Java DoubleVector.getArrayRef方法代码示例

import moa.core.DoubleVector; //导入方法依赖的package包/类
/**
 * Predicts a class for an example.
 */
public double[] getVotesForInstance(Instance inst) {
    DoubleVector combinedVote = new DoubleVector();

    if (this.trainingWeightSeenByModel > 0.0) {
        for (int i = 0; i < this.ensemble.length; i++) {
            if (this.ensembleWeights[i] > 0.0) {
                DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));

                if (vote.sumOfValues() > 0.0) {
                    vote.normalize();
                    //scale weight and prevent overflow
                    vote.scaleValues(this.ensembleWeights[i] / (1.0 * this.ensemble.length + 1));
                    combinedVote.addValues(vote);
                }
            }
        }
    }
    combinedVote.normalize();
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
public double[][] getClassDistsResultingFromBinarySplit(double splitValue) {
    DoubleVector lhsDist = new DoubleVector();
    DoubleVector rhsDist = new DoubleVector();
    for (int i = 0; i < this.attValDistPerClass.size(); i++) {
        GaussianEstimator estimator = this.attValDistPerClass.get(i);
        if (estimator != null) {
            if (splitValue < this.minValueObservedPerClass.getValue(i)) {
                rhsDist.addToValue(i, estimator.getTotalWeightObserved());
            } else if (splitValue >= this.maxValueObservedPerClass.getValue(i)) {
                lhsDist.addToValue(i, estimator.getTotalWeightObserved());
            } else {
                double[] weightDist = estimator.estimatedWeight_LessThan_EqualTo_GreaterThan_Value(splitValue);
                lhsDist.addToValue(i, weightDist[0] + weightDist[1]);
                rhsDist.addToValue(i, weightDist[2]);
            }
        }
    }
    return new double[][]{lhsDist.getArrayRef(), rhsDist.getArrayRef()};
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
public double[] getVotesForInstance(Instance inst) {
    DoubleVector combinedVote = new DoubleVector();
    for (int i = 0; i < this.ensemble.length; i++) {
        double memberWeight = getEnsembleMemberWeight(i);
        if (memberWeight > 0.0) {
            DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
            if (vote.sumOfValues() > 0.0) {
                vote.normalize();
                vote.scaleValues(memberWeight);
                combinedVote.addValues(vote);
            }
        } else {
            break;
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
public double[] getVotesForInstance(Instance inst) {
    DoubleVector combinedVote = new DoubleVector();
    if (this.trainingWeightSeenByModel > 0.0) {
        for (int i = 0; i < this.ensemble.length; i++) {
            if (this.ensembleWeights[i] > 0.0) {
                DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
                if (vote.sumOfValues() > 0.0) {
                    vote.normalize();
                    vote.scaleValues(this.ensembleWeights[i]);
                    combinedVote.addValues(vote);
                }
            }
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override
public double[] getVotesForInstance(Instance inst) {
	DoubleVector combinedVote = new DoubleVector();
	StringBuilder sb = null;
	if (VerbosityOption.getValue()>1)
		sb=new StringBuilder();
	
	for (int i = 0; i < this.ensemble.length; i++) {
		DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(transformInstance(inst,i)));
		if (VerbosityOption.getValue()>1)
				sb.append(vote.getValue(0) + ", ");
		if (this.isRegression == false && vote.sumOfValues() != 0.0){
			vote.normalize();
		}
		combinedVote.addValues(vote);
	}
	if (this.isRegression == true){
		combinedVote.scaleValues(1.0/this.ensemble.length);
	}
	if (VerbosityOption.getValue()>1){
		sb.append(combinedVote.getValue(0)  + ", ").append(inst.classValue());
		System.out.println(sb.toString());
	}
	return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
/**
 * Predicts a class for an example.
 */
public double[] getVotesForInstance(Instance inst) {
	DoubleVector combinedVote = new DoubleVector();

	if (this.trainingWeightSeenByModel > 0.0) {
		for (int i = 0; i < this.learners.length; i++) {
			if (this.weights[i][0] > 0.0) {
				DoubleVector vote = new DoubleVector(this.learners[(int) this.weights[i][1]].getVotesForInstance(inst));

				if (vote.sumOfValues() > 0.0) {
					vote.normalize();
					// scale weight and prevent overflow
					vote.scaleValues(this.weights[i][0] / (1.0 * this.learners.length + 1.0));
					combinedVote.addValues(vote);
				}
			}
		}
	}
	
	//combinedVote.normalize();
	return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override
public double[] getVotesForInstance(Instance instance) {
    Instance testInstance = instance.copy();
    if(this.ensemble == null) 
        initEnsemble(testInstance);
    DoubleVector combinedVote = new DoubleVector();

    for(int i = 0 ; i < this.ensemble.length ; ++i) {
        DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(testInstance));
        if (vote.sumOfValues() > 0.0) {
            vote.normalize();
            double acc = this.ensemble[i].evaluator.getPerformanceMeasurements()[1].getValue();
            if(! this.disableWeightedVote.isSet() && acc > 0.0) {                        
                for(int v = 0 ; v < vote.numValues() ; ++v) {
                    vote.setValue(v, vote.getValue(v) * acc);
                }
            }
            combinedVote.addValues(vote);
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override
public double[] getVotesForInstance(Instance inst) {
    DoubleVector combinedVote = new DoubleVector();
    if (this.trainingWeightSeenByModel > 0.0) {
        for (int i = 0; i < this.ensemble.size(); i++) {
            if (this.ensembleWeights.get(i) > 0.0) {
                DoubleVector vote = new DoubleVector(this.ensemble.get(i)
                        .getVotesForInstance(inst));
                if (vote.sumOfValues() > 0.0) {
                    vote.normalize();
                    vote.scaleValues(this.ensembleWeights.get(i));
                    combinedVote.addValues(vote);
                }
            }
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
/**
 * Predicts a class for an example.
 */
public double[] getVotesForInstance(Instance inst) {
	DoubleVector combinedVote = new DoubleVector();

	if (this.trainingWeightSeenByModel > 0.0) {
		for (int i = 0; i < this.ensemble.length; i++) {
			if (this.weights[i][0] > 0.0) {
				DoubleVector vote = new DoubleVector(this.ensemble[(int) this.weights[i][1]].classifier.getVotesForInstance(inst));

				if (vote.sumOfValues() > 0.0) {
					vote.normalize();
					// scale weight and prevent overflow
					vote.scaleValues(this.weights[i][0] / (1.0 * this.ensemble.length + 1.0));
					combinedVote.addValues(vote);
				}
			}
		}
	}
	
	//combinedVote.normalize();
	return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override
public double[] getVotesForInstance(Instance inst) {
    if (this.outputCodesOption.isSet()) {
        return getVotesForInstanceBinary(inst);
    }
    DoubleVector combinedVote = new DoubleVector();
    for (int i = 0; i < this.ensemble.length; i++) {
        double memberWeight = getEnsembleMemberWeight(i);
        if (memberWeight > 0.0) {
            DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
            if (vote.sumOfValues() > 0.0) {
                vote.normalize();
                vote.scaleValues(memberWeight);
                combinedVote.addValues(vote);
            }
        } else {
            break;
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
public double[][] getClassDistsResultingFromBinarySplit(int valIndex) {
    DoubleVector equalsDist = new DoubleVector();
    DoubleVector notEqualDist = new DoubleVector();
    for (int i = 0; i < this.attValDistPerClass.size(); i++) {
        DoubleVector attValDist = this.attValDistPerClass.get(i);
        if (attValDist != null) {
            for (int j = 0; j < attValDist.numValues(); j++) {
                if (j == valIndex) {
                    equalsDist.addToValue(i, attValDist.getValue(j));
                } else {
                    notEqualDist.addToValue(i, attValDist.getValue(j));
                }
            }
        }
    }
    return new double[][]{equalsDist.getArrayRef(),
                notEqualDist.getArrayRef()};
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override
public double[] getVotesForInstance(Instance inst) {
    DoubleVector combinedVote = new DoubleVector();
    for (int i = 0; i < this.ensemble.length; i++) {
        DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
        if (vote.sumOfValues() > 0.0) {
            vote.normalize();
            combinedVote.addValues(vote);
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override 
public double[] getVotesForInstance(Instance inst) {

    DoubleVector combinedVote = new DoubleVector();
    ArrayList<Integer> arr;
  
    int cmb = this.combinationOption.getChosenIndex();
    
    if (cmb == 0)    
    	arr = getMAXIndexes();
    else
    	arr = getWVDIndexes();
    
 
    if (this.trainingWeightSeenByModel > 0.0) {

        for (int i = 0; i < arr.size(); i++) {
            if (this.ensembleWeights[arr.get(i)].val > 0.0) {

                DoubleVector vote = new DoubleVector(this.ensemble[arr.get(i)].getVotesForInstance(inst));

                if (vote.sumOfValues() > 0.0) {
                    vote.normalize();
                    vote.scaleValues(this.ensembleWeights[arr.get(i)].val);
                    combinedVote.addValues(vote);
                }
            }
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
@Override
public double[] getVotesForInstance(Instance inst) {
    if (this.outputCodesOption.isSet()) {
        return getVotesForInstanceBinary(inst);
    }
    DoubleVector combinedVote = new DoubleVector();
    for (int i = 0; i < this.ensemble.length; i++) {
        DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(inst));
        if (vote.sumOfValues() > 0.0) {
            vote.normalize();
            combinedVote.addValues(vote);
        }
    }
    return combinedVote.getArrayRef();
}
 

import moa.core.DoubleVector; //导入方法依赖的package包/类
private double getMaxPosterior(double[] incomingPrediction) {
    if (incomingPrediction.length > 1) {
        DoubleVector vote = new DoubleVector(incomingPrediction);
        if (vote.sumOfValues() > 0.0) {
            vote.normalize();
        }
        incomingPrediction = vote.getArrayRef();
        outPosterior = (incomingPrediction[Utils.maxIndex(incomingPrediction)]);
    } else {
        outPosterior = 0;
    }
    return outPosterior;
}