mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
496 lines
10 KiB
C++
496 lines
10 KiB
C++
#ifndef MLPP_VECTOR_H
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#define MLPP_VECTOR_H
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#include "core/math/math_defs.h"
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#include "core/math/math_funcs.h"
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#include "core/containers/pool_vector.h"
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#include "core/containers/sort_array.h"
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#include "core/containers/vector.h"
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#include "core/error/error_macros.h"
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#include "core/os/memory.h"
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#include "core/object/reference.h"
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//REMOVE
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#include <vector>
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class MLPPMatrix;
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class MLPPVector : public Reference {
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GDCLASS(MLPPVector, Reference);
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public:
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real_t *ptrw() {
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return _data;
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}
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const real_t *ptr() const {
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return _data;
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}
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_FORCE_INLINE_ void push_back(real_t p_elem) {
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++_size;
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_data = (real_t *)memrealloc(_data, _size * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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_data[_size - 1] = p_elem;
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}
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_FORCE_INLINE_ void add_mlpp_vector(const Ref<MLPPVector> &p_other) {
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ERR_FAIL_COND(!p_other.is_valid());
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int other_size = p_other->size();
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if (other_size == 0) {
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return;
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}
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int start_offset = _size;
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_size += other_size;
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_data = (real_t *)memrealloc(_data, _size * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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const real_t *other_ptr = p_other->ptr();
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for (int i = 0; i < other_size; ++i) {
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_data[start_offset + i] = other_ptr[i];
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}
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}
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void remove(real_t p_index) {
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ERR_FAIL_INDEX(p_index, _size);
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--_size;
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if (_size == 0) {
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memfree(_data);
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_data = NULL;
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return;
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}
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for (int i = p_index; i < _size; i++) {
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_data[i] = _data[i + 1];
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}
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_data = (real_t *)memrealloc(_data, _size * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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// Removes the item copying the last value into the position of the one to
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// remove. It's generally faster than `remove`.
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void remove_unordered(int p_index) {
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ERR_FAIL_INDEX(p_index, _size);
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_size--;
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if (_size == 0) {
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memfree(_data);
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_data = NULL;
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return;
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}
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if (_size > p_index) {
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_data[p_index] = _data[_size];
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}
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_data = (real_t *)memrealloc(_data, _size * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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void erase(const real_t &p_val) {
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int idx = find(p_val);
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if (idx >= 0) {
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remove(idx);
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}
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}
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int erase_multiple_unordered(const real_t &p_val) {
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int from = 0;
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int count = 0;
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while (true) {
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int64_t idx = find(p_val, from);
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if (idx == -1) {
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break;
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}
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remove_unordered(idx);
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from = idx;
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count++;
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}
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return count;
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}
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void invert() {
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for (int i = 0; i < _size / 2; i++) {
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SWAP(_data[i], _data[_size - i - 1]);
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}
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}
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_FORCE_INLINE_ void clear() { resize(0); }
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_FORCE_INLINE_ void reset() {
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if (_data) {
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memfree(_data);
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_data = NULL;
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_size = 0;
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}
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}
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_FORCE_INLINE_ bool empty() const { return _size == 0; }
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_FORCE_INLINE_ int size() const { return _size; }
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void resize(int p_size) {
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_size = p_size;
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if (_size == 0) {
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memfree(_data);
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_data = NULL;
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return;
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}
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_data = (real_t *)memrealloc(_data, _size * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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_FORCE_INLINE_ const real_t &operator[](int p_index) const {
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CRASH_BAD_INDEX(p_index, _size);
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return _data[p_index];
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}
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_FORCE_INLINE_ real_t &operator[](int p_index) {
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CRASH_BAD_INDEX(p_index, _size);
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return _data[p_index];
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}
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_FORCE_INLINE_ real_t get_element(int p_index) const {
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ERR_FAIL_INDEX_V(p_index, _size, 0);
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return _data[p_index];
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}
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_FORCE_INLINE_ void set_element(int p_index, real_t p_val) {
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ERR_FAIL_INDEX(p_index, _size);
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_data[p_index] = p_val;
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}
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void fill(real_t p_val) {
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for (int i = 0; i < _size; i++) {
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_data[i] = p_val;
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}
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}
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void insert(int p_pos, real_t p_val) {
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ERR_FAIL_INDEX(p_pos, _size + 1);
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if (p_pos == _size) {
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push_back(p_val);
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} else {
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resize(_size + 1);
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for (int i = _size - 1; i > p_pos; i--) {
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_data[i] = _data[i - 1];
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}
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_data[p_pos] = p_val;
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}
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}
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int find(const real_t &p_val, int p_from = 0) const {
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for (int i = p_from; i < _size; i++) {
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if (_data[i] == p_val) {
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return i;
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}
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}
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return -1;
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}
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template <class C>
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void sort_custom() {
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int len = _size;
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if (len == 0) {
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return;
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}
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SortArray<real_t, C> sorter;
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sorter.sort(_data, len);
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}
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void sort() {
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sort_custom<_DefaultComparator<real_t>>();
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}
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void ordered_insert(real_t p_val) {
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int i;
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for (i = 0; i < _size; i++) {
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if (p_val < _data[i]) {
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break;
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}
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}
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insert(i, p_val);
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}
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Vector<real_t> to_vector() const {
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Vector<real_t> ret;
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ret.resize(size());
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real_t *w = ret.ptrw();
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memcpy(w, _data, sizeof(real_t) * _size);
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return ret;
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}
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PoolRealArray to_pool_vector() const {
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PoolRealArray pl;
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if (size()) {
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pl.resize(size());
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typename PoolRealArray::Write w = pl.write();
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real_t *dest = w.ptr();
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for (int i = 0; i < size(); ++i) {
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dest[i] = static_cast<real_t>(_data[i]);
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}
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}
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return pl;
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}
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Vector<uint8_t> to_byte_array() const {
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Vector<uint8_t> ret;
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ret.resize(_size * sizeof(real_t));
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uint8_t *w = ret.ptrw();
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memcpy(w, _data, sizeof(real_t) * _size);
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return ret;
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}
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Ref<MLPPVector> duplicate() const {
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Ref<MLPPVector> ret;
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ret.instance();
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ret->set_from_mlpp_vectorr(*this);
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return ret;
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}
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_FORCE_INLINE_ void set_from_mlpp_vectorr(const MLPPVector &p_from) {
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if (_size != p_from.size()) {
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resize(p_from.size());
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}
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for (int i = 0; i < p_from._size; i++) {
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_data[i] = p_from._data[i];
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}
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}
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_FORCE_INLINE_ void set_from_mlpp_vector(const Ref<MLPPVector> &p_from) {
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ERR_FAIL_COND(!p_from.is_valid());
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if (_size != p_from->size()) {
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resize(p_from->size());
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}
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for (int i = 0; i < p_from->_size; i++) {
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_data[i] = p_from->_data[i];
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}
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}
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_FORCE_INLINE_ void set_from_vector(const Vector<real_t> &p_from) {
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if (_size != p_from.size()) {
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resize(p_from.size());
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}
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resize(p_from.size());
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for (int i = 0; i < _size; i++) {
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_data[i] = p_from[i];
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}
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}
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_FORCE_INLINE_ void set_from_pool_vector(const PoolRealArray &p_from) {
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if (_size != p_from.size()) {
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resize(p_from.size());
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}
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PoolRealArray::Read r = p_from.read();
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for (int i = 0; i < _size; i++) {
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_data[i] = r[i];
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}
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}
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_FORCE_INLINE_ bool is_equal_approx(const Ref<MLPPVector> &p_with, real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const {
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ERR_FAIL_COND_V(!p_with.is_valid(), false);
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if (unlikely(this == p_with.ptr())) {
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return true;
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}
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if (_size != p_with->size()) {
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return false;
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}
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for (int i = 0; i < _size; ++i) {
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if (!Math::is_equal_approx(_data[i], p_with->_data[i], tolerance)) {
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return false;
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}
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}
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return true;
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}
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void flatten_vectors(const Vector<Ref<MLPPVector>> &A);
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Ref<MLPPVector> flatten_vectorsn(const Vector<Ref<MLPPVector>> &A) const;
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void hadamard_product(const Ref<MLPPVector> &b);
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Ref<MLPPVector> hadamard_productn(const Ref<MLPPVector> &b) const;
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void hadamard_productb(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
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void element_wise_division(const Ref<MLPPVector> &b);
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Ref<MLPPVector> element_wise_divisionn(const Ref<MLPPVector> &b) const;
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void element_wise_divisionb(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
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void scalar_multiply(real_t scalar);
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Ref<MLPPVector> scalar_multiplyn(real_t scalar) const;
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void scalar_multiplyb(real_t scalar, const Ref<MLPPVector> &a);
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void scalar_add(real_t scalar);
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Ref<MLPPVector> scalar_addn(real_t scalar) const;
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void scalar_addb(real_t scalar, const Ref<MLPPVector> &a);
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void add(const Ref<MLPPVector> &b);
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Ref<MLPPVector> addn(const Ref<MLPPVector> &b) const;
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void addb(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
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void sub(const Ref<MLPPVector> &b);
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Ref<MLPPVector> subn(const Ref<MLPPVector> &b) const;
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void subb(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
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void log();
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Ref<MLPPVector> logn() const;
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void logb(const Ref<MLPPVector> &a);
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void log10();
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Ref<MLPPVector> log10n() const;
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void log10b(const Ref<MLPPVector> &a);
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void exp();
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Ref<MLPPVector> expn() const;
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void expb(const Ref<MLPPVector> &a);
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void erf();
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Ref<MLPPVector> erfn() const;
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void erfb(const Ref<MLPPVector> &a);
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void exponentiate(real_t p);
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Ref<MLPPVector> exponentiaten(real_t p) const;
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void exponentiateb(const Ref<MLPPVector> &a, real_t p);
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void sqrt();
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Ref<MLPPVector> sqrtn() const;
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void sqrtb(const Ref<MLPPVector> &a);
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void cbrt();
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Ref<MLPPVector> cbrtn() const;
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void cbrtb(const Ref<MLPPVector> &a);
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real_t dot(const Ref<MLPPVector> &b) const;
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//std::vector<real_t> cross(std::vector<real_t> a, std::vector<real_t> b);
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void abs();
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Ref<MLPPVector> absn() const;
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void absb(const Ref<MLPPVector> &a);
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Ref<MLPPVector> zero_vec(int n) const;
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Ref<MLPPVector> one_vec(int n) const;
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Ref<MLPPVector> full_vec(int n, int k) const;
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void sin();
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Ref<MLPPVector> sinn() const;
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void sinb(const Ref<MLPPVector> &a);
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void cos();
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Ref<MLPPVector> cosn() const;
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void cosb(const Ref<MLPPVector> &a);
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void maxv(const Ref<MLPPVector> &b);
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Ref<MLPPVector> maxvn(const Ref<MLPPVector> &b) const;
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void maxvb(const Ref<MLPPVector> &a, const Ref<MLPPVector> &b);
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real_t max_element() const;
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real_t min_element() const;
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//std::vector<real_t> round(std::vector<real_t> a);
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real_t euclidean_distance(const Ref<MLPPVector> &b) const;
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real_t euclidean_distance_squared(const Ref<MLPPVector> &b) const;
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/*
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real_t norm_2(std::vector<real_t> a);
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*/
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real_t norm_sq() const;
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real_t sum_elements() const;
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//real_t cosineSimilarity(std::vector<real_t> a, std::vector<real_t> b);
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void subtract_matrix_rows(const Ref<MLPPMatrix> &B);
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Ref<MLPPVector> subtract_matrix_rowsn(const Ref<MLPPMatrix> &B) const;
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void subtract_matrix_rowsb(const Ref<MLPPVector> &a, const Ref<MLPPMatrix> &B);
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// This multiplies a, bT
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Ref<MLPPMatrix> outer_product(const Ref<MLPPVector> &b) const;
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// as_diagonal_matrix / to_diagonal_matrix
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Ref<MLPPMatrix> diagnm() const;
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String to_string();
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_FORCE_INLINE_ MLPPVector() {
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_size = 0;
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_data = NULL;
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}
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_FORCE_INLINE_ MLPPVector(const MLPPVector &p_from) {
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_size = 0;
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_data = NULL;
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resize(p_from.size());
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for (int i = 0; i < p_from._size; i++) {
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_data[i] = p_from._data[i];
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}
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}
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MLPPVector(const Vector<real_t> &p_from) {
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_size = 0;
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_data = NULL;
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resize(p_from.size());
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for (int i = 0; i < _size; i++) {
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_data[i] = p_from[i];
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}
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}
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MLPPVector(const PoolRealArray &p_from) {
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_size = 0;
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_data = NULL;
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resize(p_from.size());
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typename PoolRealArray::Read r = p_from.read();
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for (int i = 0; i < _size; i++) {
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_data[i] = r[i];
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}
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}
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_FORCE_INLINE_ ~MLPPVector() {
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if (_data) {
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reset();
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}
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}
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// TODO: These are temporary
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std::vector<real_t> to_std_vector() const;
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void set_from_std_vector(const std::vector<real_t> &p_from);
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MLPPVector(const std::vector<real_t> &p_from);
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protected:
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static void _bind_methods();
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protected:
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int _size;
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real_t *_data;
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};
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#endif
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