cavis/libnd4j/include/ops/declarable/helpers/cpu/nth_element.cpp

77 lines
3.0 KiB
C++

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