/**
* Predict the value of an instance.
*
* @param x instance
* @return value of prediction
*/
public double prediction(I x) {
double pred = b;
DoubleMatrix1D xm = new DenseDoubleMatrix1D(m.columns());
pred +=
x.operate(
(i, xi) -> {
double wi = w.getQuick(i);
DoubleMatrix1D mi = m.viewRow(i);
xm.assign(mi, (r, s) -> r + xi * s);
return xi * wi - 0.5 * xi * xi * mi.zDotProduct(mi);
},
(v1, v2) -> v1 + v2);
pred += 0.5 * xm.zDotProduct(xm);
return pred;
}
/**
* Feature-specific contribution to the prediction of the value of an instance.
*
* @param x instance
* @param i index of the feature of interest
* @param xi value of the feature of interest
* @return value of the contribution of the feature to the prediction
*/
public double prediction(I x, int i, double xi) {
double wi = w.getQuick(i);
DoubleMatrix1D mi = m.viewRow(i);
double pred = 0.0;
pred += xi * wi;
pred +=
x.operate(
(j, xj) -> {
DoubleMatrix1D mj = m.viewRow(j);
return xi * xj * mi.zDotProduct(mj);
},
(v1, v2) -> v1 + v2);
return pred;
}
/**
* Returns the Euclidean distance between two points. It is used to compute the similarity degree
* of these ones.
*
* @param x the first point
* @param y the second point
* @return the Euclidean distance between the points
*/
protected static double distnorm2(DoubleMatrix1D x, DoubleMatrix1D y) {
DoubleMatrix1D z = x.copy();
z.assign(y, Functions.minus);
return z.zDotProduct(z);
}