cavis/libnd4j/include/ops/declarable/generic/linalg/matrix_determinant.cpp

146 lines
6.4 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 GS <sgazeos@gmail.com> at 2/26/2018
//
#include <system/op_boilerplate.h>
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/lup.h>
#if NOT_EXCLUDED(OP_matrix_determinant)
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(matrix_determinant, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
REQUIRE_TRUE(input->rankOf() >=2, 0, "matrix_determinant: 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, "matrix_determinant: The last two dimmensions should be equal, but %i and %i are given", input->sizeAt(-1), input->sizeAt(-2));
return helpers::determinant(block.launchContext(), input, output);
}
DECLARE_SHAPE_FN(matrix_determinant) {
auto inShape = inputShape->at(0);
Nd4jLong const* determinantShape;
int targetRank = shape::rank(inShape) - 2; // last two dimensions will be reduced to scalar
if (targetRank == 0) { // scalar only
determinantShape = ConstantShapeHelper::getInstance().scalarShapeInfo(ArrayOptions::dataType(inShape));
}
else if (targetRank == 1) { // vector
determinantShape = ConstantShapeHelper::getInstance().vectorShapeInfo(shape::sizeAt(inShape, 0), ArrayOptions::dataType(inShape));
}
else { // only two last dimensions are excluded
determinantShape = ConstantShapeHelper::getInstance().createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), targetRank, shape::shapeOf(inShape));
}
return SHAPELIST(determinantShape);
}
DECLARE_TYPES(matrix_determinant) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}
#endif
#if NOT_EXCLUDED(OP_log_matrix_determinant)
namespace sd {
namespace ops {
DECLARE_TYPES(log_matrix_determinant) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(log_matrix_determinant, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
REQUIRE_TRUE(input->rankOf() >=2, 0, "log_matrix_determinant: 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, "log_matrix_determinant: The last two dimmensions should be equal, but %i and %i are given", input->sizeAt(-1), input->sizeAt(-2));
return helpers::logAbsDeterminant(block.launchContext(), input, output);
}
DECLARE_SHAPE_FN(log_matrix_determinant) {
auto inShape = inputShape->at(0);
Nd4jLong const* determinantShape;
int targetRank = shape::rank(inShape) - 2; // last two dimensions will be reduced to scalar
if (targetRank == 0) { // scalar only
determinantShape = ConstantShapeHelper::getInstance().scalarShapeInfo(ArrayOptions::dataType(inShape));
}
else if (targetRank == 1) { // vector
determinantShape = ConstantShapeHelper::getInstance().vectorShapeInfo(shape::sizeAt(inShape, 0), ArrayOptions::dataType(inShape));
}
else { // only two last dimensions are excluded
determinantShape = ConstantShapeHelper::getInstance().createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), targetRank, shape::shapeOf(inShape));
}
return SHAPELIST(determinantShape);
}
}
}
#endif
#if NOT_EXCLUDED(OP_logdet)
namespace sd {
namespace ops {
DECLARE_TYPES(logdet) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(logdet, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_NULLIFIED(0);
REQUIRE_TRUE(input->rankOf() >=2, 0, "logdet: 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, "logdet: The last two dimmensions should be equal, but %i and %i are given", input->sizeAt(-1), input->sizeAt(-2));
REQUIRE_TRUE(helpers::checkCholeskyInput(block.launchContext(), input), 0, "logdet: The input tensor should be positive-defined hermitian.");
return helpers::logdetFunctor(block.launchContext(), input, output);
}
DECLARE_SHAPE_FN(logdet) {
auto inShape = inputShape->at(0);
Nd4jLong const* determinantShape;
int targetRank = shape::rank(inShape) - 2; // last two dimensions will be reduced to scalar
if (targetRank == 0) { // scalar only
determinantShape = ConstantShapeHelper::getInstance().scalarShapeInfo(ArrayOptions::dataType(inShape));
}
else if (targetRank == 1) { // vector
determinantShape = ConstantShapeHelper::getInstance().vectorShapeInfo(shape::sizeAt(inShape, 0), ArrayOptions::dataType(inShape));
}
else { // only two last dimensions are excluded
determinantShape = ConstantShapeHelper::getInstance().createShapeInfo(ArrayOptions::dataType(inShape), shape::order(inShape), targetRank, shape::shapeOf(inShape));
}
return SHAPELIST(determinantShape);
}
}
}
#endif