/**
* 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
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* (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 logistic regression algorithm.
* @since 1.0.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class LiblinearL2LR extends AbstractLiblinear
{
private final int MAX_ITER_NEWTON = 100;
private final double ETA = 0.1;
/**
* Constructs the liblinear L2-regularized logistic regression algorithm.
* @param cost the cost.
* @param eps the tolerance of termination criterion.
* @param bias the bias.
*/
public LiblinearL2LR(double cost, double eps, double bias)
{
super(cost, eps, bias);
}
@Override
public float[] getWeight(AbstractTrainSpace space, int currLabel)
{
Random rand = new Random(5);
final int N = space.getInstanceSize();
final int D = space.getFeatureSize();
final double INNER_MIN = Math.min(1e-8, d_eps);
IntArrayList ys = space.getYs();
ArrayList<int[]> xs = space.getXs();
ArrayList<double[]> vs = space.getVs();
double[] alpha = new double[2*N];
double[] weight = new double[D];
double G, alpha_old, qd, d, z, gp, gpp, tmpz;
double alpha_pre = Math.min(0.001 * d_cost, 1e-8);
double innereps = 1e-2;
double Gmax;
int i, s, iter, iter_newton, iter_inner, ind1, ind2, sign;
byte yi;
int[] xi;
double[] vi = null;
int [] index = UTArray.range(N);
byte[] aY = getBinaryLabels(ys, currLabel);
double[] QD = getQD(xs, vs, 0, d_bias);
for (i=0; i<N; i++)
{
alpha[2*i ] = alpha_pre;
alpha[2*i+1] = d_cost - alpha_pre;
d = aY[i] * alpha[2*i];
xi = xs.get(i);
if (space.hasWeight()) vi = vs.get(i);
if (d != 0) updateWeight(weight, d, xi, vi, d_bias);
}
for (iter=0; iter<MAX_ITER; iter++)
{
Gmax = iter_newton = 0;
UTArray.shuffle(rand, index, N);
for (s=0; s<N; 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;
qd = QD[i];
ind1 = 2*i;
ind2 = 2*i + 1;
sign = 1;
// decide to minimize g_1(z) or g_2(z)
if (0.5 * qd * (alpha[ind2] - alpha[ind1]) + G < 0)
{
ind1 = 2*i + 1;
ind2 = 2*i;
sign = -1;
}
// g_t(z) = z*log(z) + (C-z)*log(C-z) + 0.5a(z-alpha_old)^2 + sign*G(z-alpha_old)
alpha_old = alpha[ind1];
z = alpha_old;
if (d_cost-z < 0.5*d_cost) z *= 0.1;
gp = qd * (z-alpha_old) + sign * G + Math.log(z/(d_cost-z));
Gmax = Math.max(Gmax, Math.abs(gp));
// Newton method on the sub-problem
for (iter_inner=0; iter_inner<=MAX_ITER_NEWTON; iter_inner++)
{
if (Math.abs(gp) < innereps)
break;
gpp = qd + d_cost/(d_cost-z)/z;
tmpz = z - gp/gpp;
if (tmpz <= 0) z *= ETA;
else z = tmpz;
gp = qd * (z-alpha_old) + sign * G + Math.log(z/(d_cost-z));
iter_newton++;
}
if (iter_inner > 0)
{
alpha[ind1] = z;
alpha[ind2] = d_cost-z;
d = sign * (z-alpha_old) * yi;
if (d != 0) updateWeight(weight, d, xi, vi, d_bias);
}
}
if (Gmax < d_eps)
break;
if (iter_newton <= N/10)
innereps = Math.max(INNER_MIN, 0.1*innereps);
}
weight[0] *= d_bias;
StringBuilder build = new StringBuilder();
build.append("- label = "); build.append(currLabel);
build.append(": iter = "); build.append(iter);
build.append("\n");
LOG.info(build.toString());
return UTArray.toFloatArray(weight);
}
}