From c0ee0e9c6f7ebd589ead016f36044aa2ebedbfcb Mon Sep 17 00:00:00 2001
From: linlin <jajupmochi@gmail.com>
Date: Tue, 6 Oct 2020 17:27:43 +0200
Subject: [PATCH] New translations svm.m4 (French)

---
 .../gedlib/lib/libsvm.3.22/java/libsvm/svm.m4      | 2860 ++++++++++++++++++++
 1 file changed, 2860 insertions(+)
 create mode 100644 lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/libsvm/svm.m4

diff --git a/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/libsvm/svm.m4 b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/libsvm/svm.m4
new file mode 100644
index 0000000..ca2621d
--- /dev/null
+++ b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/java/libsvm/svm.m4
@@ -0,0 +1,2860 @@
+define(`swap',`do {$1 tmp=$2; $2=$3; $3=tmp;} while(false)')
+define(`Qfloat',`float')
+define(`SIZE_OF_QFLOAT',4)
+define(`TAU',1e-12)
+changecom(`//',`')
+package libsvm;
+import java.io.*;
+import java.util.*;
+
+//
+// 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
+		Qfloat[] 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 /= SIZE_OF_QFLOAT;
+		size -= l * (16/SIZE_OF_QFLOAT);	// 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, Qfloat[][] 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
+			Qfloat[] new_data = new Qfloat[len];
+			if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
+			h.data = new_data;
+			size -= more;
+			swap(int,h.len,len);
+		}
+
+		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]);
+		swap(Qfloat[],head[i].data,head[j].data);
+		swap(int,head[i].len,head[j].len);
+		if(head[i].len > 0) lru_insert(head[i]);
+		if(head[j].len > 0) lru_insert(head[j]);
+
+		if(i>j) swap(int,i,j);
+		for(head_t h = lru_head.next; h!=lru_head; h=h.next)
+		{
+			if(h.len > i)
+			{
+				if(h.len > j)
+					swap(Qfloat,h.data[i],h.data[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 Qfloat[] 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 Qfloat[] get_Q(int column, int len);
+	abstract double[] get_QD();
+
+	void swap_index(int i, int j)
+	{
+		swap(svm_node[],x[i],x[j]);
+		if(x_square != null) swap(double,x_square[i],x_square[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);
+		swap(byte,	y[i],y[j]);
+		swap(double,	G[i],G[j]);
+		swap(byte,	alpha_status[i],alpha_status[j]);
+		swap(double,	alpha[i],alpha[j]);
+		swap(double,	p[i],p[j]);
+		swap(int,	active_set[i],active_set[j]);
+		swap(double,	G_bar[i],G_bar[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++)
+			{
+				Qfloat[] 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))
+				{
+					Qfloat[] 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))
+				{
+					Qfloat[] 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
+
+			Qfloat[] Q_i = Q.get_Q(i,active_size);
+			Qfloat[] 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 = TAU;
+				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 = TAU;
+				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("*");
+			}
+			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<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;
+		Qfloat[] 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)/TAU;
+	
+						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)/TAU;
+	
+						if (obj_diff <= obj_diff_min)
+						{
+							Gmin_idx=j;
+							obj_diff_min = obj_diff;
+						}
+					}
+				}
+			}
+		}
+
+		if(Gmax+Gmax2 < eps || Gmin_idx == -1)
+			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;
+		Qfloat[] Q_ip = null;
+		Qfloat[] 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)/TAU;
+	
+						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)/TAU;
+	
+						if (obj_diff <= obj_diff_min)
+						{
+							Gmin_idx=j;
+							obj_diff_min = obj_diff;
+						}
+					}
+				}
+			}
+		}
+
+		if(Math.max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps || Gmin_idx == -1)
+			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);
+	}
+
+	Qfloat[] get_Q(int i, int len)
+	{
+		Qfloat[][] data = new Qfloat[1][];
+		int start, j;
+		if((start = cache.get_data(i,data,len)) < len)
+		{
+			for(j=start;j<len;j++)
+				data[0][j] = (Qfloat)(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);
+		swap(byte,y[i],y[j]);
+		swap(double,QD[i],QD[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);
+	}
+
+	Qfloat[] get_Q(int i, int len)
+	{
+		Qfloat[][] data = new Qfloat[1][];
+		int start, j;
+		if((start = cache.get_data(i,data,len)) < len)
+		{
+			for(j=start;j<len;j++)
+				data[0][j] = (Qfloat)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);
+		swap(double,QD[i],QD[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 Qfloat[][] 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 Qfloat[2][2*l];
+		next_buffer = 0;
+	}
+
+	void swap_index(int i, int j)
+	{
+		swap(byte,sign[i],sign[j]);
+		swap(int,index[i],index[j]);
+		swap(double,QD[i],QD[j]);
+	}
+
+	Qfloat[] get_Q(int i, int len)
+	{
+		Qfloat[][] data = new Qfloat[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] = (Qfloat)kernel_function(real_i,j);
+		}
+
+		// reorder and copy
+		Qfloat buf[] = buffer[next_buffer];
+		next_buffer = 1 - next_buffer;
+		byte si = sign[i];
+		for(j=0;j<len;j++)
+			buf[j] = (Qfloat) 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=322; 
+	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);
+			swap(int,perm[i],perm[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;
+			}
+		}
+
+		//
+		// Labels are ordered by their first occurrence in the training set. 
+		// However, for two-class sets with -1/+1 labels and -1 appears first, 
+		// we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances.
+		//
+		if (nr_class == 2 && label[0] == -1 && label[1] == +1)
+		{
+			swap(int,label[0],label[1]);
+			swap(int,count[0],count[1]);
+			for(i=0;i<l;i++)
+			{
+				if(data_label[i] == 0)
+					data_label[i] = 1;
+				else
+					data_label[i] = 0;
+			}
+		}
+
+		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];
+			model.sv_indices = new int[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];
+					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 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][];
+			model.sv_indices = new int[nnz];
+			p = 0;
+			for(i=0;i<l;i++)
+				if(nonzero[i])
+				{
+					model.SV[p] = x[i];
+					model.sv_indices[p++] = perm[i] + 1;
+				}
+
+			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);
+					swap(int,index[start[c]+j],index[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);
+				swap(int,perm[i],perm[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 void svm_get_sv_indices(svm_model model, int[] indices)
+	{
+		if (model.sv_indices != null)
+			for(int i=0;i<model.l;i++)
+				indices[i] = model.sv_indices[i];
+	}
+
+	public static int svm_get_nr_sv(svm_model model)
+	{
+		return model.l;
+	}
+
+	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++;
+				}
+			if (nr_class == 2)
+			{
+				prob_estimates[0] = pairwise_prob[0][1];
+				prob_estimates[1] = pairwise_prob[1][0];
+			}
+			else
+				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();
+	}
+
+	private static double atof(String s)
+	{
+		return Double.valueOf(s).doubleValue();
+	}
+
+	private static int atoi(String s)
+	{
+		return Integer.parseInt(s);
+	}
+
+	private static boolean read_model_header(BufferedReader fp, svm_model model)
+	{
+		svm_parameter param = new svm_parameter();
+		model.param = param;
+		// parameters for training only won't be assigned, but arrays are assigned as NULL for safety
+		param.nr_weight = 0;
+		param.weight_label = null;
+		param.weight = null;
+
+		try
+		{
+			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 false;
+					}
+				}
+				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 false;
+					}
+				}
+				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 false;
+				}
+			}
+		}
+		catch(Exception e)
+		{
+			return false;
+		}
+		return true;
+	}
+
+	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();
+		model.rho = null;
+		model.probA = null;
+		model.probB = null;
+		model.label = null;
+		model.nSV = null;
+
+		if (read_model_header(fp, model) == false)
+		{
+			System.err.print("ERROR: failed to read model\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;
+	}
+}