deeplearning4j/deeplearning4j

/* ******************************************************************************
 *
 *
 * 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
 ******************************************************************************/

//
// Created by GS <sgazeos@gmail.com> at 11/12/2018
//

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/lup.h>
#include <system/op_boilerplate.h>

#if NOT_EXCLUDED(OP_cholesky)

namespace sd {
namespace ops {
OP_IMPL(cholesky, 1, 1, true) {
  NDArray* input = INPUT_VARIABLE(0);
  NDArray* output = OUTPUT_VARIABLE(0);

  REQUIRE_TRUE(input->rankOf() >= 2, 0, "cholesky: The rank of input array should not less than 2, but %i is given",
               input->rankOf());
  REQUIRE_TRUE(input->sizeAt(-1) == input->sizeAt(-2), 0,
               "cholesky: The last two dimensions should be equal, but %i and %i are given", input->sizeAt(-1),
               input->sizeAt(-2));
/*  REQUIRE_TRUE(helpers::checkCholeskyInput(block.launchContext(), input), 0,
               "cholesky: The input tensor should be positive-defined and symmetric.");*/
  return helpers::cholesky(block.launchContext(), input, output, block.isInplace());
}
DECLARE_TYPES(cholesky) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

}  // namespace ops
}  // namespace sd

#endif