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Added tensor methods from MLPPLinAlg to MLPPTensor3.

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Relintai 2023-04-24 11:40:46 +02:00
parent 818f9ec1af
commit 5793dc32e3
2 changed files with 200 additions and 0 deletions
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171
mlpp/lin_alg/mlpp_tensor3.cpp
View File

@ -424,6 +424,177 @@ void MLPPTensor3::set_from_image(const Ref<Image> &p_img, const int p_channels)
img->unlock();
}
/*
Vector<Ref<MLPPMatrix>> MLPPTensor3::additionnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B) {
Vector<Ref<MLPPMatrix>> res;
res.resize(A.size());
for (int i = 0; i < res.size(); i++) {
res.write[i] = additionnm(A[i], B[i]);
}
return res;
}
*/
/*
Vector<Ref<MLPPMatrix>> MLPPTensor3::element_wise_divisionnvnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B) {
Vector<Ref<MLPPMatrix>> res;
res.resize(A.size());
for (int i = 0; i < A.size(); i++) {
res.write[i] = element_wise_divisionnvnm(A[i], B[i]);
}
return res;
}
*/
/*
Vector<Ref<MLPPMatrix>> MLPPTensor3::sqrtnvt(const Vector<Ref<MLPPMatrix>> &A) {
Vector<Ref<MLPPMatrix>> res;
res.resize(A.size());
for (int i = 0; i < A.size(); i++) {
res.write[i] = sqrtnm(A[i]);
}
return res;
}
*/
/*
Vector<Ref<MLPPMatrix>> MLPPTensor3::exponentiatenvt(const Vector<Ref<MLPPMatrix>> &A, real_t p) {
Vector<Ref<MLPPMatrix>> res;
res.resize(A.size());
for (int i = 0; i < A.size(); i++) {
res.write[i] = exponentiatenm(A[i], p);
}
return res;
}
*/
/*
std::vector<std::vector<real_t>> MLPPTensor3::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 (uint32_t i = 0; i < C.size(); i++) {
C[i].resize(A[0].size());
}
for (uint32_t i = 0; i < C.size(); i++) {
for (uint32_t j = 0; j < C[i].size(); j++) {
C[i][j] = dot(A[i][j], b);
}
}
return C;
}
*/
/*
std::vector<real_t> MLPPTensor3::flatten(std::vector<std::vector<std::vector<real_t>>> A) {
std::vector<real_t> c;
for (uint32_t i = 0; i < A.size(); i++) {
std::vector<real_t> flattenedVec = flatten(A[i]);
c.insert(c.end(), flattenedVec.begin(), flattenedVec.end());
}
return c;
}
*/
/*
Vector<Ref<MLPPMatrix>> MLPPTensor3::scalar_multiplynvt(real_t scalar, Vector<Ref<MLPPMatrix>> A) {
for (int i = 0; i < A.size(); i++) {
A.write[i] = scalar_multiplynm(scalar, A[i]);
}
return A;
}
Vector<Ref<MLPPMatrix>> MLPPTensor3::scalar_addnvt(real_t scalar, Vector<Ref<MLPPMatrix>> A) {
for (int i = 0; i < A.size(); i++) {
A.write[i] = scalar_addnm(scalar, A[i]);
}
return A;
}
Vector<Ref<MLPPMatrix>> MLPPTensor3::resizenvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B) {
Vector<Ref<MLPPMatrix>> res;
res.resize(B.size());
for (int i = 0; i < res.size(); i++) {
Ref<MLPPMatrix> m;
m.instance();
m->resize(B[i]->size());
res.write[i] = m;
}
return res;
}
*/
//std::vector<std::vector<std::vector<real_t>>> hadamard_product(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B);
/*
Vector<Ref<MLPPMatrix>> MLPPTensor3::maxnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B) {
Vector<Ref<MLPPMatrix>> res;
res.resize(A.size());
for (int i = 0; i < A.size(); i++) {
res.write[i] = maxnm(A[i], B[i]);
}
return res;
}
Vector<Ref<MLPPMatrix>> MLPPTensor3::absnvt(const Vector<Ref<MLPPMatrix>> &A) {
Vector<Ref<MLPPMatrix>> res;
res.resize(A.size());
for (int i = 0; i < A.size(); i++) {
res.write[i] = absnm(A[i]);
}
return A;
}
*/
/*
real_t MLPPTensor3::norm_2(std::vector<std::vector<std::vector<real_t>>> A) {
real_t sum = 0;
for (uint32_t i = 0; i < A.size(); i++) {
for (uint32_t j = 0; j < A[i].size(); j++) {
for (uint32_t k = 0; k < A[i][j].size(); k++) {
sum += A[i][j][k] * A[i][j][k];
}
}
}
return Math::sqrt(sum);
}
*/
/*
// Bad implementation. Change this later.
std::vector<std::vector<std::vector<real_t>>> MLPPTensor3::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 (uint32_t i = 0; i < A[0].size(); i++) {
for (uint32_t j = 0; j < A[0][i].size(); j++) {
std::vector<real_t> currentVector;
currentVector.resize(A.size());
for (uint32_t k = 0; k < C.size(); k++) {
currentVector[k] = A[k][i][j];
}
currentVector = mat_vec_mult(B, currentVector);
for (uint32_t k = 0; k < C.size(); k++) {
C[k][i][j] = currentVector[k];
}
}
}
return C;
}
*/
String MLPPTensor3::to_string() {
String str;

29
mlpp/lin_alg/mlpp_tensor3.h
View File

@ -648,6 +648,35 @@ public:
void set_from_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
public:
//math api
//Vector<Ref<MLPPMatrix>> additionnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
//Vector<Ref<MLPPMatrix>> element_wise_divisionnvnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
//Vector<Ref<MLPPMatrix>> sqrtnvt(const Vector<Ref<MLPPMatrix>> &A);
//Vector<Ref<MLPPMatrix>> exponentiatenvt(const Vector<Ref<MLPPMatrix>> &A, real_t p);
//std::vector<std::vector<real_t>> tensor_vec_mult(std::vector<std::vector<std::vector<real_t>>> A, std::vector<real_t> b);
//std::vector<real_t> flatten(std::vector<std::vector<std::vector<real_t>>> A);
//Vector<Ref<MLPPMatrix>> scalar_multiplynvt(real_t scalar, Vector<Ref<MLPPMatrix>> A);
//Vector<Ref<MLPPMatrix>> scalar_addnvt(real_t scalar, Vector<Ref<MLPPMatrix>> A);
//Vector<Ref<MLPPMatrix>> resizenvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
//std::vector<std::vector<std::vector<real_t>>> hadamard_product(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B);
//Vector<Ref<MLPPMatrix>> maxnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
//Vector<Ref<MLPPMatrix>> absnvt(const Vector<Ref<MLPPMatrix>> &A);
//real_t norm_2(std::vector<std::vector<std::vector<real_t>>> A);
//std::vector<std::vector<std::vector<real_t>>> vector_wise_tensor_product(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<real_t>> B);
public:
void fill(real_t p_val) {
if (!_data) {