instances_.getCols()<=0)
{
mrs_natural nAttributes = getctrl("mrs_natural/inObservations")->to<mrs_natural>();
cout << "nAttributes = " << nAttributes << endl;
instances_.Create(nAttributes);
}
lastModePredict_ = false;
//get the incoming data and append it to the data table
for (mrs_natural ii=0; ii< inSamples_; ++ii)
{
mrs_real label = in(inObservations_-1, ii);
instances_.Append(in);
out(0,ii) = label;
out(1,ii) = label;
}//for t
}//if
else
{//predict mode
cout << "OneRClassifier::predict" << endl;
if(!lastModePredict_)
{
//get the number of class labels and build the classifier
mrs_natural nAttributes = getctrl("mrs_natural/inObservations")->to<mrs_natural>();
cout << "BUILD nAttributes = " << nAttributes << endl;
Build(nAttributes);
}//if
lastModePredict_ = true;
cout << "After lastModePredict" << endl;
//foreach row of predict data, extract the actual class, then call the
//classifier predict method. Output the actual and predicted classes.
for (mrs_natural ii=0; ii<inSamples_; ++ii)
{
//extract the actual class
mrs_natural label = (mrs_natural)in(inObservations_-1, ii);
//invoke the classifier predict method to predict the class
in.getCol(ii,row_);
mrs_natural prediction = Predict(row_);
cout << "PREDICTION = " << prediction << endl;
cout << "row_ " << row_ << endl;
//and output actual/predicted classes
out(0,ii) = (mrs_real)prediction;
out(1,ii) = (mrs_real)label;
}//for t
}//if
}//myProcess
void
SOM::myProcess(realvec& in, realvec& out)
{
mrs_string mode = getctrl("mrs_string/mode")->to<mrs_string>();
mrs_natural o,t;
mrs_real geom_dist;
mrs_real geom_dist_gauss;
int px;
int py;
MarControlAccessor acc_grid(ctrl_gridmap_);
realvec& grid_map = acc_grid.to<mrs_realvec>();
if (mode == "train")
{
mrs_real dx;
mrs_real dy;
mrs_real adj;
for (t=0; t < inSamples_; t++)
{
px = (int) in(inObservations_-2, t);
py = (int) in(inObservations_-1, t);
if ((px == -1.0)&&(px == -1.0))
{
find_grid_location(in, t);
px = (int) grid_pos_(0);
py = (int) grid_pos_(1);
}
out(0,t) = px;
out(1,t) = py;
out(2,t) = in(inObservations_-3,t);
for (int x=0; x < grid_width_; x++)
for (int y=0; y < grid_height_; y++)
{
dx = px-x;
dy = py-y;
geom_dist = sqrt((double)(dx*dx + dy*dy));
geom_dist_gauss = gaussian( geom_dist, 0.0, neigh_std_, false);
// subtract map vector from training data vector
adj = alpha_ * geom_dist_gauss;
for (o=0; o < inObservations_-3; o++)
{
adjustments_(o) = in(o,t) - grid_map(x * grid_height_ + y, o);
adjustments_(o) *= adj;
grid_map(x * grid_height_ + y, o) += adjustments_(o);
}
}
}
alpha_ *= getctrl("mrs_real/alpha_decay_train")->to<mrs_real>();
neigh_std_ *= getctrl("mrs_real/neighbourhood_decay_train")->to<mrs_real>();
}
if (mode == "init")
{
mrs_real dx;
mrs_real dy;
mrs_real adj;
mrs_real std_ = getctrl("mrs_real/std_factor_init")->to<mrs_real>();
neigh_std_ = ((0.5*(grid_width_+grid_height_)) * std_);
for (t=0; t < inSamples_; t++)
{
// no need to find grid locations, just read from the file
px = (int) in( in.getRows() - 2, t);
py = (int) in( in.getRows() - 1, t);
for(int i =0; i < inObservations_ - 3; ++i)
{
grid_map(px * grid_height_ + py, i) = in(i);
}
for (int x=0; x < grid_width_; x++)
for (int y=0; y < grid_height_; y++)
{
dx = px-x;
dy = py-y;
geom_dist = sqrt((double)(dx*dx + dy*dy));
geom_dist_gauss = gaussian( geom_dist, 0.0, neigh_std_, false);
// subtract map vector from training data vector
adj = alpha_ * geom_dist_gauss;
for (o=0; o < inObservations_-3; o++)
{
adjustments_(o) = in(o,t) - grid_map(x * grid_height_ + y, o);
adjustments_(o) *= adj;
grid_map(x * grid_height_ + y, o) += adjustments_(o);
}
}
}
//WARNING: UGLY HACK
// Last two rows of init features are x,y locations
// so we want to set all the coresponding rows in the SOM to 0
// to prevent randomness from creaping in.
for (int x=0; x < grid_width_; x++)
{
for (int y=0; y < grid_height_; y++)
{
grid_map(x * grid_height_ + y, grid_map.getCols() - 2) = 0;
grid_map(x * grid_height_ + y, grid_map.getCols() - 1) = 0;
cout << "x: " << x << " y: " << y << endl;
}
}
alpha_ *= getctrl("mrs_real/alpha_decay_init")->to<mrs_real>();
neigh_std_ *= getctrl("mrs_real/neighbourhood_decay_init")->to<mrs_real>();
}
if (mode == "predict")
{
for (t=0; t < inSamples_; t++)
{
find_grid_location(in, t);
px = (int) grid_pos_(0);
py = (int) grid_pos_(1);
out(0,t) = px;
out(1,t) = py;
out(2,t) = in(inObservations_-3,t);
}
}
}
void
SimilarityMatrix::myProcess(realvec& in, realvec& out)
{
//check if there are any elements to process at the input
//(in some cases, they may not exist!) - otherwise, do nothing
//(i.e. output is also an empty vector)
mrs_natural i, j, k, l;
if(inSamples_ > 0)
{
child_count_t child_count = marsystems_.size();
if(child_count == 1)
{
mrs_natural nfeats = in.getRows()/sizes_.getSize();
// calculate hte Covariance Matrix from the input, if defined
mrs_natural obs = 0;
for(i=0; i<sizes_.getSize(); ++i)
{
for(j=0; j<sizes_(i); j++)
{
for(k=0; (mrs_natural)k<invecs_[i].getRows(); k++)
{
invecs_[i](k, j) = in(k+obs, j);
}
}
obs += invecs_[i].getRows();
}
// normalize input features if necessary
if(ctrl_normalize_->to<mrs_string>() == "MinMax")
for(i=0; i<sizes_.getSize(); ++i)
{
invecs_[i].normObsMinMax(); // (x - min)/(max - min)
}
else if(ctrl_normalize_->to<mrs_string>() == "MeanStd")
for(i=0; i<sizes_.getSize(); ++i)
{
invecs_[i].normObs(); // (x - mean)/std
}
if(ctrl_calcCovMatrix_->to<mrs_natural>() & SimilarityMatrix::fixedStdDev)
{
MarControlAccessor acc(ctrl_covMatrix_);
realvec& covMatrix = acc.to<mrs_realvec>();
covMatrix.create(inObservations_/sizes_.getSize(), inObservations_/sizes_.getSize());
mrs_real var = ctrl_stdDev_->to<mrs_real>();
var *= var;
for(i=0; i< inObservations_/sizes_.getSize(); ++i)
{
covMatrix(i,i) = var;
}
}
else if(ctrl_calcCovMatrix_->to<mrs_natural>() & SimilarityMatrix::diagCovMatrix)
{
invecs_[0].varObs(vars_); // Faster
mrs_natural dim = vars_.getSize();
// fill covMatrix diagonal with var values (remaining values are zero)
MarControlAccessor acc(ctrl_covMatrix_);
realvec& covMatrix = acc.to<mrs_realvec>();
covMatrix.create(dim, dim);
for(i=0; i<(mrs_natural)dim; ++i)
{
covMatrix(i,i) = vars_(i);
}
}
else if(ctrl_calcCovMatrix_->to<mrs_natural>() & SimilarityMatrix::fullCovMatrix)
{
MarControlAccessor acc(ctrl_covMatrix_);
realvec& covMatrix = acc.to<mrs_realvec>();
invecs_[0].covariance(covMatrix); // Slower
}
else if(ctrl_calcCovMatrix_->to<mrs_natural>() & SimilarityMatrix::noCovMatrix)
{
ctrl_covMatrix_->setValue(realvec());
}
for(i=0; i<sizes_(0); ++i)
{
obs = 0;
invecs_[0].getCol(i, i_featVec_);
for(l=0; l<(mrs_natural)nfeats; l++)
{
stackedFeatVecs_(l,0) = i_featVec_(l);
}
for(j=1; j<sizes_.getSize(); j++)
{
for(k=0; k<sizes_(j); k++)
{
invecs_[j].getCol(k, j_featVec_);
// stack i and j feat vectors
for(l=0; l<(mrs_natural)nfeats; l++)
{
stackedFeatVecs_(l+nfeats,0) = j_featVec_(l);
}
marsystems_[0]->process(stackedFeatVecs_, metricResult_);
out(k+obs,i) = metricResult_(0,0);
}
obs += (mrs_natural)sizes_(j);
}
}
}
else
{
out.setval(0.0);
if(child_count == 0)
{
MRSWARN("SimilarityMatrix::myProcess - no Child Metric MarSystem added - outputting zero similarity matrix!");
}
else
{
MRSWARN("SimilarityMatrix::myProcess - more than one Child MarSystem exists (i.e. invalid metric) - outputting zero similarity matrix!");
}
}
}
//MATLAB_PUT(out, "simMat");
//MATLAB_EVAL(name_+"=["+name_+",simMat(:)'];");
}
