wehub-resource-sync
90 lines
3.4 KiB
C++
90 lines
3.4 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 Yurii Shyrma (iuriish@yahoo.com), created on 17.05.2018
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//
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#include <array/NDArrayFactory.h>
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#include <array/ResultSet.h>
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#include <ops/declarable/helpers/percentile.h>
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#if NOT_EXCLUDED(OP_percentile)
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namespace sd {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void _percentile(NDArray& input, NDArray& output, std::vector<LongType>& axises, const float q,
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const int interpolation) {
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const int inputRank = input.rankOf();
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if (axises.empty())
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for (int i = 0; i < inputRank; ++i) axises.push_back(i);
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else
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shape::checkDimensions(inputRank, &axises); // check, sort dimensions and remove duplicates if they are present
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auto listOfSubArrs = input.allTensorsAlongDimension(axises);
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std::vector<sd::LongType> shapeOfSubArr(listOfSubArrs.at(0)->rankOf());
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for (size_t i = 0; i < shapeOfSubArr.size(); ++i) shapeOfSubArr[i] = listOfSubArrs.at(0)->shapeOf()[i];
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auto flattenedArr = NDArrayFactory::create('c', shapeOfSubArr, input.dataType(), input.getContext());
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const int len = flattenedArr->lengthOf();
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const float fraction = 1.f - q / 100.;
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sd::LongType position = 0;
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switch (interpolation) {
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case 0: // lower
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position = static_cast<sd::LongType>(math::sd_ceil<float, T>((len - 1) * fraction));
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break;
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case 1: // higher
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position = static_cast<sd::LongType>(math::sd_floor<float, T>((len - 1) * fraction));
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break;
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case 2: // nearest
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position = static_cast<sd::LongType>(math::sd_round<float, T>((len - 1) * fraction));
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break;
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}
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position = len - position - 1;
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// FIXME: our sort impl should be used instead, so this operation might be implemented as generic
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// FIXME: parallelism !
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for (int i = 0; i < listOfSubArrs.size(); ++i) {
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auto buff = reinterpret_cast<T*>(flattenedArr->buffer());
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flattenedArr->assign(listOfSubArrs.at(i));
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std::sort(buff, buff + len);
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output.p(i, flattenedArr->e<T>(position));
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}
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delete flattenedArr;
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}
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void percentile(sd::LaunchContext* context, NDArray& input, NDArray& output, std::vector<LongType>& axises,
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const float q, const int interpolation) {
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BUILD_SINGLE_SELECTOR(input.dataType(), _percentile, (input, output, axises, q, interpolation), SD_COMMON_TYPES);
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}
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BUILD_SINGLE_TEMPLATE( void _percentile,
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(NDArray& input, NDArray& output, std::vector<LongType>& axises, const float q,
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const int interpolation),
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SD_COMMON_TYPES);
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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#endif |