52 lines
2.0 KiB
C++
52 lines
2.0 KiB
C++
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/*******************************************************************************
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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 GS <sgazeos@gmail.com>
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//
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#include <ops/declarable/helpers/confusion.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 _confusionFunctor(NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) {
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std::unique_ptr<ResultSet> arrs(output->allTensorsAlongDimension({1}));
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int lLen = labels->lengthOf();
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PRAGMA_OMP_PARALLEL_FOR_IF(lLen > Environment::getInstance()->elementwiseThreshold())
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for (int j = 0; j < lLen; ++j){
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auto label = labels->e<Nd4jLong>(j);
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auto pred = predictions->e<Nd4jLong>(j);
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T value = (weights == nullptr ? (T)1.0f : weights->e<T>(j));
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(*arrs->at(label)).p<T>(pred, value);
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}
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}
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void confusionFunctor(nd4j::LaunchContext * context, NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output) {
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auto xType = output->dataType(); // weights can be null
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BUILD_SINGLE_SELECTOR(xType, _confusionFunctor, (labels, predictions, weights, output), NUMERIC_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void _confusionFunctor, (NDArray* labels, NDArray* predictions, NDArray* weights, NDArray* output);, NUMERIC_TYPES);
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}
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}
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}
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