87 lines
3.3 KiB
C++
87 lines
3.3 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 Yurii Shyrma (iuriish@yahoo.com), created on 17.05.2018
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//
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#include <ops/declarable/helpers/percentile.h>
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#include <array/NDArrayFactory.h>
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#include <array/ResultSet.h>
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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(const NDArray& input, NDArray& output, std::vector<int>& axises, const float q, 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)
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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<Nd4jLong> shapeOfSubArr(listOfSubArrs.at(0)->rankOf());
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for(int i=0; i<shapeOfSubArr.size(); ++i)
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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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Nd4jLong position = 0;
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switch(interpolation) {
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case 0: // lower
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position = static_cast<Nd4jLong>(math::nd4j_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<Nd4jLong>(math::nd4j_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<Nd4jLong>(math::nd4j_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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}
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void percentile(sd::LaunchContext * context, const NDArray& input, NDArray& output, std::vector<int>& axises, const float q, const int interpolation) {
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BUILD_SINGLE_SELECTOR(input.dataType(), _percentile, (input, output, axises, q, interpolation), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void _percentile, (const NDArray& input, NDArray& output, std::vector<int>& axises, const float q, const int interpolation), LIBND4J_TYPES);
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}
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}
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} |