64 lines
2.1 KiB
C++
64 lines
2.1 KiB
C++
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// Created by GS <sgazeos@gmail.com> on 4/6/2018.
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//
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#include "ResultSet.h"
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#include <ops/declarable/helpers/diag.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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// Returns a batched matrix tensor with new batched diagonal values.
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// for detailed explanations please take a look on web page: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag
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template <typename T>
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static void _diagFunctor(const NDArray* input, NDArray* output) {
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const int inLength = input->lengthOf();
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PRAGMA_OMP_PARALLEL_FOR_IF(inLength > Environment::getInstance()->elementwiseThreshold())
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for(int i = 0; i < inLength; ++i)
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output->p<T>(i * (inLength + 1), (*input).e<T>(i));
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}
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void diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
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auto xType = input->dataType();
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BUILD_SINGLE_SELECTOR(xType, _diagFunctor, (input, output), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (const NDArray* input, NDArray* output);, LIBND4J_TYPES);
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void diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) {
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const int outLen = output->lengthOf();
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const int inLen = input->lengthOf();
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int i(0), j(0);
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while (j < outLen) {
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output->p(j, input->e(i));
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i += outLen + 1;
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++j;
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}
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}
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}
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}
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}
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