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deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/linalg/tri.cpp
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/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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 Yurii Shyrma, created on 31.03.2018
//
#include <ops/declarable/CustomOperations.h>
#if NOT_EXCLUDED(OP_tri)
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(tri, -2, 1, false, 0, 1) {
auto output = OUTPUT_VARIABLE(0);
const int diag = block.numI() > 2 ? INT_ARG(2) : 0;
char direction = diag <= 0 || diag == 0 || diag > 0 ? 'l': 'u';
BUILD_SINGLE_SELECTOR(output->dataType(), output->fillAsTriangular,
(1., diag, diag, *output, direction),
SD_COMMON_TYPES); // fill with unities lower triangular block of matrix
return Status::OK;
}
DECLARE_TYPES(tri) { getOpDescriptor()->setAllowedOutputTypes(0, {ALL_FLOATS, ALL_INTS}); }
DECLARE_SHAPE_FN(tri) {
const int rows = INT_ARG(0);
const int cols = block.numI() > 1 ? INT_ARG(1) : rows;
auto dtype = block.numD() ? D_ARG(0) : FLOAT32;
return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(dtype, 'c', {rows, cols}));
}
} // namespace ops
} // namespace sd
#endif