cavis/libnd4j/include/ops/declarable/generic/parity_ops/sufficient_statistics.cpp

89 lines
3.5 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
******************************************************************************/
//
// Created by george@skymind.io on 2/21/2018.
// Modified by sgazeos@gmail.com on 4/4/2018
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_sufficient_statistics)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(sufficient_statistics, 2, 3, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto axisVector = INPUT_VARIABLE(1);
auto dataCount = OUTPUT_VARIABLE(0);
auto sum = OUTPUT_VARIABLE(1);
auto squares = OUTPUT_VARIABLE(2);
std::vector<int> axis(axisVector->lengthOf());//*block.getIArguments();
// axis might be dynamic (i.e. tf mode)
helpers::adjustAxis(input->rankOf(), axisVector, axis);
input->reduceAlongDimension(reduce::SquaredNorm, *squares, axis);
input->reduceAlongDimension(reduce::Sum, *sum, axis);
auto count = NDArrayFactory::create(input->dataType(), input->lengthOf() / sum->lengthOf());
dataCount->assign(count);
if (block.numT() > 0) {
auto shift = OUTPUT_VARIABLE(3);
shift->assign(T_ARG(0));
}
return Status::OK();
}
DECLARE_TYPES(sufficient_statistics) {
getOpDescriptor()
->setAllowedInputTypes(0, {ALL_INTS, ALL_FLOATS});
getOpDescriptor()
->setAllowedInputTypes(1, {DataType::INT32, DataType::INT64});
getOpDescriptor()
->setAllowedOutputTypes(0, DataType::INHERIT);
getOpDescriptor()
->setAllowedOutputTypes(1, DataType::INHERIT);
getOpDescriptor()
->setAllowedOutputTypes(2, DataType::INHERIT);
}
DECLARE_SHAPE_FN(sufficient_statistics) {
auto axisVector = INPUT_VARIABLE(1);
std::vector<int> axis(axisVector->lengthOf());
auto input = INPUT_VARIABLE(0);
helpers::adjustAxis(input->rankOf(), axisVector, axis);
//std::vector<int> dims = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
auto scalarShape = ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inputShape->at(0)));
auto sumShape = ShapeUtils::evalReduceShapeInfo('c', axis, *input, false, false, block.workspace());
auto squareShape = ShapeUtils::evalReduceShapeInfo('c', axis, *input, false, false, block.workspace());
auto shapeList = SHAPELIST(scalarShape, sumShape, squareShape);
if (block.numT() > 0)
shapeList->push_back(ConstantShapeHelper::getInstance()->scalarShapeInfo(ArrayOptions::dataType(inputShape->at(0))));
return shapeList;
}
}
}
#endif