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pmlpp/mlpp/lin_alg/lin_alg.cpp
Relintai e5810cda01 Work on porting WGAN.
2023-02-06 02:36:22 +01:00

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//
// LinAlg.cpp
//
// Created by Marc Melikyan on 1/8/21.
//
#include "lin_alg.h"
#include "core/math/math_funcs.h"
#include "../stat/stat.h"
#include <cmath>
#include <iostream>
#include <map>
#include <random>
std::vector<std::vector<real_t>> MLPPLinAlg::gramMatrix(std::vector<std::vector<real_t>> A) {
return matmult(transpose(A), A); // AtA
}
bool MLPPLinAlg::linearIndependenceChecker(std::vector<std::vector<real_t>> A) {
if (det(gramMatrix(A), A.size()) == 0) {
return false;
}
return true;
}
std::vector<std::vector<real_t>> MLPPLinAlg::gaussianNoise(int n, int m) {
std::random_device rd;
std::default_random_engine generator(rd());
std::vector<std::vector<real_t>> A;
A.resize(n);
for (int i = 0; i < n; i++) {
A[i].resize(m);
for (int j = 0; j < m; j++) {
std::normal_distribution<real_t> distribution(0, 1); // Standard normal distribution. Mean of 0, std of 1.
A[i][j] = distribution(generator);
}
}
return A;
}
Ref<MLPPMatrix> MLPPLinAlg::gaussian_noise(int n, int m) {
std::random_device rd;
std::default_random_engine generator(rd());
std::normal_distribution<real_t> distribution(0, 1); // Standard normal distribution. Mean of 0, std of 1.
Ref<MLPPMatrix> A;
A.instance();
A->resize(Size2i(m, n));
int a_data_size = A->data_size();
real_t *a_ptr = A->ptrw();
for (int i = 0; i < a_data_size; ++i) {
a_ptr[i] = distribution(generator);
}
return A;
}
std::vector<std::vector<real_t>> MLPPLinAlg::addition(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[0].size(); j++) {
C[i][j] = A[i][j] + B[i][j];
}
}
return C;
}
std::vector<std::vector<real_t>> MLPPLinAlg::subtraction(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[0].size(); j++) {
C[i][j] = A[i][j] - B[i][j];
}
}
return C;
}
std::vector<std::vector<real_t>> MLPPLinAlg::matmult(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(B[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int k = 0; k < B.size(); k++) {
for (int j = 0; j < B[0].size(); j++) {
C[i][j] += A[i][k] * B[k][j];
}
}
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::additionm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND_V(!A.is_valid() || !B.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
ERR_FAIL_COND_V(a_size != B->size(), Ref<MLPPMatrix>());
Ref<MLPPMatrix> C;
C.instance();
C->resize(a_size);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
int data_size = A->data_size();
for (int i = 0; i < data_size; ++i) {
c_ptr[i] = a_ptr[i] + b_ptr[i];
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::subtractionm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND_V(!A.is_valid() || !B.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
ERR_FAIL_COND_V(a_size != B->size(), Ref<MLPPMatrix>());
Ref<MLPPMatrix> C;
C.instance();
C->resize(a_size);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
int data_size = A->data_size();
for (int i = 0; i < data_size; ++i) {
c_ptr[i] = a_ptr[i] - b_ptr[i];
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::matmultm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND_V(!A.is_valid() || !B.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
Size2i b_size = B->size();
ERR_FAIL_COND_V(a_size.x != b_size.y, Ref<MLPPMatrix>());
Ref<MLPPMatrix> C;
C.instance();
C->resize(Size2i(b_size.x, a_size.y));
C->fill(0);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
for (int i = 0; i < a_size.y; i++) {
for (int k = 0; k < b_size.y; k++) {
int ind_i_k = A->calculate_index(i, k);
for (int j = 0; j < b_size.x; j++) {
int ind_i_j = C->calculate_index(i, j);
int ind_k_j = B->calculate_index(k, j);
c_ptr[ind_i_j] += a_ptr[ind_i_k] * b_ptr[ind_k_j];
//C->set_element(i, j, C->get_element(i, j) + A->get_element(i, k) * B->get_element(k, j
}
}
}
return C;
}
std::vector<std::vector<real_t>> MLPPLinAlg::hadamard_product(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[0].size(); j++) {
C[i][j] = A[i][j] * B[i][j];
}
}
return C;
}
std::vector<std::vector<real_t>> MLPPLinAlg::kronecker_product(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
// [1,1,1,1] [1,2,3,4,5]
// [1,1,1,1] [1,2,3,4,5]
// [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// Resulting matrix: A.size() * B.size()
// A[0].size() * B[0].size()
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < B.size(); j++) {
std::vector<std::vector<real_t>> row;
for (int k = 0; k < A[0].size(); k++) {
row.push_back(scalarMultiply(A[i][k], B[j]));
}
C.push_back(flatten(row));
}
}
return C;
}
std::vector<std::vector<real_t>> MLPPLinAlg::elementWiseDivision(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
C[i][j] = A[i][j] / B[i][j];
}
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::hadamard_productm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND_V(!A.is_valid() || !B.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
ERR_FAIL_COND_V(a_size != B->size(), Ref<MLPPMatrix>());
Ref<MLPPMatrix> C;
C.instance();
C->resize(a_size);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
for (int i = 0; i < a_size.y; i++) {
for (int j = 0; j < a_size.x; j++) {
int ind_i_j = A->calculate_index(i, j);
c_ptr[ind_i_j] = a_ptr[ind_i_j] * b_ptr[ind_i_j];
}
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::kronecker_productm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
// [1,1,1,1] [1,2,3,4,5]
// [1,1,1,1] [1,2,3,4,5]
// [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5] [1,2,3,4,5]
// Resulting matrix: A.size() * B.size()
// A[0].size() * B[0].size()
ERR_FAIL_COND_V(!A.is_valid() || !B.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
Size2i b_size = B->size();
Ref<MLPPMatrix> C;
C.instance();
C->resize(Size2i(b_size.x * a_size.x, b_size.y * a_size.y));
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
Ref<MLPPVector> row_tmp;
row_tmp.instance();
row_tmp->resize(b_size.x);
for (int i = 0; i < a_size.y; ++i) {
for (int j = 0; j < b_size.y; ++j) {
B->get_row_into_mlpp_vector(j, row_tmp);
Vector<Ref<MLPPVector>> row;
for (int k = 0; k < a_size.x; ++k) {
row.push_back(scalar_multiplynv(a_ptr[A->calculate_index(i, k)], row_tmp));
}
Ref<MLPPVector> flattened_row = flattenv(row);
C->set_row_mlpp_vector(i * b_size.y + j, flattened_row);
}
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::element_wise_divisionm(const Ref<MLPPMatrix> &A, const Ref<MLPPMatrix> &B) {
ERR_FAIL_COND_V(!A.is_valid() || !B.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
ERR_FAIL_COND_V(a_size != B->size(), Ref<MLPPMatrix>());
Ref<MLPPMatrix> C;
C.instance();
C->resize(a_size);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = B->ptr();
real_t *c_ptr = C->ptrw();
for (int i = 0; i < a_size.y; i++) {
for (int j = 0; j < a_size.x; j++) {
int ind_i_j = A->calculate_index(i, j);
c_ptr[ind_i_j] = a_ptr[ind_i_j] / b_ptr[ind_i_j];
}
}
return C;
}
std::vector<std::vector<real_t>> MLPPLinAlg::transpose(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> AT;
AT.resize(A[0].size());
for (int i = 0; i < AT.size(); i++) {
AT[i].resize(A.size());
}
for (int i = 0; i < A[0].size(); i++) {
for (int j = 0; j < A.size(); j++) {
AT[i][j] = A[j][i];
}
}
return AT;
}
std::vector<std::vector<real_t>> MLPPLinAlg::scalarMultiply(real_t scalar, std::vector<std::vector<real_t>> A) {
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
A[i][j] *= scalar;
}
}
return A;
}
std::vector<std::vector<real_t>> MLPPLinAlg::scalarAdd(real_t scalar, std::vector<std::vector<real_t>> A) {
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
A[i][j] += scalar;
}
}
return A;
}
Ref<MLPPMatrix> MLPPLinAlg::transposem(const Ref<MLPPMatrix> &A) {
Size2i a_size = A->size();
Ref<MLPPMatrix> AT;
AT.instance();
AT->resize(Size2i(a_size.y, a_size.x));
const real_t *a_ptr = A->ptr();
real_t *at_ptr = AT->ptrw();
for (int i = 0; i < a_size.x; ++i) {
for (int j = 0; j < a_size.y; ++j) {
at_ptr[AT->calculate_index(i, j)] = a_ptr[AT->calculate_index(j, i)];
}
}
return AT;
}
Ref<MLPPMatrix> MLPPLinAlg::scalar_multiplym(real_t scalar, const Ref<MLPPMatrix> &A) {
Ref<MLPPMatrix> AN = A->duplicate();
Size2i a_size = AN->size();
real_t *an_ptr = AN->ptrw();
for (int i = 0; i < a_size.y; ++i) {
for (int j = 0; j < a_size.x; ++j) {
an_ptr[AN->calculate_index(i, j)] *= scalar;
}
}
return AN;
}
Ref<MLPPMatrix> MLPPLinAlg::scalar_addm(real_t scalar, const Ref<MLPPMatrix> &A) {
Ref<MLPPMatrix> AN = A->duplicate();
Size2i a_size = AN->size();
real_t *an_ptr = AN->ptrw();
for (int i = 0; i < a_size.y; ++i) {
for (int j = 0; j < a_size.x; ++j) {
an_ptr[AN->calculate_index(i, j)] += scalar;
}
}
return AN;
}
std::vector<std::vector<real_t>> MLPPLinAlg::log(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::log(A[i][j]);
}
}
return B;
}
std::vector<std::vector<real_t>> MLPPLinAlg::log10(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::log10(A[i][j]);
}
}
return B;
}
std::vector<std::vector<real_t>> MLPPLinAlg::exp(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::exp(A[i][j]);
}
}
return B;
}
std::vector<std::vector<real_t>> MLPPLinAlg::erf(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::erf(A[i][j]);
}
}
return B;
}
std::vector<std::vector<real_t>> MLPPLinAlg::exponentiate(std::vector<std::vector<real_t>> A, real_t p) {
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
A[i][j] = std::pow(A[i][j], p);
}
}
return A;
}
std::vector<std::vector<real_t>> MLPPLinAlg::sqrt(std::vector<std::vector<real_t>> A) {
return exponentiate(A, 0.5);
}
std::vector<std::vector<real_t>> MLPPLinAlg::cbrt(std::vector<std::vector<real_t>> A) {
return exponentiate(A, real_t(1) / real_t(3));
}
Ref<MLPPMatrix> MLPPLinAlg::logm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = Math::log(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::log10m(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = std::log10(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::expm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = Math::exp(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::erfm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = std::erf(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::exponentiatem(const Ref<MLPPMatrix> &A, real_t p) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = Math::pow(a_ptr[i], p);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::sqrtm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = Math::sqrt(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::cbrtm(const Ref<MLPPMatrix> &A) {
return exponentiatem(A, real_t(1) / real_t(3));
}
std::vector<std::vector<real_t>> MLPPLinAlg::matrixPower(std::vector<std::vector<real_t>> A, int n) {
std::vector<std::vector<real_t>> B = identity(A.size());
if (n == 0) {
return identity(A.size());
} else if (n < 0) {
A = inverse(A);
}
for (int i = 0; i < std::abs(n); i++) {
B = matmult(B, A);
}
return B;
}
std::vector<std::vector<real_t>> MLPPLinAlg::abs(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < B.size(); i++) {
for (int j = 0; j < B[i].size(); j++) {
B[i][j] = std::abs(A[i][j]);
}
}
return B;
}
Ref<MLPPMatrix> MLPPLinAlg::absm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = ABS(a_ptr[i]);
}
return out;
}
real_t MLPPLinAlg::det(std::vector<std::vector<real_t>> A, int d) {
real_t deter = 0;
std::vector<std::vector<real_t>> B;
B.resize(d);
for (int i = 0; i < d; i++) {
B[i].resize(d);
}
/* This is the base case in which the input is a 2x2 square matrix.
Recursion is performed unless and until we reach this base case,
such that we recieve a scalar as the result. */
if (d == 2) {
return A[0][0] * A[1][1] - A[0][1] * A[1][0];
}
else {
for (int i = 0; i < d; i++) {
int sub_i = 0;
for (int j = 1; j < d; j++) {
int sub_j = 0;
for (int k = 0; k < d; k++) {
if (k == i) {
continue;
}
B[sub_i][sub_j] = A[j][k];
sub_j++;
}
sub_i++;
}
deter += std::pow(-1, i) * A[0][i] * det(B, d - 1);
}
}
return deter;
}
real_t MLPPLinAlg::trace(std::vector<std::vector<real_t>> A) {
real_t trace = 0;
for (int i = 0; i < A.size(); i++) {
trace += A[i][i];
}
return trace;
}
std::vector<std::vector<real_t>> MLPPLinAlg::cofactor(std::vector<std::vector<real_t>> A, int n, int i, int j) {
std::vector<std::vector<real_t>> cof;
cof.resize(A.size());
for (int i = 0; i < cof.size(); i++) {
cof[i].resize(A.size());
}
int sub_i = 0, sub_j = 0;
for (int row = 0; row < n; row++) {
for (int col = 0; col < n; col++) {
if (row != i && col != j) {
cof[sub_i][sub_j++] = A[row][col];
if (sub_j == n - 1) {
sub_j = 0;
sub_i++;
}
}
}
}
return cof;
}
std::vector<std::vector<real_t>> MLPPLinAlg::adjoint(std::vector<std::vector<real_t>> A) {
//Resizing the initial adjoint matrix
std::vector<std::vector<real_t>> adj;
adj.resize(A.size());
for (int i = 0; i < adj.size(); i++) {
adj[i].resize(A.size());
}
// Checking for the case where the given N x N matrix is a scalar
if (A.size() == 1) {
adj[0][0] = 1;
return adj;
}
if (A.size() == 2) {
adj[0][0] = A[1][1];
adj[1][1] = A[0][0];
adj[0][1] = -A[0][1];
adj[1][0] = -A[1][0];
return adj;
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A.size(); j++) {
std::vector<std::vector<real_t>> cof = cofactor(A, int(A.size()), i, j);
// 1 if even, -1 if odd
int sign = (i + j) % 2 == 0 ? 1 : -1;
adj[j][i] = sign * det(cof, int(A.size()) - 1);
}
}
return adj;
}
// The inverse can be computed as (1 / determinant(A)) * adjoint(A)
std::vector<std::vector<real_t>> MLPPLinAlg::inverse(std::vector<std::vector<real_t>> A) {
return scalarMultiply(1 / det(A, int(A.size())), adjoint(A));
}
// This is simply the Moore-Penrose least squares approximation of the inverse.
std::vector<std::vector<real_t>> MLPPLinAlg::pinverse(std::vector<std::vector<real_t>> A) {
return matmult(inverse(matmult(transpose(A), A)), transpose(A));
}
std::vector<std::vector<real_t>> MLPPLinAlg::zeromat(int n, int m) {
std::vector<std::vector<real_t>> zeromat;
zeromat.resize(n);
for (int i = 0; i < zeromat.size(); i++) {
zeromat[i].resize(m);
}
return zeromat;
}
std::vector<std::vector<real_t>> MLPPLinAlg::onemat(int n, int m) {
return full(n, m, 1);
}
Ref<MLPPMatrix> MLPPLinAlg::zeromatm(int n, int m) {
Ref<MLPPMatrix> mat;
mat.instance();
mat->resize(Size2i(n, m));
mat->fill(0);
return mat;
}
Ref<MLPPMatrix> MLPPLinAlg::onematm(int n, int m) {
Ref<MLPPMatrix> mat;
mat.instance();
mat->resize(Size2i(n, m));
mat->fill(1);
return mat;
}
Ref<MLPPMatrix> MLPPLinAlg::fullm(int n, int m, int k) {
Ref<MLPPMatrix> mat;
mat.instance();
mat->resize(Size2i(n, m));
mat->fill(k);
return mat;
}
std::vector<std::vector<real_t>> MLPPLinAlg::full(int n, int m, int k) {
std::vector<std::vector<real_t>> full;
full.resize(n);
for (int i = 0; i < full.size(); i++) {
full[i].resize(m);
}
for (int i = 0; i < full.size(); i++) {
for (int j = 0; j < full[i].size(); j++) {
full[i][j] = k;
}
}
return full;
}
std::vector<std::vector<real_t>> MLPPLinAlg::sin(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::sin(A[i][j]);
}
}
return B;
}
std::vector<std::vector<real_t>> MLPPLinAlg::cos(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::cos(A[i][j]);
}
}
return B;
}
Ref<MLPPMatrix> MLPPLinAlg::sinm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = Math::sin(a_ptr[i]);
}
return out;
}
Ref<MLPPMatrix> MLPPLinAlg::cosm(const Ref<MLPPMatrix> &A) {
ERR_FAIL_COND_V(!A.is_valid(), Ref<MLPPVector>());
Ref<MLPPMatrix> out;
out.instance();
int data_size = A->data_size();
out->resize(A->size());
const real_t *a_ptr = A->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < data_size; ++i) {
out_ptr[i] = Math::cos(a_ptr[i]);
}
return out;
}
std::vector<real_t> MLPPLinAlg::max(std::vector<real_t> a, std::vector<real_t> b) {
std::vector<real_t> c;
c.resize(a.size());
for (int i = 0; i < c.size(); i++) {
if (a[i] >= b[i]) {
c[i] = a[i];
} else {
c[i] = b[i];
}
}
return c;
}
real_t MLPPLinAlg::max(std::vector<std::vector<real_t>> A) {
return max(flatten(A));
}
real_t MLPPLinAlg::min(std::vector<std::vector<real_t>> A) {
return min(flatten(A));
}
std::vector<std::vector<real_t>> MLPPLinAlg::round(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
B[i][j] = std::round(A[i][j]);
}
}
return B;
}
real_t MLPPLinAlg::norm_2(std::vector<std::vector<real_t>> A) {
real_t sum = 0;
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
sum += A[i][j] * A[i][j];
}
}
return std::sqrt(sum);
}
std::vector<std::vector<real_t>> MLPPLinAlg::identity(real_t d) {
std::vector<std::vector<real_t>> identityMat;
identityMat.resize(d);
for (int i = 0; i < identityMat.size(); i++) {
identityMat[i].resize(d);
}
for (int i = 0; i < identityMat.size(); i++) {
for (int j = 0; j < identityMat.size(); j++) {
if (i == j) {
identityMat[i][j] = 1;
} else {
identityMat[i][j] = 0;
}
}
}
return identityMat;
}
std::vector<std::vector<real_t>> MLPPLinAlg::cov(std::vector<std::vector<real_t>> A) {
MLPPStat stat;
std::vector<std::vector<real_t>> covMat;
covMat.resize(A.size());
for (int i = 0; i < covMat.size(); i++) {
covMat[i].resize(A.size());
}
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A.size(); j++) {
covMat[i][j] = stat.covariance(A[i], A[j]);
}
}
return covMat;
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPLinAlg::eig(std::vector<std::vector<real_t>> A) {
/*
A (the entered parameter) in most use cases will be X'X, XX', etc. and must be symmetric.
That simply means that 1) X' = X and 2) X is a square matrix. This function that computes the
eigenvalues of a matrix is utilizing Jacobi's method.
*/
real_t diagonal = true; // Perform the iterative Jacobi algorithm unless and until we reach a diagonal matrix which yields us the eigenvals.
std::map<int, int> val_to_vec;
std::vector<std::vector<real_t>> a_new;
std::vector<std::vector<real_t>> eigenvectors = identity(A.size());
do {
real_t a_ij = A[0][1];
real_t sub_i = 0;
real_t sub_j = 1;
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
if (i != j && std::abs(A[i][j]) > a_ij) {
a_ij = A[i][j];
sub_i = i;
sub_j = j;
} else if (i != j && std::abs(A[i][j]) == a_ij) {
if (i < sub_i) {
a_ij = A[i][j];
sub_i = i;
sub_j = j;
}
}
}
}
real_t a_ii = A[sub_i][sub_i];
real_t a_jj = A[sub_j][sub_j];
real_t a_ji = A[sub_j][sub_i];
real_t theta;
if (a_ii == a_jj) {
theta = M_PI / 4;
} else {
theta = 0.5 * atan(2 * a_ij / (a_ii - a_jj));
}
std::vector<std::vector<real_t>> P = identity(A.size());
P[sub_i][sub_j] = -std::sin(theta);
P[sub_i][sub_i] = std::cos(theta);
P[sub_j][sub_j] = std::cos(theta);
P[sub_j][sub_i] = std::sin(theta);
a_new = matmult(matmult(inverse(P), A), P);
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new[i].size(); j++) {
if (i != j && std::round(a_new[i][j]) == 0) {
a_new[i][j] = 0;
}
}
}
bool non_zero = false;
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new[i].size(); j++) {
if (i != j && std::round(a_new[i][j]) != 0) {
non_zero = true;
}
}
}
if (non_zero) {
diagonal = false;
} else {
diagonal = true;
}
if (a_new == A) {
diagonal = true;
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new[i].size(); j++) {
if (i != j) {
a_new[i][j] = 0;
}
}
}
}
eigenvectors = matmult(eigenvectors, P);
A = a_new;
} while (!diagonal);
std::vector<std::vector<real_t>> a_new_prior = a_new;
// Bubble Sort. Should change this later.
for (int i = 0; i < a_new.size() - 1; i++) {
for (int j = 0; j < a_new.size() - 1 - i; j++) {
if (a_new[j][j] < a_new[j + 1][j + 1]) {
real_t temp = a_new[j + 1][j + 1];
a_new[j + 1][j + 1] = a_new[j][j];
a_new[j][j] = temp;
}
}
}
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new.size(); j++) {
if (a_new[i][i] == a_new_prior[j][j]) {
val_to_vec[i] = j;
}
}
}
std::vector<std::vector<real_t>> eigen_temp = eigenvectors;
for (int i = 0; i < eigenvectors.size(); i++) {
for (int j = 0; j < eigenvectors[i].size(); j++) {
eigenvectors[i][j] = eigen_temp[i][val_to_vec[j]];
}
}
return { eigenvectors, a_new };
}
MLPPLinAlg::EigenResult MLPPLinAlg::eigen(std::vector<std::vector<real_t>> A) {
/*
A (the entered parameter) in most use cases will be X'X, XX', etc. and must be symmetric.
That simply means that 1) X' = X and 2) X is a square matrix. This function that computes the
eigenvalues of a matrix is utilizing Jacobi's method.
*/
real_t diagonal = true; // Perform the iterative Jacobi algorithm unless and until we reach a diagonal matrix which yields us the eigenvals.
std::map<int, int> val_to_vec;
std::vector<std::vector<real_t>> a_new;
std::vector<std::vector<real_t>> eigenvectors = identity(A.size());
do {
real_t a_ij = A[0][1];
real_t sub_i = 0;
real_t sub_j = 1;
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
if (i != j && std::abs(A[i][j]) > a_ij) {
a_ij = A[i][j];
sub_i = i;
sub_j = j;
} else if (i != j && std::abs(A[i][j]) == a_ij) {
if (i < sub_i) {
a_ij = A[i][j];
sub_i = i;
sub_j = j;
}
}
}
}
real_t a_ii = A[sub_i][sub_i];
real_t a_jj = A[sub_j][sub_j];
real_t a_ji = A[sub_j][sub_i];
real_t theta;
if (a_ii == a_jj) {
theta = M_PI / 4;
} else {
theta = 0.5 * atan(2 * a_ij / (a_ii - a_jj));
}
std::vector<std::vector<real_t>> P = identity(A.size());
P[sub_i][sub_j] = -std::sin(theta);
P[sub_i][sub_i] = std::cos(theta);
P[sub_j][sub_j] = std::cos(theta);
P[sub_j][sub_i] = std::sin(theta);
a_new = matmult(matmult(inverse(P), A), P);
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new[i].size(); j++) {
if (i != j && std::round(a_new[i][j]) == 0) {
a_new[i][j] = 0;
}
}
}
bool non_zero = false;
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new[i].size(); j++) {
if (i != j && std::round(a_new[i][j]) != 0) {
non_zero = true;
}
}
}
if (non_zero) {
diagonal = false;
} else {
diagonal = true;
}
if (a_new == A) {
diagonal = true;
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new[i].size(); j++) {
if (i != j) {
a_new[i][j] = 0;
}
}
}
}
eigenvectors = matmult(eigenvectors, P);
A = a_new;
} while (!diagonal);
std::vector<std::vector<real_t>> a_new_prior = a_new;
// Bubble Sort. Should change this later.
for (int i = 0; i < a_new.size() - 1; i++) {
for (int j = 0; j < a_new.size() - 1 - i; j++) {
if (a_new[j][j] < a_new[j + 1][j + 1]) {
real_t temp = a_new[j + 1][j + 1];
a_new[j + 1][j + 1] = a_new[j][j];
a_new[j][j] = temp;
}
}
}
for (int i = 0; i < a_new.size(); i++) {
for (int j = 0; j < a_new.size(); j++) {
if (a_new[i][i] == a_new_prior[j][j]) {
val_to_vec[i] = j;
}
}
}
std::vector<std::vector<real_t>> eigen_temp = eigenvectors;
for (int i = 0; i < eigenvectors.size(); i++) {
for (int j = 0; j < eigenvectors[i].size(); j++) {
eigenvectors[i][j] = eigen_temp[i][val_to_vec[j]];
}
}
EigenResult res;
res.eigen_vectors = eigenvectors;
res.eigen_values = a_new;
return res;
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPLinAlg::SVD(std::vector<std::vector<real_t>> A) {
auto [left_eigenvecs, eigenvals] = eig(matmult(A, transpose(A)));
auto [right_eigenvecs, right_eigenvals] = eig(matmult(transpose(A), A));
std::vector<std::vector<real_t>> singularvals = sqrt(eigenvals);
std::vector<std::vector<real_t>> sigma = zeromat(A.size(), A[0].size());
for (int i = 0; i < singularvals.size(); i++) {
for (int j = 0; j < singularvals[i].size(); j++) {
sigma[i][j] = singularvals[i][j];
}
}
return { left_eigenvecs, sigma, right_eigenvecs };
}
MLPPLinAlg::SDVResult MLPPLinAlg::svd(std::vector<std::vector<real_t>> A) {
EigenResult left_eigen = eigen(matmult(A, transpose(A)));
EigenResult right_eigen = eigen(matmult(transpose(A), A));
std::vector<std::vector<real_t>> singularvals = sqrt(left_eigen.eigen_values);
std::vector<std::vector<real_t>> sigma = zeromat(A.size(), A[0].size());
for (int i = 0; i < singularvals.size(); i++) {
for (int j = 0; j < singularvals[i].size(); j++) {
sigma[i][j] = singularvals[i][j];
}
}
SDVResult res;
res.U = left_eigen.eigen_vectors;
res.S = sigma;
res.Vt = right_eigen.eigen_vectors;
return res;
}
std::vector<real_t> MLPPLinAlg::vectorProjection(std::vector<real_t> a, std::vector<real_t> b) {
real_t product = dot(a, b) / dot(a, a);
return scalarMultiply(product, a); // Projection of vector a onto b. Denotated as proj_a(b).
}
std::vector<std::vector<real_t>> MLPPLinAlg::gramSchmidtProcess(std::vector<std::vector<real_t>> A) {
A = transpose(A); // C++ vectors lack a mechanism to directly index columns. So, we transpose *a copy* of A for this purpose for ease of use.
std::vector<std::vector<real_t>> B;
B.resize(A.size());
for (int i = 0; i < B.size(); i++) {
B[i].resize(A[0].size());
}
B[0] = A[0]; // We set a_1 = b_1 as an initial condition.
B[0] = scalarMultiply(1 / norm_2(B[0]), B[0]);
for (int i = 1; i < B.size(); i++) {
B[i] = A[i];
for (int j = i - 1; j >= 0; j--) {
B[i] = subtraction(B[i], vectorProjection(B[j], A[i]));
}
B[i] = scalarMultiply(1 / norm_2(B[i]), B[i]); // Very simply multiply all elements of vec B[i] by 1/||B[i]||_2
}
return transpose(B); // We re-transpose the marix.
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPLinAlg::QRD(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> Q = gramSchmidtProcess(A);
std::vector<std::vector<real_t>> R = matmult(transpose(Q), A);
return { Q, R };
}
MLPPLinAlg::QRDResult MLPPLinAlg::qrd(std::vector<std::vector<real_t>> A) {
QRDResult res;
res.Q = gramSchmidtProcess(A);
res.R = matmult(transpose(res.Q), A);
return res;
}
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPLinAlg::chol(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> L = zeromat(A.size(), A[0].size());
for (int j = 0; j < L.size(); j++) { // Matrices entered must be square. No problem here.
for (int i = j; i < L.size(); i++) {
if (i == j) {
real_t sum = 0;
for (int k = 0; k < j; k++) {
sum += L[i][k] * L[i][k];
}
L[i][j] = std::sqrt(A[i][j] - sum);
} else { // That is, i!=j
real_t sum = 0;
for (int k = 0; k < j; k++) {
sum += L[i][k] * L[j][k];
}
L[i][j] = (A[i][j] - sum) / L[j][j];
}
}
}
return { L, transpose(L) }; // Indeed, L.T is our upper triangular matrix.
}
MLPPLinAlg::CholeskyResult MLPPLinAlg::cholesky(std::vector<std::vector<real_t>> A) {
std::vector<std::vector<real_t>> L = zeromat(A.size(), A[0].size());
for (int j = 0; j < L.size(); j++) { // Matrices entered must be square. No problem here.
for (int i = j; i < L.size(); i++) {
if (i == j) {
real_t sum = 0;
for (int k = 0; k < j; k++) {
sum += L[i][k] * L[i][k];
}
L[i][j] = std::sqrt(A[i][j] - sum);
} else { // That is, i!=j
real_t sum = 0;
for (int k = 0; k < j; k++) {
sum += L[i][k] * L[j][k];
}
L[i][j] = (A[i][j] - sum) / L[j][j];
}
}
}
CholeskyResult res;
res.L = L;
res.Lt = transpose(L); // Indeed, L.T is our upper triangular matrix.
return res;
}
real_t MLPPLinAlg::sum_elements(std::vector<std::vector<real_t>> A) {
real_t sum = 0;
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
sum += A[i][j];
}
}
return sum;
}
std::vector<real_t> MLPPLinAlg::flatten(std::vector<std::vector<real_t>> A) {
std::vector<real_t> a;
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
a.push_back(A[i][j]);
}
}
return a;
}
Ref<MLPPVector> MLPPLinAlg::flattenv(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;
}
std::vector<real_t> MLPPLinAlg::solve(std::vector<std::vector<real_t>> A, std::vector<real_t> b) {
return mat_vec_mult(inverse(A), b);
}
bool MLPPLinAlg::positiveDefiniteChecker(std::vector<std::vector<real_t>> A) {
auto [eigenvectors, eigenvals] = eig(A);
std::vector<real_t> eigenvals_vec;
for (int i = 0; i < eigenvals.size(); i++) {
eigenvals_vec.push_back(eigenvals[i][i]);
}
for (int i = 0; i < eigenvals_vec.size(); i++) {
if (eigenvals_vec[i] <= 0) { // Simply check to ensure all eigenvalues are positive.
return false;
}
}
return true;
}
bool MLPPLinAlg::negativeDefiniteChecker(std::vector<std::vector<real_t>> A) {
auto [eigenvectors, eigenvals] = eig(A);
std::vector<real_t> eigenvals_vec;
for (int i = 0; i < eigenvals.size(); i++) {
eigenvals_vec.push_back(eigenvals[i][i]);
}
for (int i = 0; i < eigenvals_vec.size(); i++) {
if (eigenvals_vec[i] >= 0) { // Simply check to ensure all eigenvalues are negative.
return false;
}
}
return true;
}
bool MLPPLinAlg::zeroEigenvalue(std::vector<std::vector<real_t>> A) {
auto [eigenvectors, eigenvals] = eig(A);
std::vector<real_t> eigenvals_vec;
for (int i = 0; i < eigenvals.size(); i++) {
eigenvals_vec.push_back(eigenvals[i][i]);
}
for (int i = 0; i < eigenvals_vec.size(); i++) {
if (eigenvals_vec[i] == 0) {
return true;
}
}
return false;
}
void MLPPLinAlg::printMatrix(std::vector<std::vector<real_t>> A) {
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
std::cout << A[i][j] << " ";
}
std::cout << std::endl;
}
}
std::vector<std::vector<real_t>> MLPPLinAlg::outerProduct(std::vector<real_t> a, std::vector<real_t> b) {
std::vector<std::vector<real_t>> C;
C.resize(a.size());
for (int i = 0; i < C.size(); i++) {
C[i] = scalarMultiply(a[i], b);
}
return C;
}
Ref<MLPPMatrix> MLPPLinAlg::outer_product(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
Ref<MLPPMatrix> C;
C.instance();
Size2i size = Size2i(a->size(), b->size());
C->resize(size);
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
for (int i = 0; i < size.y; ++i) {
real_t curr_a = a_ptr[i];
for (int j = 0; j < size.x; ++j) {
C->set_element(i, j, curr_a * b_ptr[j]);
}
}
return C;
}
std::vector<real_t> MLPPLinAlg::hadamard_product(std::vector<real_t> a, std::vector<real_t> b) {
std::vector<real_t> c;
c.resize(a.size());
for (int i = 0; i < a.size(); i++) {
c[i] = a[i] * b[i];
}
return c;
}
Ref<MLPPVector> MLPPLinAlg::hadamard_productnv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
ERR_FAIL_COND_V(size != b->size(), Ref<MLPPVector>());
out->resize(size);
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] * b_ptr[i];
}
return out;
}
void MLPPLinAlg::hadamard_productv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b, Ref<MLPPVector> out) {
ERR_FAIL_COND(!a.is_valid() || !b.is_valid() || !out.is_valid());
int size = a->size();
ERR_FAIL_COND(size != b->size());
if (unlikely(out->size() != size)) {
out->resize(size);
}
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] * b_ptr[i];
}
}
std::vector<real_t> MLPPLinAlg::elementWiseDivision(std::vector<real_t> a, std::vector<real_t> b) {
std::vector<real_t> c;
c.resize(a.size());
for (int i = 0; i < a.size(); i++) {
c[i] = a[i] / b[i];
}
return c;
}
Ref<MLPPVector> MLPPLinAlg::element_wise_division(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
ERR_FAIL_COND_V(size != b->size(), Ref<MLPPVector>());
out->resize(size);
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] / b_ptr[i];
}
return out;
}
std::vector<real_t> MLPPLinAlg::scalarMultiply(real_t scalar, std::vector<real_t> a) {
for (int i = 0; i < a.size(); i++) {
a[i] *= scalar;
}
return a;
}
Ref<MLPPVector> MLPPLinAlg::scalar_multiplynv(real_t scalar, const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] * scalar;
}
return out;
}
void MLPPLinAlg::scalar_multiplyv(real_t scalar, const Ref<MLPPVector> &a, Ref<MLPPVector> out) {
ERR_FAIL_COND(!a.is_valid() || !out.is_valid());
int size = a->size();
if (unlikely(out->size() != size)) {
out->resize(size);
}
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] * scalar;
}
}
std::vector<real_t> MLPPLinAlg::scalarAdd(real_t scalar, std::vector<real_t> a) {
for (int i = 0; i < a.size(); i++) {
a[i] += scalar;
}
return a;
}
Ref<MLPPVector> MLPPLinAlg::scalar_addnv(real_t scalar, const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] + scalar;
}
return out;
}
void MLPPLinAlg::scalar_addv(real_t scalar, const Ref<MLPPVector> &a, Ref<MLPPVector> out) {
ERR_FAIL_COND(!a.is_valid() || !out.is_valid());
int size = a->size();
if (unlikely(out->size() != size)) {
out->resize(size);
}
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] + scalar;
}
}
std::vector<real_t> MLPPLinAlg::addition(std::vector<real_t> a, std::vector<real_t> b) {
std::vector<real_t> c;
c.resize(a.size());
for (int i = 0; i < a.size(); i++) {
c[i] = a[i] + b[i];
}
return c;
}
Ref<MLPPVector> MLPPLinAlg::additionnv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), Ref<MLPPVector>());
int size = a->size();
ERR_FAIL_COND_V(size != b->size(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
out->resize(size);
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] + b_ptr[i];
}
return out;
}
void MLPPLinAlg::additionv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b, Ref<MLPPVector> out) {
ERR_FAIL_COND(!a.is_valid() || !b.is_valid() || !out.is_valid());
int size = a->size();
ERR_FAIL_COND(size != b->size());
if (unlikely(out->size() != size)) {
out->resize(size);
}
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] + b_ptr[i];
}
}
std::vector<real_t> MLPPLinAlg::subtraction(std::vector<real_t> a, std::vector<real_t> b) {
std::vector<real_t> c;
c.resize(a.size());
for (int i = 0; i < a.size(); i++) {
c[i] = a[i] - b[i];
}
return c;
}
Ref<MLPPVector> MLPPLinAlg::subtractionnv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), Ref<MLPPVector>());
int size = a->size();
ERR_FAIL_COND_V(size != b->size(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
if (unlikely(size == 0)) {
return out;
}
out->resize(size);
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] - b_ptr[i];
}
return out;
}
void MLPPLinAlg::subtractionv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b, Ref<MLPPVector> out) {
ERR_FAIL_COND(!a.is_valid() || !b.is_valid() || !out.is_valid());
int size = a->size();
ERR_FAIL_COND(size != b->size());
if (unlikely(out->size() != size)) {
out->resize(size);
}
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = a_ptr[i] - b_ptr[i];
}
}
std::vector<real_t> MLPPLinAlg::subtractMatrixRows(std::vector<real_t> a, std::vector<std::vector<real_t>> B) {
for (int i = 0; i < B.size(); i++) {
a = subtraction(a, B[i]);
}
return a;
}
Ref<MLPPVector> MLPPLinAlg::subtract_matrix_rows(const Ref<MLPPVector> &a, const Ref<MLPPMatrix> &B) {
Ref<MLPPVector> c = a->duplicate();
Size2i b_size = B->size();
ERR_FAIL_COND_V(b_size.x != c->size(), c);
const real_t *b_ptr = B->ptr();
real_t *c_ptr = c->ptrw();
for (int i = 0; i < b_size.y; ++i) {
for (int j = 0; j < b_size.x; ++j) {
c_ptr[j] -= b_ptr[B->calculate_index(i, j)];
}
}
return c;
}
std::vector<real_t> MLPPLinAlg::log(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::log(a[i]);
}
return b;
}
std::vector<real_t> MLPPLinAlg::log10(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::log10(a[i]);
}
return b;
}
std::vector<real_t> MLPPLinAlg::exp(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::exp(a[i]);
}
return b;
}
std::vector<real_t> MLPPLinAlg::erf(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::erf(a[i]);
}
return b;
}
std::vector<real_t> MLPPLinAlg::exponentiate(std::vector<real_t> a, real_t p) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < b.size(); i++) {
b[i] = std::pow(a[i], p);
}
return b;
}
std::vector<real_t> MLPPLinAlg::sqrt(std::vector<real_t> a) {
return exponentiate(a, 0.5);
}
std::vector<real_t> MLPPLinAlg::cbrt(std::vector<real_t> a) {
return exponentiate(a, real_t(1) / real_t(3));
}
Ref<MLPPVector> MLPPLinAlg::logv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = Math::log(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::log10v(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = std::log10(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::expv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = Math::exp(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::erfv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = std::erf(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::exponentiatev(const Ref<MLPPVector> &a, real_t p) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = Math::pow(a_ptr[i], p);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::sqrtv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = Math::sqrt(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::cbrtv(const Ref<MLPPVector> &a) {
return exponentiatev(a, static_cast<real_t>(1) / static_cast<real_t>(3));
}
real_t MLPPLinAlg::dot(std::vector<real_t> a, std::vector<real_t> b) {
real_t c = 0;
for (int i = 0; i < a.size(); i++) {
c += a[i] * b[i];
}
return c;
}
real_t MLPPLinAlg::dotv(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
int a_size = a->size();
ERR_FAIL_COND_V(a_size != b->size(), 0);
const real_t *a_ptr = a->ptr();
const real_t *b_ptr = b->ptr();
real_t c = 0;
for (int i = 0; i < a_size; ++i) {
c += a_ptr[i] * b_ptr[i];
}
return c;
}
std::vector<real_t> MLPPLinAlg::cross(std::vector<real_t> a, std::vector<real_t> b) {
// Cross products exist in R^7 also. Though, I will limit it to R^3 as Wolfram does this.
std::vector<std::vector<real_t>> mat = { onevec(3), a, b };
real_t det1 = det({ { a[1], a[2] }, { b[1], b[2] } }, 2);
real_t det2 = -det({ { a[0], a[2] }, { b[0], b[2] } }, 2);
real_t det3 = det({ { a[0], a[1] }, { b[0], b[1] } }, 2);
return { det1, det2, det3 };
}
std::vector<real_t> MLPPLinAlg::abs(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < b.size(); i++) {
b[i] = std::abs(a[i]);
}
return b;
}
std::vector<real_t> MLPPLinAlg::zerovec(int n) {
std::vector<real_t> zerovec;
zerovec.resize(n);
return zerovec;
}
std::vector<real_t> MLPPLinAlg::onevec(int n) {
return full(n, 1);
}
std::vector<std::vector<real_t>> MLPPLinAlg::diag(std::vector<real_t> a) {
std::vector<std::vector<real_t>> B = zeromat(a.size(), a.size());
for (int i = 0; i < B.size(); i++) {
B[i][i] = a[i];
}
return B;
}
Ref<MLPPVector> MLPPLinAlg::diagm(const Ref<MLPPVector> &a) {
int a_size = a->size();
Ref<MLPPMatrix> B;
B.instance();
B->resize(Size2i(a_size, a_size));
B->fill(0);
const real_t *a_ptr = a->ptr();
real_t *b_ptr = B->ptrw();
for (int i = 0; i < a_size; ++i) {
b_ptr[B->calculate_index(i, i)] = a_ptr[i];
}
return B;
}
std::vector<real_t> MLPPLinAlg::full(int n, int k) {
std::vector<real_t> full;
full.resize(n);
for (int i = 0; i < full.size(); i++) {
full[i] = k;
}
return full;
}
Ref<MLPPVector> MLPPLinAlg::absv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = ABS(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::zerovecv(int n) {
Ref<MLPPVector> vec;
vec.instance();
vec->resize(n);
vec->fill(0);
return vec;
}
Ref<MLPPVector> MLPPLinAlg::onevecv(int n) {
Ref<MLPPVector> vec;
vec.instance();
vec->resize(n);
vec->fill(1);
return vec;
}
Ref<MLPPVector> MLPPLinAlg::fullv(int n, int k) {
Ref<MLPPVector> vec;
vec.instance();
vec->resize(n);
vec->fill(k);
return vec;
}
std::vector<real_t> MLPPLinAlg::sin(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::sin(a[i]);
}
return b;
}
std::vector<real_t> MLPPLinAlg::cos(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::cos(a[i]);
}
return b;
}
Ref<MLPPVector> MLPPLinAlg::sinv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = Math::sin(a_ptr[i]);
}
return out;
}
Ref<MLPPVector> MLPPLinAlg::cosv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), Ref<MLPPVector>());
Ref<MLPPVector> out;
out.instance();
int size = a->size();
out->resize(size);
const real_t *a_ptr = a->ptr();
real_t *out_ptr = out->ptrw();
for (int i = 0; i < size; ++i) {
out_ptr[i] = Math::cos(a_ptr[i]);
}
return out;
}
std::vector<std::vector<real_t>> MLPPLinAlg::rotate(std::vector<std::vector<real_t>> A, real_t theta, int axis) {
std::vector<std::vector<real_t>> rotationMatrix = { { std::cos(theta), -std::sin(theta) }, { std::sin(theta), std::cos(theta) } };
if (axis == 0) {
rotationMatrix = { { 1, 0, 0 }, { 0, std::cos(theta), -std::sin(theta) }, { 0, std::sin(theta), std::cos(theta) } };
} else if (axis == 1) {
rotationMatrix = { { std::cos(theta), 0, std::sin(theta) }, { 0, 1, 0 }, { -std::sin(theta), 0, std::cos(theta) } };
} else if (axis == 2) {
rotationMatrix = { { std::cos(theta), -std::sin(theta), 0 }, { std::sin(theta), std::cos(theta), 0 }, { 1, 0, 0 } };
}
return matmult(A, rotationMatrix);
}
std::vector<std::vector<real_t>> MLPPLinAlg::max(std::vector<std::vector<real_t>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (int i = 0; i < A.size(); i++) {
C[i] = max(A[i], B[i]);
}
return C;
}
real_t MLPPLinAlg::max(std::vector<real_t> a) {
int max = a[0];
for (int i = 0; i < a.size(); i++) {
if (a[i] > max) {
max = a[i];
}
}
return max;
}
real_t MLPPLinAlg::min(std::vector<real_t> a) {
int min = a[0];
for (int i = 0; i < a.size(); i++) {
if (a[i] < min) {
min = a[i];
}
}
return min;
}
std::vector<real_t> MLPPLinAlg::round(std::vector<real_t> a) {
std::vector<real_t> b;
b.resize(a.size());
for (int i = 0; i < a.size(); i++) {
b[i] = std::round(a[i]);
}
return b;
}
// Multidimensional Euclidean Distance
real_t MLPPLinAlg::euclideanDistance(std::vector<real_t> a, std::vector<real_t> b) {
real_t dist = 0;
for (int i = 0; i < a.size(); i++) {
dist += (a[i] - b[i]) * (a[i] - b[i]);
}
return std::sqrt(dist);
}
real_t MLPPLinAlg::euclidean_distance(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), 0);
int a_size = a->size();
ERR_FAIL_COND_V(a_size != b->size(), 0);
const real_t *aa = a->ptr();
const real_t *ba = b->ptr();
real_t dist = 0;
for (int i = 0; i < a_size; i++) {
dist += (aa[i] - ba[i]) * (aa[i] - ba[i]);
}
return Math::sqrt(dist);
}
real_t MLPPLinAlg::euclidean_distance_squared(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!a.is_valid() || !b.is_valid(), 0);
int a_size = a->size();
ERR_FAIL_COND_V(a_size != b->size(), 0);
const real_t *aa = a->ptr();
const real_t *ba = b->ptr();
real_t dist = 0;
for (int i = 0; i < a_size; i++) {
dist += (aa[i] - ba[i]) * (aa[i] - ba[i]);
}
return dist;
}
real_t MLPPLinAlg::norm_2(std::vector<real_t> a) {
return std::sqrt(norm_sq(a));
}
real_t MLPPLinAlg::norm_sq(std::vector<real_t> a) {
real_t n_sq = 0;
for (int i = 0; i < a.size(); i++) {
n_sq += a[i] * a[i];
}
return n_sq;
}
real_t MLPPLinAlg::norm_sqv(const Ref<MLPPVector> &a) {
ERR_FAIL_COND_V(!a.is_valid(), 0);
int size = a->size();
const real_t *a_ptr = a->ptr();
real_t n_sq = 0;
for (int i = 0; i < size; ++i) {
n_sq += a_ptr[i] * a_ptr[i];
}
return n_sq;
}
real_t MLPPLinAlg::sum_elements(std::vector<real_t> a) {
real_t sum = 0;
for (int i = 0; i < a.size(); i++) {
sum += a[i];
}
return sum;
}
real_t MLPPLinAlg::sum_elementsv(const Ref<MLPPVector> &a) {
int a_size = a->size();
const real_t *a_ptr = a->ptr();
real_t sum = 0;
for (int i = 0; i < a_size; ++i) {
sum += a_ptr[i];
}
return sum;
}
real_t MLPPLinAlg::cosineSimilarity(std::vector<real_t> a, std::vector<real_t> b) {
return dot(a, b) / (norm_2(a) * norm_2(b));
}
void MLPPLinAlg::printVector(std::vector<real_t> a) {
for (int i = 0; i < a.size(); i++) {
std::cout << a[i] << " ";
}
std::cout << std::endl;
}
std::vector<std::vector<real_t>> MLPPLinAlg::mat_vec_add(std::vector<std::vector<real_t>> A, std::vector<real_t> b) {
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
A[i][j] += b[j];
}
}
return A;
}
std::vector<real_t> MLPPLinAlg::mat_vec_mult(std::vector<std::vector<real_t>> A, std::vector<real_t> b) {
std::vector<real_t> c;
c.resize(A.size());
for (int i = 0; i < A.size(); i++) {
for (int k = 0; k < b.size(); k++) {
c[i] += A[i][k] * b[k];
}
}
return c;
}
Ref<MLPPMatrix> MLPPLinAlg::mat_vec_addv(const Ref<MLPPMatrix> &A, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!A.is_valid() || !b.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
ERR_FAIL_COND_V(a_size.x != b->size(), Ref<MLPPMatrix>());
Ref<MLPPMatrix> ret;
ret.instance();
ret->resize(a_size);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = b->ptr();
real_t *ret_ptr = ret->ptrw();
for (int i = 0; i < a_size.y; ++i) {
for (int j = 0; j < a_size.x; ++j) {
int mat_index = A->calculate_index(i, j);
ret_ptr[mat_index] = a_ptr[mat_index] + b_ptr[j];
}
}
return ret;
}
Ref<MLPPVector> MLPPLinAlg::mat_vec_multv(const Ref<MLPPMatrix> &A, const Ref<MLPPVector> &b) {
ERR_FAIL_COND_V(!A.is_valid() || !b.is_valid(), Ref<MLPPMatrix>());
Size2i a_size = A->size();
int b_size = b->size();
ERR_FAIL_COND_V(a_size.x < b->size(), Ref<MLPPMatrix>());
Ref<MLPPVector> c;
c.instance();
c->resize(a_size.y);
c->fill(0);
const real_t *a_ptr = A->ptr();
const real_t *b_ptr = b->ptr();
real_t *c_ptr = c->ptrw();
for (int i = 0; i < a_size.y; ++i) {
for (int k = 0; k < b_size; ++k) {
int mat_index = A->calculate_index(i, k);
c_ptr[i] += a_ptr[mat_index] * b_ptr[k];
}
}
return c;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::addition(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B) {
for (int i = 0; i < A.size(); i++) {
A[i] = addition(A[i], B[i]);
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::elementWiseDivision(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B) {
for (int i = 0; i < A.size(); i++) {
A[i] = elementWiseDivision(A[i], B[i]);
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::sqrt(std::vector<std::vector<std::vector<real_t>>> A) {
for (int i = 0; i < A.size(); i++) {
A[i] = sqrt(A[i]);
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::exponentiate(std::vector<std::vector<std::vector<real_t>>> A, real_t p) {
for (int i = 0; i < A.size(); i++) {
A[i] = exponentiate(A[i], p);
}
return A;
}
std::vector<std::vector<real_t>> MLPPLinAlg::tensor_vec_mult(std::vector<std::vector<std::vector<real_t>>> A, std::vector<real_t> b) {
std::vector<std::vector<real_t>> C;
C.resize(A.size());
for (int i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (int i = 0; i < C.size(); i++) {
for (int j = 0; j < C[i].size(); j++) {
C[i][j] = dot(A[i][j], b);
}
}
return C;
}
std::vector<real_t> MLPPLinAlg::flatten(std::vector<std::vector<std::vector<real_t>>> A) {
std::vector<real_t> c;
for (int i = 0; i < A.size(); i++) {
std::vector<real_t> flattenedVec = flatten(A[i]);
c.insert(c.end(), flattenedVec.begin(), flattenedVec.end());
}
return c;
}
void MLPPLinAlg::printTensor(std::vector<std::vector<std::vector<real_t>>> A) {
for (int i = 0; i < A.size(); i++) {
printMatrix(A[i]);
if (i != A.size() - 1) {
std::cout << std::endl;
}
}
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::scalarMultiply(real_t scalar, std::vector<std::vector<std::vector<real_t>>> A) {
for (int i = 0; i < A.size(); i++) {
A[i] = scalarMultiply(scalar, A[i]);
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::scalarAdd(real_t scalar, std::vector<std::vector<std::vector<real_t>>> A) {
for (int i = 0; i < A.size(); i++) {
A[i] = scalarAdd(scalar, A[i]);
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::resize(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B) {
A.resize(B.size());
for (int i = 0; i < B.size(); i++) {
A[i].resize(B[i].size());
for (int j = 0; j < B[i].size(); j++) {
A[i][j].resize(B[i][j].size());
}
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::max(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B) {
for (int i = 0; i < A.size(); i++) {
A[i] = max(A[i], B[i]);
}
return A;
}
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::abs(std::vector<std::vector<std::vector<real_t>>> A) {
for (int i = 0; i < A.size(); i++) {
A[i] = abs(A[i]);
}
return A;
}
real_t MLPPLinAlg::norm_2(std::vector<std::vector<std::vector<real_t>>> A) {
real_t sum = 0;
for (int i = 0; i < A.size(); i++) {
for (int j = 0; j < A[i].size(); j++) {
for (int k = 0; k < A[i][j].size(); k++) {
sum += A[i][j][k] * A[i][j][k];
}
}
}
return std::sqrt(sum);
}
// Bad implementation. Change this later.
std::vector<std::vector<std::vector<real_t>>> MLPPLinAlg::vector_wise_tensor_product(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<real_t>> B) {
std::vector<std::vector<std::vector<real_t>>> C;
C = resize(C, A);
for (int i = 0; i < A[0].size(); i++) {
for (int j = 0; j < A[0][i].size(); j++) {
std::vector<real_t> currentVector;
currentVector.resize(A.size());
for (int k = 0; k < C.size(); k++) {
currentVector[k] = A[k][i][j];
}
currentVector = mat_vec_mult(B, currentVector);
for (int k = 0; k < C.size(); k++) {
C[k][i][j] = currentVector[k];
}
}
}
return C;
}
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