两个向量之间的欧式距离及radial-basis-functions(RBF)

template <class DataType1, class DataType2>
double EuclideanDistance(std::vector<DataType1> &inst1, std::vector<DataType2> &inst2) {
　　if(inst1.size() != inst2.size()) {
　　　　std::cout<<"the size of the vectors is not the same ";
　　　　return -1;
　　}
　　std::vector<double> temp;
　　for(size_t i=0; i<inst1.size(); ++i) {
　　　　temp.push_back(pow(inst1.at(i)-inst2.at(i), 2.0));
　　}
　　double distance=accumulate(temp.begin(), temp.end(), 0.0);
　　distance=sqrt(distance);

　　return distance;
}

The radial-basis-functions(RBF) technique consists of choosing a function F that has the form

　　F(x)=Σw_iφ(||x-x_i||)

where {φ(||x-x_i||)|i=1,2,...,N} is a set of N arbitrary (generally nonlinear) functions, known as 

radial-basis functions, and ||•|| denotes a norm that is usually Euclidean.

Much of the theory developed on RBF networks builds on the Gaussian function, an important member

of the class of  radial-basis functions. The Gaussian function may also be viewed as a kernel--hence the 

designation of the two-stage procedure based on the Gaussian function as a kernel method.