/* * Apache License * Version 2.0, January 2004 * http://www.apache.org/licenses/ * * Copyright 2013 Aurelian Tutuianu * Copyright 2014 Aurelian Tutuianu * Copyright 2015 Aurelian Tutuianu * Copyright 2016 Aurelian Tutuianu * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * */ package rapaio.ml.classifier.svm.kernel; import rapaio.data.Frame; import rapaio.sys.WS; /** * Log Kernel * <p> * The Log kernel seems to be particularly interesting for images, but is only * conditionally positive definite. * <p> * k(x,y) = - log (\lVert x-y \rVert ^d + 1) * <p> * Created by <a href="mailto:padreati@yahoo.com">Aurelian Tutuianu</a> at 1/19/15. */ public class LogKernel extends AbstractKernel { private static final long serialVersionUID = 6198322741512752359L; private final double degree; public LogKernel(double degree) { this.degree = degree; } @Override public double eval(Frame df1, int row1, Frame df2, int row2) { return -Math.log1p(Math.pow(deltaDotProd(df1, row1, df2, row2), degree)); } @Override public Kernel newInstance() { return new LogKernel(degree); } @Override public String name() { return "Log(degree=" + WS.formatFlex(degree) + ")"; } }