77 lines
3.0 KiB
C++
77 lines
3.0 KiB
C++
/*******************************************************************************
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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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// @author sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/nth_element.h>
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#include <TAD.h>
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#include <ShapeUtils.h>
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#include <helpers/ConstantTadHelper.h>
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#include <execution/Threads.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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void nthElementFunctor_(NDArray* input, Nd4jLong n, NDArray* output, bool reverse) {
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NDArray sortedVals(*input);
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if (input->isVector()) {
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//std::vector<float> data(input->lengthOf());
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//memcpy(&data[0], input->getBuffer(), sizeof(T) * data.size());
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//size_t l = 0;
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//for (size_t l = 0; l < data.size(); ++l)
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// data[l] = input->e<float>(l);
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//auto nthPos = data.begin();
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//nthPos += n;
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//std::nth_element(data.begin(), nthPos, data.end());
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SpecialMethods<T>::sortGeneric(sortedVals.buffer(), sortedVals.shapeInfo(), reverse);
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output->p(0, sortedVals.e<T>(n));
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}
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else { // rank greater than 1
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std::vector<int> lastDims({input->rankOf() - 1});// = ShapeUtils::evalDimsToExclude(input->rankOf(), {input->rankOf() - 1});
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auto pack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(sortedVals.shapeInfo(), lastDims);
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SpecialMethods<T>::sortTadGeneric(sortedVals.buffer(), sortedVals.shapeInfo(), lastDims.data(), lastDims.size(), pack.primaryShapeInfo(), pack.primaryOffsets(), reverse);
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ResultSet rows = sortedVals.allTensorsAlongDimension(lastDims);
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Nd4jLong oL = output->lengthOf();
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e += increment) {
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auto row = rows.at(e);
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output->p(e, row->e<T>(n));
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}
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};
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samediff::Threads::parallel_for(func, 0, oL);
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}
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}
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void nthElementFunctor(nd4j::LaunchContext *launchContext, NDArray* input, Nd4jLong n, NDArray* output, bool reverse) {
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BUILD_SINGLE_SELECTOR(input->dataType(), nthElementFunctor_, (input, n, output, reverse), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void nthElementFunctor_, (NDArray* input, Nd4jLong n, NDArray* output, bool reverse), LIBND4J_TYPES);
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}
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}
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}
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