/**
* Copyright (c) 2009/09-2012/08, Regents of the University of Colorado
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* Copyright 2012/09-2013/04, 2013/11-Present, University of Massachusetts Amherst
* Copyright 2013/05-2013/10, IPSoft Inc.
*
* 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 com.clearnlp.classification.algorithm.old;
import java.util.ArrayList;
import java.util.Random;
import com.carrotsearch.hppc.IntArrayList;
import com.clearnlp.classification.train.AbstractTrainSpace;
import com.clearnlp.util.UTArray;
/**
* Liblinear L2-regularized support vector classification.
* @since 1.0.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class LiblinearL2SVC extends AbstractLiblinear
{
private byte i_lossType;
/**
* Constructs the liblinear L2-regularized support vector classification algorithm.
* @param lossType 1 for L1-loss, 2 for L2-loss.
* @param cost the cost.
* @param eps the tolerance of termination criterion.
* @param bias the bias.
*/
public LiblinearL2SVC(byte lossType, double cost, double eps, double bias)
{
super(cost, eps, bias);
i_lossType = lossType;
}
@Override
public float[] getWeight(AbstractTrainSpace space, int currLabel)
{
Random rand = new Random(5);
final int N = space.getInstanceSize();
final int D = space.getFeatureSize();
IntArrayList ys = space.getYs();
ArrayList<int[]> xs = space.getXs();
ArrayList<double[]> vs = space.getVs();
double[] alpha = new double[N];
double[] weight = new double[D];
double G, d, alpha_old;
// Projected gradient, for shrinking and stopping
double Gmax_old = Double.POSITIVE_INFINITY;
double Gmin_old = Double.NEGATIVE_INFINITY;
double violation, Gmax_new, Gmin_new;
// L1/L2 loss
double diag = 0;
double upper_bound = d_cost;
if (i_lossType == 2)
{
diag = 0.5 / d_cost;
upper_bound = Double.POSITIVE_INFINITY;
}
int active_size = N, iter, i, s;
byte yi;
int[] xi;
double[] vi = null;
int [] index = UTArray.range(N);
byte[] aY = getBinaryLabels(ys, currLabel);
double[] QD = getQD(xs, vs, diag, d_bias);
for (iter=0; iter<MAX_ITER; iter++)
{
Gmax_new = Double.NEGATIVE_INFINITY;
Gmin_new = Double.POSITIVE_INFINITY;
UTArray.shuffle(rand, index, active_size);
for (s=0; s<active_size; s++)
{
i = index[s];
yi = aY[i];
xi = xs.get(i);
if (space.hasWeight()) vi = vs.get(i);
G = getScore(weight, xi, vi, d_bias) * yi - 1;
G += alpha[i] * diag;
if (alpha[i] == 0)
{
if (G > Gmax_old)
{
active_size--;
UTArray.swap(index, s, active_size);
s--;
continue;
}
violation = Math.min(G, 0);
}
else if (alpha[i] == upper_bound)
{
if (G < Gmin_old)
{
active_size--;
UTArray.swap(index, s, active_size);
s--;
continue;
}
violation = Math.max(G, 0);
}
else
{
violation = G;
}
Gmax_new = Math.max(Gmax_new, violation);
Gmin_new = Math.min(Gmin_new, violation);
if (Math.abs(violation) > 1.0e-12)
{
alpha_old = alpha[i];
alpha[i] = Math.min(Math.max(alpha[i] - G / QD[i], 0d), upper_bound);
d = (alpha[i] - alpha_old) * yi;
if (d != 0) updateWeight(weight, d, xi, vi, d_bias);
}
}
if (Gmax_new - Gmin_new <= d_eps)
{
if (active_size == N)
break;
else
{
active_size = N;
Gmax_old = Double.POSITIVE_INFINITY;
Gmin_old = Double.NEGATIVE_INFINITY;
continue;
}
}
Gmax_old = Gmax_new;
Gmin_old = Gmin_new;
if (Gmax_old <= 0) Gmax_old = Double.POSITIVE_INFINITY;
if (Gmin_old >= 0) Gmin_old = Double.NEGATIVE_INFINITY;
}
weight[0] *= d_bias;
int nSV = 0;
for (i=0; i<N; i++)
if (alpha[i] > 0) ++nSV;
StringBuilder build = new StringBuilder();
build.append("- label = "); build.append(currLabel);
build.append(": iter = "); build.append(iter);
build.append(", nSV = "); build.append(nSV);
build.append("\n");
LOG.info(build.toString());
return UTArray.toFloatArray(weight);
}
}