Java DoubleVector.sumOfValues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[] 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();
    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
public static double[] doNaiveBayesPrediction(Instance inst,
        DoubleVector observedClassDistribution,
        AutoExpandVector<AttributeClassObserver> attributeObservers) {
    double[] votes = new double[observedClassDistribution.numValues()];
    double observedClassSum = observedClassDistribution.sumOfValues();
    for (int classIndex = 0; classIndex < votes.length; classIndex++) {
        votes[classIndex] = observedClassDistribution.getValue(classIndex)
                / observedClassSum;
        for (int attIndex = 0; attIndex < inst.numAttributes() - 1; attIndex++) {
            int instAttIndex = modelAttIndexToInstanceAttIndex(attIndex,
                    inst);
            AttributeClassObserver obs = attributeObservers.get(attIndex);
            if ((obs != null) && !inst.isMissing(instAttIndex)) {
                votes[classIndex] *= obs.probabilityOfAttributeValueGivenClass(inst.value(instAttIndex), classIndex);
            }
        }
    }
    // TODO: need logic to prevent underflow?
    return votes;
}
 

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
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();
            if ((this.useWeightOption != null)
                    && this.useWeightOption.isSet()) {
                vote.scaleValues(1.0 / (this.error[i] * this.error[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();
    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
@Override
public double probabilityOfAttributeValueGivenClass(double attVal,
        int classVal) {
    DoubleVector obs = this.attValDistPerClass.get(classVal);
    return obs != null ? (obs.getValue((int) attVal) + 1.0)
            / (obs.sumOfValues() + obs.numValues()) : 0.0;
}