1155 lines
29 KiB
C++
1155 lines
29 KiB
C++
/* $Id: tests.cpp 148 2005-04-19 15:12:26Z kamenik $ */
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/* Copyright 2004, Ondra Kamenik */
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#include "SylvException.hh"
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#include "tl_exception.hh"
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#include "gs_tensor.hh"
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#include "factory.hh"
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#include "monoms.hh"
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#include "t_container.hh"
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#include "stack_container.hh"
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#include "t_polynomial.hh"
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#include "rfs_tensor.hh"
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#include "ps_tensor.hh"
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#include "tl_static.hh"
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#include <string>
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#include <utility>
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#include <memory>
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#include <vector>
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#include <ctime>
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#include <cstdlib>
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#include <iostream>
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#include <iomanip>
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class TestRunnable
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{
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public:
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const std::string name;
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int dim; // dimension of the solved problem
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int nvar; // number of variable of the solved problem
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TestRunnable(std::string name_arg, int d, int nv)
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: name{std::move(name_arg)}, dim(d), nvar(nv)
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{
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}
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virtual ~TestRunnable() = default;
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bool test() const;
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virtual bool run() const = 0;
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protected:
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template<class _Ttype>
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static bool index_forward(const Symmetry &s, const IntSequence &nvs);
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template <class _Ttype>
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static bool index_backward(const Symmetry &s, const IntSequence &nvs);
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template <class _Ttype>
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static bool index_offset(const Symmetry &s, const IntSequence &nvs);
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static bool fold_unfold(std::unique_ptr<FTensor> folded);
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static bool
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fs_fold_unfold(int r, int nv, int dim)
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{
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Factory f;
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return fold_unfold(f.make<FFSTensor>(r, nv, dim));
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}
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static bool
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r_fold_unfold(int r, int nv, int dim)
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{
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Factory f;
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return fold_unfold(f.make<FRTensor>(r, nv, dim));
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}
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static bool
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gs_fold_unfold(int r, const Symmetry &s, const IntSequence &nvs)
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{
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Factory f;
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return fold_unfold(f.make<FGSTensor>(r, s, nvs));
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}
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static bool dense_prod(const Symmetry &bsym, const IntSequence &bnvs,
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int hdim, int hnv, int rows);
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static bool folded_monomial(int ng, int nx, int ny, int nu, int dim);
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static bool unfolded_monomial(int ng, int nx, int ny, int nu, int dim);
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static bool fold_zcont(int nf, int ny, int nu, int nup, int nbigg,
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int ng, int dim);
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static bool unfold_zcont(int nf, int ny, int nu, int nup, int nbigg,
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int ng, int dim);
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static bool folded_contraction(int r, int nv, int dim);
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static bool unfolded_contraction(int r, int nv, int dim);
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static bool poly_eval(int r, int nv, int maxdim);
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};
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bool
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TestRunnable::test() const
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{
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std::cout << "Running test <" << name << ">" << std::endl;
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clock_t start = clock();
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bool passed = run();
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clock_t end = clock();
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std::cout << "CPU time " << static_cast<double>(end-start)/CLOCKS_PER_SEC
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<< " (CPU seconds)..................";
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if (passed)
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std::cout << "passed\n\n";
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else
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std::cout << "FAILED\n\n";
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return passed;
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}
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/****************************************************/
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/* definition of TestRunnable static methods */
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/****************************************************/
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template <class _Ttype>
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bool
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TestRunnable::index_forward(const Symmetry &s, const IntSequence &nvs)
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{
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int fails = 0;
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int ndecr = 0;
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int nincr = 0;
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_Ttype dummy(0, TensorDimens(s, nvs));
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auto run = dummy.end();
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do
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{
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--run;
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ndecr++;
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auto run2 = dummy.begin();
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for (int i = 0; i < *run; i++)
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{
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++run2;
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nincr++;
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}
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if (run != run2)
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fails++;
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}
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while (run != dummy.begin());
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std::cout << "\tnumber of columns = " << dummy.ncols() << '\n'
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<< "\tnumber of increments = " << nincr << '\n'
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<< "\tnumber of decrements = " << ndecr << '\n'
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<< "\tnumber of failures = " << fails << '\n';
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return fails == 0;
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}
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template <class _Ttype>
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bool
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TestRunnable::index_backward(const Symmetry &s, const IntSequence &nvs)
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{
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int fails = 0;
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int ndecr = 0;
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int nincr = 0;
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_Ttype dummy(0, TensorDimens(s, nvs));
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auto run = dummy.begin();
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while (run != dummy.end())
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{
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auto run2 = dummy.end();
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for (int i = 0; i < dummy.ncols() - *run; i++)
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{
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--run2;
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ndecr++;
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}
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if (run != run2)
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fails++;
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++run;
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nincr++;
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}
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std::cout << "\tnumber of columns = " << dummy.ncols() << '\n'
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<< "\tnumber of increments = " << nincr << '\n'
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<< "\tnumber of decrements = " << ndecr << '\n'
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<< "\tnumber of failures = " << fails << '\n';
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return fails == 0;
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}
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template <class _Ttype>
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bool
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TestRunnable::index_offset(const Symmetry &s, const IntSequence &nvs)
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{
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int fails = 0;
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int nincr = 0;
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_Ttype dummy(0, TensorDimens(s, nvs));
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for (auto run = dummy.begin(); run != dummy.end(); ++run, nincr++)
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{
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typename _Ttype::index run2(dummy, run.getCoor());
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if (run != run2)
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fails++;
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}
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std::cout << "\tnumber of columns = " << dummy.ncols() << '\n'
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<< "\tnumber of increments = " << nincr << '\n'
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<< "\tnumber of failures = " << fails << '\n';
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return fails == 0;
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}
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bool
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TestRunnable::fold_unfold(std::unique_ptr<FTensor> folded)
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{
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auto unfolded = folded->unfold();
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auto folded2 = unfolded->fold();
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folded2->add(-1.0, *folded);
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double normInf = folded2->getNormInf();
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double norm1 = folded2->getNorm1();
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std::cout << "\tfolded size: (" << folded->nrows() << ", " << folded->ncols() << ")\n"
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<< "\tunfolded size: (" << unfolded->nrows() << ", " << unfolded->ncols() << ")\n"
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<< "\tdifference normInf: " << normInf << '\n'
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<< "\tdifference norm1: " << norm1 << '\n';
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return normInf < 1.0e-15;
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}
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bool
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TestRunnable::dense_prod(const Symmetry &bsym, const IntSequence &bnvs,
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int hdim, int hnv, int rows)
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{
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Factory f;
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FGSContainer cont{f.makeCont<FGSTensor, FGSContainer>(hnv, bnvs, bsym.dimen()-hdim+1)};
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auto fh = f.make<FGSTensor>(rows, Symmetry{hdim}, IntSequence(1, hnv));
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UGSTensor uh(*fh);
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FGSTensor fb(rows, TensorDimens(bsym, bnvs));
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fb.getData().zeros();
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clock_t s1 = clock();
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cont.multAndAdd(uh, fb);
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clock_t s2 = clock();
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UGSContainer ucont(cont);
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clock_t s3 = clock();
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UGSTensor ub(rows, fb.getDims());
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ub.getData().zeros();
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clock_t s4 = clock();
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ucont.multAndAdd(uh, ub);
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clock_t s5 = clock();
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UGSTensor btmp(fb);
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btmp.add(-1, ub);
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double norm = btmp.getData().getMax();
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double norm1 = btmp.getNorm1();
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double normInf = btmp.getNormInf();
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std::cout << "\ttime for folded product: " << static_cast<double>(s2-s1)/CLOCKS_PER_SEC << '\n'
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<< "\ttime for unfolded product: " << static_cast<double>(s5-s4)/CLOCKS_PER_SEC << '\n'
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<< "\ttime for container convert: " << static_cast<double>(s3-s2)/CLOCKS_PER_SEC << '\n'
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<< "\tunfolded difference normMax: " << norm << '\n'
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<< "\tunfolded difference norm1: " << norm1 << '\n'
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<< "\tunfolded difference normInf: " << normInf << '\n';
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return norm < 1.e-13;
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}
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bool
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TestRunnable::folded_monomial(int ng, int nx, int ny, int nu, int dim)
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{
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clock_t gen_time = clock();
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DenseDerivGenerator gen(ng, nx, ny, nu, 5, 0.3, dim);
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gen_time = clock()-gen_time;
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std::cout << "\ttime for monom generation: "
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<< static_cast<double>(gen_time)/CLOCKS_PER_SEC << '\n';
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IntSequence nvs{ny, nu};
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double maxnorm = 0;
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for (int ydim = 0; ydim <= dim; ydim++)
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{
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Symmetry s{ydim, dim-ydim};
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std::cout << "\tSymmetry: ";
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s.print();
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FGSTensor res(ng, TensorDimens(s, nvs));
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res.getData().zeros();
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clock_t stime = clock();
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for (int d = 1; d <= dim; d++)
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gen.xcont.multAndAdd(*(gen.ts[d-1]), res);
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stime = clock() - stime;
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std::cout << "\t\ttime for symmetry: "
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<< static_cast<double>(stime)/CLOCKS_PER_SEC << '\n';
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const FGSTensor &mres = gen.rcont.get(s);
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res.add(-1.0, mres);
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double normtmp = res.getData().getMax();
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std::cout << "\t\terror normMax: " << normtmp << '\n';
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if (normtmp > maxnorm)
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maxnorm = normtmp;
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}
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return maxnorm < 1.0e-10;
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}
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bool
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TestRunnable::unfolded_monomial(int ng, int nx, int ny, int nu, int dim)
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{
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clock_t gen_time = clock();
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DenseDerivGenerator gen(ng, nx, ny, nu, 5, 0.3, dim);
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gen_time = clock()-gen_time;
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std::cout << "\ttime for monom generation: "
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<< static_cast<double>(gen_time)/CLOCKS_PER_SEC << '\n';
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clock_t u_time = clock();
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gen.unfold();
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u_time = clock() - u_time;
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std::cout << "\ttime for monom unfolding: "
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<< static_cast<double>(u_time)/CLOCKS_PER_SEC << '\n';
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IntSequence nvs{ny, nu};
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double maxnorm = 0;
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for (int ydim = 0; ydim <= dim; ydim++)
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{
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Symmetry s{ydim, dim-ydim};
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std::cout << "\tSymmetry: ";
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s.print();
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UGSTensor res(ng, TensorDimens(s, nvs));
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res.getData().zeros();
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clock_t stime = clock();
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for (int d = 1; d <= dim; d++)
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gen.uxcont.multAndAdd(*(gen.uts[d-1]), res);
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stime = clock() - stime;
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std::cout << "\t\ttime for symmetry: "
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<< static_cast<double>(stime)/CLOCKS_PER_SEC << '\n';
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const FGSTensor &mres = gen.rcont.get(s);
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FGSTensor foldres(res);
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foldres.add(-1.0, mres);
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double normtmp = foldres.getData().getMax();
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std::cout << "\t\terror normMax: " << normtmp << '\n';
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if (normtmp > maxnorm)
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maxnorm = normtmp;
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}
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return maxnorm < 1.0e-10;
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}
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bool
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TestRunnable::fold_zcont(int nf, int ny, int nu, int nup, int nbigg,
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int ng, int dim)
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{
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clock_t gen_time = clock();
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SparseDerivGenerator dg(nf, ny, nu, nup, nbigg, ng,
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5, 0.55, dim);
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gen_time = clock()-gen_time;
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for (int d = 1; d <= dim; d++)
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std::cout << "\tfill of dim=" << d << " tensor: "
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<< std::setprecision(2) << std::fixed << std::setw(6)
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<< 100*dg.ts[d-1].getFillFactor()
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<< std::setprecision(6) << std::defaultfloat << " %\n";
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std::cout << "\ttime for monom generation: "
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<< static_cast<double>(gen_time)/CLOCKS_PER_SEC << '\n';
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IntSequence nvs{ny, nu, nup, 1};
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double maxnorm = 0.0;
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// form ZContainer
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FoldedZContainer zc(&dg.bigg, nbigg, &dg.g, ng, ny, nu);
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for (int d = 2; d <= dim; d++)
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for (auto &si : SymmetrySet(d, 4))
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{
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std::cout << "\tSymmetry: ";
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si.print();
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FGSTensor res(nf, TensorDimens(si, nvs));
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res.getData().zeros();
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clock_t stime = clock();
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for (int l = 1; l <= si.dimen(); l++)
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zc.multAndAdd(dg.ts[l-1], res);
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stime = clock() - stime;
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std::cout << "\t\ttime for symmetry: "
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<< static_cast<double>(stime)/CLOCKS_PER_SEC << '\n';
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const FGSTensor &mres = dg.rcont.get(si);
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res.add(-1.0, mres);
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double normtmp = res.getData().getMax();
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std::cout << "\t\terror normMax: " << normtmp << '\n';
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if (normtmp > maxnorm)
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maxnorm = normtmp;
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}
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return maxnorm < 1.0e-10;
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}
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bool
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TestRunnable::unfold_zcont(int nf, int ny, int nu, int nup, int nbigg,
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int ng, int dim)
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{
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clock_t gen_time = clock();
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SparseDerivGenerator dg(nf, ny, nu, nup, nbigg, ng,
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5, 0.55, dim);
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gen_time = clock()-gen_time;
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for (int d = 1; d <= dim; d++)
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std::cout << "\tfill of dim=" << d << " tensor: "
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<< std::setprecision(2) << std::fixed << std::setw(6)
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<< 100*dg.ts[d-1].getFillFactor()
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<< std::setprecision(6) << std::defaultfloat << " %\n";
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std::cout << "\ttime for monom generation: "
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<< static_cast<double>(gen_time)/CLOCKS_PER_SEC << '\n';
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clock_t con_time = clock();
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UGSContainer uG_cont(dg.bigg);
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UGSContainer ug_cont(dg.g);
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con_time = clock()-con_time;
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std::cout << "\ttime for container unfold: "
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<< static_cast<double>(con_time)/CLOCKS_PER_SEC << '\n';
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IntSequence nvs{ny, nu, nup, 1};
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double maxnorm = 0.0;
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// form ZContainer
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UnfoldedZContainer zc(&uG_cont, nbigg, &ug_cont, ng, ny, nu);
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for (int d = 2; d <= dim; d++)
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for (auto &si : SymmetrySet(d, 4))
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{
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std::cout << "\tSymmetry: ";
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si.print();
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UGSTensor res(nf, TensorDimens(si, nvs));
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res.getData().zeros();
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clock_t stime = clock();
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for (int l = 1; l <= si.dimen(); l++)
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zc.multAndAdd(dg.ts[l-1], res);
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stime = clock() - stime;
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std::cout << "\t\ttime for symmetry: "
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<< static_cast<double>(stime)/CLOCKS_PER_SEC << '\n';
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FGSTensor fold_res(res);
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const FGSTensor &mres = dg.rcont.get(si);
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fold_res.add(-1.0, mres);
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double normtmp = fold_res.getData().getMax();
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std::cout << "\t\terror normMax: " << normtmp << '\n';
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if (normtmp > maxnorm)
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maxnorm = normtmp;
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}
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return maxnorm < 1.0e-10;
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}
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bool
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TestRunnable::folded_contraction(int r, int nv, int dim)
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{
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Factory fact;
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Vector x{fact.makeVector(nv)};
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auto forig = fact.make<FFSTensor>(r, nv, dim);
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auto f = std::make_unique<FFSTensor>(*forig);
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clock_t ctime = clock();
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for (int d = dim-1; d > 0; d--)
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f = std::make_unique<FFSTensor>(*f, ConstVector(x));
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ctime = clock() - ctime;
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Vector res(forig->nrows());
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res.zeros();
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f->multaVec(res, x);
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UFSTensor u(*forig);
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clock_t utime = clock();
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URSingleTensor ux(x, dim);
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Vector v(u.nrows());
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v.zeros();
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u.multaVec(v, ux.getData());
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utime = clock() - utime;
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v.add(-1.0, res);
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std::cout << "\ttime for folded contraction: "
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<< static_cast<double>(ctime)/CLOCKS_PER_SEC << '\n'
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<< "\ttime for unfolded power: "
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<< static_cast<double>(utime)/CLOCKS_PER_SEC << '\n'
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<< "\terror normMax: " << v.getMax() << '\n'
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<< "\terror norm1: " << v.getNorm1() << '\n';
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return (v.getMax() < 1.e-10);
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}
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bool
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TestRunnable::unfolded_contraction(int r, int nv, int dim)
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{
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Factory fact;
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Vector x{fact.makeVector(nv)};
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auto forig = fact.make<FFSTensor>(r, nv, dim);
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UFSTensor uorig(*forig);
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auto u = std::make_unique<UFSTensor>(uorig);
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clock_t ctime = clock();
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for (int d = dim-1; d > 0; d--)
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u = std::make_unique<UFSTensor>(*u, ConstVector(x));
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ctime = clock() - ctime;
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Vector res(uorig.nrows());
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res.zeros();
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u->multaVec(res, x);
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clock_t utime = clock();
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URSingleTensor ux(x, dim);
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Vector v(uorig.nrows());
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v.zeros();
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uorig.multaVec(v, ux.getData());
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utime = clock() - utime;
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v.add(-1.0, res);
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std::cout << "\ttime for unfolded contraction: "
|
|
<< static_cast<double>(ctime)/CLOCKS_PER_SEC << '\n'
|
|
<< "\ttime for unfolded power: "
|
|
<< static_cast<double>(utime)/CLOCKS_PER_SEC << '\n'
|
|
<< "\terror normMax: " << v.getMax() << '\n'
|
|
<< "\terror norm1: " << v.getNorm1() << '\n';
|
|
|
|
return (v.getMax() < 1.e-10);
|
|
}
|
|
|
|
bool
|
|
TestRunnable::poly_eval(int r, int nv, int maxdim)
|
|
{
|
|
Factory fact;
|
|
Vector x{fact.makeVector(nv)};
|
|
|
|
Vector out_ft(r);
|
|
out_ft.zeros();
|
|
Vector out_fh(r);
|
|
out_fh.zeros();
|
|
Vector out_ut(r);
|
|
out_ut.zeros();
|
|
Vector out_uh(r);
|
|
out_uh.zeros();
|
|
|
|
FTensorPolynomial fp{fact.makePoly<FFSTensor, FTensorPolynomial>(r, nv, maxdim)};
|
|
|
|
clock_t ft_cl = clock();
|
|
fp.evalTrad(out_ft, x);
|
|
ft_cl = clock() - ft_cl;
|
|
std::cout << "\ttime for folded power eval: "
|
|
<< static_cast<double>(ft_cl)/CLOCKS_PER_SEC << '\n';
|
|
|
|
clock_t fh_cl = clock();
|
|
fp.evalHorner(out_fh, x);
|
|
fh_cl = clock() - fh_cl;
|
|
std::cout << "\ttime for folded horner eval: "
|
|
<< static_cast<double>(fh_cl)/CLOCKS_PER_SEC << '\n';
|
|
|
|
UTensorPolynomial up{fp};
|
|
|
|
clock_t ut_cl = clock();
|
|
up.evalTrad(out_ut, x);
|
|
ut_cl = clock() - ut_cl;
|
|
std::cout << "\ttime for unfolded power eval: "
|
|
<< static_cast<double>(ut_cl)/CLOCKS_PER_SEC << '\n';
|
|
|
|
clock_t uh_cl = clock();
|
|
up.evalHorner(out_uh, x);
|
|
uh_cl = clock() - uh_cl;
|
|
std::cout << "\ttime for unfolded horner eval: "
|
|
<< static_cast<double>(uh_cl)/CLOCKS_PER_SEC << '\n';
|
|
|
|
out_ft.add(-1.0, out_ut);
|
|
double max_ft = out_ft.getMax();
|
|
out_fh.add(-1.0, out_ut);
|
|
double max_fh = out_fh.getMax();
|
|
out_uh.add(-1.0, out_ut);
|
|
double max_uh = out_uh.getMax();
|
|
|
|
std::cout << "\tfolded power error norm max: " << max_ft << '\n'
|
|
<< "\tfolded horner error norm max: " << max_fh << '\n'
|
|
<< "\tunfolded horner error norm max: " << max_uh << '\n';
|
|
|
|
return (max_ft+max_fh+max_uh < 1.0e-10);
|
|
}
|
|
|
|
/****************************************************/
|
|
/* definition of TestRunnable subclasses */
|
|
/****************************************************/
|
|
class SmallIndexForwardFold : public TestRunnable
|
|
{
|
|
public:
|
|
SmallIndexForwardFold()
|
|
: TestRunnable("small index forward for fold (44)(222)", 5, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3};
|
|
IntSequence nvs{4, 2};
|
|
return index_forward<FGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallIndexForwardUnfold : public TestRunnable
|
|
{
|
|
public:
|
|
SmallIndexForwardUnfold()
|
|
: TestRunnable("small index forward for unfold (44)(222)", 5, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3};
|
|
IntSequence nvs{4, 2};
|
|
return index_forward<UGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class IndexForwardFold : public TestRunnable
|
|
{
|
|
public:
|
|
IndexForwardFold()
|
|
: TestRunnable("index forward for fold (55)(222)(22)", 7, 5)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3, 2};
|
|
IntSequence nvs{5, 2, 2};
|
|
return index_forward<FGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class IndexForwardUnfold : public TestRunnable
|
|
{
|
|
public:
|
|
IndexForwardUnfold()
|
|
: TestRunnable("index forward for unfold (55)(222)(22)", 7, 5)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3, 2};
|
|
IntSequence nvs{5, 2, 2};
|
|
return index_forward<UGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallIndexBackwardFold : public TestRunnable
|
|
{
|
|
public:
|
|
SmallIndexBackwardFold()
|
|
: TestRunnable("small index backward for fold (3)(3)(222)", 5, 3)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{1, 1, 3};
|
|
IntSequence nvs{3, 3, 2};
|
|
return index_backward<FGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class IndexBackwardFold : public TestRunnable
|
|
{
|
|
public:
|
|
IndexBackwardFold()
|
|
: TestRunnable("index backward for fold (44)(222)(44)", 7, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3, 2};
|
|
IntSequence nvs{4, 2, 4};
|
|
return index_backward<FGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallIndexBackwardUnfold : public TestRunnable
|
|
{
|
|
public:
|
|
SmallIndexBackwardUnfold()
|
|
: TestRunnable("small index backward for unfold (3)(3)(222)", 5, 3)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{1, 1, 3};
|
|
IntSequence nvs{3, 3, 2};
|
|
return index_backward<UGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class IndexBackwardUnfold : public TestRunnable
|
|
{
|
|
public:
|
|
IndexBackwardUnfold()
|
|
: TestRunnable("index backward for unfold (44)(222)(44)", 7, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3, 2};
|
|
IntSequence nvs{4, 2, 4};
|
|
return index_backward<UGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallIndexOffsetFold : public TestRunnable
|
|
{
|
|
public:
|
|
SmallIndexOffsetFold()
|
|
: TestRunnable("small index offset for fold (44)(222)", 5, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3};
|
|
IntSequence nvs{4, 2};
|
|
return index_offset<FGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallIndexOffsetUnfold : public TestRunnable
|
|
{
|
|
public:
|
|
SmallIndexOffsetUnfold()
|
|
: TestRunnable("small index offset for unfold (44)(222)", 5, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3};
|
|
IntSequence nvs{4, 2};
|
|
return index_offset<UGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class IndexOffsetFold : public TestRunnable
|
|
{
|
|
public:
|
|
IndexOffsetFold()
|
|
: TestRunnable("index offset for fold (55)(222)(22)", 5, 5)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3, 2};
|
|
IntSequence nvs{5, 2, 2};
|
|
return index_offset<FGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class IndexOffsetUnfold : public TestRunnable
|
|
{
|
|
public:
|
|
IndexOffsetUnfold()
|
|
: TestRunnable("index offset for unfold (55)(222)(22)", 7, 5)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 3, 2};
|
|
IntSequence nvs{5, 2, 2};
|
|
return index_offset<UGSTensor>(s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallFoldUnfoldFS : public TestRunnable
|
|
{
|
|
public:
|
|
SmallFoldUnfoldFS()
|
|
: TestRunnable("small fold-unfold for full symmetry (444)", 3, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return fs_fold_unfold(5, 4, 3);
|
|
}
|
|
};
|
|
|
|
class SmallFoldUnfoldGS : public TestRunnable
|
|
{
|
|
public:
|
|
SmallFoldUnfoldGS()
|
|
: TestRunnable("small fold-unfold for gen symmetry (3)(33)(22)", 5, 3)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{1, 2, 2};
|
|
IntSequence nvs{3, 3, 2};
|
|
return gs_fold_unfold(5, s, nvs);
|
|
}
|
|
};
|
|
|
|
class FoldUnfoldFS : public TestRunnable
|
|
{
|
|
public:
|
|
FoldUnfoldFS()
|
|
: TestRunnable("fold-unfold for full symmetry (9999)", 4, 9)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return fs_fold_unfold(5, 9, 4);
|
|
}
|
|
};
|
|
|
|
class FoldUnfoldGS : public TestRunnable
|
|
{
|
|
public:
|
|
FoldUnfoldGS()
|
|
: TestRunnable("fold-unfold for gen symmetry (66)(2)(66)", 5, 6)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
Symmetry s{2, 1, 2};
|
|
IntSequence nvs{6, 2, 6};
|
|
return gs_fold_unfold(5, s, nvs);
|
|
}
|
|
};
|
|
|
|
class SmallFoldUnfoldR : public TestRunnable
|
|
{
|
|
public:
|
|
SmallFoldUnfoldR()
|
|
: TestRunnable("small fold-unfold for row full symmetry (333)", 3, 3)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return r_fold_unfold(5, 3, 3);
|
|
}
|
|
};
|
|
|
|
class FoldUnfoldR : public TestRunnable
|
|
{
|
|
public:
|
|
FoldUnfoldR()
|
|
: TestRunnable("fold-unfold for row full symmetry (66666)", 5, 6)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return r_fold_unfold(5, 6, 5);
|
|
}
|
|
};
|
|
|
|
class SmallDenseProd : public TestRunnable
|
|
{
|
|
public:
|
|
SmallDenseProd()
|
|
: TestRunnable("small dense prod bsym=1-2,nvs=3-2,h=2-3,r=2", 3, 3)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
IntSequence bnvs{3, 2};
|
|
return dense_prod(Symmetry{1, 2}, bnvs, 2, 3, 2);
|
|
}
|
|
};
|
|
|
|
class DenseProd : public TestRunnable
|
|
{
|
|
public:
|
|
DenseProd()
|
|
: TestRunnable("dense prod bsym=2-3,nvs=10-7,h=3-15,r=10", 5, 15)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
IntSequence bnvs{10, 7};
|
|
return dense_prod(Symmetry{2, 3}, bnvs, 3, 15, 10);
|
|
}
|
|
};
|
|
|
|
class BigDenseProd : public TestRunnable
|
|
{
|
|
public:
|
|
BigDenseProd()
|
|
: TestRunnable("dense prod bsym=3-2,nvs=13-11,h=3-20,r=20", 6, 20)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
IntSequence bnvs{13, 11};
|
|
return dense_prod(Symmetry{3, 2}, bnvs, 3, 20, 20);
|
|
}
|
|
};
|
|
|
|
class SmallFoldedMonomial : public TestRunnable
|
|
{
|
|
public:
|
|
SmallFoldedMonomial()
|
|
: TestRunnable("folded vrs. monoms (g,x,y,u)=(10,4,5,3), dim=4", 4, 8)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return folded_monomial(10, 4, 5, 3, 4);
|
|
}
|
|
};
|
|
|
|
class FoldedMonomial : public TestRunnable
|
|
{
|
|
public:
|
|
FoldedMonomial()
|
|
: TestRunnable("folded vrs. monoms (g,x,y,u)=(20,12,10,5), dim=4", 4, 15)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return folded_monomial(20, 12, 10, 5, 4);
|
|
}
|
|
};
|
|
|
|
class SmallUnfoldedMonomial : public TestRunnable
|
|
{
|
|
public:
|
|
SmallUnfoldedMonomial()
|
|
: TestRunnable("unfolded vrs. monoms (g,x,y,u)=(10,4,5,3), dim=4", 4, 8)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return unfolded_monomial(10, 4, 5, 3, 4);
|
|
}
|
|
};
|
|
|
|
class UnfoldedMonomial : public TestRunnable
|
|
{
|
|
public:
|
|
UnfoldedMonomial()
|
|
: TestRunnable("unfolded vrs. monoms (g,x,y,u)=(20,12,10,5), dim=4", 4, 15)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return unfolded_monomial(20, 12, 10, 5, 4);
|
|
}
|
|
};
|
|
|
|
class FoldedContractionSmall : public TestRunnable
|
|
{
|
|
public:
|
|
FoldedContractionSmall()
|
|
: TestRunnable("folded contraction small (r=5, nv=4, dim=3)", 3, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return folded_contraction(5, 4, 3);
|
|
}
|
|
};
|
|
|
|
class FoldedContractionBig : public TestRunnable
|
|
{
|
|
public:
|
|
FoldedContractionBig()
|
|
: TestRunnable("folded contraction big (r=20, nv=12, dim=5)", 5, 12)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return folded_contraction(20, 12, 5);
|
|
}
|
|
};
|
|
|
|
class UnfoldedContractionSmall : public TestRunnable
|
|
{
|
|
public:
|
|
UnfoldedContractionSmall()
|
|
: TestRunnable("unfolded contraction small (r=5, nv=4, dim=3)", 3, 4)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return unfolded_contraction(5, 4, 3);
|
|
}
|
|
};
|
|
|
|
class UnfoldedContractionBig : public TestRunnable
|
|
{
|
|
public:
|
|
UnfoldedContractionBig()
|
|
: TestRunnable("unfolded contraction big (r=20, nv=12, dim=5)", 5, 12)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return unfolded_contraction(20, 12, 5);
|
|
}
|
|
};
|
|
|
|
class PolyEvalSmall : public TestRunnable
|
|
{
|
|
public:
|
|
PolyEvalSmall()
|
|
: TestRunnable("polynomial evaluation small (r=4, nv=5, maxdim=4)", 4, 5)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return poly_eval(4, 5, 4);
|
|
}
|
|
};
|
|
|
|
class PolyEvalBig : public TestRunnable
|
|
{
|
|
public:
|
|
PolyEvalBig()
|
|
: TestRunnable("polynomial evaluation big (r=244, nv=97, maxdim=2)", 2, 97)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return poly_eval(244, 97, 2);
|
|
}
|
|
};
|
|
|
|
class FoldZContSmall : public TestRunnable
|
|
{
|
|
public:
|
|
FoldZContSmall()
|
|
: TestRunnable("folded Z container (r=3,ny=2,nu=2,nup=1,G=2,g=2,dim=3)",
|
|
3, 8)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return fold_zcont(3, 2, 2, 1, 2, 2, 3);
|
|
}
|
|
};
|
|
|
|
class FoldZCont : public TestRunnable
|
|
{
|
|
public:
|
|
FoldZCont()
|
|
: TestRunnable("folded Z container (r=13,ny=5,nu=7,nup=4,G=6,g=7,dim=4)",
|
|
4, 25)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return fold_zcont(13, 5, 7, 4, 6, 7, 4);
|
|
}
|
|
};
|
|
|
|
class UnfoldZContSmall : public TestRunnable
|
|
{
|
|
public:
|
|
UnfoldZContSmall()
|
|
: TestRunnable("unfolded Z container (r=3,ny=2,nu=2,nup=1,G=2,g=2,dim=3)",
|
|
3, 8)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return unfold_zcont(3, 2, 2, 1, 2, 2, 3);
|
|
}
|
|
};
|
|
|
|
class UnfoldZCont : public TestRunnable
|
|
{
|
|
public:
|
|
UnfoldZCont()
|
|
: TestRunnable("unfolded Z container (r=13,ny=5,nu=7,nup=4,G=6,g=7,dim=4",
|
|
4, 25)
|
|
{
|
|
}
|
|
bool
|
|
run() const override
|
|
{
|
|
return unfold_zcont(13, 5, 7, 4, 6, 7, 4);
|
|
}
|
|
};
|
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int
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main()
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{
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std::vector<std::unique_ptr<TestRunnable>> all_tests;
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// fill in vector of all tests
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all_tests.push_back(std::make_unique<SmallIndexForwardFold>());
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all_tests.push_back(std::make_unique<SmallIndexForwardUnfold>());
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all_tests.push_back(std::make_unique<IndexForwardFold>());
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all_tests.push_back(std::make_unique<IndexForwardUnfold>());
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all_tests.push_back(std::make_unique<SmallIndexBackwardFold>());
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all_tests.push_back(std::make_unique<IndexBackwardFold>());
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all_tests.push_back(std::make_unique<SmallIndexBackwardUnfold>());
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all_tests.push_back(std::make_unique<IndexBackwardUnfold>());
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all_tests.push_back(std::make_unique<SmallIndexOffsetFold>());
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all_tests.push_back(std::make_unique<SmallIndexOffsetUnfold>());
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all_tests.push_back(std::make_unique<IndexOffsetFold>());
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all_tests.push_back(std::make_unique<IndexOffsetUnfold>());
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all_tests.push_back(std::make_unique<SmallFoldUnfoldFS>());
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all_tests.push_back(std::make_unique<SmallFoldUnfoldGS>());
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all_tests.push_back(std::make_unique<FoldUnfoldFS>());
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all_tests.push_back(std::make_unique<FoldUnfoldGS>());
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all_tests.push_back(std::make_unique<SmallFoldUnfoldR>());
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all_tests.push_back(std::make_unique<FoldUnfoldR>());
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all_tests.push_back(std::make_unique<SmallDenseProd>());
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all_tests.push_back(std::make_unique<DenseProd>());
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all_tests.push_back(std::make_unique<BigDenseProd>());
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all_tests.push_back(std::make_unique<SmallFoldedMonomial>());
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all_tests.push_back(std::make_unique<FoldedMonomial>());
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all_tests.push_back(std::make_unique<SmallUnfoldedMonomial>());
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all_tests.push_back(std::make_unique<UnfoldedMonomial>());
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all_tests.push_back(std::make_unique<FoldedContractionSmall>());
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all_tests.push_back(std::make_unique<FoldedContractionBig>());
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all_tests.push_back(std::make_unique<UnfoldedContractionSmall>());
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all_tests.push_back(std::make_unique<UnfoldedContractionBig>());
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all_tests.push_back(std::make_unique<PolyEvalSmall>());
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all_tests.push_back(std::make_unique<PolyEvalBig>());
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all_tests.push_back(std::make_unique<FoldZContSmall>());
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all_tests.push_back(std::make_unique<FoldZCont>());
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all_tests.push_back(std::make_unique<UnfoldZContSmall>());
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all_tests.push_back(std::make_unique<UnfoldZCont>());
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// find maximum dimension and maximum nvar
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int dmax = 0;
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int nvmax = 0;
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for (const auto &test : all_tests)
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{
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if (dmax < test->dim)
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dmax = test->dim;
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if (nvmax < test->nvar)
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nvmax = test->nvar;
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}
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TLStatic::init(dmax, nvmax); // initialize library
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// launch the tests
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int success = 0;
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for (const auto &test : all_tests)
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{
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try
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{
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if (test->test())
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success++;
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}
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catch (const TLException &e)
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{
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std::cout << "Caught TL exception in <" << test->name << ">:\n";
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e.print();
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}
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catch (SylvException &e)
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{
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std::cout << "Caught Sylv exception in <" << test->name << ">:\n";
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e.printMessage();
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}
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}
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int nfailed = all_tests.size() - success;
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std::cout << "There were " << nfailed << " tests that failed out of "
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<< all_tests.size() << " tests run." << std::endl;
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if (nfailed)
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return EXIT_FAILURE;
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else
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return EXIT_SUCCESS;
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}
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