Now KBP KB thingy works. Kinda

src/edu/stanford/nlp/classify/LinearClassifier.java

@@ -200,15 +200,8 @@ public Counter<L> scoresOf(RVFDatum<L, F> example) {
    */
   private Counter<L> scoresOfRVFDatum(RVFDatum<L, F> example) {
     Counter<L> scores = new ClassicCounter<L>();
-    // Index the features in the datum
-    Counter<F> asCounter = example.asFeaturesCounter();
-    Counter<Integer> asIndexedCounter = new ClassicCounter<>(asCounter.size());
-    for (Map.Entry<F, Double> entry : asCounter.entrySet()) {
-      asIndexedCounter.setCount(featureIndex.indexOf(entry.getKey()), entry.getValue());
-    }
-    // Set the scores appropriately
     for (L l : labels()) {
-      scores.setCount(l, scoreOfRVFDatum(asIndexedCounter, l));
+      scores.setCount(l, scoreOfRVFDatum(example, l));
     }
     //System.out.println("Scores are: " + scores + "   (gold: " + example.label() + ")");
     return scores;
@@ -235,20 +228,8 @@ private double scoreOfRVFDatum(RVFDatum<L, F> example, L label) {
     int iLabel = labelIndex.indexOf(label);
     double score = 0.0;
     Counter<F> features = example.asFeaturesCounter();
-    for (Map.Entry<F, Double> entry : features.entrySet()) {
-      score += weight(entry.getKey(), iLabel) * entry.getValue();
-    }
-    return score + thresholds[iLabel];
-  }
-
-  /** Returns the score of the RVFDatum for the specified label.
-   *  Ignores the true label of the RVFDatum.
-   */
-  private double scoreOfRVFDatum(Counter<Integer> features, L label) {
-    int iLabel = labelIndex.indexOf(label);
-    double score = 0.0;
-    for (Map.Entry<Integer, Double> entry : features.entrySet()) {
-      score += weight(entry.getKey(), iLabel) * entry.getValue();
+    for (F f : features.keySet()) {
+      score += weight(f, iLabel) * features.getCount(f);
     }
     return score + thresholds[iLabel];
   }