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com.github.chen0040.libsvm.SupportVectorMachine Maven / Gradle / Ivy
package com.github.chen0040.libsvm;
import java.io.*;
import java.util.Random;
import java.util.StringTokenizer;
//
// 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 static 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= 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)
do {float tmp = h.data[i]; h.data[i]=h.data[j]; h.data[j] = tmp;} while(false);
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 SupportVectorMachineNode[][] 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)
{
do {SupportVectorMachineNode[] tmp = x[i]; x[i]=x[j]; x[j] = tmp;} while(false);
if(x_square != null) do {double tmp = x_square[i]; x_square[i]=x_square[j]; x_square[j] = tmp;} while(false);
}
private static double powi(double base, int times)
{
double tmp = base, ret = 1.0;
for(int t = times; t>0; t/=2)
{
if( isOdd(t) ) ret *= tmp;
tmp = tmp * tmp;
}
return ret;
}
private static boolean isOdd(int t) {
return Math.abs(t) % 2 == 1;
}
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, SupportVectorMachineNode[][] x_, svm_parameter param)
{
this.kernel_type = param.kernel_type;
this.degree = param.degree;
this.gamma = param.gamma;
this.coef0 = param.coef0;
x = x_.clone();
if(kernel_type == svm_parameter.RBF)
{
x_square = new double[l];
for(int i=0;i y[j].index)
++j;
else
++i;
}
}
return sum;
}
static double k_function(SupportVectorMachineNode[] x, SupportVectorMachineNode[] 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 = 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);
do {byte tmp = y[i]; y[i]=y[j]; y[j] = tmp;} while(false);
do {double tmp = G[i]; G[i]=G[j]; G[j] = tmp;} while(false);
do {byte tmp = alpha_status[i]; alpha_status[i]=alpha_status[j]; alpha_status[j] = tmp;} while(false);
do {double tmp = alpha[i]; alpha[i]=alpha[j]; alpha[j] = tmp;} while(false);
do {double tmp = p[i]; p[i]=p[j]; p[j] = tmp;} while(false);
do {int tmp = active_set[i]; active_set[i]=active_set[j]; active_set[j] = tmp;} while(false);
do {double tmp = G_bar[i]; G_bar[i]=G_bar[j]; G_bar[j] = tmp;} while(false);
}
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 2*active_size*(l-active_size))
{
for(i=active_size;iInteger.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();
SupportVectorMachine.info(".");
}
if(select_working_set(working_set)!=0)
{
// reconstruct the whole gradient
reconstruct_gradient();
// reset active set size and check
active_size = l;
SupportVectorMachine.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= max_iter)
{
if(active_size < l)
{
// reconstruct the whole gradient to calculate objective value
reconstruct_gradient();
active_size = l;
SupportVectorMachine.info("*");
}
System.err.print("\nWARNING: reaching max number of iterations\n");
}
// calculate rho
si.rho = calculate_rho();
// calculate objective value
{
double v = 0;
int i;
for(i=0;i= 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= 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) }
// findNode maximal violating pair first
for(i=0;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 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 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= 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= 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) }
// findNode maximal violating pair first
int i;
for(i=0;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 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 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 = y_.clone();
cache = new Cache(prob.l,(long)(param.cache_size*(1<<20)));
QD = new double[prob.l];
for(int i=0;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;i0)
y[i] = +1;
else
y[i] = -1;
double sum_pos = nu*l/2;
double sum_neg = nu*l/2;
for(i=0;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;
}
}
}
SupportVectorMachine.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 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;i0) 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= 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)= min_step)
{
newA = A + stepsize * dA;
newB = B + stepsize * dB;
// New function value
newf = 0.0;
for (i=0;i= 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=max_iter)
SupportVectorMachine.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;tmax_error)
max_error=error;
}
if (max_error=max_iter)
SupportVectorMachine.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];
// naive shuffle
for(i=0;i0)
p_count++;
else
n_count++;
if(p_count==0 && n_count==0)
for(j=begin;j 0 && n_count == 0)
for(j=begin;j 0)
for(j=begin;j 5*std)
count=count+1;
else
mae+=Math.abs(ymv[i]);
mae /= (prob.l-count);
SupportVectorMachine.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 0) ++nSV;
model.l = nSV;
model.SV = new SupportVectorMachineNode[nSV][];
model.sv_coef[0] = new double[nSV];
model.sv_indices = new int[nSV];
int j = 0;
for(i=0;i 0)
{
model.SV[j] = prob.x[i];
model.sv_coef[0][j] = f.alpha[i];
model.sv_indices[j] = i+1;
++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 train 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)
SupportVectorMachine.info("WARNING: training data in only one class. See README for details.\n");
SupportVectorMachineNode[][] x = new SupportVectorMachineNode[l][];
int i;
for(i=0;i 0)
nonzero[si+k] = true;
for(k=0;k 0)
nonzero[sj+k] = true;
++p;
}
// build output
model.nr_class = nr_class;
model.label = new int[nr_class];
for(i=0;i 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];
// naive 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;i0)?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 0)
++vote[i];
else
++vote[j];
p++;
}
int vote_max_idx = 0;
for(i=1;i vote[vote_max_idx])
vote_max_idx = i;
return model.label[vote_max_idx];
}
}
public static double svm_predict(svm_model model, SupportVectorMachineNode[] 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, SupportVectorMachineNode[] 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 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 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 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;
}
}
