Author: ssc
Date: Fri Jan 28 14:23:38 2011
New Revision: 1064691
URL: http://svn.apache.org/viewvc?rev=1064691&view=rev
Log:
added Locale.ENGLISH to fix test failures
Modified:
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java
Modified:
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java?rev=1064691&r1=1064690&r2=1064691&view=diff
==============================================================================
---
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java
(original)
+++
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/RunLogistic.java
Fri Jan 28 14:23:38 2011
@@ -34,6 +34,7 @@ import java.io.BufferedReader;
import java.io.File;
import java.io.IOException;
import java.io.PrintStream;
+import java.util.Locale;
public final class RunLogistic {
@@ -64,28 +65,28 @@ public final class RunLogistic {
csv.firstLine(line);
line = in.readLine();
if (showScores) {
- output.printf("\"%s\",\"%s\",\"%s\"\n", "target", "model-output",
"log-likelihood");
+ output.printf(Locale.ENGLISH, "\"%s\",\"%s\",\"%s\"\n", "target",
"model-output", "log-likelihood");
}
while (line != null) {
Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
int target = csv.processLine(line, v);
double score = lr.classifyScalar(v);
if (showScores) {
- output.printf("%d,%.3f,%.6f\n", target, score,
lr.logLikelihood(target, v));
+ output.printf(Locale.ENGLISH, "%d,%.3f,%.6f\n", target, score,
lr.logLikelihood(target, v));
}
collector.add(target, score);
line = in.readLine();
}
if (showAuc) {
- output.printf("AUC = %.2f\n", collector.auc());
+ output.printf(Locale.ENGLISH, "AUC = %.2f\n", collector.auc());
}
if (showConfusion) {
Matrix m = collector.confusion();
- output.printf("confusion: [[%.1f, %.1f], [%.1f, %.1f]]\n",
+ output.printf(Locale.ENGLISH, "confusion: [[%.1f, %.1f], [%.1f,
%.1f]]\n",
m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
m = collector.entropy();
- output.printf("entropy: [[%.1f, %.1f], [%.1f, %.1f]]\n",
+ output.printf(Locale.ENGLISH, "entropy: [[%.1f, %.1f], [%.1f,
%.1f]]\n",
m.get(0, 0), m.get(1, 0), m.get(0, 1), m.get(1, 1));
}
}
Modified:
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java
URL:
http://svn.apache.org/viewvc/mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java?rev=1064691&r1=1064690&r2=1064691&view=diff
==============================================================================
---
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java
(original)
+++
mahout/trunk/examples/src/main/java/org/apache/mahout/classifier/sgd/TrainLogistic.java
Fri Jan 28 14:23:38 2011
@@ -42,6 +42,7 @@ import java.io.PrintStream;
import java.io.Writer;
import java.nio.charset.Charset;
import java.util.List;
+import java.util.Locale;
/**
@@ -92,7 +93,7 @@ public final class TrainLogistic {
}
double p = lr.classifyScalar(input);
if (scores) {
- output.printf("%10d %2d %10.2f %2.4f %10.4f %10.4f\n",
+ output.printf(Locale.ENGLISH, "%10d %2d %10.2f %2.4f %10.4f
%10.4f\n",
samples, targetValue, lr.currentLearningRate(), p, logP,
logPEstimate);
}
@@ -111,13 +112,13 @@ public final class TrainLogistic {
modelOutput.close();
}
- output.printf("%d\n", lmp.getNumFeatures());
- output.printf("%s ~ ", lmp.getTargetVariable());
+ output.printf(Locale.ENGLISH, "%d\n", lmp.getNumFeatures());
+ output.printf(Locale.ENGLISH, "%s ~ ", lmp.getTargetVariable());
String sep = "";
for (String v : csv.getPredictors()) {
double weight = predictorWeight(lr, 0, csv, v);
if (weight != 0) {
- output.printf("%s%.3f*%s", sep, weight, v);
+ output.printf(Locale.ENGLISH, "%s%.3f*%s", sep, weight, v);
sep = " + ";
}
}
@@ -127,11 +128,11 @@ public final class TrainLogistic {
for (String key : csv.getTraceDictionary().keySet()) {
double weight = predictorWeight(lr, row, csv, key);
if (weight != 0) {
- output.printf("%20s %.5f\n", key, weight);
+ output.printf(Locale.ENGLISH, "%20s %.5f\n", key, weight);
}
}
for (int column = 0; column < lr.getBeta().numCols(); column++) {
- output.printf("%15.9f ", lr.getBeta().get(row, column));
+ output.printf(Locale.ENGLISH, "%15.9f ", lr.getBeta().get(row,
column));
}
output.println();
}