Author: joern
Date: Fri Apr 4 09:36:27 2014
New Revision: 1584580
URL: http://svn.apache.org/r1584580
Log:
OPENNLP-569 Fixing remaining issues with the unit tests. Thanks to Vinh Khuc
for providing a patch.
Added:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodFunctionTest.java
Modified:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/LineSearchTest.java
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction.java
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction02.java
Modified:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/LineSearchTest.java
URL:
http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/LineSearchTest.java?rev=1584580&r1=1584579&r2=1584580&view=diff
==============================================================================
---
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/LineSearchTest.java
(original)
+++
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/LineSearchTest.java
Fri Apr 4 09:36:27 2014
@@ -27,7 +27,7 @@ import org.junit.Test;
public class LineSearchTest {
public static final double TOLERANCE = 0.01;
- // @Test
+ @Test
public void testLineSearchDeterminesSaneStepLength01() {
DifferentiableFunction objectiveFunction = new QuadraticFunction();
// given
@@ -44,7 +44,7 @@ public class LineSearchTest {
assertTrue(succCond);
}
- // @Test
+ @Test
public void testLineSearchDeterminesSaneStepLength02() {
DifferentiableFunction objectiveFunction = new QuadraticFunction02();
// given
@@ -61,7 +61,7 @@ public class LineSearchTest {
assertTrue(succCond);
}
- //@Test
+ @Test
public void testLineSearchFailsWithWrongDirection01() {
DifferentiableFunction objectiveFunction = new QuadraticFunction();
// given
@@ -79,7 +79,7 @@ public class LineSearchTest {
assertEquals(0.0, stepSize, TOLERANCE);
}
- // @Test
+ @Test
public void testLineSearchFailsWithWrongDirection02() {
DifferentiableFunction objectiveFunction = new QuadraticFunction02();
// given
@@ -97,7 +97,7 @@ public class LineSearchTest {
assertEquals(0.0, stepSize, TOLERANCE);
}
- // @Test
+ @Test
public void testLineSearchFailsWithWrongDirection03() {
DifferentiableFunction objectiveFunction = new QuadraticFunction();
// given
@@ -115,7 +115,7 @@ public class LineSearchTest {
assertEquals(0.0, stepSize, TOLERANCE);
}
- // @Test
+ @Test
public void testLineSearchFailsWithWrongDirection04() {
DifferentiableFunction objectiveFunction = new QuadraticFunction02();
// given
@@ -133,8 +133,8 @@ public class LineSearchTest {
assertEquals(0.0, stepSize, TOLERANCE);
}
- // @Test
- public void testLineSearchFailsAtMaxima01() {
+ @Test
+ public void testLineSearchFailsAtMinimum01() {
DifferentiableFunction objectiveFunction = new QuadraticFunction02();
// given
double[] testX = new double[] { 0 };
@@ -151,8 +151,8 @@ public class LineSearchTest {
assertEquals(0.0, stepSize, TOLERANCE);
}
- // @Test
- public void testLineSearchFailsAtMaxima02() {
+ @Test
+ public void testLineSearchFailsAtMinimum02() {
DifferentiableFunction objectiveFunction = new QuadraticFunction02();
// given
double[] testX = new double[] { 0 };
Added:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodFunctionTest.java
URL:
http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodFunctionTest.java?rev=1584580&view=auto
==============================================================================
---
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodFunctionTest.java
(added)
+++
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihoodFunctionTest.java
Fri Apr 4 09:36:27 2014
@@ -0,0 +1,247 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package opennlp.tools.ml.maxent.quasinewton;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.Map;
+
+import opennlp.tools.ml.model.DataIndexer;
+import opennlp.tools.ml.model.OnePassRealValueDataIndexer;
+import opennlp.tools.ml.model.RealValueFileEventStream;
+
+import org.junit.Test;
+
+public class NegLogLikelihoodFunctionTest {
+ public final double TOLERANCE01 = 1.0E-06;
+ public final double TOLERANCE02 = 1.0E-10;
+
+ @Test
+ public void testDomainDimensionSanity() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ int correctDomainDimension = testDataIndexer.getPredLabels().length
+ * testDataIndexer.getOutcomeLabels().length;
+ // then
+ assertEquals(correctDomainDimension, objectFunction.getDomainDimension());
+ }
+
+ @Test
+ public void testInitialSanity() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ double[] initial = objectFunction.getInitialPoint();
+ // then
+ for (int i = 0; i < initial.length; i++) {
+ assertEquals(0.0, initial[i], TOLERANCE01);
+ }
+ }
+
+ @Test
+ public void testGradientSanity() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ double[] initial = objectFunction.getInitialPoint();
+ /** valueAt() must be always called before gradientAt() */
+ objectFunction.valueAt(initial);
+ double[] gradientAtInitial = objectFunction.gradientAt(initial);
+ // then
+ assertNotNull(gradientAtInitial);
+ }
+
+ @Test
+ public void testValueAtInitialPoint() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ double value = objectFunction.valueAt(objectFunction.getInitialPoint());
+ double expectedValue = 13.86294361;
+ // then
+ assertEquals(expectedValue, value, TOLERANCE01);
+ }
+
+ @Test
+ public void testValueAtNonInitialPoint01() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ double[] nonInitialPoint = new double[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
+ double value = objectFunction.valueAt(nonInitialPoint);
+ double expectedValue = 23.862943611198894;
+ // then
+ assertEquals(expectedValue, value, TOLERANCE01);
+ }
+
+ @Test
+ public void testValueAtNonInitialPoint02() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ double[] nonInitialPoint = new double[] { 3, 2, 3, 2, 3, 2, 3, 2, 3, 2 };
+ double value =
objectFunction.valueAt(dealignDoubleArrayForTestData(nonInitialPoint,
+ testDataIndexer.getPredLabels(),
+ testDataIndexer.getOutcomeLabels()));
+ double expectedValue = 118.16321972109903;
+ // then
+ assertEquals(expectedValue, value, TOLERANCE02);
+ }
+
+ @Test
+ public void testGradientAtInitialPoint() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ /** valueAt() must be always called before gradientAt() */
+ objectFunction.valueAt(objectFunction.getInitialPoint());
+ double[] gradientAtInitialPoint =
objectFunction.gradientAt(objectFunction.getInitialPoint());
+ double[] expectedGradient = new double[] { -9.0, -14.0, -17.0, 20.0, 8.5,
9.0, 14.0, 17.0, -20.0, -8.5 };
+ // then
+ assertTrue(compareDoubleArray(expectedGradient, gradientAtInitialPoint,
+ testDataIndexer, TOLERANCE01));
+ }
+
+ @Test
+ public void testGradientAtNonInitialPoint() throws IOException {
+ // given
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt",
"UTF-8");
+ DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
+ NegLogLikelihoodFunction objectFunction = new
NegLogLikelihoodFunction(testDataIndexer);
+ // when
+ double[] nonInitialPoint = new double[] { 0.2, 0.5, 0.2, 0.5, 0.2, 0.5,
0.2, 0.5, 0.2, 0.5 };
+ /** valueAt() must be always called before gradientAt() */
+ objectFunction.valueAt(nonInitialPoint);
+ double[] gradientAtNonInitialPoint =
+
objectFunction.gradientAt(dealignDoubleArrayForTestData(nonInitialPoint,
+ testDataIndexer.getPredLabels(),
+ testDataIndexer.getOutcomeLabels()));
+ double[] expectedGradient =
+ new double[] { -8.742040938155275, -10.81798632847702,
+ 30.311389857093182, 1.5513000872985572,
+ 2.0513491106450754, 10.142040938155278,
+ 12.217986328477027, -28.911389857093162,
+ -0.15130008729855715, -0.6513491106450751 };
+ // then
+ assertTrue(compareDoubleArray(expectedGradient, gradientAtNonInitialPoint,
+ testDataIndexer, TOLERANCE01));
+ }
+
+ private double[] alignDoubleArrayForTestData(double[] expected,
+ String[] predLabels, String[] outcomeLabels) {
+ double[] aligned = new double[predLabels.length * outcomeLabels.length];
+
+ String[] sortedPredLabels = predLabels.clone();
+ String[] sortedOutcomeLabels = outcomeLabels.clone();
+ Arrays.sort(sortedPredLabels);
+ Arrays.sort(sortedOutcomeLabels);
+
+ Map<String, Integer> invertedPredIndex = new HashMap<String, Integer>();
+ Map<String, Integer> invertedOutcomeIndex = new HashMap<String,
Integer>();
+ for (int i = 0; i < predLabels.length; i++) {
+ invertedPredIndex.put(predLabels[i], i);
+ }
+ for (int i = 0; i < outcomeLabels.length; i++) {
+ invertedOutcomeIndex.put(outcomeLabels[i], i);
+ }
+
+ for (int i = 0; i < sortedOutcomeLabels.length; i++) {
+ for (int j = 0; j < sortedPredLabels.length; j++) {
+ aligned[i * sortedPredLabels.length + j] =
expected[invertedOutcomeIndex
+ .get(sortedOutcomeLabels[i])
+ * sortedPredLabels.length
+ + invertedPredIndex.get(sortedPredLabels[j])];
+ }
+ }
+ return aligned;
+ }
+
+ private double[] dealignDoubleArrayForTestData(double[] expected,
+ String[] predLabels, String[] outcomeLabels) {
+ double[] dealigned = new double[predLabels.length * outcomeLabels.length];
+
+ String[] sortedPredLabels = predLabels.clone();
+ String[] sortedOutcomeLabels = outcomeLabels.clone();
+ Arrays.sort(sortedPredLabels);
+ Arrays.sort(sortedOutcomeLabels);
+
+ Map<String, Integer> invertedPredIndex = new HashMap<String, Integer>();
+ Map<String, Integer> invertedOutcomeIndex = new HashMap<String, Integer>();
+ for (int i = 0; i < predLabels.length; i++) {
+ invertedPredIndex.put(predLabels[i], i);
+ }
+ for (int i = 0; i < outcomeLabels.length; i++) {
+ invertedOutcomeIndex.put(outcomeLabels[i], i);
+ }
+
+ for (int i = 0; i < sortedOutcomeLabels.length; i++) {
+ for (int j = 0; j < sortedPredLabels.length; j++) {
+ dealigned[invertedOutcomeIndex.get(sortedOutcomeLabels[i])
+ * sortedPredLabels.length
+ + invertedPredIndex.get(sortedPredLabels[j])] = expected[i
+ * sortedPredLabels.length + j];
+ }
+ }
+
+ return dealigned;
+ }
+
+ private boolean compareDoubleArray(double[] expected, double[] actual,
+ DataIndexer indexer, double tolerance) {
+ double[] alignedActual = alignDoubleArrayForTestData(
+ actual, indexer.getPredLabels(), indexer.getOutcomeLabels());
+
+ if (expected.length != alignedActual.length) {
+ return false;
+ }
+
+ for (int i = 0; i < alignedActual.length; i++) {
+ if (Math.abs(alignedActual[i] - expected[i]) > tolerance) {
+ return false;
+ }
+ }
+ return true;
+ }
+}
Modified:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java
URL:
http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java?rev=1584580&r1=1584579&r2=1584580&view=diff
==============================================================================
---
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java
(original)
+++
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNPrepAttachTest.java
Fri Apr 4 09:36:27 2014
@@ -52,7 +52,8 @@ public class QNPrepAttachTest {
trainParams.put(AbstractEventTrainer.DATA_INDEXER_PARAM,
AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE);
trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
- trainParams.put(QNTrainer.L2COST_PARAM, Double.toString(2.0)); // L2-cost
higher than the default
+ // use L2-cost higher than the default
+ trainParams.put(QNTrainer.L2COST_PARAM, Double.toString(2.0));
MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null)
.train(createTrainingStream());
Modified:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java
URL:
http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java?rev=1584580&r1=1584579&r2=1584580&view=diff
==============================================================================
---
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java
(original)
+++
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QNTrainerTest.java
Fri Apr 4 09:36:27 2014
@@ -16,44 +16,35 @@
*/
package opennlp.tools.ml.maxent.quasinewton;
-import static opennlp.tools.ml.PrepAttachDataUtil.createTrainingStream;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertTrue;
-import java.io.BufferedReader;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.DataOutputStream;
import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.util.ArrayList;
-import java.util.List;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.BinaryFileDataReader;
import opennlp.tools.ml.model.DataIndexer;
-import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.GenericModelReader;
import opennlp.tools.ml.model.GenericModelWriter;
-import opennlp.tools.ml.model.MaxentModel;
import opennlp.tools.ml.model.OnePassRealValueDataIndexer;
import opennlp.tools.ml.model.RealValueFileEventStream;
-import opennlp.tools.ml.model.TwoPassDataIndexer;
-import opennlp.tools.ml.perceptron.PerceptronPrepAttachTest;
import org.junit.Test;
public class QNTrainerTest {
- private static int ITERATIONS = 10;
+ private static int ITERATIONS = 50;
@Test
public void testTrainModelReturnsAQNModel() throws Exception {
// given
- RealValueFileEventStream rvfes1 = new
RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
// when
QNModel trainedModel = new QNTrainer(false).trainModel(ITERATIONS,
testDataIndexer);
@@ -64,30 +55,30 @@ public class QNTrainerTest {
@Test
public void testInTinyDevSet() throws Exception {
// given
- RealValueFileEventStream rvfes1 = new
RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
// when
QNModel trainedModel = new QNTrainer(15, true).trainModel(ITERATIONS,
testDataIndexer);
- String[] features2Classify = new String[] {"feature2","feature3",
"feature3", "feature3","feature3", "feature3", "feature3","feature3",
"feature3", "feature3","feature3", "feature3"};
+ String[] features2Classify = new String[] {
+ "feature2","feature3", "feature3",
+ "feature3","feature3", "feature3",
+ "feature3","feature3", "feature3",
+ "feature3","feature3", "feature3"};
double[] eval = trainedModel.eval(features2Classify);
// then
assertNotNull(eval);
}
-
- @Test
- public void testInBigDevSet() throws IOException {
- QNModel trainedModel = new QNTrainer(10, 1000,
true).trainModel(ITERATIONS, new TwoPassDataIndexer(createTrainingStream()));
- // then
- testModel(trainedModel);
- }
@Test
public void testModel() throws IOException {
// given
- RealValueFileEventStream rvfes1 = new
RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new
OnePassRealValueDataIndexer(rvfes1,1);
// when
- QNModel trainedModel = new QNTrainer(15,
true).trainModel(ITERATIONS, testDataIndexer);
+ QNModel trainedModel = new QNTrainer(15, true).trainModel(
+ ITERATIONS, testDataIndexer);
assertTrue(trainedModel.equals(trainedModel));
assertFalse(trainedModel.equals(null));
@@ -96,14 +87,15 @@ public class QNTrainerTest {
@Test
public void testSerdeModel() throws IOException {
// given
- RealValueFileEventStream rvfes1 = new
RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
+ RealValueFileEventStream rvfes1 = new RealValueFileEventStream(
+
"src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt");
DataIndexer testDataIndexer = new
OnePassRealValueDataIndexer(rvfes1,1);
// when
- // QNModel trainedModel = new QNTrainer(5, 500, true).trainModel(new
TwoPassDataIndexer(createTrainingStream()));
QNModel trainedModel = new QNTrainer(5, 700,
true).trainModel(ITERATIONS, testDataIndexer);
ByteArrayOutputStream modelBytes = new ByteArrayOutputStream();
- GenericModelWriter modelWriter = new
GenericModelWriter(trainedModel, new DataOutputStream(modelBytes));
+ GenericModelWriter modelWriter = new
GenericModelWriter(trainedModel,
+ new DataOutputStream(modelBytes));
modelWriter.persist();
modelWriter.close();
@@ -114,7 +106,11 @@ public class QNTrainerTest {
assertTrue(trainedModel.equals(deserModel));
- String[] features2Classify = new String[] {"feature2","feature3",
"feature3", "feature3","feature3", "feature3", "feature3","feature3",
"feature3", "feature3","feature3", "feature3"};
+ String[] features2Classify = new String[] {
+ "feature2","feature3", "feature3",
+ "feature3","feature3", "feature3",
+ "feature3","feature3", "feature3",
+ "feature3","feature3", "feature3"};
double[] eval01 = trainedModel.eval(features2Classify);
double[] eval02 = deserModel.eval(features2Classify);
@@ -123,51 +119,4 @@ public class QNTrainerTest {
assertEquals(eval01[i], eval02[i], 0.00000001);
}
}
-
- public static void testModel(MaxentModel model) throws IOException {
- List<Event> devEvents = readPpaFile("devset");
-
- int total = 0;
- int correct = 0;
- for (Event ev: devEvents) {
- String targetLabel = ev.getOutcome();
- double[] ocs = model.eval(ev.getContext());
-
- int best = 0;
- for (int i=1; i<ocs.length; i++)
- if (ocs[i] > ocs[best])
- best = i;
- String predictedLabel = model.getOutcome(best);
-
- if (targetLabel.equals(predictedLabel))
- correct++;
- total++;
- }
-
- double accuracy = correct/(double)total;
- System.out.println("Accuracy on PPA devset: (" + correct + "/" + total +
") " + accuracy);
- }
-
- private static List<Event> readPpaFile(String filename) throws IOException {
-
- List<Event> events = new ArrayList<Event>();
-
- InputStream in =
PerceptronPrepAttachTest.class.getResourceAsStream("/data/ppa/" +
- filename);
-
- try {
- BufferedReader reader = new BufferedReader(new InputStreamReader(in,
"UTF-8"));
- String line;
- while ((line = reader.readLine()) != null) {
- String[] items = line.split("\\s+");
- String label = items[5];
- String[] context = { "verb=" + items[1], "noun=" + items[2],
- "prep=" + items[3], "prep_obj=" + items[4] };
- events.add(new Event(label, context));
- }
- } finally {
- in.close();
- }
- return events;
- }
}
Modified:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction.java
URL:
http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction.java?rev=1584580&r1=1584579&r2=1584580&view=diff
==============================================================================
---
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction.java
(original)
+++
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction.java
Fri Apr 4 09:36:27 2014
@@ -17,17 +17,18 @@
package opennlp.tools.ml.maxent.quasinewton;
/**
- * sample function for unit tests of LineSearch
+ * Sample function for unit tests of LineSearch
*/
public class QuadraticFunction implements DifferentiableFunction {
public double valueAt(double[] x) {
- // -(x-2)^2 + 4;
- return (Math.pow(x[0] - 2.0, 2.0) * -1.0) + 4.0;
+ // (x-2)^2 + 4;
+ return Math.pow(x[0] - 2, 2) + 4;
}
public double[] gradientAt(double[] x) {
- return new double[] {(-2.0 * x[0]) + 4.0};
+ // 2(x-2)
+ return new double[] {2 * (x[0]- 2)};
}
public int getDomainDimension() {
Modified:
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction02.java
URL:
http://svn.apache.org/viewvc/opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction02.java?rev=1584580&r1=1584579&r2=1584580&view=diff
==============================================================================
---
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction02.java
(original)
+++
opennlp/trunk/opennlp-tools/src/test/java/opennlp/tools/ml/maxent/quasinewton/QuadraticFunction02.java
Fri Apr 4 09:36:27 2014
@@ -17,17 +17,17 @@
package opennlp.tools.ml.maxent.quasinewton;
/**
- * sample function for unit tests of LineSearch
+ * Sample function for unit tests of LineSearch
*/
public class QuadraticFunction02 implements DifferentiableFunction {
public double valueAt(double[] x) {
- // -x^2;
- return Math.pow(x[0], 2) * -1;
+ // x^2;
+ return Math.pow(x[0], 2);
}
public double[] gradientAt(double[] x) {
- // -2x
- return new double[] {-2.0 * x[0]};
+ // 2x
+ return new double[] {2 * x[0]};
}
public int getDomainDimension() {
