Added MLPPTensor3 class. It's a copy of the MLPPMatrix class for now.

SCsub

@@ -9,6 +9,7 @@ sources = [
 
     "mlpp/lin_alg/mlpp_vector.cpp",
     "mlpp/lin_alg/mlpp_matrix.cpp",
+    "mlpp/lin_alg/mlpp_tensor3.cpp",
 
     "mlpp/activation/activation.cpp",
     "mlpp/ann/ann.cpp",

config.py

@@ -8,6 +8,7 @@ def get_doc_classes():
     return [
         "MLPPVector",
         "MLPPMatrix",
+        "MLPPTensor3",
 
         "MLPPUtilities",
         "MLPPReg",

mlpp/lin_alg/mlpp_tensor3.cpp

@@ -0,0 +1,137 @@
+
+#include "mlpp_tensor3.h"
+
+String MLPPTensor3::to_string() {
+	String str;
+
+	str += "[MLPPTensor3: \n";
+
+	for (int y = 0; y < _size.y; ++y) {
+		str += "  [ ";
+
+		for (int x = 0; x < _size.x; ++x) {
+			str += String::num(_data[_size.x * y + x]);
+			str += " ";
+		}
+
+		str += "]\n";
+	}
+
+	str += "]";
+
+	return str;
+}
+
+std::vector<real_t> MLPPTensor3::to_flat_std_vector() const {
+	std::vector<real_t> ret;
+	ret.resize(data_size());
+	real_t *w = &ret[0];
+	memcpy(w, _data, sizeof(real_t) * data_size());
+	return ret;
+}
+
+void MLPPTensor3::set_from_std_vectors(const std::vector<std::vector<real_t>> &p_from) {
+	if (p_from.size() == 0) {
+		reset();
+		return;
+	}
+
+	resize(Size2i(p_from[0].size(), p_from.size()));
+
+	if (data_size() == 0) {
+		reset();
+		return;
+	}
+
+	for (uint32_t i = 0; i < p_from.size(); ++i) {
+		const std::vector<real_t> &r = p_from[i];
+
+		ERR_CONTINUE(r.size() != static_cast<uint32_t>(_size.x));
+
+		int start_index = i * _size.x;
+
+		const real_t *from_ptr = &r[0];
+		for (int j = 0; j < _size.x; j++) {
+			_data[start_index + j] = from_ptr[j];
+		}
+	}
+}
+
+std::vector<std::vector<real_t>> MLPPTensor3::to_std_vector() {
+	std::vector<std::vector<real_t>> ret;
+
+	ret.resize(_size.y);
+
+	for (int i = 0; i < _size.y; ++i) {
+		std::vector<real_t> row;
+
+		for (int j = 0; j < _size.x; ++j) {
+			row.push_back(_data[calculate_index(i, j)]);
+		}
+
+		ret[i] = row;
+	}
+
+	return ret;
+}
+
+void MLPPTensor3::set_row_std_vector(int p_index_y, const std::vector<real_t> &p_row) {
+	ERR_FAIL_COND(p_row.size() != static_cast<uint32_t>(_size.x));
+	ERR_FAIL_INDEX(p_index_y, _size.y);
+
+	int ind_start = p_index_y * _size.x;
+
+	const real_t *row_ptr = &p_row[0];
+
+	for (int i = 0; i < _size.x; ++i) {
+		_data[ind_start + i] = row_ptr[i];
+	}
+}
+
+MLPPTensor3::MLPPTensor3(const std::vector<std::vector<real_t>> &p_from) {
+	_data = NULL;
+
+	set_from_std_vectors(p_from);
+}
+
+void MLPPTensor3::_bind_methods() {
+	ClassDB::bind_method(D_METHOD("add_row", "row"), &MLPPTensor3::add_row_pool_vector);
+	ClassDB::bind_method(D_METHOD("add_row_mlpp_vector", "row"), &MLPPTensor3::add_row_mlpp_vector);
+	ClassDB::bind_method(D_METHOD("add_rows_mlpp_matrix", "other"), &MLPPTensor3::add_rows_mlpp_matrix);
+
+	ClassDB::bind_method(D_METHOD("remove_row", "index"), &MLPPTensor3::remove_row);
+	ClassDB::bind_method(D_METHOD("remove_row_unordered", "index"), &MLPPTensor3::remove_row_unordered);
+	ClassDB::bind_method(D_METHOD("swap_row", "index_1", "index_2"), &MLPPTensor3::swap_row);
+
+	ClassDB::bind_method(D_METHOD("clear"), &MLPPTensor3::clear);
+	ClassDB::bind_method(D_METHOD("reset"), &MLPPTensor3::reset);
+	ClassDB::bind_method(D_METHOD("empty"), &MLPPTensor3::empty);
+
+	ClassDB::bind_method(D_METHOD("data_size"), &MLPPTensor3::data_size);
+	ClassDB::bind_method(D_METHOD("size"), &MLPPTensor3::size);
+
+	ClassDB::bind_method(D_METHOD("resize", "size"), &MLPPTensor3::resize);
+
+	ClassDB::bind_method(D_METHOD("get_element", "index_x", "index_y"), &MLPPTensor3::get_element_bind);
+	ClassDB::bind_method(D_METHOD("set_element", "index_x", "index_y", "val"), &MLPPTensor3::set_element_bind);
+
+	ClassDB::bind_method(D_METHOD("get_row_pool_vector", "index_y"), &MLPPTensor3::get_row_pool_vector);
+	ClassDB::bind_method(D_METHOD("get_row_mlpp_vector", "index_y"), &MLPPTensor3::get_row_mlpp_vector);
+	ClassDB::bind_method(D_METHOD("get_row_into_mlpp_vector", "index_y", "target"), &MLPPTensor3::get_row_into_mlpp_vector);
+
+	ClassDB::bind_method(D_METHOD("set_row_pool_vector", "index_y", "row"), &MLPPTensor3::set_row_pool_vector);
+	ClassDB::bind_method(D_METHOD("set_row_mlpp_vector", "index_y", "row"), &MLPPTensor3::set_row_mlpp_vector);
+
+	ClassDB::bind_method(D_METHOD("fill", "val"), &MLPPTensor3::fill);
+
+	ClassDB::bind_method(D_METHOD("to_flat_pool_vector"), &MLPPTensor3::to_flat_pool_vector);
+	ClassDB::bind_method(D_METHOD("to_flat_byte_array"), &MLPPTensor3::to_flat_byte_array);
+
+	ClassDB::bind_method(D_METHOD("duplicate"), &MLPPTensor3::duplicate);
+
+	ClassDB::bind_method(D_METHOD("set_from_mlpp_vectors_array", "from"), &MLPPTensor3::set_from_mlpp_vectors_array);
+	ClassDB::bind_method(D_METHOD("set_from_arrays", "from"), &MLPPTensor3::set_from_arrays);
+	ClassDB::bind_method(D_METHOD("set_from_mlpp_matrix", "from"), &MLPPTensor3::set_from_mlpp_matrix);
+
+	ClassDB::bind_method(D_METHOD("is_equal_approx", "with", "tolerance"), &MLPPTensor3::is_equal_approx, CMP_EPSILON);
+}

mlpp/lin_alg/mlpp_tensor3.h

@@ -0,0 +1,662 @@
+#ifndef MLPP_TENSOR3_H
+#define MLPP_TENSOR3_H
+
+#include "core/math/math_defs.h"
+
+#include "core/containers/pool_vector.h"
+#include "core/containers/sort_array.h"
+#include "core/containers/vector.h"
+#include "core/error/error_macros.h"
+#include "core/math/vector2i.h"
+#include "core/os/memory.h"
+
+#include "core/object/reference.h"
+
+#include "mlpp_matrix.h"
+#include "mlpp_vector.h"
+
+class MLPPTensor3 : public Reference {
+	GDCLASS(MLPPTensor3, Reference);
+
+public:
+	real_t *ptrw() {
+		return _data;
+	}
+
+	const real_t *ptr() const {
+		return _data;
+	}
+
+	_FORCE_INLINE_ void add_row(const Vector<real_t> &p_row) {
+		if (p_row.size() == 0) {
+			return;
+		}
+
+		if (_size.x == 0) {
+			_size.x = p_row.size();
+		}
+
+		ERR_FAIL_COND(_size.x != p_row.size());
+
+		int ci = data_size();
+
+		++_size.y;
+
+		_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+
+		const real_t *row_arr = p_row.ptr();
+
+		for (int i = 0; i < p_row.size(); ++i) {
+			_data[ci + i] = row_arr[i];
+		}
+	}
+
+	_FORCE_INLINE_ void add_row_pool_vector(const PoolRealArray &p_row) {
+		if (p_row.size() == 0) {
+			return;
+		}
+
+		if (_size.x == 0) {
+			_size.x = p_row.size();
+		}
+
+		ERR_FAIL_COND(_size.x != p_row.size());
+
+		int ci = data_size();
+
+		++_size.y;
+
+		_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+
+		PoolRealArray::Read rread = p_row.read();
+		const real_t *row_arr = rread.ptr();
+
+		for (int i = 0; i < p_row.size(); ++i) {
+			_data[ci + i] = row_arr[i];
+		}
+	}
+
+	_FORCE_INLINE_ void add_row_mlpp_vector(const Ref<MLPPVector> &p_row) {
+		ERR_FAIL_COND(!p_row.is_valid());
+
+		int p_row_size = p_row->size();
+
+		if (p_row_size == 0) {
+			return;
+		}
+
+		if (_size.x == 0) {
+			_size.x = p_row_size;
+		}
+
+		ERR_FAIL_COND(_size.x != p_row_size);
+
+		int ci = data_size();
+
+		++_size.y;
+
+		_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+
+		const real_t *row_ptr = p_row->ptr();
+
+		for (int i = 0; i < p_row_size; ++i) {
+			_data[ci + i] = row_ptr[i];
+		}
+	}
+
+	_FORCE_INLINE_ void add_rows_mlpp_matrix(const Ref<MLPPMatrix> &p_other) {
+		ERR_FAIL_COND(!p_other.is_valid());
+
+		int other_data_size = p_other->data_size();
+
+		if (other_data_size == 0) {
+			return;
+		}
+
+		Size2i other_size = p_other->size();
+
+		if (_size.x == 0) {
+			_size.x = other_size.x;
+		}
+
+		ERR_FAIL_COND(other_size.x != _size.x);
+
+		int start_offset = data_size();
+
+		_size.y += other_size.y;
+
+		_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+
+		const real_t *other_ptr = p_other->ptr();
+
+		for (int i = 0; i < other_data_size; ++i) {
+			_data[start_offset + i] = other_ptr[i];
+		}
+	}
+
+	void remove_row(real_t p_index) {
+		ERR_FAIL_INDEX(p_index, _size.y);
+
+		--_size.y;
+
+		int ds = data_size();
+
+		if (ds == 0) {
+			memfree(_data);
+			_data = NULL;
+			return;
+		}
+
+		for (int i = p_index * _size.x; i < ds; ++i) {
+			_data[i] = _data[i + _size.x];
+		}
+
+		_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+	}
+
+	// Removes the item copying the last value into the position of the one to
+	// remove. It's generally faster than `remove`.
+	void remove_row_unordered(int p_index) {
+		ERR_FAIL_INDEX(p_index, _size.y);
+
+		--_size.y;
+
+		int ds = data_size();
+
+		if (ds == 0) {
+			memfree(_data);
+			_data = NULL;
+			return;
+		}
+
+		int start_ind = p_index * _size.x;
+		int end_ind = (p_index + 1) * _size.x;
+
+		for (int i = start_ind; i < end_ind; ++i) {
+			_data[i] = _data[ds + i];
+		}
+
+		_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+	}
+
+	void swap_row(int p_index_1, int p_index_2) {
+		ERR_FAIL_INDEX(p_index_1, _size.y);
+		ERR_FAIL_INDEX(p_index_2, _size.y);
+
+		int ind1_start = p_index_1 * _size.x;
+		int ind2_start = p_index_2 * _size.x;
+
+		for (int i = 0; i < _size.x; ++i) {
+			SWAP(_data[ind1_start + i], _data[ind2_start + i]);
+		}
+	}
+
+	_FORCE_INLINE_ void clear() { resize(Size2i()); }
+	_FORCE_INLINE_ void reset() {
+		if (_data) {
+			memfree(_data);
+			_data = NULL;
+			_size = Vector2i();
+		}
+	}
+
+	_FORCE_INLINE_ bool empty() const { return data_size() == 0; }
+	_FORCE_INLINE_ int data_size() const { return _size.x * _size.y; }
+	_FORCE_INLINE_ Size2i size() const { return _size; }
+
+	void resize(const Size2i &p_size) {
+		_size = p_size;
+
+		int ds = data_size();
+
+		if (ds == 0) {
+			if (_data) {
+				memfree(_data);
+				_data = NULL;
+			}
+
+			return;
+		}
+
+		_data = (real_t *)memrealloc(_data, ds * sizeof(real_t));
+		CRASH_COND_MSG(!_data, "Out of memory");
+	}
+
+	_FORCE_INLINE_ int calculate_index(int p_index_y, int p_index_x) const {
+		return p_index_y * _size.x + p_index_x;
+	}
+
+	_FORCE_INLINE_ const real_t &operator[](int p_index) const {
+		CRASH_BAD_INDEX(p_index, data_size());
+		return _data[p_index];
+	}
+	_FORCE_INLINE_ real_t &operator[](int p_index) {
+		CRASH_BAD_INDEX(p_index, data_size());
+		return _data[p_index];
+	}
+
+	_FORCE_INLINE_ real_t get_element(int p_index_y, int p_index_x) const {
+		ERR_FAIL_INDEX_V(p_index_x, _size.x, 0);
+		ERR_FAIL_INDEX_V(p_index_y, _size.y, 0);
+
+		return _data[p_index_y * _size.x + p_index_x];
+	}
+	_FORCE_INLINE_ real_t get_element(int p_index_y, int p_index_x) {
+		ERR_FAIL_INDEX_V(p_index_x, _size.x, 0);
+		ERR_FAIL_INDEX_V(p_index_y, _size.y, 0);
+
+		return _data[p_index_y * _size.x + p_index_x];
+	}
+
+	_FORCE_INLINE_ real_t get_element_bind(int p_index_y, int p_index_x) const {
+		ERR_FAIL_INDEX_V(p_index_x, _size.x, 0);
+		ERR_FAIL_INDEX_V(p_index_y, _size.y, 0);
+
+		return static_cast<real_t>(_data[p_index_y * _size.x + p_index_x]);
+	}
+
+	_FORCE_INLINE_ void set_element(int p_index_y, int p_index_x, real_t p_val) {
+		ERR_FAIL_INDEX(p_index_x, _size.x);
+		ERR_FAIL_INDEX(p_index_y, _size.y);
+
+		_data[p_index_y * _size.x + p_index_x] = p_val;
+	}
+
+	_FORCE_INLINE_ void set_element_bind(int p_index_y, int p_index_x, real_t p_val) {
+		ERR_FAIL_INDEX(p_index_x, _size.x);
+		ERR_FAIL_INDEX(p_index_y, _size.y);
+
+		_data[p_index_y * _size.x + p_index_x] = p_val;
+	}
+
+	_FORCE_INLINE_ Vector<real_t> get_row_vector(int p_index_y) {
+		ERR_FAIL_INDEX_V(p_index_y, _size.y, Vector<real_t>());
+
+		Vector<real_t> ret;
+
+		if (unlikely(_size.x == 0)) {
+			return ret;
+		}
+
+		ret.resize(_size.x);
+
+		int ind_start = p_index_y * _size.x;
+
+		real_t *row_ptr = ret.ptrw();
+
+		for (int i = 0; i < _size.x; ++i) {
+			row_ptr[i] = _data[ind_start + i];
+		}
+	}
+
+	_FORCE_INLINE_ PoolRealArray get_row_pool_vector(int p_index_y) {
+		ERR_FAIL_INDEX_V(p_index_y, _size.y, PoolRealArray());
+
+		PoolRealArray ret;
+
+		if (unlikely(_size.x == 0)) {
+			return ret;
+		}
+
+		ret.resize(_size.x);
+
+		int ind_start = p_index_y * _size.x;
+
+		PoolRealArray::Write w = ret.write();
+		real_t *row_ptr = w.ptr();
+
+		for (int i = 0; i < _size.x; ++i) {
+			row_ptr[i] = _data[ind_start + i];
+		}
+
+		return ret;
+	}
+
+	_FORCE_INLINE_ Ref<MLPPVector> get_row_mlpp_vector(int p_index_y) {
+		ERR_FAIL_INDEX_V(p_index_y, _size.y, Ref<MLPPVector>());
+
+		Ref<MLPPVector> ret;
+		ret.instance();
+
+		if (unlikely(_size.x == 0)) {
+			return ret;
+		}
+
+		ret->resize(_size.x);
+
+		int ind_start = p_index_y * _size.x;
+
+		real_t *row_ptr = ret->ptrw();
+
+		for (int i = 0; i < _size.x; ++i) {
+			row_ptr[i] = _data[ind_start + i];
+		}
+
+		return ret;
+	}
+
+	_FORCE_INLINE_ void get_row_into_mlpp_vector(int p_index_y, Ref<MLPPVector> target) const {
+		ERR_FAIL_COND(!target.is_valid());
+		ERR_FAIL_INDEX(p_index_y, _size.y);
+
+		if (unlikely(target->size() != _size.x)) {
+			target->resize(_size.x);
+		}
+
+		int ind_start = p_index_y * _size.x;
+
+		real_t *row_ptr = target->ptrw();
+
+		for (int i = 0; i < _size.x; ++i) {
+			row_ptr[i] = _data[ind_start + i];
+		}
+	}
+
+	_FORCE_INLINE_ void set_row_vector(int p_index_y, const Vector<real_t> &p_row) {
+		ERR_FAIL_COND(p_row.size() != _size.x);
+		ERR_FAIL_INDEX(p_index_y, _size.y);
+
+		int ind_start = p_index_y * _size.x;
+
+		const real_t *row_ptr = p_row.ptr();
+
+		for (int i = 0; i < _size.x; ++i) {
+			_data[ind_start + i] = row_ptr[i];
+		}
+	}
+
+	_FORCE_INLINE_ void set_row_pool_vector(int p_index_y, const PoolRealArray &p_row) {
+		ERR_FAIL_COND(p_row.size() != _size.x);
+		ERR_FAIL_INDEX(p_index_y, _size.y);
+
+		int ind_start = p_index_y * _size.x;
+
+		PoolRealArray::Read r = p_row.read();
+		const real_t *row_ptr = r.ptr();
+
+		for (int i = 0; i < _size.x; ++i) {
+			_data[ind_start + i] = row_ptr[i];
+		}
+	}
+
+	_FORCE_INLINE_ void set_row_mlpp_vector(int p_index_y, const Ref<MLPPVector> &p_row) {
+		ERR_FAIL_COND(!p_row.is_valid());
+		ERR_FAIL_COND(p_row->size() != _size.x);
+		ERR_FAIL_INDEX(p_index_y, _size.y);
+
+		int ind_start = p_index_y * _size.x;
+
+		const real_t *row_ptr = p_row->ptr();
+
+		for (int i = 0; i < _size.x; ++i) {
+			_data[ind_start + i] = row_ptr[i];
+		}
+	}
+
+	void fill(real_t p_val) {
+		if (!_data) {
+			return;
+		}
+
+		int ds = data_size();
+		for (int i = 0; i < ds; ++i) {
+			_data[i] = p_val;
+		}
+	}
+
+	Vector<real_t> to_flat_vector() const {
+		Vector<real_t> ret;
+		ret.resize(data_size());
+		real_t *w = ret.ptrw();
+		memcpy(w, _data, sizeof(real_t) * data_size());
+		return ret;
+	}
+
+	PoolRealArray to_flat_pool_vector() const {
+		PoolRealArray pl;
+		if (data_size()) {
+			pl.resize(data_size());
+			typename PoolRealArray::Write w = pl.write();
+			real_t *dest = w.ptr();
+
+			for (int i = 0; i < data_size(); ++i) {
+				dest[i] = static_cast<real_t>(_data[i]);
+			}
+		}
+		return pl;
+	}
+
+	Vector<uint8_t> to_flat_byte_array() const {
+		Vector<uint8_t> ret;
+		ret.resize(data_size() * sizeof(real_t));
+		uint8_t *w = ret.ptrw();
+		memcpy(w, _data, sizeof(real_t) * data_size());
+		return ret;
+	}
+
+	Ref<MLPPMatrix> duplicate() const {
+		Ref<MLPPMatrix> ret;
+		ret.instance();
+
+		//ret->set_from_mlpp_matrixr(*this);
+
+		return ret;
+	}
+
+	_FORCE_INLINE_ void set_from_mlpp_matrix(const Ref<MLPPMatrix> &p_from) {
+		ERR_FAIL_COND(!p_from.is_valid());
+
+		//resize(p_from->size());
+		//for (int i = 0; i < p_from->data_size(); ++i) {
+			//_data[i] = p_from->_data[i];
+		//}
+	}
+
+	_FORCE_INLINE_ void set_from_mlpp_matrixr(const MLPPMatrix &p_from) {
+		//resize(p_from.size());
+		//for (int i = 0; i < p_from.data_size(); ++i) {
+			//_data[i] = p_from._data[i];
+		//}
+	}
+
+	_FORCE_INLINE_ void set_from_mlpp_vectors(const Vector<Ref<MLPPVector>> &p_from) {
+		if (p_from.size() == 0) {
+			reset();
+			return;
+		}
+
+		if (!p_from[0].is_valid()) {
+			reset();
+			return;
+		}
+
+		resize(Size2i(p_from[0]->size(), p_from.size()));
+
+		if (data_size() == 0) {
+			reset();
+			return;
+		}
+
+		for (int i = 0; i < p_from.size(); ++i) {
+			const Ref<MLPPVector> &r = p_from[i];
+
+			ERR_CONTINUE(!r.is_valid());
+			ERR_CONTINUE(r->size() != _size.x);
+
+			int start_index = i * _size.x;
+
+			const real_t *from_ptr = r->ptr();
+			for (int j = 0; j < _size.x; j++) {
+				_data[start_index + j] = from_ptr[j];
+			}
+		}
+	}
+
+	_FORCE_INLINE_ void set_from_mlpp_vectors_array(const Array &p_from) {
+		if (p_from.size() == 0) {
+			reset();
+			return;
+		}
+
+		Ref<MLPPVector> v0 = p_from[0];
+
+		if (!v0.is_valid()) {
+			reset();
+			return;
+		}
+
+		resize(Size2i(v0->size(), p_from.size()));
+
+		if (data_size() == 0) {
+			reset();
+			return;
+		}
+
+		for (int i = 0; i < p_from.size(); ++i) {
+			Ref<MLPPVector> r = p_from[i];
+
+			ERR_CONTINUE(!r.is_valid());
+			ERR_CONTINUE(r->size() != _size.x);
+
+			int start_index = i * _size.x;
+
+			const real_t *from_ptr = r->ptr();
+			for (int j = 0; j < _size.x; j++) {
+				_data[start_index + j] = from_ptr[j];
+			}
+		}
+	}
+
+	_FORCE_INLINE_ void set_from_vectors(const Vector<Vector<real_t>> &p_from) {
+		if (p_from.size() == 0) {
+			reset();
+			return;
+		}
+
+		resize(Size2i(p_from[0].size(), p_from.size()));
+
+		if (data_size() == 0) {
+			reset();
+			return;
+		}
+
+		for (int i = 0; i < p_from.size(); ++i) {
+			const Vector<real_t> &r = p_from[i];
+
+			ERR_CONTINUE(r.size() != _size.x);
+
+			int start_index = i * _size.x;
+
+			const real_t *from_ptr = r.ptr();
+			for (int j = 0; j < _size.x; j++) {
+				_data[start_index + j] = from_ptr[j];
+			}
+		}
+	}
+
+	_FORCE_INLINE_ void set_from_arrays(const Array &p_from) {
+		if (p_from.size() == 0) {
+			reset();
+			return;
+		}
+
+		PoolRealArray p0arr = p_from[0];
+
+		resize(Size2i(p0arr.size(), p_from.size()));
+
+		if (data_size() == 0) {
+			reset();
+			return;
+		}
+
+		for (int i = 0; i < p_from.size(); ++i) {
+			PoolRealArray r = p_from[i];
+
+			ERR_CONTINUE(r.size() != _size.x);
+
+			int start_index = i * _size.x;
+
+			PoolRealArray::Read read = r.read();
+			const real_t *from_ptr = read.ptr();
+			for (int j = 0; j < _size.x; j++) {
+				_data[start_index + j] = from_ptr[j];
+			}
+		}
+	}
+
+	_FORCE_INLINE_ bool is_equal_approx(const Ref<MLPPTensor3> &p_with, real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const {
+		ERR_FAIL_COND_V(!p_with.is_valid(), false);
+
+		if (unlikely(this == p_with.ptr())) {
+			return true;
+		}
+
+		if (_size != p_with->size()) {
+			return false;
+		}
+
+		int ds = data_size();
+
+		for (int i = 0; i < ds; ++i) {
+			if (!Math::is_equal_approx(_data[i], p_with->_data[i], tolerance)) {
+				return false;
+			}
+		}
+
+		return true;
+	}
+
+	String to_string();
+
+	_FORCE_INLINE_ MLPPTensor3() {
+		_data = NULL;
+	}
+	_FORCE_INLINE_ MLPPTensor3(const MLPPMatrix &p_from) {
+		_data = NULL;
+
+		//resize(p_from.size());
+		//for (int i = 0; i < p_from.data_size(); ++i) {
+		//	_data[i] = p_from._data[i];
+		//}
+	}
+
+	MLPPTensor3(const Vector<Vector<real_t>> &p_from) {
+		_data = NULL;
+
+		set_from_vectors(p_from);
+	}
+
+	MLPPTensor3(const Array &p_from) {
+		_data = NULL;
+
+		set_from_arrays(p_from);
+	}
+
+	_FORCE_INLINE_ ~MLPPTensor3() {
+		if (_data) {
+			reset();
+		}
+	}
+
+	// TODO: These are temporary
+	std::vector<real_t> to_flat_std_vector() const;
+	void set_from_std_vectors(const std::vector<std::vector<real_t>> &p_from);
+	std::vector<std::vector<real_t>> to_std_vector();
+	void set_row_std_vector(int p_index_y, const std::vector<real_t> &p_row);
+	MLPPTensor3(const std::vector<std::vector<real_t>> &p_from);
+
+protected:
+	static void _bind_methods();
+
+protected:
+	Size2i _size;
+	real_t *_data;
+};
+
+#endif

register_types.cpp

@@ -25,6 +25,7 @@ SOFTWARE.
 
 #include "mlpp/data/data.h"
 #include "mlpp/lin_alg/mlpp_matrix.h"
+#include "mlpp/lin_alg/mlpp_tensor3.h"
 #include "mlpp/lin_alg/mlpp_vector.h"
 
 #include "mlpp/activation/activation.h"
@@ -74,6 +75,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
 	if (p_level == MODULE_REGISTRATION_LEVEL_SCENE) {
 		ClassDB::register_class<MLPPVector>();
 		ClassDB::register_class<MLPPMatrix>();
+		ClassDB::register_class<MLPPTensor3>();
 
 		ClassDB::register_class<MLPPUtilities>();
 		ClassDB::register_class<MLPPReg>();