int main(int argc, char** argv)
{
// Dimension of the input data. In our case, it should be 10=5*2 (5points underneath the foot and 2 is for position and velocity) for each model. In total, we need 6 models one per each force/torque.
int dimX = 2;
// Number of training data. In our case it is 150.
int numSamplesY = 150;
Matrix X(numSamplesY,dimX);
ColumnVector y(numSamplesY);
ColumnVector xTest(dimX);
DiagonalMatrix Dprecision(dimX);
// constant parameters
double cutoff = 0;
double sigma = 0.1;
int minNumPts = 2;
// Position and velocity for the five points. The size should be 10 and we need 6 sets of these.
xTest.element(0) = 25;
xTest.element(1) = 450;
for (int i=0; i<dimX; ++i) {
Dprecision.element(i,i) = sigma;
}
double beta[dimX+1];
double *outBetaPtr;
outBetaPtr = &beta[0];
// This part will be replaced by the recorded data from previous steps
for (int i=0; i<numSamplesY; ++i) {
X.element(i,0) = i;
X.element(i,1) = 2*i*i;
y.element(i) = 3*i;
}
// Calling the learning algorithm
localLinearWeightedRegression(X, y, xTest, Dprecision, cutoff, minNumPts, dimX, numSamplesY, outBetaPtr);
// output is y = beta0 + beta1*x1 + ... + beta10*x10
// we need 6 sets of these
double predict;
predict = *outBetaPtr + *(outBetaPtr+1) * xTest.element(0) + *(outBetaPtr+2) * xTest.element(1);
printf("predicted value ==> %f\n", predict );
return 0;
}
float Interpolate1D::predict(const float & x) const
{
DenseMatrix<float > xTest(1,1);
xTest.column(0)[0] = x - m_xMean->v()[0];
Covariance<float, RbfKernel<float> > covTest;
covTest.create(xTest, *m_xTrain, *m_rbf);
DenseMatrix<float> KxKtraininv(covTest.K().numRows(),
m_covTrain->Kinv().numCols() );
covTest.K().mult(KxKtraininv, m_covTrain->Kinv() );
/// yPred = Ktest * inv(Ktrain) * yTrain
DenseMatrix<float> yPred(1,1);
KxKtraininv.mult(yPred, *m_yTrain);
return (yPred.column(0)[0] + m_yMean->v()[0]);
}
