3#ifndef cMHN_NONTT_LEARN_THETA_H
4#define cMHN_NONTT_LEARN_THETA_H
50 template<
class T, pRC::Size D,
class S>
51 std::tuple<pRC::Tensor<T, D, D>, std::map<std::string, std::string>,
52 std::map<std::string, double>>
54 std::string
const &output, std::map<S, T>
const &pD,
56 T const &toleranceOptimizer,
T const &toleranceSolverP,
57 T const &toleranceSolverQ)
59 auto tempTheta = theta;
67 std::map<std::string, double> logInfoNumbers{{
"Score", score()},
68 {
"Iterations", at_iter},
72 std::map<std::string, std::string> logInfoNames{
75 writeTheta(output, header, tempTheta, logInfoNames, logInfoNumbers);
77 std::cout <<
"cMHN learning started (nonTT):" << std::endl;
78 std::cout <<
"\tScore Name:\t" << logInfoNames[
"Score Name"]
80 std::cout <<
"\tRegulator Name:\t" << logInfoNames[
"Regulator Name"]
93 pInit = pInit / norm<1>(pInit);
98 &toleranceOptimizer, &toleranceSolverP,
99 &toleranceSolverQ](
auto const &tempTheta,
auto &g)
110 [&output, &header, &score, &at_iter, &startTime, &logInfoNames,
111 &logInfoNumbers](
auto const &tempTheta)
114 logInfoNumbers[
"Iterations"] = at_iter;
115 logInfoNumbers[
"Score"] = score();
116 logInfoNumbers[
"Time"] =
119 std::cout <<
"cMHN learning in progress (nonTT):" << std::endl;
120 std::cout << std::defaultfloat;
121 std::cout <<
"\tIteration:\t" << logInfoNumbers[
"Iterations"]
123 std::cout << std::scientific;
124 std::cout <<
"\tLambda:\t\t" << logInfoNumbers[
"Lambda"]
126 std::cout <<
"\tScore:\t\t" << logInfoNumbers[
"Score"]
128 std::cout <<
"\tTime:\t\t" << logInfoNumbers[
"Time"]
130 std::cout << std::defaultfloat;
132 writeTheta(output, header, tempTheta, logInfoNames,
137 return std::make_tuple(tempTheta, logInfoNames, logInfoNumbers);
Class storing all relevant information for a regulator.
Definition regulator.hpp:30
auto & lambda()
Definition regulator.hpp:58
auto name() const
Definition regulator.hpp:68
Class storing all relevant information for a score.
Definition score.hpp:27
auto name() const
Definition score.hpp:54
Class storing an MHN operator represented by a theta matrix (for non TT calculations)
Definition mhn_operator.hpp:24
Definition type_traits.hpp:57
Definition threefry.hpp:24
pRC::Float<> T
Definition externs_nonTT.hpp:1
std::tuple< pRC::Tensor< T, D, D >, std::map< std::string, std::string >, std::map< std::string, double > > learnTheta(pRC::Tensor< T, D, D > const &theta, std::string const &header, std::string const &output, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, T const &toleranceOptimizer, T const &toleranceSolverP, T const &toleranceSolverQ)
Optimizes an MHN represented by a theta matrix to best describe a given data distribution.
Definition learn_theta.hpp:53
std::tuple< T, pRC::Tensor< T, D, D > > calculateScoreAndGradient(nonTT::MHNOperator< T, D > const &op, std::map< S, T > const &pD, cMHN::Score< T > const &Score, cMHN::Regulator< T, D > const &Regulator, T const &toleranceSolverQ=1e-4)
Calculate score and gradient of a theta matrix given some data distribution pD.
Definition calculate_score_and_gradient.hpp:35
static auto writeTheta(std::string const &filename, std::string const &header, pRC::Tensor< T, D, D > const &theta, std::map< std::string, std::string > const &logInfoNames={}, std::map< std::string, double > const &logInfoNumbers={})
Writes a theta matrix to file, including additional logging information at the bottom.
Definition write_theta.hpp:29
static constexpr auto makeConstantSequence()
Definition sequence.hpp:402
static constexpr auto random(RandomEngine &rng, D &distribution)
Definition random.hpp:12
Size Index
Definition type_traits.hpp:21
static Float< 64 > getTimeInSeconds()
Definition stopwatch.hpp:23