com.expleague.ml.methods.seq.automaton.evaluation.NonOptimizedStateEvaluation Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of jmll

Various ML methods implemented by myself and my students

There is a newer version: 1.4.9

package com.expleague.ml.methods.seq.automaton.evaluation;


import com.expleague.commons.math.vectors.Vec;
import com.expleague.commons.seq.Seq;
import com.expleague.ml.methods.seq.automaton.DFA;
import com.expleague.ml.data.set.DataSet;
import com.expleague.ml.methods.seq.automaton.AutomatonStats;

import java.util.function.Function;

public class NonOptimizedStateEvaluation implements Function, Double> {
  @Override
  public Double apply(AutomatonStats automatonStats) {
    final DataSet> dataSet = automatonStats.getDataSet();
    final Vec target = automatonStats.getTarget();
    final DFA automaton = automatonStats.getAutomaton();
    final double[] stateSum = new double[automaton.getStateCount()];
    final double[] stateSum2 = new double[automaton.getStateCount()];
    final int[] stateSize = new int[automaton.getStateCount()];

    for (int i = 0; i < target.length(); i++) {
      final int to = automaton.run(dataSet.at(i));
      final double w = target.get(i);
      stateSum[to] += w;
      stateSum2[to] += w * w;
      stateSize[to]++;
    }

    double score = 0;
    for (int i = 0; i < automaton.getStateCount(); i++) {
      if (stateSize[i] > 0)
        score += stateSum2[i] - stateSum[i] * stateSum[i] / stateSize[i];
    }

    return score;
  }
}