7b14a9f603
* Added implementation for random_gamma op. * Added implementation for random_poisson op and support classes. * Added helpers for random_poisson and random_gamma ops. * Implementation of random_poisson. The first working edition. * Implementation of random_poisson. Parallelized working edition. * Implementation of random_gamma. Parallelized working edition with alpha only. * Added cuda implementation for helper of poisson distribution. * Corrected shape calculation with random_gamma and tests. * Finished cpu implementation for gamma distribution. * Finished cuda implementation for random_gamma op. * Refactored cpu helpers for random_gamma and random_poisson ops. * Refactored cuda helpers for gamma and poisson distribution. * Refactored cuda helper for gamma distribution. * Refactored cpu helper for random_poisson op. * Refactored cpu helper for random_gamma op.
67 lines
2.3 KiB
C++
67 lines
2.3 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 George A. Shulinok <sgazeos@gmail.com>
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_random_poisson)
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#include <ops/declarable/headers/random.h>
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#include <ops/declarable/helpers/random.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(random_poisson, 2, 1, false, 0, 0) {
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// gamma distribution
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auto rng = block.randomGenerator();
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auto shape = INPUT_VARIABLE(0);
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auto lambda = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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auto seed = 0;
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if (block.getIArguments()->size()) {
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seed = INT_ARG(0);
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}
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rng.setSeed(seed);
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helpers::fillRandomPoisson(block.launchContext(), rng, lambda, output);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(random_poisson) {
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auto in = INPUT_VARIABLE(0);
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auto shape = in->template asVectorT<Nd4jLong>();
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auto lambdaShape = inputShape->at(1);
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auto dtype = ArrayOptions::dataType(lambdaShape);
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for (auto d = 0; d < shape::rank(lambdaShape); ++d ) {
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shape.emplace_back(shape::sizeAt(lambdaShape, d));
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}
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auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(dtype, 'c', shape);
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return SHAPELIST(newShape);
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}
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DECLARE_TYPES(random_poisson) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {ALL_INTS})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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}
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}
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#endif |