| /external/eigen/doc/snippets/ |
| TopicAliasing_mult1.cpp | 1 MatrixXf matA(2,2);
2 matA << 2, 0, 0, 2;
3 matA = matA * matA;
4 cout << matA;
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| TopicAliasing_mult3.cpp | 1 MatrixXf matA(2,2);
2 matA << 2, 0, 0, 2;
3 matA.noalias() = matA * matA;
4 cout << matA;
|
| TopicAliasing_mult2.cpp | 1 MatrixXf matA(2,2), matB(2,2);
2 matA << 2, 0, 0, 2;
5 matB = matA * matA;
9 matB.noalias() = matA * matA;
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| Tutorial_AdvancedInitialization_Block.cpp | 1 MatrixXf matA(2, 2);
2 matA << 1, 2, 3, 4;
4 matB << matA, matA/10, matA/10, matA;
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| /external/chromium-trace/catapult/tracing/third_party/gl-matrix/spec/gl-matrix/ |
| mat2d-spec.js | 24 var out, matA, matB, identity, result;
27 matA = [1, 2,
58 beforeEach(function() { result = mat2d.clone(matA); });
59 it("should return a 6 element array initialized to the values in matA", function() { expect(result).toBeEqualish(matA); });
63 beforeEach(function() { result = mat2d.copy(out, matA); });
64 it("should place values into out", function() { expect(out).toBeEqualish(matA); });
76 beforeEach(function() { result = mat2d.invert(out, matA); });
80 it("should not modify matA", function() { expect(matA).toBeEqualish(oldA); })
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| mat2-spec.js | 24 var out, matA, matB, identity, result;
27 matA = [1, 2,
46 beforeEach(function() { result = mat2.clone(matA); });
47 it("should return a 4 element array initialized to the values in matA", function() { expect(result).toBeEqualish(matA); });
51 beforeEach(function() { result = mat2.copy(out, matA); });
52 it("should place values into out", function() { expect(out).toBeEqualish(matA); });
64 beforeEach(function() { result = mat2.transpose(out, matA); });
68 it("should not modify matA", function() { expect(matA).toBeEqualish([1, 2, 3, 4]); })
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| mat3-spec.js | 26 var out, matA, matB, identity, result;
29 matA = [1, 0, 0,
48 matA = [1, 0, 0, 0,
52 result = mat3.normalFromMat4(out, matA);
59 mat4.translate(matA, matA, [2, 4, 6]);
60 mat4.rotateX(matA, matA, Math.PI / 2);
62 result = mat3.normalFromMat4(out, matA);
73 mat4.scale(matA, matA, [2, 3, 4])
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| mat4-spec.js | 25 var out, matA, matB, identity, result;
29 matA = [1, 0, 0, 0,
56 beforeEach(function() { result = mat4.clone(matA); });
57 it("should return a 16 element array initialized to the values in matA", function() { expect(result).toBeEqualish(matA); });
61 beforeEach(function() { result = mat4.copy(out, matA); });
62 it("should place values into out", function() { expect(out).toBeEqualish(matA); });
74 beforeEach(function() { result = mat4.transpose(out, matA); });
85 it("should not modify matA", function() {
86 expect(matA).toBeEqualish(
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| vec2-spec.js | 405 var matA;
406 beforeEach(function() { matA = [1, 2, 3, 4]; });
409 beforeEach(function() { result = vec2.transformMat2(out, vecA, matA); });
414 it("should not modify matA", function() { expect(matA).toBeEqualish([1, 2, 3, 4]); });
418 beforeEach(function() { result = vec2.transformMat2(vecA, vecA, matA); });
422 it("should not modify matA", function() { expect(matA).toBeEqualish([1, 2, 3, 4]); });
427 var matA;
428 beforeEach(function() { matA = [1, 2, 3, 4, 5, 6]; })
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| /external/eigen/Eigen/src/Eigenvalues/ |
| GeneralizedSelfAdjointEigenSolver.h | 83 * \param[in] matA Selfadjoint matrix in matrix pencil.
92 * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the
107 GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB,
109 : Base(matA.cols())
111 compute(matA, matB, options);
116 * \param[in] matA Selfadjoint matrix in matrix pencil.
130 * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite
154 GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB,
164 compute(const MatrixType& matA, const MatrixType& matB, int options)
166 eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows())
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| HessenbergDecomposition.h | 270 static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp);
280 * Performs a tridiagonal decomposition of \a matA in place.
282 * \param matA the input selfadjoint matrix
285 * The result is written in the lower triangular part of \a matA.
292 void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp)
294 eigen_assert(matA.rows()==matA.cols());
295 Index n = matA.rows();
303 matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
304 matA.col(i).coeffRef(i+1) = beta
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| Tridiagonalization.h | 26 void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
323 * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place.
325 * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced.
331 * and lower sub-diagonal of the matrix \a matA.
339 * \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$.
346 void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
352 Index n = matA.rows();
353 eigen_assert(n==matA.cols());
361 matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta)
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| SelfAdjointEigenSolver.h | 333 SelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true)
334 : m_eivec(matA.cols(), matA.cols()),
335 m_eivalues(matA.cols()),
336 m_subdiag(matA.cols() > 1 ? matA.cols() - 1 : 1),
339 static_cast<GeneralizedSelfAdjointEigenSolver<MatrixType>*>(this)->compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly);
347 void compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true)
349 compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly);
374 * \param matA the input selfadjoint matri
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| /frameworks/rs/java/tests/RsBLAS_Benchmark/src/com/example/android/rs/blasbenchmark/ |
| BNNMTest.java | 33 private Allocation matA;
227 matA = Allocation.createTyped(mRS, a_type);
230 matA.copyFrom(a_byte);
234 mBLAS.BNNM(matA, a_offset, matB, b_offset, matC, c_offset, c_mult_int);
297 matA = Allocation.createTyped(mRS, a_type);
301 matA.copyFrom(a_byte);
305 mBLAS.BNNM(matA, a_offset, matB, b_offset, matC, c_offset, c_mult_int);
346 matA = Allocation.createTyped(mRS, a_type);
350 matA.copyFrom(a_byte);
354 mBLAS.BNNM(matA, a_offset, matB, b_offset, matC, c_offset, c_mult_int)
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| SGEMMTest.java | 33 private Allocation matA;
150 matA = Allocation.createTyped(mRS, a_type);
154 matA.copyFrom(a_float);
159 1.0f, matA, matB, 0.f, matC);
205 matA = Allocation.createTyped(mRS, a_type);
209 matA.copyFrom(a_float);
214 1.0f, matA, matB, 0.f, matC);
253 matA = Allocation.createTyped(mRS, a_type);
257 matA.copyFrom(a_float);
262 1.0f, matA, matB, 0.f, matC)
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| /external/neven/Embedded/common/src/b_TensorEm/ |
| Int32Mat.h | 106 * matA: the square matrix, array of size ( matWidthA * matWidthA )
111 * tmpMatA: matrix of same size as matA
115 const int32* matA,
123 /** same as _solve(), but matA gets overwritten, and tmpMatA is not needed:
124 * saves memory when matA is large;
129 int32* matA,
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| Int32Mat.c | 210 const int32* matA,
218 bbs_memcpy32( tmpMatA, matA, ( matWidthA * matWidthA ) * bbs_SIZEOF32( int32 ) );
232 int32* matA,
246 int32* matL = matA;
|
| /frameworks/base/graphics/java/android/graphics/ |
| ColorMatrix.java | 176 * as applying matB and then applying matA.
178 * It is legal for either matA or matB to be the same colormatrix as this.
181 public void setConcat(ColorMatrix matA, ColorMatrix matB) {
183 if (matA == this || matB == this) {
189 final float[] a = matA.mArray;
|
| /cts/tests/tests/rsblas/src/android/renderscript/cts/ |
| IntrinsicBLAS.java | 239 for (Allocation matA : mMatrix) {
248 Element elemA = matA.getType().getElement();
249 if (validateGEMV(elemA, trans, matA, vecX, incX, vecY, incY)) {
252 mBLAS.SGEMV(trans, alphaS, matA, vecX, incX, betaS, vecY, incY);
254 mBLAS.DGEMV(trans, alphaD, matA, vecX, incX, betaD, vecY, incY);
256 mBLAS.CGEMV(trans, alphaC, matA, vecX, incX, betaC, vecY, incY);
258 mBLAS.ZGEMV(trans, alphaZ, matA, vecX, incX, betaZ, vecY, incY);
265 mBLAS.SGEMV(trans, alphaS, matA, vecX, incX, betaS, vecY, incY);
270 mBLAS.DGEMV(trans, alphaD, matA, vecX, incX, betaD, vecY, incY);
275 mBLAS.CGEMV(trans, alphaC, matA, vecX, incX, betaC, vecY, incY)
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| /external/eigen/Eigen/src/Eigen2Support/ |
| SVD.h | 109 MatrixType matA(matrix);
124 m_sigma[k] = matA.col(k).end(m-k).norm();
127 if (matA(k,k) < 0.0)
129 matA.col(k).end(m-k) /= m_sigma[k];
130 matA(k,k) += 1.0;
140 Scalar t = matA.col(k).end(m-k).eigen2_dot(matA.col(j).end(m-k)); // FIXME dot product or cwise prod + .sum() ??
141 t = -t/matA(k,k);
142 matA.col(j).end(m-k) += t * matA.col(k).end(m-k)
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| /external/opencv/cv/src/ |
| cvcornersubpix.cpp | 233 CvMat matA, matInvA;
239 cvInitMatHeader( &matA, 2, 2, CV_64F, A );
242 cvInvert( &matA, &matInvA, CV_SVD );
|
| /external/skia/src/effects/ |
| SkColorMatrix.cpp | 132 void SkColorMatrix::setConcat(const SkColorMatrix& matA, const SkColorMatrix& matB) {
133 SetConcat(fMat, matA.fMat, matB.fMat);
|
| /external/eigen/test/ |
| cholesky.cpp | 308 MatrixType matA;
309 matA << 1, 1, 1, 1;
312 VectorType vecX = matA.ldlt().solve(vecB);
313 VERIFY_IS_APPROX(matA * vecX, vecB);
|
| /external/opencv3/modules/video/src/ |
| compat_video.cpp | 307 cv::Mat matA = cv::cvarrToMat(arrA), matB = cv::cvarrToMat(arrB);
310 cv::Mat matM = cv::estimateRigidTransform(matA, matB, full_affine != 0);
|
| /external/eigen/blas/ |
| level3_impl.h | 272 Matrix<Scalar,Dynamic,Dynamic,ColMajor> matA(size,size);
275 matA.triangularView<Upper>() = matrix(a,size,size,*lda);
276 matA.triangularView<Lower>() = matrix(a,size,size,*lda).transpose();
280 matA.triangularView<Lower>() = matrix(a,size,size,*lda);
281 matA.triangularView<Upper>() = matrix(a,size,size,*lda).transpose();
284 matrix(c, *m, *n, *ldc) += alpha * matA * matrix(b, *m, *n, *ldb);
286 matrix(c, *m, *n, *ldc) += alpha * matrix(b, *m, *n, *ldb) * matA;
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