deeplearning4j--deeplearning4j
wehub-resource-sync
101 行
2.9 KiB
C++
101 行
2.9 KiB
C++
/* ******************************************************************************
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*
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#ifndef LIBND4J_HESSENBERGANDSCHUR_H
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#define LIBND4J_HESSENBERGANDSCHUR_H
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#include <array/NDArray.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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// this class implements Hessenberg decomposition of square matrix using orthogonal similarity transformation
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// A = Q H Q^T
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// Q - orthogonal matrix
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// H - Hessenberg matrix
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template <typename T>
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class Hessenberg {
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// suppose we got input square NxN matrix
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public:
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NDArray *_Q; // {N,N}
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NDArray *_H; // {N,N}
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explicit Hessenberg(NDArray* matrix);
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~Hessenberg() {
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delete _Q;
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delete _H;
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}
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private:
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void evalData();
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};
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// this class implements real Schur decomposition of square matrix using orthogonal similarity transformation
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// A = U T U^T
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// T - real quasi-upper-triangular matrix - block upper triangular matrix where the blocks on the diagonal are 1×1 or
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// 2×2 with complex eigenvalues U - real orthogonal matrix
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template <typename T>
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class Schur {
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// suppose we got input square NxN matrix
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public:
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NDArray *t; // {N,N}
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NDArray *u; // {N,N}
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explicit Schur(NDArray& matrix);
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void splitTwoRows(const int ind, const T shift);
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void calcShift(const int ind, const int iter, T& shift, NDArray& shiftInfo);
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void initFrancisQR(const int ind1, const int ind2, NDArray& shiftVec, int& ind3, NDArray& householderVec);
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void doFrancisQR(const int ind1, const int ind2, const int ind3, NDArray& householderVec);
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void calcFromHessenberg();
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private:
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static const int _maxItersPerRow = 40;
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void evalData(NDArray& matrix);
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//////////////////////////////////////////////////////////////////////////
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SD_INLINE int getSmallSubdiagEntry(const int inInd) {
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int outInd = inInd;
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while (outInd > 0) {
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T factor = math::sd_abs<T,T>(t->t<T>(outInd - 1, outInd - 1)) + math::sd_abs<T,T>(t->t<T>(outInd, outInd));
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if (math::sd_abs<T,T>(t->t<T>(outInd, outInd - 1)) <= DataTypeUtils::eps<T>() * factor) break;
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outInd--;
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}
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return outInd;
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}
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};
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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#endif // LIBND4J_HESSENBERGANDSCHUR_H
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