/** * Use this constructor to fit a thick separator, where the positive and negative sides of the * hyperplane will be given the specified separate thicknesses. * * @param n The name of the classifier. * @param r The desired learning rate value. * @param t The desired threshold value. * @param pt The desired positive thickness. * @param nt The desired negative thickness. **/ public SparseAveragedPerceptron(String n, double r, double t, double pt, double nt) { super(n); Parameters p = new Parameters(); p.learningRate = r; p.threshold = t; p.positiveThickness = pt; p.negativeThickness = nt; setParameters(p); }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 4; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
public Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 2; baseLTU = new SparseAveragedPerceptron(p); } }
Parameters() { SparseAveragedPerceptron.Parameters p = new SparseAveragedPerceptron.Parameters(); p.learningRate = .1; p.thickness = 4; baseLTU = new SparseAveragedPerceptron(p); } }
/** * Retrieves the parameters that are set in this learner. * * @return An object containing all the values of the parameters that control the behavior of * this learning algorithm. **/ public Learner.Parameters getParameters() { Parameters p = new Parameters((SparsePerceptron.Parameters) super.getParameters()); return p; }