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MLPPMatrix math api rework pt6.

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Relintai 2023-04-25 10:54:56 +02:00
parent b7f453afed
commit be7b0e037e
2 changed files with 330 additions and 82 deletions
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364
mlpp/lin_alg/mlpp_matrix.cpp
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@ -898,26 +898,59 @@ std::vector<std::vector<real_t>> MLPPMatrix::matrixPower(std::vector<std::vector
}
*/
Ref<MLPPMatrix> MLPPMatrix::absnm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
void MLPPMatrix::abs() {
int ds = data_size();
real_t *out_ptr = ptrw();
for (int i = 0; i < ds; ++i) {
out_ptr[i] = ABS(out_ptr[i]);
}
}
Ref<MLPPMatrix> MLPPMatrix::absn() const {
Ref<MLPPMatrix> out;
out.instance();
out->resize(size());
int data_size = A->data_size();
out->resize(A->size());
int ds = data_size();
const real_t *a_ptr = A->ptr();
const real_t *a_ptr = ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
for (int i = 0; i < ds; ++i) {
out_ptr[i] = ABS(a_ptr[i]);
}
return out;
}
void MLPPMatrix::absb(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND(!A.is_valid());
real_t MLPPMatrix::detm(const Ref<MLPPMatrix> &A, int d) {
Size2i a_size = A->size();
if (a_size != size()) {
resize(a_size);
}
int ds = data_size();
const real_t *a_ptr = A->ptr();
real_t *out_ptr = ptrw();
for (int i = 0; i < ds; ++i) {
out_ptr[i] = ABS(a_ptr[i]);
}
}
real_t MLPPMatrix::det(int d) const {
if (d == -1) {
return detb(Ref<MLPPMatrix>(this), _size.y);
} else {
return detb(Ref<MLPPMatrix>(this), d);
}
}
real_t MLPPMatrix::detb(const Ref<MLPPMatrix> &A, int d) const {
ERR_FAIL_COND_V(!A.is_valid(), 0);
real_t deter = 0;
@ -947,7 +980,7 @@ real_t MLPPMatrix::detm(const Ref<MLPPMatrix> &A, int d) {
sub_i++;
}
deter += Math::pow(static_cast<real_t>(-1), static_cast<real_t>(i)) * A->get_element(0, i) * detm(B, d - 1);
deter += Math::pow(static_cast<real_t>(-1), static_cast<real_t>(i)) * A->get_element(0, i) * B->det(d - 1);
}
}
@ -964,10 +997,10 @@ real_t MLPPMatrix::trace(std::vector<std::vector<real_t>> A) {
}
*/
Ref<MLPPMatrix> MLPPMatrix::cofactornm(const Ref<MLPPMatrix> &A, int n, int i, int j) {
Ref<MLPPMatrix> MLPPMatrix::cofactor(int n, int i, int j) const {
Ref<MLPPMatrix> cof;
cof.instance();
cof->resize(A->size());
cof->resize(_size);
int sub_i = 0;
int sub_j = 0;
@ -975,7 +1008,7 @@ Ref<MLPPMatrix> MLPPMatrix::cofactornm(const Ref<MLPPMatrix> &A, int n, int i, i
for (int row = 0; row < n; row++) {
for (int col = 0; col < n; col++) {
if (row != i && col != j) {
cof->set_element(sub_i, sub_j++, A->get_element(row, col));
cof->set_element(sub_i, sub_j++, get_element(row, col));
if (sub_j == n - 1) {
sub_j = 0;
@ -987,53 +1020,122 @@ Ref<MLPPMatrix> MLPPMatrix::cofactornm(const Ref<MLPPMatrix> &A, int n, int i, i
return cof;
}
Ref<MLPPMatrix> MLPPMatrix::adjointnm(const Ref<MLPPMatrix> &A) {
void MLPPMatrix::cofactoro(int n, int i, int j, Ref<MLPPMatrix> out) const {
ERR_FAIL_COND(!out.is_valid());
if (unlikely(out->size() != _size)) {
out->resize(_size);
}
int sub_i = 0;
int sub_j = 0;
for (int row = 0; row < n; row++) {
for (int col = 0; col < n; col++) {
if (row != i && col != j) {
out->set_element(sub_i, sub_j++, get_element(row, col));
if (sub_j == n - 1) {
sub_j = 0;
sub_i++;
}
}
}
}
}
Ref<MLPPMatrix> MLPPMatrix::adjoint() const {
Ref<MLPPMatrix> adj;
ERR_FAIL_COND_V(!A.is_valid(), adj);
Size2i a_size = A->size();
ERR_FAIL_COND_V(a_size.x != a_size.y, adj);
ERR_FAIL_COND_V(_size.x != _size.y, adj);
//Resizing the initial adjoint matrix
adj.instance();
adj->resize(a_size);
adj->resize(_size);
// Checking for the case where the given N x N matrix is a scalar
if (a_size.y == 1) {
if (_size.y == 1) {
adj->set_element(0, 0, 1);
return adj;
}
if (a_size.y == 2) {
adj->set_element(0, 0, A->get_element(1, 1));
adj->set_element(1, 1, A->get_element(0, 0));
if (_size.y == 2) {
adj->set_element(0, 0, get_element(1, 1));
adj->set_element(1, 1, get_element(0, 0));
adj->set_element(0, 1, -A->get_element(0, 1));
adj->set_element(1, 0, -A->get_element(1, 0));
adj->set_element(0, 1, -get_element(0, 1));
adj->set_element(1, 0, -get_element(1, 0));
return adj;
}
for (int i = 0; i < a_size.y; i++) {
for (int j = 0; j < a_size.x; j++) {
Ref<MLPPMatrix> cof = cofactornm(A, a_size.y, i, j);
for (int i = 0; i < _size.y; i++) {
for (int j = 0; j < _size.x; j++) {
Ref<MLPPMatrix> cof = cofactor(_size.y, i, j);
// 1 if even, -1 if odd
int sign = (i + j) % 2 == 0 ? 1 : -1;
adj->set_element(j, i, sign * detm(cof, int(a_size.y) - 1));
adj->set_element(j, i, sign * cof->det(int(_size.y) - 1));
}
}
return adj;
}
Ref<MLPPMatrix> MLPPMatrix::inversenm(const Ref<MLPPMatrix> &A) {
return adjointnm(A)->scalar_multiplyn(1 / detm(A, int(A->size().y)));
void MLPPMatrix::adjointo(Ref<MLPPMatrix> out) const {
ERR_FAIL_COND(!out.is_valid());
ERR_FAIL_COND(_size.x != _size.y);
//Resizing the initial adjoint matrix
if (unlikely(out->size() != _size)) {
out->resize(_size);
}
// Checking for the case where the given N x N matrix is a scalar
if (_size.y == 1) {
out->set_element(0, 0, 1);
return;
}
if (_size.y == 2) {
out->set_element(0, 0, get_element(1, 1));
out->set_element(1, 1, get_element(0, 0));
out->set_element(0, 1, -get_element(0, 1));
out->set_element(1, 0, -get_element(1, 0));
return;
}
for (int i = 0; i < _size.y; i++) {
for (int j = 0; j < _size.x; j++) {
Ref<MLPPMatrix> cof = cofactor(_size.y, i, j);
// 1 if even, -1 if odd
int sign = (i + j) % 2 == 0 ? 1 : -1;
out->set_element(j, i, sign * cof->det(int(_size.y) - 1));
}
}
}
Ref<MLPPMatrix> MLPPMatrix::pinversenm(const Ref<MLPPMatrix> &A) {
return inversenm(A->multn(A)->transposen())->multn(A->transposen());
Ref<MLPPMatrix> MLPPMatrix::inverse() const {
return adjoint()->scalar_multiplyn(1 / det());
}
Ref<MLPPMatrix> MLPPMatrix::zeromatnm(int n, int m) {
void MLPPMatrix::inverseo(Ref<MLPPMatrix> out) const {
ERR_FAIL_COND(!out.is_valid());
out->set_from_mlpp_matrix(adjoint()->scalar_multiplyn(1 / det()));
}
Ref<MLPPMatrix> MLPPMatrix::pinverse() const {
return multn(Ref<MLPPMatrix>(this))->transposen()->inverse()->multn(transposen());
}
void MLPPMatrix::pinverseo(Ref<MLPPMatrix> out) const {
ERR_FAIL_COND(!out.is_valid());
out->set_from_mlpp_matrix(multn(Ref<MLPPMatrix>(this))->transposen()->inverse()->multn(transposen()));
}
Ref<MLPPMatrix> MLPPMatrix::zero_mat(int n, int m) const {
Ref<MLPPMatrix> mat;
mat.instance();
@ -1042,7 +1144,7 @@ Ref<MLPPMatrix> MLPPMatrix::zeromatnm(int n, int m) {
return mat;
}
Ref<MLPPMatrix> MLPPMatrix::onematnm(int n, int m) {
Ref<MLPPMatrix> MLPPMatrix::one_mat(int n, int m) const {
Ref<MLPPMatrix> mat;
mat.instance();
@ -1051,7 +1153,7 @@ Ref<MLPPMatrix> MLPPMatrix::onematnm(int n, int m) {
return mat;
}
Ref<MLPPMatrix> MLPPMatrix::fullnm(int n, int m, int k) {
Ref<MLPPMatrix> MLPPMatrix::full_mat(int n, int m, int k) const {
Ref<MLPPMatrix> mat;
mat.instance();
@ -1061,42 +1163,89 @@ Ref<MLPPMatrix> MLPPMatrix::fullnm(int n, int m, int k) {
return mat;
}
Ref<MLPPMatrix> MLPPMatrix::sinnm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
void MLPPMatrix::sin() {
int ds = data_size();
real_t *out_ptr = ptrw();
for (int i = 0; i < ds; ++i) {
out_ptr[i] = Math::sin(out_ptr[i]);
}
}
Ref<MLPPMatrix> MLPPMatrix::sinn() const {
Ref<MLPPMatrix> out;
out.instance();
out->resize(size());
int data_size = A->data_size();
out->resize(A->size());
int ds = data_size();
const real_t *a_ptr = A->ptr();
const real_t *a_ptr = ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
for (int i = 0; i < ds; ++i) {
out_ptr[i] = Math::sin(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPMatrix::cosnm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
void MLPPMatrix::sinb(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND(!A.is_valid());
Ref<MLPPMatrix> out;
out.instance();
if (A->size() != _size) {
resize(A->size());
}
int data_size = A->data_size();
out->resize(A->size());
int ds = A->data_size();
const real_t *a_ptr = A->ptr();
real_t *out_ptr = ptrw();
for (int i = 0; i < ds; ++i) {
out_ptr[i] = Math::sin(a_ptr[i]);
}
}
void MLPPMatrix::cos() {
int ds = data_size();
real_t *out_ptr = ptrw();
for (int i = 0; i < ds; ++i) {
out_ptr[i] = Math::cos(out_ptr[i]);
}
}
Ref<MLPPMatrix> MLPPMatrix::cosn() const {
Ref<MLPPMatrix> out;
out.instance();
out->resize(size());
int ds = data_size();
const real_t *a_ptr = ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
for (int i = 0; i < ds; ++i) {
out_ptr[i] = Math::cos(a_ptr[i]);
}
return out;
}
void MLPPMatrix::cosb(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND(!A.is_valid());
if (A->size() != _size) {
resize(A->size());
}
int ds = A->data_size();
const real_t *a_ptr = A->ptr();
real_t *out_ptr = ptrw();
for (int i = 0; i < ds; ++i) {
out_ptr[i] = Math::cos(a_ptr[i]);
}
}
/*
std::vector<std::vector<real_t>> MLPPMatrix::rotate(std::vector<std::vector<real_t>> A, real_t theta, int axis) {
@ -1113,23 +1262,58 @@ std::vector<std::vector<real_t>> MLPPMatrix::rotate(std::vector<std::vector<real
}
*/
Ref<MLPPMatrix> MLPPMatrix::maxnm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
void MLPPMatrix::max(const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND(!B.is_valid());
ERR_FAIL_COND(_size != B->size());
const real_t *b_ptr = B->ptr();
real_t *c_ptr = ptrw();
int ds = data_size();
for (int i = 0; i < ds; ++i) {
c_ptr[i] = MAX(c_ptr[i], b_ptr[i]);
}
}
Ref<MLPPMatrix> MLPPMatrix::maxn(const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND_V(!B.is_valid(), Ref<MLPPMatrix>());
ERR_FAIL_COND_V(_size != B->size(), Ref<MLPPMatrix>());
Ref<MLPPMatrix> C;
C.instance();
C->resize(A->size());
C->resize(_size);
const real_t *a_ptr = A->ptr();
const real_t *a_ptr = ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
int size = A->data_size();
int ds = data_size();
for (int i = 0; i < size; i++) {
for (int i = 0; i < ds; ++i) {
c_ptr[i] = MAX(a_ptr[i], b_ptr[i]);
}
return C;
}
void MLPPMatrix::maxb(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND(!A.is_valid() || !B.is_valid());
Size2i a_size = A->size();
ERR_FAIL_COND(a_size != B->size());
if (_size != a_size) {
resize(a_size);
}
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = ptrw();
int data_size = A->data_size();
for (int i = 0; i < data_size; ++i) {
c_ptr[i] = MAX(a_ptr[i], b_ptr[i]);
}
}
/*
real_t MLPPMatrix::max(std::vector<std::vector<real_t>> A) {
@ -1167,7 +1351,26 @@ real_t MLPPMatrix::norm_2(std::vector<std::vector<real_t>> A) {
}
*/
Ref<MLPPMatrix> MLPPMatrix::identitym(int d) {
void MLPPMatrix::identity() {
fill(0);
real_t *im_ptr = ptrw();
int d = MIN(_size.x, _size.y);
for (int i = 0; i < d; i++) {
im_ptr[calculate_index(i, i)] = 1;
}
}
Ref<MLPPMatrix> MLPPMatrix::identityn() const {
Ref<MLPPMatrix> identity_mat;
identity_mat.instance();
identity_mat->resize(_size);
identity_mat->identity();
return identity_mat;
}
Ref<MLPPMatrix> MLPPMatrix::identity_mat(int d) const {
Ref<MLPPMatrix> identity_mat;
identity_mat.instance();
identity_mat->resize(Size2i(d, d));
@ -1182,29 +1385,27 @@ Ref<MLPPMatrix> MLPPMatrix::identitym(int d) {
return identity_mat;
}
Ref<MLPPMatrix> MLPPMatrix::covnm(const Ref<MLPPMatrix> &A) {
Ref<MLPPMatrix> MLPPMatrix::cov() const {
MLPPStat stat;
Ref<MLPPMatrix> cov_mat;
cov_mat.instance();
Size2i a_size = A->size();
cov_mat->resize(a_size);
cov_mat->resize(_size);
Ref<MLPPVector> a_i_row_tmp;
a_i_row_tmp.instance();
a_i_row_tmp->resize(a_size.x);
a_i_row_tmp->resize(_size.x);
Ref<MLPPVector> a_j_row_tmp;
a_j_row_tmp.instance();
a_j_row_tmp->resize(a_size.x);
a_j_row_tmp->resize(_size.x);
for (int i = 0; i < a_size.y; ++i) {
A->get_row_into_mlpp_vector(i, a_i_row_tmp);
for (int i = 0; i < _size.y; ++i) {
get_row_into_mlpp_vector(i, a_i_row_tmp);
for (int j = 0; j < a_size.x; ++j) {
A->get_row_into_mlpp_vector(j, a_j_row_tmp);
for (int j = 0; j < _size.x; ++j) {
get_row_into_mlpp_vector(j, a_j_row_tmp);
cov_mat->set_element(i, j, stat.covariancev(a_i_row_tmp, a_j_row_tmp));
}
@ -1212,6 +1413,33 @@ Ref<MLPPMatrix> MLPPMatrix::covnm(const Ref<MLPPMatrix> &A) {
return cov_mat;
}
void MLPPMatrix::covo(Ref<MLPPMatrix> out) const {
ERR_FAIL_COND(!out.is_valid());
MLPPStat stat;
if (unlikely(out->size() != _size)) {
out->resize(_size);
}
Ref<MLPPVector> a_i_row_tmp;
a_i_row_tmp.instance();
a_i_row_tmp->resize(_size.x);
Ref<MLPPVector> a_j_row_tmp;
a_j_row_tmp.instance();
a_j_row_tmp->resize(_size.x);
for (int i = 0; i < _size.y; ++i) {
get_row_into_mlpp_vector(i, a_i_row_tmp);
for (int j = 0; j < _size.x; ++j) {
get_row_into_mlpp_vector(j, a_j_row_tmp);
out->set_element(i, j, stat.covariancev(a_i_row_tmp, a_j_row_tmp));
}
}
}
MLPPMatrix::EigenResult MLPPMatrix::eigen(Ref<MLPPMatrix> A) {
EigenResult res;
@ -1228,7 +1456,7 @@ MLPPMatrix::EigenResult MLPPMatrix::eigen(Ref<MLPPMatrix> A) {
HashMap<int, int> val_to_vec;
Ref<MLPPMatrix> a_new;
Ref<MLPPMatrix> eigenvectors = identitym(A->size().y);
Ref<MLPPMatrix> eigenvectors = identity_mat(A->size().y);
Size2i a_size = A->size();
do {
@ -1265,13 +1493,13 @@ MLPPMatrix::EigenResult MLPPMatrix::eigen(Ref<MLPPMatrix> A) {
theta = 0.5 * atan(2 * a_ij / (a_ii - a_jj));
}
Ref<MLPPMatrix> P = identitym(A->size().y);
Ref<MLPPMatrix> P = identity_mat(A->size().y);
P->set_element(sub_i, sub_j, -Math::sin(theta));
P->set_element(sub_i, sub_i, Math::cos(theta));
P->set_element(sub_j, sub_j, Math::cos(theta));
P->set_element(sub_j, sub_i, Math::sin(theta));
a_new = inversenm(P)->multn(A)->multn(P);
a_new = P->inverse()->multn(A)->multn(P);
Size2i a_new_size = a_new->size();
@ -1364,7 +1592,7 @@ MLPPMatrix::SVDResult MLPPMatrix::svd(const Ref<MLPPMatrix> &A) {
EigenResult right_eigen = eigen(A->transposen()->multn(A));
Ref<MLPPMatrix> singularvals = left_eigen.eigen_values->sqrtn();
Ref<MLPPMatrix> sigma = zeromatnm(a_size.y, a_size.x);
Ref<MLPPMatrix> sigma = zero_mat(a_size.y, a_size.x);
Size2i singularvals_size = singularvals->size();

48
mlpp/lin_alg/mlpp_matrix.h
View File

@ -655,27 +655,44 @@ public:
//std::vector<std::vector<real_t>> matrixPower(std::vector<std::vector<real_t>> A, int n);
Ref<MLPPMatrix> absnm(const Ref<MLPPMatrix> &A);
void abs();
Ref<MLPPMatrix> absn() const;
void absb(const Ref<MLPPMatrix> &A);
real_t detm(const Ref<MLPPMatrix> &A, int d);
real_t det(int d = -1) const;
real_t detb(const Ref<MLPPMatrix> &A, int d) const;
//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> cofactor(int n, int i, int j) const;
void cofactoro(int n, int i, int j, Ref<MLPPMatrix> out) const;
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> adjoint() const;
void adjointo(Ref<MLPPMatrix> out) const;
Ref<MLPPMatrix> sinnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> cosnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> inverse() const;
void inverseo(Ref<MLPPMatrix> out) const;
Ref<MLPPMatrix> pinverse() const;
void pinverseo(Ref<MLPPMatrix> out) const;
Ref<MLPPMatrix> zero_mat(int n, int m) const;
Ref<MLPPMatrix> one_mat(int n, int m) const;
Ref<MLPPMatrix> full_mat(int n, int m, int k) const;
void sin();
Ref<MLPPMatrix> sinn() const;
void sinb(const Ref<MLPPMatrix> &A);
void cos();
Ref<MLPPMatrix> cosn() const;
void cosb(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);
void max(const Ref<MLPPMatrix> &B);
Ref<MLPPMatrix> maxn(const Ref<MLPPMatrix> &B);
void maxb(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);
@ -684,9 +701,12 @@ public:
//real_t norm_2(std::vector<std::vector<real_t>> A);
Ref<MLPPMatrix> identitym(int d);
void identity();
Ref<MLPPMatrix> identityn() const;
Ref<MLPPMatrix> identity_mat(int d) const;
Ref<MLPPMatrix> covnm(const Ref<MLPPMatrix> &A);
Ref<MLPPMatrix> cov() const;
void covo(Ref<MLPPMatrix> out) const;
struct EigenResult {
Ref<MLPPMatrix> eigen_vectors;