Package edu.stanford.nlp.optimization

Examples of edu.stanford.nlp.optimization.QNMinimizer.minimize()

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      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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    }
    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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