/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.mathutil.libsvm;
import java.io.BufferedOutputStream;
import java.io.BufferedReader;
import java.io.DataOutputStream;
import java.io.FileOutputStream;
import java.io.FileReader;
import java.io.IOException;
import java.util.Random;
import java.util.StringTokenizer;
import org.encog.util.arrayutil.Array;
import org.encog.util.csv.CSVFormat;
/**
* This class was taken from the libsvm package. We have made some
* modifications for use in Encog.
*
* The libsvm Copyright/license is listed here.
*
* Copyright (c) 2000-2010 Chih-Chung Chang and Chih-Jen Lin
* 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.
*
* 3. Neither name of copyright holders nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
*
* 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 REGENTS 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.
*
*/
//
// Kernel Cache
//
// l is the number of total data items
// size is the cache size limit in bytes
//
class Cache {
private final int l;
private long size;
private final class head_t
{
head_t prev, next; // a cicular list
float[] data;
int len; // data[0,len) is cached in this entry
}
private final head_t[] head;
private head_t lru_head;
Cache(int l_, long size_)
{
l = l_;
size = size_;
head = new head_t[l];
for(int i=0;i<l;i++) head[i] = new head_t();
size /= 4;
size -= l * (16/4); // sizeof(head_t) == 16
size = Math.max(size, 2* (long) l); // cache must be large enough for two columns
lru_head = new head_t();
lru_head.next = lru_head.prev = lru_head;
}
private void lru_delete(head_t h)
{
// delete from current location
h.prev.next = h.next;
h.next.prev = h.prev;
}
private void lru_insert(head_t h)
{
// insert to last position
h.next = lru_head;
h.prev = lru_head.prev;
h.prev.next = h;
h.next.prev = h;
}
// request data [0,len)
// return some position p where [p,len) need to be filled
// (p >= len if nothing needs to be filled)
// java: simulate pointer using single-element array
int get_data(int index, float[][] data, int len)
{
head_t h = head[index];
if(h.len > 0) lru_delete(h);
int more = len - h.len;
if(more > 0)
{
// free old space
while(size < more)
{
head_t old = lru_head.next;
lru_delete(old);
size += old.len;
old.data = null;
old.len = 0;
}
// allocate new space
float[] new_data = new float[len];
if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
h.data = new_data;
size -= more;
do {int tmp=h.len; h.len=len; len=tmp;} while(false);
}
lru_insert(h);
data[0] = h.data;
return len;
}
void swap_index(int i, int j)
{
if(i==j) return;
if(head[i].len > 0) lru_delete(head[i]);
if(head[j].len > 0) lru_delete(head[j]);
do {float[] tmp=head[i].data; head[i].data=head[j].data; head[j].data=tmp;} while(false);
do {int tmp=head[i].len; head[i].len=head[j].len; head[j].len=tmp;} while(false);
if(head[i].len > 0) lru_insert(head[i]);
if(head[j].len > 0) lru_insert(head[j]);
if(i>j) do {int tmp=i; i=j; j=tmp;} while(false);
for(head_t h = lru_head.next; h!=lru_head; h=h.next)
{
if(h.len > i)
{
if(h.len > j)
Array.swap(h.data, i, j);
else
{
// give up
lru_delete(h);
size += h.len;
h.data = null;
h.len = 0;
}
}
}
}
}
//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
abstract class QMatrix {
abstract float[] get_Q(int column, int len);
abstract double[] get_QD();
abstract void swap_index(int i, int j);
};
abstract class Kernel extends QMatrix {
private svm_node[][] x;
private final double[] x_square;
// svm_parameter
private final int kernel_type;
private final int degree;
private final double gamma;
private final double coef0;
abstract float[] get_Q(int column, int len);
abstract double[] get_QD();
void swap_index(int i, int j)
{
Array.swap(x, i, j);;
if(x_square != null) {
Array.swap(x_square, i, j);
}
}
private static double powi(double base, int times)
{
double tmp = base, ret = 1.0;
for(int t=times; t>0; t/=2)
{
if(t%2==1) ret*=tmp;
tmp = tmp * tmp;
}
return ret;
}
double kernel_function(int i, int j)
{
switch(kernel_type)
{
case svm_parameter.LINEAR:
return dot(x[i],x[j]);
case svm_parameter.POLY:
return powi(gamma*dot(x[i],x[j])+coef0,degree);
case svm_parameter.RBF:
return Math.exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
case svm_parameter.SIGMOID:
return Math.tanh(gamma*dot(x[i],x[j])+coef0);
case svm_parameter.PRECOMPUTED:
return x[i][(int)(x[j][0].value)].value;
default:
return 0; // java
}
}
Kernel(int l, svm_node[][] x_, svm_parameter param)
{
this.kernel_type = param.kernel_type;
this.degree = param.degree;
this.gamma = param.gamma;
this.coef0 = param.coef0;
x = (svm_node[][])x_.clone();
if(kernel_type == svm_parameter.RBF)
{
x_square = new double[l];
for(int i=0;i<l;i++)
x_square[i] = dot(x[i],x[i]);
}
else x_square = null;
}
static double dot(svm_node[] x, svm_node[] y)
{
double sum = 0;
int xlen = x.length;
int ylen = y.length;
int i = 0;
int j = 0;
while(i < xlen && j < ylen)
{
if(x[i].index == y[j].index)
sum += x[i++].value * y[j++].value;
else
{
if(x[i].index > y[j].index)
++j;
else
++i;
}
}
return sum;
}
static double k_function(svm_node[] x, svm_node[] y,
svm_parameter param)
{
switch(param.kernel_type)
{
case svm_parameter.LINEAR:
return dot(x,y);
case svm_parameter.POLY:
return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
case svm_parameter.RBF:
{
double sum = 0;
int xlen = x.length;
int ylen = y.length;
int i = 0;
int j = 0;
while(i < xlen && j < ylen)
{
if(x[i].index == y[j].index)
{
double d = x[i++].value - y[j++].value;
sum += d*d;
}
else if(x[i].index > y[j].index)
{
sum += y[j].value * y[j].value;
++j;
}
else
{
sum += x[i].value * x[i].value;
++i;
}
}
while(i < xlen)
{
sum += x[i].value * x[i].value;
++i;
}
while(j < ylen)
{
sum += y[j].value * y[j].value;
++j;
}
return Math.exp(-param.gamma*sum);
}
case svm_parameter.SIGMOID:
return Math.tanh(param.gamma*dot(x,y)+param.coef0);
case svm_parameter.PRECOMPUTED:
return x[(int)(y[0].value)].value;
default:
return 0; // java
}
}
}
// An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
// Solves:
//
// min 0.5(\alpha^T Q \alpha) + p^T \alpha
//
// y^T \alpha = \delta
// y_i = +1 or -1
// 0 <= alpha_i <= Cp for y_i = 1
// 0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
// Q, p, y, Cp, Cn, and an initial feasible point \alpha
// l is the size of vectors and matrices
// eps is the stopping tolerance
//
// solution will be put in \alpha, objective value will be put in obj
//
class Solver {
int active_size;
byte[] y;
double[] G; // gradient of objective function
static final byte LOWER_BOUND = 0;
static final byte UPPER_BOUND = 1;
static final byte FREE = 2;
byte[] alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE
double[] alpha;
QMatrix Q;
double[] QD;
double eps;
double Cp,Cn;
double[] p;
int[] active_set;
double[] G_bar; // gradient, if we treat free variables as 0
int l;
boolean unshrink; // XXX
static final double INF = java.lang.Double.POSITIVE_INFINITY;
double get_C(int i)
{
return (y[i] > 0)? Cp : Cn;
}
void update_alpha_status(int i)
{
if(alpha[i] >= get_C(i))
alpha_status[i] = UPPER_BOUND;
else if(alpha[i] <= 0)
alpha_status[i] = LOWER_BOUND;
else alpha_status[i] = FREE;
}
boolean is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
boolean is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
boolean is_free(int i) { return alpha_status[i] == FREE; }
// java: information about solution except alpha,
// because we cannot return multiple values otherwise...
static class SolutionInfo {
double obj;
double rho;
double upper_bound_p;
double upper_bound_n;
double r; // for Solver_NU
}
void swap_index(int i, int j)
{
Q.swap_index(i,j);
Array.swap(y, i, j);
Array.swap(G, i, j);
Array.swap(alpha_status, i, j);
Array.swap(alpha, i, j);
Array.swap(p, i, j);
Array.swap(active_set, i, j);
Array.swap(G_bar, i, j);
}
void reconstruct_gradient()
{
// reconstruct inactive elements of G from G_bar and free variables
if(active_size == l) return;
int i,j;
int nr_free = 0;
for(j=active_size;j<l;j++)
G[j] = G_bar[j] + p[j];
for(j=0;j<active_size;j++)
if(is_free(j))
nr_free++;
if(2*nr_free < active_size)
svm.info("\nWARNING: using -h 0 may be faster\n");
if (nr_free*l > 2*active_size*(l-active_size))
{
for(i=active_size;i<l;i++)
{
float[] Q_i = Q.get_Q(i,active_size);
for(j=0;j<active_size;j++)
if(is_free(j))
G[i] += alpha[j] * Q_i[j];
}
}
else
{
for(i=0;i<active_size;i++)
if(is_free(i))
{
float[] Q_i = Q.get_Q(i,l);
double alpha_i = alpha[i];
for(j=active_size;j<l;j++)
G[j] += alpha_i * Q_i[j];
}
}
}
void Solve(int l, QMatrix Q, double[] p_, byte[] y_,
double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
{
this.l = l;
this.Q = Q;
QD = Q.get_QD();
p = (double[])p_.clone();
y = (byte[])y_.clone();
alpha = (double[])alpha_.clone();
this.Cp = Cp;
this.Cn = Cn;
this.eps = eps;
this.unshrink = false;
// initialize alpha_status
{
alpha_status = new byte[l];
for(int i=0;i<l;i++)
update_alpha_status(i);
}
// initialize active set (for shrinking)
{
active_set = new int[l];
for(int i=0;i<l;i++)
active_set[i] = i;
active_size = l;
}
// initialize gradient
{
G = new double[l];
G_bar = new double[l];
int i;
for(i=0;i<l;i++)
{
G[i] = p[i];
G_bar[i] = 0;
}
for(i=0;i<l;i++)
if(!is_lower_bound(i))
{
float[] Q_i = Q.get_Q(i,l);
double alpha_i = alpha[i];
int j;
for(j=0;j<l;j++)
G[j] += alpha_i*Q_i[j];
if(is_upper_bound(i))
for(j=0;j<l;j++)
G_bar[j] += get_C(i) * Q_i[j];
}
}
// optimization step
int iter = 0;
int max_iter = Math.max(10000000, l>Integer.MAX_VALUE/100 ? Integer.MAX_VALUE : 100*l);
int counter = Math.min(l,1000)+1;
int[] working_set = new int[2];
while(iter < max_iter)
{
// show progress and do shrinking
if(--counter == 0)
{
counter = Math.min(l,1000);
if(shrinking!=0) do_shrinking();
svm.info(".");
}
if(select_working_set(working_set)!=0)
{
// reconstruct the whole gradient
reconstruct_gradient();
// reset active set size and check
active_size = l;
svm.info("*");
if(select_working_set(working_set)!=0)
break;
else
counter = 1; // do shrinking next iteration
}
int i = working_set[0];
int j = working_set[1];
++iter;
// update alpha[i] and alpha[j], handle bounds carefully
float[] Q_i = Q.get_Q(i,active_size);
float[] Q_j = Q.get_Q(j,active_size);
double C_i = get_C(i);
double C_j = get_C(j);
double old_alpha_i = alpha[i];
double old_alpha_j = alpha[j];
if(y[i]!=y[j])
{
double quad_coef = QD[i]+QD[j]+2*Q_i[j];
if (quad_coef <= 0)
quad_coef = 1e-12;
double delta = (-G[i]-G[j])/quad_coef;
double diff = alpha[i] - alpha[j];
alpha[i] += delta;
alpha[j] += delta;
if(diff > 0)
{
if(alpha[j] < 0)
{
alpha[j] = 0;
alpha[i] = diff;
}
}
else
{
if(alpha[i] < 0)
{
alpha[i] = 0;
alpha[j] = -diff;
}
}
if(diff > C_i - C_j)
{
if(alpha[i] > C_i)
{
alpha[i] = C_i;
alpha[j] = C_i - diff;
}
}
else
{
if(alpha[j] > C_j)
{
alpha[j] = C_j;
alpha[i] = C_j + diff;
}
}
}
else
{
double quad_coef = QD[i]+QD[j]-2*Q_i[j];
if (quad_coef <= 0)
quad_coef = 1e-12;
double delta = (G[i]-G[j])/quad_coef;
double sum = alpha[i] + alpha[j];
alpha[i] -= delta;
alpha[j] += delta;
if(sum > C_i)
{
if(alpha[i] > C_i)
{
alpha[i] = C_i;
alpha[j] = sum - C_i;
}
}
else
{
if(alpha[j] < 0)
{
alpha[j] = 0;
alpha[i] = sum;
}
}
if(sum > C_j)
{
if(alpha[j] > C_j)
{
alpha[j] = C_j;
alpha[i] = sum - C_j;
}
}
else
{
if(alpha[i] < 0)
{
alpha[i] = 0;
alpha[j] = sum;
}
}
}
// update G
double delta_alpha_i = alpha[i] - old_alpha_i;
double delta_alpha_j = alpha[j] - old_alpha_j;
for(int k=0;k<active_size;k++)
{
G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
}
// update alpha_status and G_bar
{
boolean ui = is_upper_bound(i);
boolean uj = is_upper_bound(j);
update_alpha_status(i);
update_alpha_status(j);
int k;
if(ui != is_upper_bound(i))
{
Q_i = Q.get_Q(i,l);
if(ui)
for(k=0;k<l;k++)
G_bar[k] -= C_i * Q_i[k];
else
for(k=0;k<l;k++)
G_bar[k] += C_i * Q_i[k];
}
if(uj != is_upper_bound(j))
{
Q_j = Q.get_Q(j,l);
if(uj)
for(k=0;k<l;k++)
G_bar[k] -= C_j * Q_j[k];
else
for(k=0;k<l;k++)
G_bar[k] += C_j * Q_j[k];
}
}
}
if(iter >= max_iter)
{
if(active_size < l)
{
// reconstruct the whole gradient to calculate objective value
reconstruct_gradient();
active_size = l;
svm.info("*");
}
svm.info("\nWARNING: reaching max number of iterations");
}
// calculate rho
si.rho = calculate_rho();
// calculate objective value
{
double v = 0;
int i;
for(i=0;i<l;i++)
v += alpha[i] * (G[i] + p[i]);
si.obj = v/2;
}
// put back the solution
{
for(int i=0;i<l;i++)
alpha_[active_set[i]] = alpha[i];
}
si.upper_bound_p = Cp;
si.upper_bound_n = Cn;
svm.info("\noptimization finished, #iter = "+iter+"\n");
}
// return 1 if already optimal, return 0 otherwise
int select_working_set(int[] working_set)
{
// return i,j such that
// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
// j: mimimizes the decrease of obj value
// (if quadratic coefficeint <= 0, replace it with tau)
// -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
double Gmax = -INF;
double Gmax2 = -INF;
int Gmax_idx = -1;
int Gmin_idx = -1;
double obj_diff_min = INF;
for(int t=0;t<active_size;t++)
if(y[t]==+1)
{
if(!is_upper_bound(t))
if(-G[t] >= Gmax)
{
Gmax = -G[t];
Gmax_idx = t;
}
}
else
{
if(!is_lower_bound(t))
if(G[t] >= Gmax)
{
Gmax = G[t];
Gmax_idx = t;
}
}
int i = Gmax_idx;
float[] Q_i = null;
if(i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
Q_i = Q.get_Q(i,active_size);
for(int j=0;j<active_size;j++)
{
if(y[j]==+1)
{
if (!is_lower_bound(j))
{
double grad_diff=Gmax+G[j];
if (G[j] >= Gmax2)
Gmax2 = G[j];
if (grad_diff > 0)
{
double obj_diff;
double quad_coef = QD[i]+QD[j]-2.0*y[i]*Q_i[j];
if (quad_coef > 0)
obj_diff = -(grad_diff*grad_diff)/quad_coef;
else
obj_diff = -(grad_diff*grad_diff)/1e-12;
if (obj_diff <= obj_diff_min)
{
Gmin_idx=j;
obj_diff_min = obj_diff;
}
}
}
}
else
{
if (!is_upper_bound(j))
{
double grad_diff= Gmax-G[j];
if (-G[j] >= Gmax2)
Gmax2 = -G[j];
if (grad_diff > 0)
{
double obj_diff;
double quad_coef = QD[i]+QD[j]+2.0*y[i]*Q_i[j];
if (quad_coef > 0)
obj_diff = -(grad_diff*grad_diff)/quad_coef;
else
obj_diff = -(grad_diff*grad_diff)/1e-12;
if (obj_diff <= obj_diff_min)
{
Gmin_idx=j;
obj_diff_min = obj_diff;
}
}
}
}
}
if(Gmax+Gmax2 < eps)
return 1;
working_set[0] = Gmax_idx;
working_set[1] = Gmin_idx;
return 0;
}
private boolean be_shrunk(int i, double Gmax1, double Gmax2)
{
if(is_upper_bound(i))
{
if(y[i]==+1)
return(-G[i] > Gmax1);
else
return(-G[i] > Gmax2);
}
else if(is_lower_bound(i))
{
if(y[i]==+1)
return(G[i] > Gmax2);
else
return(G[i] > Gmax1);
}
else
return(false);
}
void do_shrinking()
{
int i;
double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) }
double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) }
// find maximal violating pair first
for(i=0;i<active_size;i++)
{
if(y[i]==+1)
{
if(!is_upper_bound(i))
{
if(-G[i] >= Gmax1)
Gmax1 = -G[i];
}
if(!is_lower_bound(i))
{
if(G[i] >= Gmax2)
Gmax2 = G[i];
}
}
else
{
if(!is_upper_bound(i))
{
if(-G[i] >= Gmax2)
Gmax2 = -G[i];
}
if(!is_lower_bound(i))
{
if(G[i] >= Gmax1)
Gmax1 = G[i];
}
}
}
if(unshrink == false && Gmax1 + Gmax2 <= eps*10)
{
unshrink = true;
reconstruct_gradient();
active_size = l;
}
for(i=0;i<active_size;i++)
if (be_shrunk(i, Gmax1, Gmax2))
{
active_size--;
while (active_size > i)
{
if (!be_shrunk(active_size, Gmax1, Gmax2))
{
swap_index(i,active_size);
break;
}
active_size--;
}
}
}
double calculate_rho()
{
double r;
int nr_free = 0;
double ub = INF, lb = -INF, sum_free = 0;
for(int i=0;i<active_size;i++)
{
double yG = y[i]*G[i];
if(is_lower_bound(i))
{
if(y[i] > 0)
ub = Math.min(ub,yG);
else
lb = Math.max(lb,yG);
}
else if(is_upper_bound(i))
{
if(y[i] < 0)
ub = Math.min(ub,yG);
else
lb = Math.max(lb,yG);
}
else
{
++nr_free;
sum_free += yG;
}
}
if(nr_free>0)
r = sum_free/nr_free;
else
r = (ub+lb)/2;
return r;
}
}
//
// Solver for nu-svm classification and regression
//
// additional constraint: e^T \alpha = constant
//
final class Solver_NU extends Solver
{
private SolutionInfo si;
void Solve(int l, QMatrix Q, double[] p, byte[] y,
double[] alpha, double Cp, double Cn, double eps,
SolutionInfo si, int shrinking)
{
this.si = si;
super.Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking);
}
// return 1 if already optimal, return 0 otherwise
int select_working_set(int[] working_set)
{
// return i,j such that y_i = y_j and
// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
// j: minimizes the decrease of obj value
// (if quadratic coefficeint <= 0, replace it with tau)
// -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
double Gmaxp = -INF;
double Gmaxp2 = -INF;
int Gmaxp_idx = -1;
double Gmaxn = -INF;
double Gmaxn2 = -INF;
int Gmaxn_idx = -1;
int Gmin_idx = -1;
double obj_diff_min = INF;
for(int t=0;t<active_size;t++)
if(y[t]==+1)
{
if(!is_upper_bound(t))
if(-G[t] >= Gmaxp)
{
Gmaxp = -G[t];
Gmaxp_idx = t;
}
}
else
{
if(!is_lower_bound(t))
if(G[t] >= Gmaxn)
{
Gmaxn = G[t];
Gmaxn_idx = t;
}
}
int ip = Gmaxp_idx;
int in = Gmaxn_idx;
float[] Q_ip = null;
float[] Q_in = null;
if(ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
Q_ip = Q.get_Q(ip,active_size);
if(in != -1)
Q_in = Q.get_Q(in,active_size);
for(int j=0;j<active_size;j++)
{
if(y[j]==+1)
{
if (!is_lower_bound(j))
{
double grad_diff=Gmaxp+G[j];
if (G[j] >= Gmaxp2)
Gmaxp2 = G[j];
if (grad_diff > 0)
{
double obj_diff;
double quad_coef = QD[ip]+QD[j]-2*Q_ip[j];
if (quad_coef > 0)
obj_diff = -(grad_diff*grad_diff)/quad_coef;
else
obj_diff = -(grad_diff*grad_diff)/1e-12;
if (obj_diff <= obj_diff_min)
{
Gmin_idx=j;
obj_diff_min = obj_diff;
}
}
}
}
else
{
if (!is_upper_bound(j))
{
double grad_diff=Gmaxn-G[j];
if (-G[j] >= Gmaxn2)
Gmaxn2 = -G[j];
if (grad_diff > 0)
{
double obj_diff;
double quad_coef = QD[in]+QD[j]-2*Q_in[j];
if (quad_coef > 0)
obj_diff = -(grad_diff*grad_diff)/quad_coef;
else
obj_diff = -(grad_diff*grad_diff)/1e-12;
if (obj_diff <= obj_diff_min)
{
Gmin_idx=j;
obj_diff_min = obj_diff;
}
}
}
}
}
if(Math.max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps)
return 1;
if(y[Gmin_idx] == +1)
working_set[0] = Gmaxp_idx;
else
working_set[0] = Gmaxn_idx;
working_set[1] = Gmin_idx;
return 0;
}
private boolean be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
{
if(is_upper_bound(i))
{
if(y[i]==+1)
return(-G[i] > Gmax1);
else
return(-G[i] > Gmax4);
}
else if(is_lower_bound(i))
{
if(y[i]==+1)
return(G[i] > Gmax2);
else
return(G[i] > Gmax3);
}
else
return(false);
}
void do_shrinking()
{
double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
// find maximal violating pair first
int i;
for(i=0;i<active_size;i++)
{
if(!is_upper_bound(i))
{
if(y[i]==+1)
{
if(-G[i] > Gmax1) Gmax1 = -G[i];
}
else if(-G[i] > Gmax4) Gmax4 = -G[i];
}
if(!is_lower_bound(i))
{
if(y[i]==+1)
{
if(G[i] > Gmax2) Gmax2 = G[i];
}
else if(G[i] > Gmax3) Gmax3 = G[i];
}
}
if(unshrink == false && Math.max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10)
{
unshrink = true;
reconstruct_gradient();
active_size = l;
}
for(i=0;i<active_size;i++)
if (be_shrunk(i, Gmax1, Gmax2, Gmax3, Gmax4))
{
active_size--;
while (active_size > i)
{
if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
{
swap_index(i,active_size);
break;
}
active_size--;
}
}
}
double calculate_rho()
{
int nr_free1 = 0,nr_free2 = 0;
double ub1 = INF, ub2 = INF;
double lb1 = -INF, lb2 = -INF;
double sum_free1 = 0, sum_free2 = 0;
for(int i=0;i<active_size;i++)
{
if(y[i]==+1)
{
if(is_lower_bound(i))
ub1 = Math.min(ub1,G[i]);
else if(is_upper_bound(i))
lb1 = Math.max(lb1,G[i]);
else
{
++nr_free1;
sum_free1 += G[i];
}
}
else
{
if(is_lower_bound(i))
ub2 = Math.min(ub2,G[i]);
else if(is_upper_bound(i))
lb2 = Math.max(lb2,G[i]);
else
{
++nr_free2;
sum_free2 += G[i];
}
}
}
double r1,r2;
if(nr_free1 > 0)
r1 = sum_free1/nr_free1;
else
r1 = (ub1+lb1)/2;
if(nr_free2 > 0)
r2 = sum_free2/nr_free2;
else
r2 = (ub2+lb2)/2;
si.r = (r1+r2)/2;
return (r1-r2)/2;
}
}
//
// Q matrices for various formulations
//
class SVC_Q extends Kernel
{
private final byte[] y;
private final Cache cache;
private final double[] QD;
SVC_Q(svm_problem prob, svm_parameter param, byte[] y_)
{
super(prob.l, prob.x, param);
y = (byte[])y_.clone();
cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
QD = new double[prob.l];
for(int i=0;i<prob.l;i++)
QD[i] = kernel_function(i,i);
}
float[] get_Q(int i, int len)
{
float[][] data = new float[1][];
int start, j;
if((start = cache.get_data(i,data,len)) < len)
{
for(j=start;j<len;j++)
data[0][j] = (float)(y[i]*y[j]*kernel_function(i,j));
}
return data[0];
}
double[] get_QD()
{
return QD;
}
void swap_index(int i, int j)
{
cache.swap_index(i,j);
super.swap_index(i,j);
Array.swap(y, i, j);
Array.swap(QD, i, j);
}
}
class ONE_CLASS_Q extends Kernel
{
private final Cache cache;
private final double[] QD;
ONE_CLASS_Q(svm_problem prob, svm_parameter param)
{
super(prob.l, prob.x, param);
cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
QD = new double[prob.l];
for(int i=0;i<prob.l;i++)
QD[i] = kernel_function(i,i);
}
float[] get_Q(int i, int len)
{
float[][] data = new float[1][];
int start, j;
if((start = cache.get_data(i,data,len)) < len)
{
for(j=start;j<len;j++)
data[0][j] = (float)kernel_function(i,j);
}
return data[0];
}
double[] get_QD()
{
return QD;
}
void swap_index(int i, int j)
{
cache.swap_index(i,j);
super.swap_index(i,j);
Array.swap(QD, i, j);
}
}
class SVR_Q extends Kernel
{
private final int l;
private final Cache cache;
private final byte[] sign;
private final int[] index;
private int next_buffer;
private float[][] buffer;
private final double[] QD;
SVR_Q(svm_problem prob, svm_parameter param)
{
super(prob.l, prob.x, param);
l = prob.l;
cache = new Cache(l,(long)(param.cache_size*(1<<20)));
QD = new double[2*l];
sign = new byte[2*l];
index = new int[2*l];
for(int k=0;k<l;k++)
{
sign[k] = 1;
sign[k+l] = -1;
index[k] = k;
index[k+l] = k;
QD[k] = kernel_function(k,k);
QD[k+l] = QD[k];
}
buffer = new float[2][2*l];
next_buffer = 0;
}
void swap_index(int i, int j)
{
Array.swap(sign, i, j);
Array.swap(index, i, j);
Array.swap(QD, i, j);
}
float[] get_Q(int i, int len)
{
float[][] data = new float[1][];
int j, real_i = index[i];
if(cache.get_data(real_i,data,l) < l)
{
for(j=0;j<l;j++)
data[0][j] = (float)kernel_function(real_i,j);
}
// reorder and copy
float buf[] = buffer[next_buffer];
next_buffer = 1 - next_buffer;
byte si = sign[i];
for(j=0;j<len;j++)
buf[j] = (float) si * sign[j] * data[0][index[j]];
return buf;
}
double[] get_QD()
{
return QD;
}
}
public class svm {
//
// construct and solve various formulations
//
public static final int LIBSVM_VERSION=311;
public static final Random rand = new Random();
private static svm_print_interface svm_print_stdout = new svm_print_interface()
{
public void print(String s)
{
//System.out.print(s);
//System.out.flush();
}
};
private static svm_print_interface svm_print_string = svm_print_stdout;
static void info(String s)
{
svm_print_string.print(s);
}
private static void solve_c_svc(svm_problem prob, svm_parameter param,
double[] alpha, Solver.SolutionInfo si,
double Cp, double Cn)
{
int l = prob.l;
double[] minus_ones = new double[l];
byte[] y = new byte[l];
int i;
for(i=0;i<l;i++)
{
alpha[i] = 0;
minus_ones[i] = -1;
if(prob.y[i] > 0) y[i] = +1; else y[i] = -1;
}
Solver s = new Solver();
s.Solve(l, new SVC_Q(prob,param,y), minus_ones, y,
alpha, Cp, Cn, param.eps, si, param.shrinking);
double sum_alpha=0;
for(i=0;i<l;i++)
sum_alpha += alpha[i];
if (Cp==Cn)
svm.info("nu = "+sum_alpha/(Cp*prob.l)+"\n");
for(i=0;i<l;i++)
alpha[i] *= y[i];
}
private static void solve_nu_svc(svm_problem prob, svm_parameter param,
double[] alpha, Solver.SolutionInfo si)
{
int i;
int l = prob.l;
double nu = param.nu;
byte[] y = new byte[l];
for(i=0;i<l;i++)
if(prob.y[i]>0)
y[i] = +1;
else
y[i] = -1;
double sum_pos = nu*l/2;
double sum_neg = nu*l/2;
for(i=0;i<l;i++)
if(y[i] == +1)
{
alpha[i] = Math.min(1.0,sum_pos);
sum_pos -= alpha[i];
}
else
{
alpha[i] = Math.min(1.0,sum_neg);
sum_neg -= alpha[i];
}
double[] zeros = new double[l];
for(i=0;i<l;i++)
zeros[i] = 0;
Solver_NU s = new Solver_NU();
s.Solve(l, new SVC_Q(prob,param,y), zeros, y,
alpha, 1.0, 1.0, param.eps, si, param.shrinking);
double r = si.r;
svm.info("C = "+1/r+"\n");
for(i=0;i<l;i++)
alpha[i] *= y[i]/r;
si.rho /= r;
si.obj /= (r*r);
si.upper_bound_p = 1/r;
si.upper_bound_n = 1/r;
}
private static void solve_one_class(svm_problem prob, svm_parameter param,
double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double[] zeros = new double[l];
byte[] ones = new byte[l];
int i;
int n = (int)(param.nu*prob.l); // # of alpha's at upper bound
for(i=0;i<n;i++)
alpha[i] = 1;
if(n<prob.l)
alpha[n] = param.nu * prob.l - n;
for(i=n+1;i<l;i++)
alpha[i] = 0;
for(i=0;i<l;i++)
{
zeros[i] = 0;
ones[i] = 1;
}
Solver s = new Solver();
s.Solve(l, new ONE_CLASS_Q(prob,param), zeros, ones,
alpha, 1.0, 1.0, param.eps, si, param.shrinking);
}
private static void solve_epsilon_svr(svm_problem prob, svm_parameter param,
double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double[] alpha2 = new double[2*l];
double[] linear_term = new double[2*l];
byte[] y = new byte[2*l];
int i;
for(i=0;i<l;i++)
{
alpha2[i] = 0;
linear_term[i] = param.p - prob.y[i];
y[i] = 1;
alpha2[i+l] = 0;
linear_term[i+l] = param.p + prob.y[i];
y[i+l] = -1;
}
Solver s = new Solver();
s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
alpha2, param.C, param.C, param.eps, si, param.shrinking);
double sum_alpha = 0;
for(i=0;i<l;i++)
{
alpha[i] = alpha2[i] - alpha2[i+l];
sum_alpha += Math.abs(alpha[i]);
}
svm.info("nu = "+sum_alpha/(param.C*l)+"\n");
}
private static void solve_nu_svr(svm_problem prob, svm_parameter param,
double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double C = param.C;
double[] alpha2 = new double[2*l];
double[] linear_term = new double[2*l];
byte[] y = new byte[2*l];
int i;
double sum = C * param.nu * l / 2;
for(i=0;i<l;i++)
{
alpha2[i] = alpha2[i+l] = Math.min(sum,C);
sum -= alpha2[i];
linear_term[i] = - prob.y[i];
y[i] = 1;
linear_term[i+l] = prob.y[i];
y[i+l] = -1;
}
Solver_NU s = new Solver_NU();
s.Solve(2*l, new SVR_Q(prob,param), linear_term, y,
alpha2, C, C, param.eps, si, param.shrinking);
svm.info("epsilon = "+(-si.r)+"\n");
for(i=0;i<l;i++)
alpha[i] = alpha2[i] - alpha2[i+l];
}
//
// decision_function
//
static class decision_function
{
double[] alpha;
double rho;
};
static decision_function svm_train_one(
svm_problem prob, svm_parameter param,
double Cp, double Cn)
{
double[] alpha = new double[prob.l];
Solver.SolutionInfo si = new Solver.SolutionInfo();
switch(param.svm_type)
{
case svm_parameter.C_SVC:
solve_c_svc(prob,param,alpha,si,Cp,Cn);
break;
case svm_parameter.NU_SVC:
solve_nu_svc(prob,param,alpha,si);
break;
case svm_parameter.ONE_CLASS:
solve_one_class(prob,param,alpha,si);
break;
case svm_parameter.EPSILON_SVR:
solve_epsilon_svr(prob,param,alpha,si);
break;
case svm_parameter.NU_SVR:
solve_nu_svr(prob,param,alpha,si);
break;
}
svm.info("obj = "+si.obj+", rho = "+si.rho+"\n");
// output SVs
int nSV = 0;
int nBSV = 0;
for(int i=0;i<prob.l;i++)
{
if(Math.abs(alpha[i]) > 0)
{
++nSV;
if(prob.y[i] > 0)
{
if(Math.abs(alpha[i]) >= si.upper_bound_p)
++nBSV;
}
else
{
if(Math.abs(alpha[i]) >= si.upper_bound_n)
++nBSV;
}
}
}
svm.info("nSV = "+nSV+", nBSV = "+nBSV+"\n");
decision_function f = new decision_function();
f.alpha = alpha;
f.rho = si.rho;
return f;
}
// Platt's binary SVM Probablistic Output: an improvement from Lin et al.
private static void sigmoid_train(int l, double[] dec_values, double[] labels,
double[] probAB)
{
double A, B;
double prior1=0, prior0 = 0;
int i;
for (i=0;i<l;i++)
if (labels[i] > 0) prior1+=1;
else prior0+=1;
int max_iter=100; // Maximal number of iterations
double min_step=1e-10; // Minimal step taken in line search
double sigma=1e-12; // For numerically strict PD of Hessian
double eps=1e-5;
double hiTarget=(prior1+1.0)/(prior1+2.0);
double loTarget=1/(prior0+2.0);
double[] t= new double[l];
double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize;
double newA,newB,newf,d1,d2;
int iter;
// Initial Point and Initial Fun Value
A=0.0; B=Math.log((prior0+1.0)/(prior1+1.0));
double fval = 0.0;
for (i=0;i<l;i++)
{
if (labels[i]>0) t[i]=hiTarget;
else t[i]=loTarget;
fApB = dec_values[i]*A+B;
if (fApB>=0)
fval += t[i]*fApB + Math.log(1+Math.exp(-fApB));
else
fval += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
}
for (iter=0;iter<max_iter;iter++)
{
// Update Gradient and Hessian (use H' = H + sigma I)
h11=sigma; // numerically ensures strict PD
h22=sigma;
h21=0.0;g1=0.0;g2=0.0;
for (i=0;i<l;i++)
{
fApB = dec_values[i]*A+B;
if (fApB >= 0)
{
p=Math.exp(-fApB)/(1.0+Math.exp(-fApB));
q=1.0/(1.0+Math.exp(-fApB));
}
else
{
p=1.0/(1.0+Math.exp(fApB));
q=Math.exp(fApB)/(1.0+Math.exp(fApB));
}
d2=p*q;
h11+=dec_values[i]*dec_values[i]*d2;
h22+=d2;
h21+=dec_values[i]*d2;
d1=t[i]-p;
g1+=dec_values[i]*d1;
g2+=d1;
}
// Stopping Criteria
if (Math.abs(g1)<eps && Math.abs(g2)<eps)
break;
// Finding Newton direction: -inv(H') * g
det=h11*h22-h21*h21;
dA=-(h22*g1 - h21 * g2) / det;
dB=-(-h21*g1+ h11 * g2) / det;
gd=g1*dA+g2*dB;
stepsize = 1; // Line Search
while (stepsize >= min_step)
{
newA = A + stepsize * dA;
newB = B + stepsize * dB;
// New function value
newf = 0.0;
for (i=0;i<l;i++)
{
fApB = dec_values[i]*newA+newB;
if (fApB >= 0)
newf += t[i]*fApB + Math.log(1+Math.exp(-fApB));
else
newf += (t[i] - 1)*fApB +Math.log(1+Math.exp(fApB));
}
// Check sufficient decrease
if (newf<fval+0.0001*stepsize*gd)
{
A=newA;B=newB;fval=newf;
break;
}
else
stepsize = stepsize / 2.0;
}
if (stepsize < min_step)
{
svm.info("Line search fails in two-class probability estimates\n");
break;
}
}
if (iter>=max_iter)
svm.info("Reaching maximal iterations in two-class probability estimates\n");
probAB[0]=A;probAB[1]=B;
}
private static double sigmoid_predict(double decision_value, double A, double B)
{
double fApB = decision_value*A+B;
if (fApB >= 0)
return Math.exp(-fApB)/(1.0+Math.exp(-fApB));
else
return 1.0/(1+Math.exp(fApB)) ;
}
// Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
private static void multiclass_probability(int k, double[][] r, double[] p)
{
int t,j;
int iter = 0, max_iter=Math.max(100,k);
double[][] Q=new double[k][k];
double[] Qp=new double[k];
double pQp, eps=0.005/k;
for (t=0;t<k;t++)
{
p[t]=1.0/k; // Valid if k = 1
Q[t][t]=0;
for (j=0;j<t;j++)
{
Q[t][t]+=r[j][t]*r[j][t];
Q[t][j]=Q[j][t];
}
for (j=t+1;j<k;j++)
{
Q[t][t]+=r[j][t]*r[j][t];
Q[t][j]=-r[j][t]*r[t][j];
}
}
for (iter=0;iter<max_iter;iter++)
{
// stopping condition, recalculate QP,pQP for numerical accuracy
pQp=0;
for (t=0;t<k;t++)
{
Qp[t]=0;
for (j=0;j<k;j++)
Qp[t]+=Q[t][j]*p[j];
pQp+=p[t]*Qp[t];
}
double max_error=0;
for (t=0;t<k;t++)
{
double error=Math.abs(Qp[t]-pQp);
if (error>max_error)
max_error=error;
}
if (max_error<eps) break;
for (t=0;t<k;t++)
{
double diff=(-Qp[t]+pQp)/Q[t][t];
p[t]+=diff;
pQp=(pQp+diff*(diff*Q[t][t]+2*Qp[t]))/(1+diff)/(1+diff);
for (j=0;j<k;j++)
{
Qp[j]=(Qp[j]+diff*Q[t][j])/(1+diff);
p[j]/=(1+diff);
}
}
}
if (iter>=max_iter)
svm.info("Exceeds max_iter in multiclass_prob\n");
}
// Cross-validation decision values for probability estimates
private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
{
int i;
int nr_fold = 5;
int[] perm = new int[prob.l];
double[] dec_values = new double[prob.l];
// random shuffle
for(i=0;i<prob.l;i++) perm[i]=i;
for(i=0;i<prob.l;i++)
{
int j = i+rand.nextInt(prob.l-i);
Array.swap(perm, i, j);
}
for(i=0;i<nr_fold;i++)
{
int begin = i*prob.l/nr_fold;
int end = (i+1)*prob.l/nr_fold;
int j,k;
svm_problem subprob = new svm_problem();
subprob.l = prob.l-(end-begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
k=0;
for(j=0;j<begin;j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
for(j=end;j<prob.l;j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
int p_count=0,n_count=0;
for(j=0;j<k;j++)
if(subprob.y[j]>0)
p_count++;
else
n_count++;
if(p_count==0 && n_count==0)
for(j=begin;j<end;j++)
dec_values[perm[j]] = 0;
else if(p_count > 0 && n_count == 0)
for(j=begin;j<end;j++)
dec_values[perm[j]] = 1;
else if(p_count == 0 && n_count > 0)
for(j=begin;j<end;j++)
dec_values[perm[j]] = -1;
else
{
svm_parameter subparam = (svm_parameter)param.clone();
subparam.probability=0;
subparam.C=1.0;
subparam.nr_weight=2;
subparam.weight_label = new int[2];
subparam.weight = new double[2];
subparam.weight_label[0]=+1;
subparam.weight_label[1]=-1;
subparam.weight[0]=Cp;
subparam.weight[1]=Cn;
svm_model submodel = svm_train(subprob,subparam);
for(j=begin;j<end;j++)
{
double[] dec_value=new double[1];
svm_predict_values(submodel,prob.x[perm[j]],dec_value);
dec_values[perm[j]]=dec_value[0];
// ensure +1 -1 order; reason not using CV subroutine
dec_values[perm[j]] *= submodel.label[0];
}
}
}
sigmoid_train(prob.l,dec_values,prob.y,probAB);
}
// Return parameter of a Laplace distribution
private static double svm_svr_probability(svm_problem prob, svm_parameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.l];
double mae = 0;
svm_parameter newparam = (svm_parameter)param.clone();
newparam.probability = 0;
svm_cross_validation(prob,newparam,nr_fold,ymv);
for(i=0;i<prob.l;i++)
{
ymv[i]=prob.y[i]-ymv[i];
mae += Math.abs(ymv[i]);
}
mae /= prob.l;
double std=Math.sqrt(2*mae*mae);
int count=0;
mae=0;
for(i=0;i<prob.l;i++)
if (Math.abs(ymv[i]) > 5*std)
count=count+1;
else
mae+=Math.abs(ymv[i]);
mae /= (prob.l-count);
svm.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+mae+"\n");
return mae;
}
// label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
// perm, length l, must be allocated before calling this subroutine
private static void svm_group_classes(svm_problem prob, int[] nr_class_ret, int[][] label_ret, int[][] start_ret, int[][] count_ret, int[] perm)
{
int l = prob.l;
int max_nr_class = 16;
int nr_class = 0;
int[] label = new int[max_nr_class];
int[] count = new int[max_nr_class];
int[] data_label = new int[l];
int i;
for(i=0;i<l;i++)
{
int this_label = (int)(prob.y[i]);
int j;
for(j=0;j<nr_class;j++)
{
if(this_label == label[j])
{
++count[j];
break;
}
}
data_label[i] = j;
if(j == nr_class)
{
if(nr_class == max_nr_class)
{
max_nr_class *= 2;
int[] new_data = new int[max_nr_class];
System.arraycopy(label,0,new_data,0,label.length);
label = new_data;
new_data = new int[max_nr_class];
System.arraycopy(count,0,new_data,0,count.length);
count = new_data;
}
label[nr_class] = this_label;
count[nr_class] = 1;
++nr_class;
}
}
int[] start = new int[nr_class];
start[0] = 0;
for(i=1;i<nr_class;i++)
start[i] = start[i-1]+count[i-1];
for(i=0;i<l;i++)
{
perm[start[data_label[i]]] = i;
++start[data_label[i]];
}
start[0] = 0;
for(i=1;i<nr_class;i++)
start[i] = start[i-1]+count[i-1];
nr_class_ret[0] = nr_class;
label_ret[0] = label;
start_ret[0] = start;
count_ret[0] = count;
}
//
// Interface functions
//
public static svm_model svm_train(svm_problem prob, svm_parameter param)
{
svm_model model = new svm_model();
model.param = param;
if(param.svm_type == svm_parameter.ONE_CLASS ||
param.svm_type == svm_parameter.EPSILON_SVR ||
param.svm_type == svm_parameter.NU_SVR)
{
// regression or one-class-svm
model.nr_class = 2;
model.label = null;
model.nSV = null;
model.probA = null; model.probB = null;
model.sv_coef = new double[1][];
if(param.probability == 1 &&
(param.svm_type == svm_parameter.EPSILON_SVR ||
param.svm_type == svm_parameter.NU_SVR))
{
model.probA = new double[1];
model.probA[0] = svm_svr_probability(prob,param);
}
decision_function f = svm_train_one(prob,param,0,0);
model.rho = new double[1];
model.rho[0] = f.rho;
int nSV = 0;
int i;
for(i=0;i<prob.l;i++)
if(Math.abs(f.alpha[i]) > 0) ++nSV;
model.l = nSV;
model.SV = new svm_node[nSV][];
model.sv_coef[0] = new double[nSV];
int j = 0;
for(i=0;i<prob.l;i++)
if(Math.abs(f.alpha[i]) > 0)
{
model.SV[j] = prob.x[i];
model.sv_coef[0][j] = f.alpha[i];
++j;
}
}
else
{
// classification
int l = prob.l;
int[] tmp_nr_class = new int[1];
int[][] tmp_label = new int[1][];
int[][] tmp_start = new int[1][];
int[][] tmp_count = new int[1][];
int[] perm = new int[l];
// group training data of the same class
svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
int nr_class = tmp_nr_class[0];
int[] label = tmp_label[0];
int[] start = tmp_start[0];
int[] count = tmp_count[0];
if(nr_class == 1)
svm.info("WARNING: training data in only one class. See README for details.\n");
svm_node[][] x = new svm_node[l][];
int i;
for(i=0;i<l;i++)
x[i] = prob.x[perm[i]];
// calculate weighted C
double[] weighted_C = new double[nr_class];
for(i=0;i<nr_class;i++)
weighted_C[i] = param.C;
for(i=0;i<param.nr_weight;i++)
{
int j;
for(j=0;j<nr_class;j++)
if(param.weight_label[i] == label[j])
break;
if(j == nr_class)
System.err.print("WARNING: class label "+param.weight_label[i]+" specified in weight is not found\n");
else
weighted_C[j] *= param.weight[i];
}
// train k*(k-1)/2 models
boolean[] nonzero = new boolean[l];
for(i=0;i<l;i++)
nonzero[i] = false;
decision_function[] f = new decision_function[nr_class*(nr_class-1)/2];
double[] probA=null,probB=null;
if (param.probability == 1)
{
probA=new double[nr_class*(nr_class-1)/2];
probB=new double[nr_class*(nr_class-1)/2];
}
int p = 0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
svm_problem sub_prob = new svm_problem();
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
sub_prob.l = ci+cj;
sub_prob.x = new svm_node[sub_prob.l][];
sub_prob.y = new double[sub_prob.l];
int k;
for(k=0;k<ci;k++)
{
sub_prob.x[k] = x[si+k];
sub_prob.y[k] = +1;
}
for(k=0;k<cj;k++)
{
sub_prob.x[ci+k] = x[sj+k];
sub_prob.y[ci+k] = -1;
}
if(param.probability == 1)
{
double[] probAB=new double[2];
svm_binary_svc_probability(sub_prob,param,weighted_C[i],weighted_C[j],probAB);
probA[p]=probAB[0];
probB[p]=probAB[1];
}
f[p] = svm_train_one(sub_prob,param,weighted_C[i],weighted_C[j]);
for(k=0;k<ci;k++)
if(!nonzero[si+k] && Math.abs(f[p].alpha[k]) > 0)
nonzero[si+k] = true;
for(k=0;k<cj;k++)
if(!nonzero[sj+k] && Math.abs(f[p].alpha[ci+k]) > 0)
nonzero[sj+k] = true;
++p;
}
// build output
model.nr_class = nr_class;
model.label = new int[nr_class];
for(i=0;i<nr_class;i++)
model.label[i] = label[i];
model.rho = new double[nr_class*(nr_class-1)/2];
for(i=0;i<nr_class*(nr_class-1)/2;i++)
model.rho[i] = f[i].rho;
if(param.probability == 1)
{
model.probA = new double[nr_class*(nr_class-1)/2];
model.probB = new double[nr_class*(nr_class-1)/2];
for(i=0;i<nr_class*(nr_class-1)/2;i++)
{
model.probA[i] = probA[i];
model.probB[i] = probB[i];
}
}
else
{
model.probA=null;
model.probB=null;
}
int nnz = 0;
int[] nz_count = new int[nr_class];
model.nSV = new int[nr_class];
for(i=0;i<nr_class;i++)
{
int nSV = 0;
for(int j=0;j<count[i];j++)
if(nonzero[start[i]+j])
{
++nSV;
++nnz;
}
model.nSV[i] = nSV;
nz_count[i] = nSV;
}
svm.info("Total nSV = "+nnz+"\n");
model.l = nnz;
model.SV = new svm_node[nnz][];
p = 0;
for(i=0;i<l;i++)
if(nonzero[i]) model.SV[p++] = x[i];
int[] nz_start = new int[nr_class];
nz_start[0] = 0;
for(i=1;i<nr_class;i++)
nz_start[i] = nz_start[i-1]+nz_count[i-1];
model.sv_coef = new double[nr_class-1][];
for(i=0;i<nr_class-1;i++)
model.sv_coef[i] = new double[nnz];
p = 0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
// classifier (i,j): coefficients with
// i are in sv_coef[j-1][nz_start[i]...],
// j are in sv_coef[i][nz_start[j]...]
int si = start[i];
int sj = start[j];
int ci = count[i];
int cj = count[j];
int q = nz_start[i];
int k;
for(k=0;k<ci;k++)
if(nonzero[si+k])
model.sv_coef[j-1][q++] = f[p].alpha[k];
q = nz_start[j];
for(k=0;k<cj;k++)
if(nonzero[sj+k])
model.sv_coef[i][q++] = f[p].alpha[ci+k];
++p;
}
}
return model;
}
// Stratified cross validation
public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
{
int i;
int[] fold_start = new int[nr_fold+1];
int l = prob.l;
int[] perm = new int[l];
// stratified cv may not give leave-one-out rate
// Each class to l folds -> some folds may have zero elements
if((param.svm_type == svm_parameter.C_SVC ||
param.svm_type == svm_parameter.NU_SVC) && nr_fold < l)
{
int[] tmp_nr_class = new int[1];
int[][] tmp_label = new int[1][];
int[][] tmp_start = new int[1][];
int[][] tmp_count = new int[1][];
svm_group_classes(prob,tmp_nr_class,tmp_label,tmp_start,tmp_count,perm);
int nr_class = tmp_nr_class[0];
int[] start = tmp_start[0];
int[] count = tmp_count[0];
// random shuffle and then data grouped by fold using the array perm
int[] fold_count = new int[nr_fold];
int c;
int[] index = new int[l];
for(i=0;i<l;i++)
index[i]=perm[i];
for (c=0; c<nr_class; c++)
for(i=0;i<count[c];i++)
{
int j = i+rand.nextInt(count[c]-i);
Array.swap(index, start[c] + j, start[c] + i);
}
for(i=0;i<nr_fold;i++)
{
fold_count[i] = 0;
for (c=0; c<nr_class;c++)
fold_count[i]+=(i+1)*count[c]/nr_fold-i*count[c]/nr_fold;
}
fold_start[0]=0;
for (i=1;i<=nr_fold;i++)
fold_start[i] = fold_start[i-1]+fold_count[i-1];
for (c=0; c<nr_class;c++)
for(i=0;i<nr_fold;i++)
{
int begin = start[c]+i*count[c]/nr_fold;
int end = start[c]+(i+1)*count[c]/nr_fold;
for(int j=begin;j<end;j++)
{
perm[fold_start[i]] = index[j];
fold_start[i]++;
}
}
fold_start[0]=0;
for (i=1;i<=nr_fold;i++)
fold_start[i] = fold_start[i-1]+fold_count[i-1];
}
else
{
for(i=0;i<l;i++) perm[i]=i;
for(i=0;i<l;i++)
{
int j = i+rand.nextInt(l-i);
Array.swap(perm, i, j);
}
for(i=0;i<=nr_fold;i++)
fold_start[i]=i*l/nr_fold;
}
for(i=0;i<nr_fold;i++)
{
int begin = fold_start[i];
int end = fold_start[i+1];
int j,k;
svm_problem subprob = new svm_problem();
subprob.l = l-(end-begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
k=0;
for(j=0;j<begin;j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
for(j=end;j<l;j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
svm_model submodel = svm_train(subprob,param);
if(param.probability==1 &&
(param.svm_type == svm_parameter.C_SVC ||
param.svm_type == svm_parameter.NU_SVC))
{
double[] prob_estimates= new double[svm_get_nr_class(submodel)];
for(j=begin;j<end;j++)
target[perm[j]] = svm_predict_probability(submodel,prob.x[perm[j]],prob_estimates);
}
else
for(j=begin;j<end;j++)
target[perm[j]] = svm_predict(submodel,prob.x[perm[j]]);
}
}
public static int svm_get_svm_type(svm_model model)
{
return model.param.svm_type;
}
public static int svm_get_nr_class(svm_model model)
{
return model.nr_class;
}
public static void svm_get_labels(svm_model model, int[] label)
{
if (model.label != null)
for(int i=0;i<model.nr_class;i++)
label[i] = model.label[i];
}
public static double svm_get_svr_probability(svm_model model)
{
if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
model.probA!=null)
return model.probA[0];
else
{
System.err.print("Model doesn't contain information for SVR probability inference\n");
return 0;
}
}
public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
{
int i;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
{
double[] sv_coef = model.sv_coef[0];
double sum = 0;
for(i=0;i<model.l;i++)
sum += sv_coef[i] * Kernel.k_function(x,model.SV[i],model.param);
sum -= model.rho[0];
dec_values[0] = sum;
if(model.param.svm_type == svm_parameter.ONE_CLASS)
return (sum>0)?1:-1;
else
return sum;
}
else
{
int nr_class = model.nr_class;
int l = model.l;
double[] kvalue = new double[l];
for(i=0;i<l;i++)
kvalue[i] = Kernel.k_function(x,model.SV[i],model.param);
int[] start = new int[nr_class];
start[0] = 0;
for(i=1;i<nr_class;i++)
start[i] = start[i-1]+model.nSV[i-1];
int[] vote = new int[nr_class];
for(i=0;i<nr_class;i++)
vote[i] = 0;
int p=0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
double sum = 0;
int si = start[i];
int sj = start[j];
int ci = model.nSV[i];
int cj = model.nSV[j];
int k;
double[] coef1 = model.sv_coef[j-1];
double[] coef2 = model.sv_coef[i];
for(k=0;k<ci;k++)
sum += coef1[si+k] * kvalue[si+k];
for(k=0;k<cj;k++)
sum += coef2[sj+k] * kvalue[sj+k];
sum -= model.rho[p];
dec_values[p] = sum;
if(dec_values[p] > 0)
++vote[i];
else
++vote[j];
p++;
}
int vote_max_idx = 0;
for(i=1;i<nr_class;i++)
if(vote[i] > vote[vote_max_idx])
vote_max_idx = i;
return model.label[vote_max_idx];
}
}
public static double svm_predict(svm_model model, svm_node[] x)
{
int nr_class = model.nr_class;
double[] dec_values;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
dec_values = new double[1];
else
dec_values = new double[nr_class*(nr_class-1)/2];
double pred_result = svm_predict_values(model, x, dec_values);
return pred_result;
}
public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
{
if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
model.probA!=null && model.probB!=null)
{
int i;
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm_predict_values(model, x, dec_values);
double min_prob=1e-7;
double[][] pairwise_prob=new double[nr_class][nr_class];
int k=0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
pairwise_prob[j][i]=1-pairwise_prob[i][j];
k++;
}
multiclass_probability(nr_class,pairwise_prob,prob_estimates);
int prob_max_idx = 0;
for(i=1;i<nr_class;i++)
if(prob_estimates[i] > prob_estimates[prob_max_idx])
prob_max_idx = i;
return model.label[prob_max_idx];
}
else
return svm_predict(model, x);
}
static final String svm_type_table[] =
{
"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",
};
static final String kernel_type_table[]=
{
"linear","polynomial","rbf","sigmoid","precomputed"
};
public static void svm_save_model(String model_file_name, svm_model model) throws IOException
{
DataOutputStream fp = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(model_file_name)));
svm_parameter param = model.param;
fp.writeBytes("svm_type "+svm_type_table[param.svm_type]+"\n");
fp.writeBytes("kernel_type "+kernel_type_table[param.kernel_type]+"\n");
if(param.kernel_type == svm_parameter.POLY)
fp.writeBytes("degree "+param.degree+"\n");
if(param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.RBF ||
param.kernel_type == svm_parameter.SIGMOID)
fp.writeBytes("gamma "+param.gamma+"\n");
if(param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.SIGMOID)
fp.writeBytes("coef0 "+param.coef0+"\n");
int nr_class = model.nr_class;
int l = model.l;
fp.writeBytes("nr_class "+nr_class+"\n");
fp.writeBytes("total_sv "+l+"\n");
{
fp.writeBytes("rho");
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
fp.writeBytes(" "+model.rho[i]);
fp.writeBytes("\n");
}
if(model.label != null)
{
fp.writeBytes("label");
for(int i=0;i<nr_class;i++)
fp.writeBytes(" "+model.label[i]);
fp.writeBytes("\n");
}
if(model.probA != null) // regression has probA only
{
fp.writeBytes("probA");
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
fp.writeBytes(" "+model.probA[i]);
fp.writeBytes("\n");
}
if(model.probB != null)
{
fp.writeBytes("probB");
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
fp.writeBytes(" "+model.probB[i]);
fp.writeBytes("\n");
}
if(model.nSV != null)
{
fp.writeBytes("nr_sv");
for(int i=0;i<nr_class;i++)
fp.writeBytes(" "+model.nSV[i]);
fp.writeBytes("\n");
}
fp.writeBytes("SV\n");
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
for(int i=0;i<l;i++)
{
for(int j=0;j<nr_class-1;j++)
fp.writeBytes(sv_coef[j][i]+" ");
svm_node[] p = SV[i];
if(param.kernel_type == svm_parameter.PRECOMPUTED)
fp.writeBytes("0:"+(int)(p[0].value));
else
for(int j=0;j<p.length;j++)
fp.writeBytes(p[j].index+":"+p[j].value+" ");
fp.writeBytes("\n");
}
fp.close();
}
public static void svm_save_model(DataOutputStream fp, svm_model model) throws IOException
{
svm_parameter param = model.param;
fp.writeBytes("svm_type "+svm_type_table[param.svm_type]+"\n");
fp.writeBytes("kernel_type "+kernel_type_table[param.kernel_type]+"\n");
if(param.kernel_type == svm_parameter.POLY)
fp.writeBytes("degree "+param.degree+"\n");
if(param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.RBF ||
param.kernel_type == svm_parameter.SIGMOID)
fp.writeBytes("gamma "+param.gamma+"\n");
if(param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.SIGMOID)
fp.writeBytes("coef0 "+param.coef0+"\n");
int nr_class = model.nr_class;
int l = model.l;
fp.writeBytes("nr_class "+nr_class+"\n");
fp.writeBytes("total_sv "+l+"\n");
{
fp.writeBytes("rho");
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
fp.writeBytes(" "+model.rho[i]);
fp.writeBytes("\n");
}
if(model.label != null)
{
fp.writeBytes("label");
for(int i=0;i<nr_class;i++)
fp.writeBytes(" "+model.label[i]);
fp.writeBytes("\n");
}
if(model.probA != null) // regression has probA only
{
fp.writeBytes("probA");
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
fp.writeBytes(" "+model.probA[i]);
fp.writeBytes("\n");
}
if(model.probB != null)
{
fp.writeBytes("probB");
for(int i=0;i<nr_class*(nr_class-1)/2;i++)
fp.writeBytes(" "+model.probB[i]);
fp.writeBytes("\n");
}
if(model.nSV != null)
{
fp.writeBytes("nr_sv");
for(int i=0;i<nr_class;i++)
fp.writeBytes(" "+model.nSV[i]);
fp.writeBytes("\n");
}
fp.writeBytes("SV\n");
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
for(int i=0;i<l;i++)
{
for(int j=0;j<nr_class-1;j++)
fp.writeBytes(sv_coef[j][i]+" ");
svm_node[] p = SV[i];
if(param.kernel_type == svm_parameter.PRECOMPUTED)
fp.writeBytes("0:"+(int)(p[0].value));
else
for(int j=0;j<p.length;j++)
fp.writeBytes(p[j].index+":"+p[j].value+" ");
fp.writeBytes("\n");
}
}
private static double atof(String s)
{
return CSVFormat.EG_FORMAT.parse(s);
}
private static int atoi(String s)
{
return Integer.parseInt(s);
}
public static svm_model svm_load_model(String model_file_name) throws IOException
{
return svm_load_model(new BufferedReader(new FileReader(model_file_name)));
}
public static svm_model svm_load_model(BufferedReader fp) throws IOException
{
// read parameters
svm_model model = new svm_model();
svm_parameter param = new svm_parameter();
model.param = param;
model.rho = null;
model.probA = null;
model.probB = null;
model.label = null;
model.nSV = null;
while(true)
{
String cmd = fp.readLine();
String arg = cmd.substring(cmd.indexOf(' ')+1);
if(cmd.startsWith("svm_type"))
{
int i;
for(i=0;i<svm_type_table.length;i++)
{
if(arg.indexOf(svm_type_table[i])!=-1)
{
param.svm_type=i;
break;
}
}
if(i == svm_type_table.length)
{
System.err.print("unknown svm type.\n");
return null;
}
}
else if(cmd.startsWith("kernel_type"))
{
int i;
for(i=0;i<kernel_type_table.length;i++)
{
if(arg.indexOf(kernel_type_table[i])!=-1)
{
param.kernel_type=i;
break;
}
}
if(i == kernel_type_table.length)
{
System.err.print("unknown kernel function.\n");
return null;
}
}
else if(cmd.startsWith("degree"))
param.degree = atoi(arg);
else if(cmd.startsWith("gamma"))
param.gamma = atof(arg);
else if(cmd.startsWith("coef0"))
param.coef0 = atof(arg);
else if(cmd.startsWith("nr_class"))
model.nr_class = atoi(arg);
else if(cmd.startsWith("total_sv"))
model.l = atoi(arg);
else if(cmd.startsWith("rho"))
{
int n = model.nr_class * (model.nr_class-1)/2;
model.rho = new double[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.rho[i] = atof(st.nextToken());
}
else if(cmd.startsWith("label"))
{
int n = model.nr_class;
model.label = new int[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.label[i] = atoi(st.nextToken());
}
else if(cmd.startsWith("probA"))
{
int n = model.nr_class*(model.nr_class-1)/2;
model.probA = new double[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.probA[i] = atof(st.nextToken());
}
else if(cmd.startsWith("probB"))
{
int n = model.nr_class*(model.nr_class-1)/2;
model.probB = new double[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.probB[i] = atof(st.nextToken());
}
else if(cmd.startsWith("nr_sv"))
{
int n = model.nr_class;
model.nSV = new int[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.nSV[i] = atoi(st.nextToken());
}
else if(cmd.startsWith("SV"))
{
break;
}
else
{
System.err.print("unknown text in model file: ["+cmd+"]\n");
return null;
}
}
// read sv_coef and SV
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = new double[m][l];
model.SV = new svm_node[l][];
for(int i=0;i<l;i++)
{
String line = fp.readLine();
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
for(int k=0;k<m;k++)
model.sv_coef[k][i] = atof(st.nextToken());
int n = st.countTokens()/2;
model.SV[i] = new svm_node[n];
for(int j=0;j<n;j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = atoi(st.nextToken());
model.SV[i][j].value = atof(st.nextToken());
}
}
fp.close();
return model;
}
public static String svm_check_parameter(svm_problem prob, svm_parameter param)
{
// svm_type
int svm_type = param.svm_type;
if(svm_type != svm_parameter.C_SVC &&
svm_type != svm_parameter.NU_SVC &&
svm_type != svm_parameter.ONE_CLASS &&
svm_type != svm_parameter.EPSILON_SVR &&
svm_type != svm_parameter.NU_SVR)
return "unknown svm type";
// kernel_type, degree
int kernel_type = param.kernel_type;
if(kernel_type != svm_parameter.LINEAR &&
kernel_type != svm_parameter.POLY &&
kernel_type != svm_parameter.RBF &&
kernel_type != svm_parameter.SIGMOID &&
kernel_type != svm_parameter.PRECOMPUTED)
return "unknown kernel type";
if(param.gamma < 0)
return "gamma < 0";
if(param.degree < 0)
return "degree of polynomial kernel < 0";
// cache_size,eps,C,nu,p,shrinking
if(param.cache_size <= 0)
return "cache_size <= 0";
if(param.eps <= 0)
return "eps <= 0";
if(svm_type == svm_parameter.C_SVC ||
svm_type == svm_parameter.EPSILON_SVR ||
svm_type == svm_parameter.NU_SVR)
if(param.C <= 0)
return "C <= 0";
if(svm_type == svm_parameter.NU_SVC ||
svm_type == svm_parameter.ONE_CLASS ||
svm_type == svm_parameter.NU_SVR)
if(param.nu <= 0 || param.nu > 1)
return "nu <= 0 or nu > 1";
if(svm_type == svm_parameter.EPSILON_SVR)
if(param.p < 0)
return "p < 0";
if(param.shrinking != 0 &&
param.shrinking != 1)
return "shrinking != 0 and shrinking != 1";
if(param.probability != 0 &&
param.probability != 1)
return "probability != 0 and probability != 1";
if(param.probability == 1 &&
svm_type == svm_parameter.ONE_CLASS)
return "one-class SVM probability output not supported yet";
// check whether nu-svc is feasible
if(svm_type == svm_parameter.NU_SVC)
{
int l = prob.l;
int max_nr_class = 16;
int nr_class = 0;
int[] label = new int[max_nr_class];
int[] count = new int[max_nr_class];
int i;
for(i=0;i<l;i++)
{
int this_label = (int)prob.y[i];
int j;
for(j=0;j<nr_class;j++)
if(this_label == label[j])
{
++count[j];
break;
}
if(j == nr_class)
{
if(nr_class == max_nr_class)
{
max_nr_class *= 2;
int[] new_data = new int[max_nr_class];
System.arraycopy(label,0,new_data,0,label.length);
label = new_data;
new_data = new int[max_nr_class];
System.arraycopy(count,0,new_data,0,count.length);
count = new_data;
}
label[nr_class] = this_label;
count[nr_class] = 1;
++nr_class;
}
}
for(i=0;i<nr_class;i++)
{
int n1 = count[i];
for(int j=i+1;j<nr_class;j++)
{
int n2 = count[j];
if(param.nu*(n1+n2)/2 > Math.min(n1,n2))
return "specified nu is infeasible";
}
}
}
return null;
}
public static int svm_check_probability_model(svm_model model)
{
if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
model.probA!=null && model.probB!=null) ||
((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
model.probA!=null))
return 1;
else
return 0;
}
public static void svm_set_print_string_function(svm_print_interface print_func)
{
if (print_func == null)
svm_print_string = svm_print_stdout;
else
svm_print_string = print_func;
}
}