mirror of https://github.com/Relintai/pmlpp.git
Uncomment and change add_row apis to add_feature_map in MLPPTensor3.
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be0c2f11b5
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@ -91,19 +91,6 @@ std::vector<std::vector<std::vector<real_t>>> MLPPTensor3::to_std_vector() {
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return ret;
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}
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void MLPPTensor3::set_row_std_vector(int p_index_y, const std::vector<real_t> &p_row) {
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ERR_FAIL_COND(p_row.size() != static_cast<uint32_t>(_size.x));
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ERR_FAIL_INDEX(p_index_y, _size.y);
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int ind_start = p_index_y * _size.x;
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const real_t *row_ptr = &p_row[0];
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for (int i = 0; i < _size.x; ++i) {
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_data[ind_start + i] = row_ptr[i];
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}
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}
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MLPPTensor3::MLPPTensor3(const std::vector<std::vector<std::vector<real_t>>> &p_from) {
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_data = NULL;
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@ -27,21 +27,18 @@ public:
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return _data;
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}
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/*
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_FORCE_INLINE_ void add_row(const Vector<real_t> &p_row) {
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_FORCE_INLINE_ void add_feature_map(const Vector<real_t> &p_row) {
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if (p_row.size() == 0) {
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return;
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}
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if (_size.x == 0) {
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_size.x = p_row.size();
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}
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int fms = feature_map_data_size();
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ERR_FAIL_COND(_size.x != p_row.size());
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ERR_FAIL_COND(fms != p_row.size());
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int ci = data_size();
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++_size.y;
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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@ -53,20 +50,18 @@ public:
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}
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}
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_FORCE_INLINE_ void add_row_pool_vector(const PoolRealArray &p_row) {
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_FORCE_INLINE_ void add_feature_map_pool_vector(const PoolRealArray &p_row) {
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if (p_row.size() == 0) {
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return;
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}
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if (_size.x == 0) {
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_size.x = p_row.size();
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}
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int fms = feature_map_data_size();
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ERR_FAIL_COND(_size.x != p_row.size());
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ERR_FAIL_COND(fms != p_row.size());
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int ci = data_size();
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++_size.y;
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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@ -79,7 +74,7 @@ public:
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}
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}
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_FORCE_INLINE_ void add_row_mlpp_vector(const Ref<MLPPVector> &p_row) {
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_FORCE_INLINE_ void add_feature_map_mlpp_vector(const Ref<MLPPVector> &p_row) {
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ERR_FAIL_COND(!p_row.is_valid());
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int p_row_size = p_row->size();
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@ -88,15 +83,13 @@ public:
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return;
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}
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if (_size.x == 0) {
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_size.x = p_row_size;
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}
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int fms = feature_map_data_size();
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ERR_FAIL_COND(_size.x != p_row_size);
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ERR_FAIL_COND(fms != p_row_size);
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int ci = data_size();
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++_size.y;
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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@ -108,41 +101,38 @@ public:
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}
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}
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_FORCE_INLINE_ void add_rows_mlpp_matrix(const Ref<MLPPMatrix> &p_other) {
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ERR_FAIL_COND(!p_other.is_valid());
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_FORCE_INLINE_ void add_feature_map_mlpp_matrix(const Ref<MLPPMatrix> &p_matrix) {
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ERR_FAIL_COND(!p_matrix.is_valid());
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int other_data_size = p_other->data_size();
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int other_data_size = p_matrix->data_size();
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if (other_data_size == 0) {
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return;
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}
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Size2i other_size = p_other->size();
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Size2i matrix_size = p_matrix->size();
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Size2i fms = feature_map_size();
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if (_size.x == 0) {
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_size.x = other_size.x;
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}
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ERR_FAIL_COND(other_size.x != _size.x);
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ERR_FAIL_COND(fms != matrix_size);
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int start_offset = data_size();
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_size.y += other_size.y;
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++_size.z;
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_data = (real_t *)memrealloc(_data, 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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const real_t *other_ptr = p_matrix->ptr();
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for (int i = 0; i < other_data_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_row(real_t p_index) {
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ERR_FAIL_INDEX(p_index, _size.y);
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void remove_feature_map(int p_index) {
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ERR_FAIL_INDEX(p_index, _size.z);
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--_size.y;
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--_size.z;
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int ds = data_size();
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@ -152,8 +142,10 @@ public:
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return;
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}
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for (int i = p_index * _size.x; i < ds; ++i) {
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_data[i] = _data[i + _size.x];
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int fmds = feature_map_data_size();
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for (int i = calculate_feature_map_index(p_index); i < ds; ++i) {
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_data[i] = _data[i + fmds];
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}
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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@ -162,10 +154,10 @@ public:
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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_row_unordered(int p_index) {
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ERR_FAIL_INDEX(p_index, _size.y);
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void remove_feature_map_unordered(int p_index) {
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ERR_FAIL_INDEX(p_index, _size.z);
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--_size.y;
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--_size.z;
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int ds = data_size();
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@ -175,8 +167,8 @@ public:
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return;
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}
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int start_ind = p_index * _size.x;
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int end_ind = (p_index + 1) * _size.x;
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int start_ind = calculate_feature_map_index(p_index);
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int end_ind = calculate_feature_map_index(p_index + 1);
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for (int i = start_ind; i < end_ind; ++i) {
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_data[i] = _data[ds + i];
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@ -186,18 +178,19 @@ public:
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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void swap_row(int p_index_1, int p_index_2) {
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ERR_FAIL_INDEX(p_index_1, _size.y);
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ERR_FAIL_INDEX(p_index_2, _size.y);
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void swap_feature_map(int p_index_1, int p_index_2) {
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ERR_FAIL_INDEX(p_index_1, _size.z);
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ERR_FAIL_INDEX(p_index_2, _size.z);
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int ind1_start = p_index_1 * _size.x;
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int ind2_start = p_index_2 * _size.x;
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int ind1_start = calculate_feature_map_index(p_index_1);
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int ind2_start = calculate_feature_map_index(p_index_2);
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for (int i = 0; i < _size.x; ++i) {
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int fmds = feature_map_data_size();
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for (int i = 0; i < fmds; ++i) {
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SWAP(_data[ind1_start + i], _data[ind2_start + i]);
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}
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}
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*/
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_FORCE_INLINE_ void clear() { resize(Size3i()); }
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_FORCE_INLINE_ void reset() {
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@ -209,6 +202,8 @@ public:
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}
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_FORCE_INLINE_ bool empty() const { return _size == Size3i(); }
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_FORCE_INLINE_ int feature_map_data_size() const { return _size.x * _size.y; }
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_FORCE_INLINE_ Size2i feature_map_size() const { return Size2i(_size.x, _size.y); }
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_FORCE_INLINE_ int data_size() const { return _size.x * _size.y * _size.z; }
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_FORCE_INLINE_ Size3i size() const { return _size; }
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@ -234,6 +229,10 @@ public:
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return p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z;
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}
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_FORCE_INLINE_ int calculate_feature_map_index(int p_index_z) const {
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return _size.x * _size.y * p_index_z;
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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, data_size());
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return _data[p_index];
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@ -669,7 +668,6 @@ public:
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std::vector<real_t> to_flat_std_vector() const;
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void set_from_std_vectors(const std::vector<std::vector<std::vector<real_t>>> &p_from);
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std::vector<std::vector<std::vector<real_t>>> to_std_vector();
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void set_row_std_vector(int p_index_y, const std::vector<real_t> &p_row);
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MLPPTensor3(const std::vector<std::vector<std::vector<real_t>>> &p_from);
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protected:
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