Issue 5: Upgrade to version 3.0 of libsvm

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

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.
+

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

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)