cavis/libnd4j/include/ops/declarable/generic/random/exponential.cpp

78 lines
2.7 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 raver119@gmail.com
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_random_exponential)
#include <ops/declarable/headers/random.h>
#include <helpers/RandomLauncher.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(random_exponential, 1, 1, true, 1, 0) {
// uniform distribution
auto rng = block.randomGenerator();
// FIXME: to be implemented
/*
if (rng == nullptr)
return Status::THROW("RNG is null, aborting...");
auto x = INPUT_VARIABLE(0);
auto z = OUTPUT_VARIABLE(0);
if (block.width() == 1)
functions::random::RandomFunction<T>::template execTransform<randomOps::ExponentialDistribution<T>>(block.getRNG(), z->getBuffer(), z->getShapeInfo(), block.getTArguments()->data());
else {
auto y = INPUT_VARIABLE(1);
REQUIRE_TRUE(y->isSameShape(z), 0, "ExponentialDistribution: Y shape should be equal to Z shape");
functions::random::RandomFunction<T>::template execTransform<randomOps::ExponentialDistribution<T>>(block.getRNG(), y->getBuffer(), y->getShapeInfo(), z->getBuffer(), z->getShapeInfo(), block.getTArguments()->data());
}
STORE_RESULT(*z);
*/
auto z = OUTPUT_VARIABLE(0);
auto lambda = T_ARG(0);
RandomLauncher::fillExponential(block.launchContext(), rng, z, lambda);
return Status::OK();
}
DECLARE_SHAPE_FN(random_exponential) {
auto in = INPUT_VARIABLE(0);
auto shape = in->template asVectorT<Nd4jLong>();
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(block.dataType(), 'c', shape);
return SHAPELIST(newShape);
}
DECLARE_TYPES(random_exponential) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}
#endif