Package edu.stanford.nlp.optimization

Examples of edu.stanford.nlp.optimization.QNMinimizer$QNInfo$ZeroGradient

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    //maxFuncIter = 10;
    // 1: QNMinimizer, 2: SGD
    switch (MINIMIZER) {
    case (1): {
      QNMinimizer qn = new QNMinimizer(op.trainOptions.qnEstimates, true);
      qn.useMinPackSearch();
      qn.useDiagonalScaling();
      qn.terminateOnAverageImprovement(true);
      qn.terminateOnNumericalZero(true);
      qn.terminateOnRelativeNorm(true);

      theta = qn.minimize(gcFunc, op.trainOptions.qnTolerance, theta, op.trainOptions.qnIterationsPerBatch);
      break;
    }
    case 2:{
      //Minimizer smd = new SGDMinimizer();      double tol = 1e-4;      theta = smd.minimize(gcFunc,tol,theta,op.trainOptions.qnIterationsPerBatch);
      double lastCost = 0, currCost = 0;
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    tagDist = Distribution.laplaceSmoothedDistribution(tagCounter, tagCounter.size(), 0.5);
    tagCounter = null;
    applyThresholds(data);

    verbose("Making classifier...");
    QNMinimizer minim = new QNMinimizer();//new ResultStoringMonitor(5, "weights"));
//    minim.shutUp();

    LinearClassifierFactory factory = new LinearClassifierFactory(minim);
    factory.setTol(tol);
    factory.setSigma(sigma);
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      }
    }
    int totalFeatures = sumValues[numFeatures - 1] + numValues[numFeatures - 1] + 1;
    System.err.println("total feats " + totalFeatures);
    LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>(totalFeatures, numClasses, newdata, labels, prior, sigma, 0.0);
    Minimizer<DiffFunction> min = new QNMinimizer();
    double[] argmin = min.minimize(objective, 1e-4, objective.initial());
    double[][] wts = objective.to2D(argmin);
    System.out.println("weights have dimension " + wts.length);
    return new NBWeights(wts, numValues);
  }
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  private NBWeights trainWeightsCL(int[][] data, int[] labels, int numFeatures, int numClasses) {

    LogConditionalEqConstraintFunction objective = new LogConditionalEqConstraintFunction(numFeatures, numClasses, data, labels, prior, sigma, 0.0);
    Minimizer<DiffFunction> min = new QNMinimizer();
    double[] argmin = min.minimize(objective, 1e-4, objective.initial());
    double[][][] wts = objective.to3D(argmin);
    double[] priors = objective.priors(argmin);
    return new NBWeights(priors, wts);
  }
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      LogisticObjectiveFunction lof = null;
      if(data instanceof Dataset<?,?>)
        lof = new LogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getLabelsArray(), prior,dataWeights);
      else if(data instanceof RVFDataset<?,?>)
        lof = new LogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getValuesArray(), data.getLabelsArray(), prior,dataWeights);
      minim = new QNMinimizer(lof);
      weights = minim.minimize(lof, 1e-4, new double[data.numFeatureTypes()]);

    featureIndex = data.featureIndex;
    classes[0] = data.labelIndex.get(0);
    classes[1] = data.labelIndex.get(1);
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      else if(data instanceof RVFDataset<?,?>)
        lof = new LogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getValuesArray(), data.getLabelsArray(), prior);
      if (l1reg > 0.0) {
        minim = ReflectionLoading.loadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", l1reg);
      } else {
        minim = new QNMinimizer(lof);
      }
      weights = minim.minimize(lof, tol, new double[data.numFeatureTypes()]);
    } else {
      BiasedLogisticObjectiveFunction lof = new BiasedLogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getLabelsArray(), prior);
      if (l1reg > 0.0) {
        minim = ReflectionLoading.loadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", l1reg);
      } else {
        minim = new QNMinimizer(lof);
      }
      weights = minim.minimize(lof, tol, new double[data.numFeatureTypes()]);
    }

    featureIndex = data.featureIndex;
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    LogisticObjectiveFunction lof = null;
    if(data instanceof Dataset<?,?>)
      lof = new LogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getLabelsArray(), new LogPrior(LogPrior.LogPriorType.QUADRATIC),dataWeights);
    else if(data instanceof RVFDataset<?,?>)
      lof = new LogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getValuesArray(), data.getLabelsArray(), new LogPrior(LogPrior.LogPriorType.QUADRATIC),dataWeights);
    minim = new QNMinimizer(lof);
    weights = minim.minimize(lof, 1e-4, new double[data.numFeatureTypes()]);

    featureIndex = data.featureIndex;
    classes[0] = data.labelIndex.get(0);
    classes[1] = data.labelIndex.get(1);
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      else if(data instanceof RVFDataset<?,?>)
        lof = new LogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getValuesArray(), data.getLabelsArray(), prior);
      if (l1reg > 0.0) {
        minim = ReflectionLoading.loadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", l1reg);
      } else {
        minim = new QNMinimizer(lof);
      }
      weights = minim.minimize(lof, tol, new double[data.numFeatureTypes()]);
    } else {
      BiasedLogisticObjectiveFunction lof = new BiasedLogisticObjectiveFunction(data.numFeatureTypes(), data.getDataArray(), data.getLabelsArray(), prior);
      if (l1reg > 0.0) {
        minim = ReflectionLoading.loadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", l1reg);
      } else {
        minim = new QNMinimizer(lof);
      }
      weights = minim.minimize(lof, tol, new double[data.numFeatureTypes()]);
    }

    featureIndex = data.featureIndex;
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