hephaestus/main.cpp at master · adrianariton/hephaestus · GitHub

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#include <cmath>
#include <iostream>
#include "athena/athena.h"


#include "hephaestus/hephaestus.cpp"
#include "hephaestus/Heph_Utils.h"


/**
 * Use
    #include "hephaestus.cpp"
    #include "Heph_Utils.h"
 * if you work in the same folder
*/

USE_HEPHAESTUS_CPP
using namespace std;

int main() {
    LET A = TENSOR(bigreal)({1, 2, 3,
                             4, 5, 6,
                             7, 1, 0}, MATRIX_3X3) DONE
    LET B = TENSOR(bigreal)({0, 1, 1,
                             1, 1, 1,
                             0, 1, 0}, MATRIX_3X3) DONE

    LET A_einstein = __(A, "^alpha_beta") DONE
    LET B_einstein = __(B, "^beta_gamma") DONE

    LET AxB_matrix_prod = __m(A_einstein, B_einstein) DONE

    LOG "Tensor matrix product:\n" DONE
    FOREACH(i, indices, IN_TENSOR, AxB_matrix_prod.tensor,
        LOG AxB_matrix_prod.at(indices) ALSO SPACE DONE
    )

    LOG_ALONE("")

    // OR JUST DO
    AxB_matrix_prod = __m(A, "^alpha_beta", B, "^beta_gamma") DONE
    LOG "Same tensor matrix product:\n" DONE

    FOREACH(i, indices, IN_TENSOR, AxB_matrix_prod.tensor,
        LOG AxB_matrix_prod.at(indices) ALSO SPACE DONE
    )

    LOG_ALONE("")

    // OR DEFINE A PRODUCT
    DEF_TENSOR_PRODUCT_TO_INDEXED_TENSOR( matrixprod, bigreal, _x, "^alpha_beta", _y, "^beta_gamma", _x.dim() == 2 && _y.dim() == 2);
    DEF_TENSOR_PRODUCT_TO_INDEXED_TENSOR( dblmatrixprod, long double, _x, "^alpha_beta", _y, "^beta_gamma", _x.dim() == 2 && _y.dim() == 2);

    AxB_matrix_prod = DEFN_matrixprod(A, B) DONE
    LOG "Same tensor matrix product but defined:\n" DONE

    FOREACH(i, indices, IN_TENSOR, AxB_matrix_prod.tensor,
        LOG AxB_matrix_prod.at(indices) ALSO SPACE DONE
    )

    LOG_ALONE("")

    srand (time(NULL));
    auto rnd = [] () -> long double {
        return rand() % 10 + 1;
    };

    LET Rando = HTensor<long double>::random_distribution(rnd, HShape({6, 6}));
    LET Rando1 = HTensor<long double>::random_distribution(rnd, HShape({6, 6}));

    LET Rando_sq = DEFN_dblmatrixprod(Rando, Rando1) DONE

    LOG "Random tensor with random values\n" DONE

    FOREACH(i, indices, IN_TENSOR, Rando_sq.tensor,
        LOG Rando_sq.at(indices) ALSO SPACE DONE
    )

    return 0;
}