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Added the matrix manipulation methods to MLPPMatrix.

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This commit is contained in:
Relintai 2023-04-24 08:24:59 +02:00
parent a34aed1696
commit cfea6f9d83
4 changed files with 1211 additions and 31 deletions
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58
mlpp/lin_alg/lin_alg.cpp
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@ -950,35 +950,6 @@ real_t MLPPLinAlg::sum_elements(std::vector<std::vector<real_t>> A) {
}
*/
Ref<MLPPVector> MLPPLinAlg::flattenmnv(const Vector<Ref<MLPPVector>> &A) {
Ref<MLPPVector> a;
a.instance();
int vsize = 0;
for (int i = 0; i < A.size(); ++i) {
vsize += A[i]->size();
}
a->resize(vsize);
int a_index = 0;
real_t *a_ptr = a->ptrw();
for (int i = 0; i < A.size(); ++i) {
const Ref<MLPPVector> &r = A[i];
int r_size = r->size();
const real_t *r_ptr = r->ptr();
for (int j = 0; j < r_size; ++j) {
a_ptr[a_index] = r_ptr[j];
++a_index;
}
}
return a;
}
Ref<MLPPVector> MLPPLinAlg::flattenvvnv(const Ref<MLPPMatrix> &A) {
int data_size = A->data_size();
@ -1053,6 +1024,35 @@ bool MLPPLinAlg::zeroEigenvalue(std::vector<std::vector<real_t>> A) {
}
*/
Ref<MLPPVector> MLPPLinAlg::flattenmnv(const Vector<Ref<MLPPVector>> &A) {
Ref<MLPPVector> a;
a.instance();
int vsize = 0;
for (int i = 0; i < A.size(); ++i) {
vsize += A[i]->size();
}
a->resize(vsize);
int a_index = 0;
real_t *a_ptr = a->ptrw();
for (int i = 0; i < A.size(); ++i) {
const Ref<MLPPVector> &r = A[i];
int r_size = r->size();
const real_t *r_ptr = r->ptr();
for (int j = 0; j < r_size; ++j) {
a_ptr[a_index] = r_ptr[j];
++a_index;
}
}
return a;
}
Ref<MLPPMatrix> MLPPLinAlg::outer_product(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
Ref<MLPPMatrix> C;
C.instance();

4
mlpp/lin_alg/lin_alg.h
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@ -125,7 +125,6 @@ public:
//real_t sum_elements(std::vector<std::vector<real_t>> A);
Ref<MLPPVector> flattenmnv(const Vector<Ref<MLPPVector>> &A);
Ref<MLPPVector> flattenvvnv(const Ref<MLPPMatrix> &A);
/*
@ -140,6 +139,8 @@ public:
// VECTOR FUNCTIONS
Ref<MLPPVector> flattenmnv(const Vector<Ref<MLPPVector>> &A);
Ref<MLPPMatrix> outer_product(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b); // This multiplies a, bT
Ref<MLPPVector> hadamard_productnv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
@ -200,7 +201,6 @@ public:
real_t norm_sqv(const Ref<MLPPVector> &a);
real_t sum_elementsv(const Ref<MLPPVector> &a);
//real_t cosineSimilarity(std::vector<real_t> a, std::vector<real_t> b);

1065
mlpp/lin_alg/mlpp_matrix.cpp
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File diff suppressed because it is too large Load Diff

115
mlpp/lin_alg/mlpp_matrix.h
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@ -583,6 +583,121 @@ public:
}
}
//TODO remove these
Ref<MLPPVector> scalar_multiplynv(real_t scalar, const Ref<MLPPVector> &a);
Ref<MLPPVector> flattenmnv(const Vector<Ref<MLPPVector>> &A);
//std::vector<std::vector<real_t>> gramMatrix(std::vector<std::vector<real_t>> A);
//bool linearIndependenceChecker(std::vector<std::vector<real_t>> A);
Ref<MLPPMatrix> gaussian_noise(int n, int m);
Ref<MLPPMatrix> additionnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> subtractionnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> matmultnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> hadamard_productnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> kronecker_productnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> element_wise_divisionnvnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> transposenm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> scalar_multiplynm(real_t scalar, const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> scalar_addnm(real_t scalar, const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> lognm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> log10nm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> expnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> erfnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> exponentiatenm(const Ref<MLPPMatrix> &A, real_t p);
Ref<MLPPMatrix> sqrtnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> cbrtnm(const Ref<MLPPMatrix> &A);
//std::vector<std::vector<real_t>> matrixPower(std::vector<std::vector<real_t>> A, int n);
Ref<MLPPMatrix> absnm(const Ref<MLPPMatrix> &A);
real_t detm(const Ref<MLPPMatrix> &A, int d);
//real_t trace(std::vector<std::vector<real_t>> A);
Ref<MLPPMatrix> cofactornm(const Ref<MLPPMatrix> &A, int n, int i, int j);
Ref<MLPPMatrix> adjointnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> inversenm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> pinversenm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> zeromatnm(int n, int m);
Ref<MLPPMatrix> onematnm(int n, int m);
Ref<MLPPMatrix> fullnm(int n, int m, int k);
Ref<MLPPMatrix> sinnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> cosnm(const Ref<MLPPMatrix> &A);
//std::vector<std::vector<real_t>> rotate(std::vector<std::vector<real_t>> A, real_t theta, int axis = -1);
Ref<MLPPMatrix> maxnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B);
//real_t max(std::vector<std::vector<real_t>> A);
//real_t min(std::vector<std::vector<real_t>> A);
//std::vector<std::vector<real_t>> round(std::vector<std::vector<real_t>> A);
//real_t norm_2(std::vector<std::vector<real_t>> A);
Ref<MLPPMatrix> identitym(int d);
Ref<MLPPMatrix> covnm(const Ref<MLPPMatrix> &A);
struct EigenResult {
Ref<MLPPMatrix> eigen_vectors;
Ref<MLPPMatrix> eigen_values;
};
EigenResult eigen(Ref<MLPPMatrix> A);
struct SVDResult {
Ref<MLPPMatrix> U;
Ref<MLPPMatrix> S;
Ref<MLPPMatrix> Vt;
};
SVDResult svd(const Ref<MLPPMatrix> &A);
//std::vector<real_t> vectorProjection(std::vector<real_t> a, std::vector<real_t> b);
//std::vector<std::vector<real_t>> gramSchmidtProcess(std::vector<std::vector<real_t>> A);
/*
struct QRDResult {
std::vector<std::vector<real_t>> Q;
std::vector<std::vector<real_t>> R;
};
*/
//QRDResult qrd(std::vector<std::vector<real_t>> A);
/*
struct CholeskyResult {
std::vector<std::vector<real_t>> L;
std::vector<std::vector<real_t>> Lt;
};
CholeskyResult cholesky(std::vector<std::vector<real_t>> A);
*/
//real_t sum_elements(std::vector<std::vector<real_t>> A);
Ref<MLPPVector> flattenvvnv(const Ref<MLPPMatrix> &A);
/*
std::vector<real_t> solve(std::vector<std::vector<real_t>> A, std::vector<real_t> b);
bool positiveDefiniteChecker(std::vector<std::vector<real_t>> A);
bool negativeDefiniteChecker(std::vector<std::vector<real_t>> A);
bool zeroEigenvalue(std::vector<std::vector<real_t>> A);
*/
_FORCE_INLINE_ bool is_equal_approx(const Ref<MLPPMatrix> &p_with, real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const {
ERR_FAIL_COND_V(!p_with.is_valid(), false);