/**   softperc.cpp: a perceptron model with sigmoid outputs   (c) 2006-2007 Hsuan-Tien Lin   (some code copied from perceptron.cpp in LEMGA (c) Ling Li)**/#include <assert.h>#include <cmath>#include <numeric>#include "softperc.h"REGISTER_CREATOR(lemga::SoftPerc);#define INPUT_SUM(w,x)  \    std::inner_product(x.begin(), x.end(), w.begin(), w.back())namespace lemga {Output SoftPerc::operator() (const Input& x) const {    assert(x.size() == n_input());    REAL sum = INPUT_SUM(wgt, x);    return Output(1, tanh(scale * sum));}typedef std::vector<REAL> RVEC;typedef std::vector<RVEC> RMAT;#define DOTPROD(x,y) std::inner_product(x.begin(), x.end(), y.begin(), .0)// The version without bias#define DOTPROD_NB(x,y) std::inner_product(x.begin(),x.end()-1,y.begin(),.0)inline void normalize (RVEC& v, REAL thr = 0) {    REAL s = DOTPROD(v, v);    assert(s > 0);    if (s <= thr) return;    s = 1 / std::sqrt(s);    for (RVEC::iterator p = v.begin(); p != v.end(); ++p)        *p *= s;}void SoftPerc::train() {    Perceptron::train();    normalize(wgt);}bool SoftPerc::serialize (std::ostream& os, ver_list& vl) const {    SERIALIZE_PARENT(Perceptron, os, vl, 1);    return (os << scale << '\n');}bool SoftPerc::unserialize (std::istream& is, ver_list& vl, const id_t& d) {    if (d != id() && d != NIL_ID) return false;    UNSERIALIZE_PARENT(Perceptron, is, vl, 1, v);    return (is >> scale);}} // namespace lemga