diff --git a/CHANGES.txt b/CHANGES.txt
index 5bee6c2..99e0c3a 100644
--- a/CHANGES.txt
+++ b/CHANGES.txt
@@ -1,5 +1,6 @@
 1.4.1-SNAPSHOT
-- updated to liblinear 1.7 
+- upgraded lisvm 3.0 + rely on external lib instead of modified code
+- upgraded liblinear 1.7 
 - can specify weighting schemes per field
 - added TestWeightingSchemes
 - can remap the attribute numbers before generating the vector file
diff --git a/lib/libsvm-3.0.jar b/lib/libsvm-3.0.jar
new file mode 100644
index 0000000..6ed1a25
Binary files /dev/null and b/lib/libsvm-3.0.jar differ
diff --git a/libLinear.copyright.txt b/libLinear.copyright.txt
index 65ad0ec..c319013 100644
--- a/libLinear.copyright.txt
+++ b/libLinear.copyright.txt
@@ -1,5 +1,4 @@
-
-Copyright (c) 2007-2008 The LIBLINEAR Project.
+Copyright (c) 2007-2010 The LIBLINEAR Project.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
@@ -29,3 +28,4 @@ PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
diff --git a/libSVM.copyright.txt b/libSVM.copyright.txt
index 5af6ebb..58a2d82 100644
--- a/libSVM.copyright.txt
+++ b/libSVM.copyright.txt
@@ -1,4 +1,4 @@
-Copyright (c) 2000-2006 Chih-Chung Chang and Chih-Jen Lin
+Copyright (c) 2000-2010 Chih-Chung Chang and Chih-Jen Lin
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/src/java/com/digitalpebble/classification/libsvm/LibSVMModelCreator.java b/src/java/com/digitalpebble/classification/libsvm/LibSVMModelCreator.java
index a015555..c364cc7 100755
--- a/src/java/com/digitalpebble/classification/libsvm/LibSVMModelCreator.java
+++ b/src/java/com/digitalpebble/classification/libsvm/LibSVMModelCreator.java
@@ -29,7 +29,6 @@
 import libsvm.svm_node;
 import libsvm.svm_parameter;
 import libsvm.svm_problem;
-import libsvm.svm_problem_impl;
 
 import com.digitalpebble.classification.Document;
 import com.digitalpebble.classification.Learner;
@@ -94,12 +93,6 @@ public void internal_learn() throws Exception {
 		} else {
 			model = svm.svm_train(prob, param);
 			svm.svm_save_model(model_file_name, model);
-			// dump linear weights in lexicon
-			try {
-				double[] weights = model.getLinearWeights();
-				this.lexicon.setLinearWeight(weights);
-			} catch (Exception e) {
-			}
 		}
 	}
 
@@ -221,12 +214,15 @@ private void read_problem(File learningFile) throws IOException {
 				max_index = Math.max(max_index, x[m - 1].index);
 			vx.addElement(x);
 		}
-		prob = new svm_problem_impl(vy.size());
-		for (int i = 0; i < prob.size(); i++)
-			prob.setNodes(i, (svm_node[]) vx.elementAt(i));
-		for (int i = 0; i < prob.size(); i++) {
+		prob = new svm_problem();
+		prob.l=vy.size();
+		prob.y = new double[prob.l];
+		prob.x = new svm_node[prob.l][];
+		for (int i = 0; i < prob.l; i++)
+			prob.x[i]= (svm_node[]) vx.elementAt(i);
+		for (int i = 0; i < prob.l; i++) {
 			double labell = Double.parseDouble((String) vy.elementAt(i));
-			prob.setLabel(i, labell);
+			prob.y[i]= labell;
 		}
 		if (param.gamma == 0)
 			param.gamma = 1.0 / max_index;
@@ -246,13 +242,13 @@ private void do_cross_validation() {
 		int total_correct = 0;
 		double total_error = 0;
 		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
-		double size = prob.size();
-		double[] target = new double[prob.size()];
+		double size = prob.l;
+		double[] target = new double[prob.l];
 		svm.svm_cross_validation(prob, param, this.nfold, target);
 		if (param.svm_type == svm_parameter.EPSILON_SVR
 				|| param.svm_type == svm_parameter.NU_SVR) {
-			for (i = 0; i < prob.size(); i++) {
-				double y = prob.getLabel(i);
+			for (i = 0; i < prob.l; i++) {
+				double y = prob.y[i];
 				double v = target[i];
 				total_error += (v - y) * (v - y);
 				sumv += v;
@@ -274,7 +270,7 @@ private void do_cross_validation() {
 		int numclasses = lexicon.getLabelNum();
 		double[][] confMatrix = new double[numclasses][numclasses];
 		for (i = 0; i < size; i++) {
-			double expected = prob.getLabel(i);
+			double expected = prob.y[i];
 			if (target[i] == expected)
 				++total_correct;
 			confMatrix[(int) target[i]][(int) expected]++;
@@ -328,13 +324,13 @@ private void do_cross_validation() {
 		Map<Integer, String> inverted = lexicon.getInvertedIndex();
 		for (i = 0; i < size; i++) {
 			StringBuffer sb = new StringBuffer();
-			double expected = prob.getLabel(i);
+			double expected = prob.y[i];
 			if (target[i] == expected)
 				continue;
 			sb.append("expected: ").append(lexicon.getLabel((int) expected))
 					.append("\tfound:").append(
 							lexicon.getLabel((int) target[i]));
-			svm_node[] nodes = prob.getNodes(i);
+			svm_node[] nodes = prob.x[i];
 			for (svm_node node : nodes) {
 				String attLabel = inverted.get(new Integer(node.index));
 				if (attLabel == null)
diff --git a/src/java/libsvm/svm.java b/src/java/libsvm/svm.java
deleted file mode 100644
index 3434ac9..0000000
--- a/src/java/libsvm/svm.java
+++ /dev/null
@@ -1,2811 +0,0 @@
-package libsvm;
-import java.io.BufferedReader;
-import java.io.DataOutputStream;
-import java.io.FileOutputStream;
-import java.io.FileReader;
-import java.io.IOException;
-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 int size;
-	private final class head_t
-	{
-		head_t prev, next;	// a cicular list
-		float[] data;
-		int len;		// data[0,len) is cached in this entry
-	}
-	private final head_t[] head;
-	private head_t lru_head;
-
-	Cache(int l_, int size_)
-	{
-		l = l_;
-		size = size_;
-		head = new head_t[l];
-		for(int i=0;i<l;i++) head[i] = new head_t();
-		size /= 4;
-		size -= l * (16/4);	// sizeof(head_t) == 16
-		size = Math.max(size, 2*l);  // cache must be large enough for two columns
-		lru_head = new head_t();
-		lru_head.next = lru_head.prev = lru_head;
-	}
-
-	private void lru_delete(head_t h)
-	{
-		// delete from current location
-		h.prev.next = h.next;
-		h.next.prev = h.prev;
-	}
-
-	private void lru_insert(head_t h)
-	{
-		// insert to last position
-		h.next = lru_head;
-		h.prev = lru_head.prev;
-		h.prev.next = h;
-		h.next.prev = h;
-	}
-
-	// request data [0,len)
-	// return some position p where [p,len) need to be filled
-	// (p >= len if nothing needs to be filled)
-	// java: simulate pointer using single-element array
-	int get_data(int index, float[][] data, int len)
-	{
-		head_t h = head[index];
-		if(h.len > 0) lru_delete(h);
-		int more = len - h.len;
-
-		if(more > 0)
-		{
-			// free old space
-			while(size < more)
-			{
-				head_t old = lru_head.next;
-				lru_delete(old);
-				size += old.len;
-				old.data = null;
-				old.len = 0;
-			}
-
-			// allocate new space
-			float[] new_data = new float[len];
-			if(h.data != null) System.arraycopy(h.data,0,new_data,0,h.len);
-			h.data = new_data;
-			size -= more;
-			do {int _=h.len; h.len=len; len=_;} 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[] _=head[i].data; head[i].data=head[j].data; head[j].data=_;} while(false);
-		do {int _=head[i].len; head[i].len=head[j].len; head[j].len=_;} 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 _=i; i=j; j=_;} 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 _=h.data[i]; h.data[i]=h.data[j]; h.data[j]=_;} 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 float[] get_QD();
-	abstract void swap_index(int i, int j);
-};
-
-abstract class Kernel extends QMatrix {
-	private svm_node[][] x;
-	private final double[] x_square;
-
-	// svm_parameter
-	private final int kernel_type;
-	private final int degree;
-	private final double gamma;
-	private final double coef0;
-
-	abstract float[] get_Q(int column, int len);
-	abstract float[] get_QD();
-
-	void swap_index(int i, int j)
-	{
-		do {svm_node[] _=x[i]; x[i]=x[j]; x[j]=_;} while(false);
-		if(x_square != null) do {double _=x_square[i]; x_square[i]=x_square[j]; x_square[j]=_;} 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(t%2==1) ret*=tmp;
-	                tmp = tmp * tmp;
-        	}
-	        return ret;
-	}
-
-	private static double tanh(double x)
-	{
-		double e = Math.exp(x);
-		return 1.0-2.0/(e*e+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 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;
-	}
-
-	final 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;
-	}
-
-	final 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 tanh(param.gamma*dot(x,y)+param.coef0);
-			case svm_parameter.PRECOMPUTED:
-				return 	x[(int)(y[0].value)].value;
-			default:
-				return 0;	// java
-		}
-	}
-}
-
-// Generalized SMO+SVMlight algorithm
-// Solves:
-//
-//	min 0.5(\alpha^T Q \alpha) + b^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, b, y, Cp, Cn, and an initial feasible point \alpha
-//	l is the size of vectors and matrices
-//	eps is the stopping criterion
-//
-// 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;
-	float[] QD;
-	double eps;
-	double Cp,Cn;
-	double[] b;
-	int[] active_set;
-	double[] G_bar;		// gradient, if we treat free variables as 0
-	int l;
-	boolean unshrinked;	// 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);
-		do {byte _=y[i]; y[i]=y[j]; y[j]=_;} while(false);
-		do {double _=G[i]; G[i]=G[j]; G[j]=_;} while(false);
-		do {byte _=alpha_status[i]; alpha_status[i]=alpha_status[j]; alpha_status[j]=_;} while(false);
-		do {double _=alpha[i]; alpha[i]=alpha[j]; alpha[j]=_;} while(false);
-		do {double _=b[i]; b[i]=b[j]; b[j]=_;} while(false);
-		do {int _=active_set[i]; active_set[i]=active_set[j]; active_set[j]=_;} while(false);
-		do {double _=G_bar[i]; G_bar[i]=G_bar[j]; G_bar[j]=_;} while(false);
-	}
-
-	void reconstruct_gradient()
-	{
-		// reconstruct inactive elements of G from G_bar and free variables
-
-		if(active_size == l) return;
-
-		int i;
-		for(i=active_size;i<l;i++)
-			G[i] = G_bar[i] + b[i];
-
-		for(i=0;i<active_size;i++)
-			if(is_free(i))
-			{
-				float[] Q_i = Q.get_Q(i,l);
-				double alpha_i = alpha[i];
-				for(int j=active_size;j<l;j++)
-					G[j] += alpha_i * Q_i[j];
-			}
-	}
-
-	void Solve(int l, QMatrix Q, double[] b_, byte[] y_,
-		   double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, int shrinking)
-	{
-		this.l = l;
-		this.Q = Q;
-		QD = Q.get_QD();
-		b = (double[])b_.clone();
-		y = (byte[])y_.clone();
-		alpha = (double[])alpha_.clone();
-		this.Cp = Cp;
-		this.Cn = Cn;
-		this.eps = eps;
-		this.unshrinked = 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] = b[i];
-				G_bar[i] = 0;
-			}
-			for(i=0;i<l;i++)
-				if(!is_lower_bound(i))
-				{
-					float[] Q_i = Q.get_Q(i,l);
-					double alpha_i = alpha[i];
-					int j;
-					for(j=0;j<l;j++)
-						G[j] += alpha_i*Q_i[j];
-					if(is_upper_bound(i))
-						for(j=0;j<l;j++)
-							G_bar[j] += get_C(i) * Q_i[j];
-				}
-		}
-
-		// optimization step
-
-		int iter = 0;
-		int counter = Math.min(l,1000)+1;
-		int[] working_set = new int[2];
-
-		while(true)
-		{
-			// show progress and do shrinking
-
-			if(--counter == 0)
-			{
-				counter = Math.min(l,1000);
-				if(shrinking!=0) do_shrinking();
-				// System.err.print(".");
-			}
-
-			if(select_working_set(working_set)!=0)
-			{
-				// reconstruct the whole gradient
-				reconstruct_gradient();
-				// reset active set size and check
-				active_size = l;
-				// System.err.print("*");
-				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 = Q_i[i]+Q_j[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 = Q_i[i]+Q_j[j]-2*Q_i[j];
-				if (quad_coef <= 0)
-					quad_coef = 1e-12;
-				double delta = (G[i]-G[j])/quad_coef;
-				double sum = alpha[i] + alpha[j];
-				alpha[i] -= delta;
-				alpha[j] += delta;
-
-				if(sum > C_i)
-				{
-					if(alpha[i] > C_i)
-					{
-						alpha[i] = C_i;
-						alpha[j] = sum - C_i;
-					}
-				}
-				else
-				{
-					if(alpha[j] < 0)
-					{
-						alpha[j] = 0;
-						alpha[i] = sum;
-					}
-				}
-				if(sum > C_j)
-				{
-					if(alpha[j] > C_j)
-					{
-						alpha[j] = C_j;
-						alpha[i] = sum - C_j;
-					}
-				}
-				else
-				{
-					if(alpha[i] < 0)
-					{
-						alpha[i] = 0;
-						alpha[j] = sum;
-					}
-				}
-			}
-
-			// update G
-
-			double delta_alpha_i = alpha[i] - old_alpha_i;
-			double delta_alpha_j = alpha[j] - old_alpha_j;
-
-			for(int k=0;k<active_size;k++)
-			{
-				G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
-			}
-
-			// update alpha_status and G_bar
-
-			{
-				boolean ui = is_upper_bound(i);
-				boolean uj = is_upper_bound(j);
-				update_alpha_status(i);
-				update_alpha_status(j);
-				int k;
-				if(ui != is_upper_bound(i))
-				{
-					Q_i = Q.get_Q(i,l);
-					if(ui)
-						for(k=0;k<l;k++)
-							G_bar[k] -= C_i * Q_i[k];
-					else
-						for(k=0;k<l;k++)
-							G_bar[k] += C_i * Q_i[k];
-				}
-
-				if(uj != is_upper_bound(j))
-				{
-					Q_j = Q.get_Q(j,l);
-					if(uj)
-						for(k=0;k<l;k++)
-							G_bar[k] -= C_j * Q_j[k];
-					else
-						for(k=0;k<l;k++)
-							G_bar[k] += C_j * Q_j[k];
-				}
-			}
-
-		}
-
-		// 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] + b[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;
-
-		// System.out.print("\noptimization finished, #iter = "+iter+"\n");
-	}
-
-	// return 1 if already optimal, return 0 otherwise
-	int select_working_set(int[] working_set)
-	{
-		// return i,j such that
-		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
-		// j: mimimizes the decrease of obj value
-		//    (if quadratic coefficeint <= 0, replace it with tau)
-		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
-		
-		double Gmax = -INF;
-		double Gmax2 = -INF;
-		int Gmax_idx = -1;
-		int Gmin_idx = -1;
-		double obj_diff_min = INF;
-	
-		for(int t=0;t<active_size;t++)
-			if(y[t]==+1)	
-			{
-				if(!is_upper_bound(t))
-					if(-G[t] >= Gmax)
-					{
-						Gmax = -G[t];
-						Gmax_idx = t;
-					}
-			}
-			else
-			{
-				if(!is_lower_bound(t))
-					if(G[t] >= Gmax)
-					{
-						Gmax = G[t];
-						Gmax_idx = t;
-					}
-			}
-	
-		int i = Gmax_idx;
-		float[] Q_i = null;
-		if(i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
-			Q_i = Q.get_Q(i,active_size);
-	
-		for(int j=0;j<active_size;j++)
-		{
-			if(y[j]==+1)
-			{
-				if (!is_lower_bound(j))
-				{
-					double grad_diff=Gmax+G[j];
-					if (G[j] >= Gmax2)
-						Gmax2 = G[j];
-					if (grad_diff > 0)
-					{
-						double obj_diff; 
-						double quad_coef=Q_i[i]+QD[j]-2*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=Q_i[i]+QD[j]+2*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;
-	}
-
-	// return 1 if already optimal, return 0 otherwise
-	int max_violating_pair(int[] working_set)
-	{
-		// return i,j which maximize -grad(f)^T d , under constraint
-		// if alpha_i == C, d != +1
-		// if alpha_i == 0, d != -1
-
-		double Gmax1 = -INF;		// max { -y_i * grad(f)_i | i in I_up(\alpha) }
-		int Gmax1_idx = -1;
-
-		int Gmax2_idx = -1;
-		double Gmax2 = -INF;		// max { y_i * grad(f)_i | i in I_low(\alpha) }
-
-		for(int i=0;i<active_size;i++)
-		{
-			if(y[i]==+1)	// y = +1
-			{
-				if(!is_upper_bound(i))	// d = +1
-				{
-					if(-G[i] >= Gmax1)
-					{
-						Gmax1 = -G[i];
-						Gmax1_idx = i;
-					}
-				}
-				if(!is_lower_bound(i))	// d = -1
-				{
-					if(G[i] >= Gmax2)
-					{
-						Gmax2 = G[i];
-						Gmax2_idx = i;
-					}
-				}
-			}
-			else		// y = -1
-			{
-				if(!is_upper_bound(i))	// d = +1
-				{
-					if(-G[i] >= Gmax2)
-					{
-						Gmax2 = -G[i];
-						Gmax2_idx = i;
-					}
-				}
-				if(!is_lower_bound(i))	// d = -1
-				{
-					if(G[i] >= Gmax1)
-					{
-						Gmax1 = G[i];
-						Gmax1_idx = i;
-					}
-				}
-			}
-		}
-
-		if(Gmax1+Gmax2 < eps)
-	 		return 1;
-
-		working_set[0] = Gmax1_idx;
-		working_set[1] = Gmax2_idx;
-		return 0;
-	}
-
-	void do_shrinking()
-	{
-		int i,j,k;
-		int[] working_set = new int[2];
-		if(max_violating_pair(working_set)!=0) return;
-		i = working_set[0];
-		j = working_set[1];
-		double Gm1 = -y[j]*G[j];
-		double Gm2 = y[i]*G[i];
-
-		// shrink
-	
-		for(k=0;k<active_size;k++)
-		{
-			if(is_lower_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(-G[k] >= Gm1) continue;
-				}
-				else	if(-G[k] >= Gm2) continue;
-			}
-			else if(is_upper_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(G[k] >= Gm2) continue;
-				}
-				else	if(G[k] >= Gm1) continue;
-			}
-			else continue;
-
-			--active_size;
-			swap_index(k,active_size);
-			--k;	// look at the newcomer
-		}
-
-		// unshrink, check all variables again before final iterations
-
-		if(unshrinked || -(Gm1 + Gm2) > eps*10) return;
-
-		unshrinked = true;
-		reconstruct_gradient();
-
-		for(k=l-1;k>=active_size;k--)
-		{
-			if(is_lower_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(-G[k] < Gm1) continue;
-				}
-				else	if(-G[k] < Gm2) continue;
-			}
-			else if(is_upper_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(G[k] < Gm2) continue;
-				}
-				else	if(G[k] < Gm1) continue;
-			}
-			else continue;
-
-			swap_index(k,active_size);
-			active_size++;
-			++k;	// look at the newcomer
-		}
-	}
-
-	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[] b, byte[] y,
-		   double[] alpha, double Cp, double Cn, double eps,
-		   SolutionInfo si, int shrinking)
-	{
-		this.si = si;
-		super.Solve(l,Q,b,y,alpha,Cp,Cn,eps,si,shrinking);
-	}
-
-	// return 1 if already optimal, return 0 otherwise
-	int select_working_set(int[] working_set)
-	{
-		// return i,j such that y_i = y_j and
-		// i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
-		// j: minimizes the decrease of obj value
-		//    (if quadratic coefficeint <= 0, replace it with tau)
-		//    -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
-	
-		double Gmaxp = -INF;
-		double Gmaxp2 = -INF;
-		int Gmaxp_idx = -1;
-	
-		double Gmaxn = -INF;
-		double Gmaxn2 = -INF;
-		int Gmaxn_idx = -1;
-	
-		int Gmin_idx = -1;
-		double obj_diff_min = INF;
-	
-		for(int t=0;t<active_size;t++)
-			if(y[t]==+1)
-			{
-				if(!is_upper_bound(t))
-					if(-G[t] >= Gmaxp)
-					{
-						Gmaxp = -G[t];
-						Gmaxp_idx = t;
-					}
-			}
-			else
-			{
-				if(!is_lower_bound(t))
-					if(G[t] >= Gmaxn)
-					{
-						Gmaxn = G[t];
-						Gmaxn_idx = t;
-					}
-			}
-	
-		int ip = Gmaxp_idx;
-		int in = Gmaxn_idx;
-		float[] Q_ip = null;
-		float[] Q_in = null;
-		if(ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
-			Q_ip = Q.get_Q(ip,active_size);
-		if(in != -1)
-			Q_in = Q.get_Q(in,active_size);
-	
-		for(int j=0;j<active_size;j++)
-		{
-			if(y[j]==+1)
-			{
-				if (!is_lower_bound(j))	
-				{
-					double grad_diff=Gmaxp+G[j];
-					if (G[j] >= Gmaxp2)
-						Gmaxp2 = G[j];
-					if (grad_diff > 0)
-					{
-						double obj_diff; 
-						double quad_coef = Q_ip[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 = Q_in[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;
-	}
-
-	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 k;
-		for(k=0;k<active_size;k++)
-		{
-			if(!is_upper_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(-G[k] > Gmax1) Gmax1 = -G[k];
-				}
-				else	if(-G[k] > Gmax3) Gmax3 = -G[k];
-			}
-			if(!is_lower_bound(k))
-			{
-				if(y[k]==+1)
-				{	
-					if(G[k] > Gmax2) Gmax2 = G[k];
-				}
-				else	if(G[k] > Gmax4) Gmax4 = G[k];
-			}
-		}
-
-		// shrinking
-
-		double Gm1 = -Gmax2;
-		double Gm2 = -Gmax1;
-		double Gm3 = -Gmax4;
-		double Gm4 = -Gmax3;
-
-		for(k=0;k<active_size;k++)
-		{
-			if(is_lower_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(-G[k] >= Gm1) continue;
-				}
-				else	if(-G[k] >= Gm3) continue;
-			}
-			else if(is_upper_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(G[k] >= Gm2) continue;
-				}
-				else	if(G[k] >= Gm4) continue;
-			}
-			else continue;
-
-			--active_size;
-			swap_index(k,active_size);
-			--k;	// look at the newcomer
-		}
-
-		// unshrink, check all variables again before final iterations
-
-		if(unshrinked || Math.max(-(Gm1+Gm2),-(Gm3+Gm4)) > eps*10) return;
-	
-		unshrinked = true;
-		reconstruct_gradient();
-
-		for(k=l-1;k>=active_size;k--)
-		{
-			if(is_lower_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(-G[k] < Gm1) continue;
-				}
-				else	if(-G[k] < Gm3) continue;
-			}
-			else if(is_upper_bound(k))
-			{
-				if(y[k]==+1)
-				{
-					if(G[k] < Gm2) continue;
-				}
-				else	if(G[k] < Gm4) continue;
-			}
-			else continue;
-
-			swap_index(k,active_size);
-			active_size++;
-			++k;	// look at the newcomer
-		}
-	}
-	
-	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 float[] QD;
-
-	SVC_Q(svm_problem prob, svm_parameter param, byte[] y_)
-	{
-		super(prob.size(), prob.getMatrix(), param);
-		y = (byte[])y_.clone();
-		cache = new Cache(prob.size(),(int)(param.cache_size*(1<<20)));
-		QD = new float[prob.size()];
-		for(int i=0;i<prob.size();i++)
-			QD[i]= (float)kernel_function(i,i);
-	}
-
-	float[] get_Q(int i, int len)
-	{
-		float[][] data = new float[1][];
-		int start;
-		if((start = cache.get_data(i,data,len)) < len)
-		{
-			for(int j=start;j<len;j++)
-				data[0][j] = (float)(y[i]*y[j]*kernel_function(i,j));
-		}
-		return data[0];
-	}
-
-	float[] get_QD()
-	{
-		return QD;
-	}
-
-	void swap_index(int i, int j)
-	{
-		cache.swap_index(i,j);
-		super.swap_index(i,j);
-		do {byte _=y[i]; y[i]=y[j]; y[j]=_;} while(false);
-		do {float _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
-	}
-}
-
-class ONE_CLASS_Q extends Kernel
-{
-	private final Cache cache;
-	private final float[] QD;
-
-	ONE_CLASS_Q(svm_problem prob, svm_parameter param)
-	{
-		super(prob.size(), prob.getMatrix(), param);
-		cache = new Cache(prob.size(),(int)(param.cache_size*(1<<20)));
-		QD = new float[prob.size()];
-		for(int i=0;i<prob.size();i++)
-			QD[i]= (float)kernel_function(i,i);
-	}
-
-	float[] get_Q(int i, int len)
-	{
-		float[][] data = new float[1][];
-		int start;
-		if((start = cache.get_data(i,data,len)) < len)
-		{
-			for(int j=start;j<len;j++)
-				data[0][j] = (float)kernel_function(i,j);
-		}
-		return data[0];
-	}
-
-	float[] get_QD()
-	{
-		return QD;
-	}
-
-	void swap_index(int i, int j)
-	{
-		cache.swap_index(i,j);
-		super.swap_index(i,j);
-		do {float _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
-	}
-}
-
-class SVR_Q extends Kernel
-{
-	private final int l;
-	private final Cache cache;
-	private final byte[] sign;
-	private final int[] index;
-	private int next_buffer;
-	private float[][] buffer;
-	private final float[] QD;
-
-	SVR_Q(svm_problem prob, svm_parameter param)
-	{
-		super(prob.size(), prob.getMatrix(), param);
-		l = prob.size();
-		cache = new Cache(l,(int)(param.cache_size*(1<<20)));
-		QD = new float[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] = (float)kernel_function(k,k);
-			QD[k+l] = QD[k];
-		}
-		buffer = new float[2][2*l];
-		next_buffer = 0;
-	}
-
-	void swap_index(int i, int j)
-	{
-		do {byte _=sign[i]; sign[i]=sign[j]; sign[j]=_;} while(false);
-		do {int _=index[i]; index[i]=index[j]; index[j]=_;} while(false);
-		do {float _=QD[i]; QD[i]=QD[j]; QD[j]=_;} while(false);
-	}
-
-	float[] get_Q(int i, int len)
-	{
-		float[][] data = new float[1][];
-		int real_i = index[i];
-		if(cache.get_data(real_i,data,l) < l)
-		{
-			for(int j=0;j<l;j++)
-				data[0][j] = (float)kernel_function(real_i,j);
-		}
-
-		// reorder and copy
-		float buf[] = buffer[next_buffer];
-		next_buffer = 1 - next_buffer;
-		byte si = sign[i];
-		for(int j=0;j<len;j++)
-			buf[j] = si * sign[j] * data[0][index[j]];
-		return buf;
-	}
-
-	float[] get_QD()
-	{
-		return QD;
-	}
-}
-
-public class svm {
-	//
-	// construct and solve various formulations
-	//
-	private static void solve_c_svc(svm_problem prob, svm_parameter param,
-					double[] alpha, Solver.SolutionInfo si,
-					double Cp, double Cn)
-	{
-		int l = prob.size();
-		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.getLabel(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)
-		//	System.out.print("nu = "+sum_alpha/(Cp*prob.size())+"\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.size();
-		double nu = param.nu;
-
-		byte[] y = new byte[l];
-
-		for(i=0;i<l;i++)
-			if(prob.getLabel(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;
-
-		// System.out.print("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.size();
-		double[] zeros = new double[l];
-		byte[] ones = new byte[l];
-		int i;
-
-		int n = (int)(param.nu*prob.size());	// # of alpha's at upper bound
-
-		for(i=0;i<n;i++)
-			alpha[i] = 1;
-		if(n<prob.size())
-			alpha[n] = param.nu * prob.size() - 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.size();
-		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.getLabel(i);
-			y[i] = 1;
-
-			alpha2[i+l] = 0;
-			linear_term[i+l] = param.p + prob.getLabel(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]);
-		}
-		// System.out.print("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.size();
-		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.getLabel(i);
-			y[i] = 1;
-
-			linear_term[i+l] = prob.getLabel(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);
-
-		// System.out.print("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.size()];
-		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;
-		}
-
-		// System.out.print("obj = "+si.obj+", rho = "+si.rho+"\n");
-
-		// output SVs
-
-		int nSV = 0;
-		int nBSV = 0;
-		for(int i=0;i<prob.size();i++)
-		{
-			if(Math.abs(alpha[i]) > 0)
-			{
-				++nSV;
-				if(prob.getLabel(i) > 0)
-				{
-					if(Math.abs(alpha[i]) >= si.upper_bound_p)
-					++nBSV;
-				}
-				else
-				{
-					if(Math.abs(alpha[i]) >= si.upper_bound_n)
-						++nBSV;
-				}
-			}
-		}
-
-		// System.out.print("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-3;	// 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)
-			{
-				System.err.print("Line search fails in two-class probability estimates\n");
-				break;
-			}
-		}
-		
-		if (iter>=max_iter)
-			System.err.print("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)
-			System.err.print("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.size()];
-		double[] dec_values = new double[prob.size()];
-
-		// random shuffle
-		for(i=0;i<prob.size();i++) perm[i]=i;
-		for(i=0;i<prob.size();i++)
-		{
-			int j = i+(int)(Math.random()*(prob.size()-i));
-			do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
-		}
-		for(i=0;i<nr_fold;i++)
-		{
-			int begin = i*prob.size()/nr_fold;
-			int end = (i+1)*prob.size()/nr_fold;
-			int j,k;
-		
-      int subSize = prob.size()-(end-begin);
-      svm_problem subprob = new svm_problem_impl(subSize);
-			
-			k=0;
-			for(j=0;j<begin;j++)
-			{
-        subprob.setNodes(k, prob.getNodes(perm[j]));
-        subprob.setLabel(k, prob.getLabel(perm[j]));
-				++k;
-			}
-			for(j=end;j<prob.size();j++)
-			{
-        subprob.setNodes(k, prob.getNodes(perm[j]));
-        subprob.setLabel(k, prob.getLabel(perm[j]));
-				++k;
-			}
-			int p_count=0,n_count=0;
-			for(j=0;j<k;j++)
-				if(subprob.getLabel(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.getNodes(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.size(),dec_values,prob.getLabelArray(),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.size()];
-		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.size();i++)
-		{
-			ymv[i]=prob.getLabel(i)-ymv[i];
-			mae += Math.abs(ymv[i]);
-		}		
-		mae /= prob.size();
-		double std=Math.sqrt(2*mae*mae);
-		int count=0;
-		mae=0;
-		for(i=0;i<prob.size();i++)
-			if (Math.abs(ymv[i]) > 5*std) 
-				count=count+1;
-			else 
-				mae+=Math.abs(ymv[i]);
-		mae /= (prob.size()-count);
-		System.err.print("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.size();
-		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.getLabel(i));
-			int j;
-			for(j=0;j<nr_class;j++)
-			{
-				if(this_label == label[j])
-				{
-					++count[j];
-					break;
-				}
-			}
-			data_label[i] = j;
-			if(j == nr_class)
-			{
-				if(nr_class == max_nr_class)
-				{
-					max_nr_class *= 2;
-					int[] new_data = new int[max_nr_class];
-					System.arraycopy(label,0,new_data,0,label.length);
-					label = new_data;
-					new_data = new int[max_nr_class];
-					System.arraycopy(count,0,new_data,0,count.length);
-					count = new_data;					
-				}
-				label[nr_class] = this_label;
-				count[nr_class] = 1;
-				++nr_class;
-			}
-		}
-
-		int[] start = new int[nr_class];
-		start[0] = 0;
-		for(i=1;i<nr_class;i++)
-			start[i] = start[i-1]+count[i-1];
-		for(i=0;i<l;i++)
-		{
-			perm[start[data_label[i]]] = i;
-			++start[data_label[i]];
-		}
-		start[0] = 0;
-		for(i=1;i<nr_class;i++)
-			start[i] = start[i-1]+count[i-1];
-
-		nr_class_ret[0] = nr_class;
-		label_ret[0] = label;
-		start_ret[0] = start;
-		count_ret[0] = count;
-	}
-
-	//
-	// Interface functions
-	//
-	public static svm_model svm_train(svm_problem prob, svm_parameter param)
-	{
-		svm_model model = new svm_model();
-		model.param = param;
-
-		if(param.svm_type == svm_parameter.ONE_CLASS ||
-		   param.svm_type == svm_parameter.EPSILON_SVR ||
-		   param.svm_type == svm_parameter.NU_SVR)
-		{
-			// regression or one-class-svm
-			model.nr_class = 2;
-			model.label = null;
-			model.nSV = null;
-			model.probA = null; model.probB = null;
-			model.sv_coef = new double[1][];
-
-			if(param.probability == 1 &&
-			   (param.svm_type == svm_parameter.EPSILON_SVR ||
-			    param.svm_type == svm_parameter.NU_SVR))
-			{
-				model.probA = new double[1];
-				model.probA[0] = svm_svr_probability(prob,param);
-			}
-
-			decision_function f = svm_train_one(prob,param,0,0);
-			model.rho = new double[1];
-			model.rho[0] = f.rho;
-
-			int nSV = 0;
-			int i;
-			for(i=0;i<prob.size();i++)
-				if(Math.abs(f.alpha[i]) > 0) ++nSV;
-			model.l = nSV;
-			model.SV = new svm_node[nSV][];
-			model.sv_coef[0] = new double[nSV];
-			int j = 0;
-			for(i=0;i<prob.size();i++)
-				if(Math.abs(f.alpha[i]) > 0)
-				{
-					model.SV[j] = prob.getNodes(i);
-					model.sv_coef[0][j] = f.alpha[i];
-					++j;
-				}
-		}
-		else
-		{
-			// classification
-			int l = prob.size();
-			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];
-			svm_node[][] x = new svm_node[l][];
-			int i;
-			for(i=0;i<l;i++)
-        x[i] = prob.getNodes(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++)
-				{
-					int si = start[i], sj = start[j];
-					int ci = count[i], cj = count[j];
-          svm_problem sub_prob = new svm_problem_impl(ci+cj);
-					int k;
-					for(k=0;k<ci;k++)
-					{
-						//sub_prob.x[k] = x[si+k];
-						//sub_prob.y[k] = +1;
-            sub_prob.setNodes(k, x[si+k]);
-            sub_prob.setLabel(k, +1);
-					}
-					for(k=0;k<cj;k++)
-					{
-            sub_prob.setNodes(ci+k, x[sj+k]);
-            sub_prob.setLabel(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;
-			}
-
-			// System.out.print("Total nSV = "+nnz+"\n");
-
-			model.l = nnz;
-			model.SV = new svm_node[nnz][];
-			p = 0;
-			for(i=0;i<l;i++)
-				if(nonzero[i]) model.SV[p++] = x[i];
-
-			int[] nz_start = new int[nr_class];
-			nz_start[0] = 0;
-			for(i=1;i<nr_class;i++)
-				nz_start[i] = nz_start[i-1]+nz_count[i-1];
-
-			model.sv_coef = new double[nr_class-1][];
-			for(i=0;i<nr_class-1;i++)
-				model.sv_coef[i] = new double[nnz];
-
-			p = 0;
-			for(i=0;i<nr_class;i++)
-				for(int j=i+1;j<nr_class;j++)
-				{
-					// classifier (i,j): coefficients with
-					// i are in sv_coef[j-1][nz_start[i]...],
-					// j are in sv_coef[i][nz_start[j]...]
-
-					int si = start[i];
-					int sj = start[j];
-					int ci = count[i];
-					int cj = count[j];
-
-					int q = nz_start[i];
-					int k;
-					for(k=0;k<ci;k++)
-						if(nonzero[si+k])
-							model.sv_coef[j-1][q++] = f[p].alpha[k];
-					q = nz_start[j];
-					for(k=0;k<cj;k++)
-						if(nonzero[sj+k])
-							model.sv_coef[i][q++] = f[p].alpha[ci+k];
-					++p;
-				}
-		}
-		return model;
-	}
-	
-	// Stratified cross validation
-	public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
-	{
-		int i;
-		int[] fold_start = new int[nr_fold+1];
-		int l = prob.size();
-		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[] label = tmp_label[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+(int)(Math.random()*(count[c]-i));
-					do {int _=index[start[c]+j]; index[start[c]+j]=index[start[c]+i]; index[start[c]+i]=_;} while(false);
-				}
-			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+(int)(Math.random()*(l-i));
-				do {int _=perm[i]; perm[i]=perm[j]; perm[j]=_;} while(false);
-			}
-			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;
-			      
-      int subSize = l-(end-begin);
-      svm_problem subprob = new svm_problem_impl(subSize);
-
-			k=0;
-			for(j=0;j<begin;j++)
-			{
-				// subprob.x[k] = prob.x[perm[j]];
-        subprob.setNodes(k, prob.getNodes(perm[j]));
-				//subprob.y[k] = prob.y[perm[j]];
-        subprob.setLabel(k, prob.getLabel(perm[j]));
-				++k;
-			}
-			for(j=end;j<l;j++)
-			{
-				// subprob.x[k] = prob.x[perm[j]];
-				// subprob.y[k] = prob.y[perm[j]];
-        subprob.setNodes(k, prob.getNodes(perm[j]));
-        subprob.setLabel(k, prob.getLabel(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);
-          target[perm[j]] = svm_predict_probability(submodel,prob.getNodes(perm[j]),prob_estimates);
-			}
-			else
-				for(j=begin;j<end;j++)
-          target[perm[j]] = svm_predict(submodel,prob.getNodes(perm[j]));
-		}
-	}
-
-	public static int svm_get_svm_type(svm_model model)
-	{
-		return model.param.svm_type;
-	}
-
-	public static int svm_get_nr_class(svm_model model)
-	{
-		return model.nr_class;
-	}
-
-	public static void svm_get_labels(svm_model model, int[] label)
-	{
-		if (model.label != null)
-			for(int i=0;i<model.nr_class;i++)
-				label[i] = model.label[i];
-	}
-
-	public static double svm_get_svr_probability(svm_model model)
-	{
-		if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
-		    model.probA!=null)
-		return model.probA[0];
-		else
-		{
-			System.err.print("Model doesn't contain information for SVR probability inference\n");
-			return 0;
-		}
-	}
-
-	public static void svm_predict_values(svm_model model, svm_node[] x, 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)
-		{
-			double[] sv_coef = model.sv_coef[0];
-			double sum = 0;
-			for(int 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;
-		}
-		else
-		{
-			int i;
-			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 p=0;
-			int pos=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[pos++] = sum;					
-				}
-		}
-	}
-
-	public static double svm_predict(svm_model model, svm_node[] x)
-	{
-		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[] res = new double[1];
-			svm_predict_values(model, x, res);
-
-			if(model.param.svm_type == svm_parameter.ONE_CLASS)
-				return (res[0]>0)?1:-1;
-			else
-				return res[0];
-		}
-		else
-		{
-			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);
-
-			int[] vote = new int[nr_class];
-			for(i=0;i<nr_class;i++)
-				vote[i] = 0;
-			int pos=0;
-			for(i=0;i<nr_class;i++)
-				for(int j=i+1;j<nr_class;j++)
-				{
-					if(dec_values[pos++] > 0)
-						++vote[i];
-					else
-						++vote[j];
-				}
-
-			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_probability(svm_model model, svm_node[] x, double[] prob_estimates)
-	{
-		if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
-		    model.probA!=null && model.probB!=null)
-		{
-			int i;
-			int nr_class = model.nr_class;
-			double[] dec_values = new double[nr_class*(nr_class-1)/2];
-			svm_predict_values(model, x, dec_values);
-
-			double min_prob=1e-7;
-			double[][] pairwise_prob=new double[nr_class][nr_class];
-			
-			int k=0;
-			for(i=0;i<nr_class;i++)
-				for(int j=i+1;j<nr_class;j++)
-				{
-					pairwise_prob[i][j]=Math.min(Math.max(sigmoid_predict(dec_values[k],model.probA[k],model.probB[k]),min_prob),1-min_prob);
-					pairwise_prob[j][i]=1-pairwise_prob[i][j];
-					k++;
-				}
-			multiclass_probability(nr_class,pairwise_prob,prob_estimates);
-
-			int prob_max_idx = 0;
-			for(i=1;i<nr_class;i++)
-				if(prob_estimates[i] > prob_estimates[prob_max_idx])
-					prob_max_idx = i;
-			return model.label[prob_max_idx];
-		}
-		else 
-			return svm_predict(model, x);
-	}
-
-	static final String svm_type_table[] =
-	{
-		"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",
-	};
-
-	static final String kernel_type_table[]=
-	{
-		"linear","polynomial","rbf","sigmoid","precomputed"
-	};
-
-	public static void svm_save_model(String model_file_name, svm_model model) throws IOException
-	{
-		DataOutputStream fp = new DataOutputStream(new 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);
-	}
-
-	public static svm_model svm_load_model(String model_file_name) throws IOException
-	{
-		BufferedReader fp = new BufferedReader(new FileReader(model_file_name));
-
-		// read parameters
-
-		svm_model model = new svm_model();
-		svm_parameter param = new svm_parameter();
-		model.param = param;
-		model.rho = null;
-		model.probA = null;
-		model.probB = null;
-		model.label = null;
-		model.nSV = null;
-
-		while(true)
-		{
-			String cmd = fp.readLine();
-			String arg = cmd.substring(cmd.indexOf(' ')+1);
-
-			if(cmd.startsWith("svm_type"))
-			{
-				int i;
-				for(i=0;i<svm_type_table.length;i++)
-				{
-					if(arg.indexOf(svm_type_table[i])!=-1)
-					{
-						param.svm_type=i;
-						break;
-					}
-				}
-				if(i == svm_type_table.length)
-				{
-					System.err.print("unknown svm type.\n");
-					return null;
-				}
-			}
-			else if(cmd.startsWith("kernel_type"))
-			{
-				int i;
-				for(i=0;i<kernel_type_table.length;i++)
-				{
-					if(arg.indexOf(kernel_type_table[i])!=-1)
-					{
-						param.kernel_type=i;
-						break;
-					}
-				}
-				if(i == kernel_type_table.length)
-				{
-					System.err.print("unknown kernel function.\n");
-					return null;
-				}
-			}
-			else if(cmd.startsWith("degree"))
-				param.degree = atoi(arg);
-			else if(cmd.startsWith("gamma"))
-				param.gamma = atof(arg);
-			else if(cmd.startsWith("coef0"))
-				param.coef0 = atof(arg);
-			else if(cmd.startsWith("nr_class"))
-				model.nr_class = atoi(arg);
-			else if(cmd.startsWith("total_sv"))
-				model.l = atoi(arg);
-			else if(cmd.startsWith("rho"))
-			{
-				int n = model.nr_class * (model.nr_class-1)/2;
-				model.rho = new double[n];
-				StringTokenizer st = new StringTokenizer(arg);
-				for(int i=0;i<n;i++)
-					model.rho[i] = atof(st.nextToken());
-			}
-			else if(cmd.startsWith("label"))
-			{
-				int n = model.nr_class;
-				model.label = new int[n];
-				StringTokenizer st = new StringTokenizer(arg);
-				for(int i=0;i<n;i++)
-					model.label[i] = atoi(st.nextToken());					
-			}
-			else if(cmd.startsWith("probA"))
-			{
-				int n = model.nr_class*(model.nr_class-1)/2;
-				model.probA = new double[n];
-				StringTokenizer st = new StringTokenizer(arg);
-				for(int i=0;i<n;i++)
-					model.probA[i] = atof(st.nextToken());					
-			}
-			else if(cmd.startsWith("probB"))
-			{
-				int n = model.nr_class*(model.nr_class-1)/2;
-				model.probB = new double[n];
-				StringTokenizer st = new StringTokenizer(arg);
-				for(int i=0;i<n;i++)
-					model.probB[i] = atof(st.nextToken());					
-			}
-			else if(cmd.startsWith("nr_sv"))
-			{
-				int n = model.nr_class;
-				model.nSV = new int[n];
-				StringTokenizer st = new StringTokenizer(arg);
-				for(int i=0;i<n;i++)
-					model.nSV[i] = atoi(st.nextToken());
-			}
-			else if(cmd.startsWith("SV"))
-			{
-				break;
-			}
-			else
-			{
-				System.err.print("unknown text in model file\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.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.size();
-			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.getLabel(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;
-	}
-}
diff --git a/src/java/libsvm/svm_model.java b/src/java/libsvm/svm_model.java
deleted file mode 100644
index 49028a6..0000000
--- a/src/java/libsvm/svm_model.java
+++ /dev/null
@@ -1,55 +0,0 @@
-//
-// svm_model
-//
-package libsvm;
-
-public class svm_model implements java.io.Serializable {
-  svm_parameter param; // parameter
-  int nr_class; // number of classes, = 2 in regression/one class svm
-  int l; // total #SV
-  svm_node[][] SV; // SVs (SV[l])
-  double[][] sv_coef; // coefficients for SVs in decision functions
-                      // (sv_coef[n-1][l])
-  double[] rho; // constants in decision functions (rho[n*(n-1)/2])
-  double[] probA; // pariwise probability information
-  double[] probB;
-  // for classification only
-  int[] label; // label of each class (label[n])
-  int[] nSV; // number of SVs for each class (nSV[n])
-
-  // nSV[0] + nSV[1] + ... + nSV[n-1] = l
-  double[] linWeights;
-  
-  
-  // returns the weights for each attribute in a linear model
-  public double[] getLinearWeights() throws Exception {
-    if(l == 0) throw new Exception("Model not trained");
-    if(param.kernel_type != svm_parameter.LINEAR) throw new Exception("Model is not a linear kernel");
-    if(nr_class != 2) throw new Exception("Model is not binary");
-    return _getLinearWeights();
-  }
-  
-  private double[] _getLinearWeights() {
-    if(this.linWeights != null) return this.linWeights;    
-    int highestIndex = 0;    
-    // find the highest index in the SVs    
-    for(int i = 0; i < SV.length; i++) {
-      svm_node[] currentnodes = SV[i];
-      for(int j = 0; j < currentnodes.length; j++) {
-        if (highestIndex<currentnodes[j].index){
-          highestIndex=currentnodes[j].index;
-        }
-      }
-    }
-    double[] weights = new double[highestIndex + 1];
-    for(int i = 0; i < SV.length; i++) {
-      double alpha = sv_coef[0][i];
-      svm_node[] currentnodes = SV[i];
-      for(int j = 0; j < currentnodes.length; j++) {
-        weights[currentnodes[j].index] += currentnodes[j].value * alpha;
-      }
-    }
-    this.linWeights = weights;
-    return this.linWeights;
-  }
-};
diff --git a/src/java/libsvm/svm_node.java b/src/java/libsvm/svm_node.java
deleted file mode 100644
index 9ab0a10..0000000
--- a/src/java/libsvm/svm_node.java
+++ /dev/null
@@ -1,6 +0,0 @@
-package libsvm;
-public class svm_node implements java.io.Serializable
-{
-	public int index;
-	public double value;
-}
diff --git a/src/java/libsvm/svm_parameter.java b/src/java/libsvm/svm_parameter.java
deleted file mode 100644
index 429f041..0000000
--- a/src/java/libsvm/svm_parameter.java
+++ /dev/null
@@ -1,47 +0,0 @@
-package libsvm;
-public class svm_parameter implements Cloneable,java.io.Serializable
-{
-	/* svm_type */
-	public static final int C_SVC = 0;
-	public static final int NU_SVC = 1;
-	public static final int ONE_CLASS = 2;
-	public static final int EPSILON_SVR = 3;
-	public static final int NU_SVR = 4;
-
-	/* kernel_type */
-	public static final int LINEAR = 0;
-	public static final int POLY = 1;
-	public static final int RBF = 2;
-	public static final int SIGMOID = 3;
-	public static final int PRECOMPUTED = 4;
-
-	public int svm_type;
-	public int kernel_type;
-	public int degree;	// for poly
-	public double gamma;	// for poly/rbf/sigmoid
-	public double coef0;	// for poly/sigmoid
-
-	// these are for training only
-	public double cache_size; // in MB
-	public double eps;	// stopping criteria
-	public double C;	// for C_SVC, EPSILON_SVR and NU_SVR
-	public int nr_weight;		// for C_SVC
-	public int[] weight_label;	// for C_SVC
-	public double[] weight;		// for C_SVC
-	public double nu;	// for NU_SVC, ONE_CLASS, and NU_SVR
-	public double p;	// for EPSILON_SVR
-	public int shrinking;	// use the shrinking heuristics
-	public int probability; // do probability estimates
-
-	public Object clone() 
-	{
-		try 
-		{
-			return super.clone();
-		} catch (CloneNotSupportedException e) 
-		{
-			return null;
-		}
-	}
-
-}
diff --git a/src/java/libsvm/svm_problem.java b/src/java/libsvm/svm_problem.java
deleted file mode 100644
index 18360e2..0000000
--- a/src/java/libsvm/svm_problem.java
+++ /dev/null
@@ -1,11 +0,0 @@
-package libsvm;
-
-public interface svm_problem {
-  public int size();
-  public double getLabel(int i);
-  public svm_node[] getNodes(int i);
-  public void setNodes(int i,svm_node[] nodes);
-  public void setLabel(int i,double label);
-  public double[] getLabelArray();
-  public svm_node[][] getMatrix();
-}
\ No newline at end of file
diff --git a/src/java/libsvm/svm_problem_impl.java b/src/java/libsvm/svm_problem_impl.java
deleted file mode 100644
index fd226be..0000000
--- a/src/java/libsvm/svm_problem_impl.java
+++ /dev/null
@@ -1,35 +0,0 @@
-package libsvm;
-public class svm_problem_impl implements java.io.Serializable, svm_problem
-{
-	private int l;
-  private double[] y;
-  private svm_node[][] x;
-  
-  public svm_problem_impl(int size){
-    l=size;
-    x = new svm_node[size][];
-    y = new double[size];
-  }
-  
-  public double getLabel(int i) {
-    return y[i];
-  }
-  public svm_node[] getNodes(int i) {
-    return x[i];
-  }
-  public void setNodes(int i,svm_node[] nodes) {
-    x[i]=nodes;
-  }
-  public void setLabel(int i,double label) {
-    y[i]=label;
-  }
-  public int size() {
-    return l;
-  }
-  public double[] getLabelArray(){
-    return y;
-  }
-  public svm_node[][] getMatrix(){
-    return x;
-  }
-}