Ejemplo n.º 1
0
double MultiClassExp::getConfidence(const FeatureVec& feature_vec, const std::vector<double>& weights_plus, const std::vector<double>& weights_minus) 
{
    double confidence_plus = std::inner_product(feature_vec.begin(), feature_vec.end(),
						weights_plus.begin(), 0.0);
    double confidence_minus = std::inner_product(feature_vec.begin(), feature_vec.end(),
						 weights_minus.begin(), 0.0);
    double confidence = confidence_plus-confidence_minus;
    return confidence;
}
Ejemplo n.º 2
0
/*the document is index:value format because there is less features,centorid is array format for quick access
 *now it's changed into cosine!2011-07-22
 * */
float KNN::InnerProduct(FeatureVec & document,vector<float> & centorid, int catIndex)
{
	/*part of cosine*/
	float similarity=0.0;
	float finalScore=0.0;
	float totalDoc=0.0;

	FeatVecIte it;
	for(it=document.begin();it!=document.end();it++)
	{
		similarity+=((*it).weight*centorid[(*it).index]);//direct access the vector
	}

	//float docLen=inner_product(centorid.begin(),centorid.end(),centorid.begin(),0.0);//tip:可以提前计算好。@zhqm

	return similarity;
}
Ejemplo n.º 3
0
int KNN::MaxProbCat(FeatureVec & document,vector<vector<float> > & Centoroid,float & score)
{
	float MaxScoreNow=0.0;
	int   MaxCatIndex=-1;

	int currentCatIndex=0;

	

    float docDotProduct=0.0;
	FeatVecIte dit;
	for(dit=document.begin();dit!=document.end();dit++)
	{
		docDotProduct+=(*dit).weight*(*dit).weight;	//tip:@zhqm totalDoc +=
	}
    docDotProduct = sqrt(docDotProduct);

    if (docDotProduct == 0){
        score = 0.0;
        return MaxCatIndex;
    }

	vector<vector<float> >::iterator it;
	for(it=Centoroid.begin();it!=Centoroid.end();it++,currentCatIndex++)
	{
		float score=InnerProduct(document,(*it), currentCatIndex);
              score=(float)score/(docDotProduct * m_centoroidFeatureDotProduct.at(currentCatIndex));
		//cout<<"cat="<<currentCatIndex<<"\tscore="<<score<<endl;

		if(score>MaxScoreNow)//a better class
		{
			MaxScoreNow=score;
			MaxCatIndex=currentCatIndex;

		}
	}

	score=MaxScoreNow;
	return MaxCatIndex;
}