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