| /external/ceres-solver/internal/ceres/ |
| covariance.cc | 31 #include "ceres/covariance.h"
41 Covariance::Covariance(const Covariance::Options& options) {
45 Covariance::~Covariance() {
48 bool Covariance::Compute(
54 bool Covariance::GetCovarianceBlock(const double* parameter_block1,
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| covariance_impl.cc | 63 #include "ceres/covariance.h"
78 CovarianceImpl::CovarianceImpl(const Covariance::Options& options)
104 << "Covariance::GetCovarianceBlock called before Covariance::Compute";
106 << "Covariance::GetCovarianceBlock called when Covariance::Compute "
110 // covariance block is also zero.
134 // Find where in the covariance matrix the block is located.
146 // covariance block begin.
153 LOG(ERROR) << "Unable to find covariance block for
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| covariance_impl.h | 38 #include "ceres/covariance.h"
51 explicit CovarianceImpl(const Covariance::Options& options);
77 Covariance::Options options_;
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| covariance_test.cc | 31 #include "ceres/covariance.h"
96 Covariance::Options options;
285 void ComputeAndCompareCovarianceBlocks(const Covariance::Options& options,
288 // covariance computation is correct.
315 Covariance covariance(options);
316 EXPECT_TRUE(covariance.Compute(covariance_blocks, &problem_));
322 GetCovarianceBlockAndCompare(block1, block2, covariance, expected_covariance);
324 GetCovarianceBlockAndCompare(block2, block1, covariance, expected_covariance);
331 const Covariance& covariance
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| /external/ceres-solver/include/ceres/ |
| covariance.h | 56 // This class allows the user to evaluate the covariance for a
63 // non-linear least squares solve is to analyze the covariance of the
71 // independent variable x with mean f(x) and identity covariance. Then
77 // And the covariance of x* is given by
84 // If J(x*) is rank deficient, then the covariance matrix C(x*) is
89 // Note that in the above, we assumed that the covariance
95 // Where S is a positive semi-definite matrix denoting the covariance
100 // and the corresponding covariance estimate of x* is given by
105 // covariance matrix not equal to identity, then it is the user's
109 // is the inverse square root of the covariance matrix S
[all...] |
| normal_prior.h | 53 // where, mu is a vector and S is a covariance matrix, then, A =
55 // covariance, also known as the stiffness matrix. There are however
57 // which would be the case if the covariance matrix S is rank
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| ceres.h | 41 #include "ceres/covariance.h"
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| /external/eigen/unsupported/Eigen/src/LevenbergMarquardt/ |
| LMcovar.h | 59 /* form the full lower triangle of the covariance matrix */
76 /* symmetrize the covariance matrix in r. */
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| /external/eigen/unsupported/Eigen/src/NonLinearOptimization/ |
| covar.h | 46 /* form the full lower triangle of the covariance matrix */
63 /* symmetrize the covariance matrix in r. */
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| /frameworks/native/services/sensorservice/ |
| Fusion.h | 40 * the predicated covariance matrix is made of 4 3x3 sub-matrices and it is
52 * the process noise covariance matrix
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| /external/srec/srec/include/ |
| hmm_desc.h | 39 #define DIAG (1<<4) /* Diagonal covariance model */
40 #define FULL (2<<4) /* Full covariance model */
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| hmm_type.h | 39 typedef double covdata; /* covariance data */
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| all_defs.h | 50 #define EIGEN 1 /* for full covariance probability calc. */
60 #define ITEM_WEIGHT 1 // item weighting for covariance calc.
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| pre_desc.h | 70 prdata grand_mod_cov; /* grand covariance modulus */
71 prdata grand_mod_cov_gaussian; /* grand covariance modulus */
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| /packages/inputmethods/LatinIME/native/jni/src/suggest/core/layout/ |
| normal_distribution_2d.h | 28 // Normal distribution on a 2D plane. The covariance is always zero, but the distribution can be
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| /external/chromium_org/third_party/protobuf/java/src/main/java/com/google/protobuf/ |
| Message.java | 53 // (From MessageLite, re-declared here only for return type covariance.)
98 // (From MessageLite, re-declared here only for return type covariance.)
107 // covariance.)
129 // covariance.)
213 // covariance.)
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| /external/ceres-solver/docs/source/ |
| solving.rst | [all...] |
| features.rst | 70 * **Covariance estimation** - Evaluate the sensitivity/uncertainty of
71 the solution by evaluating all or part of the covariance
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| /external/protobuf/java/src/main/java/com/google/protobuf/ |
| Message.java | 61 // (From MessageLite, re-declared here only for return type covariance.)
154 // (From MessageLite, re-declared here only for return type covariance.)
163 // covariance.)
185 // covariance.)
201 // covariance.)
281 // covariance.)
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| /external/eigen/Eigen/src/Eigen2Support/ |
| LeastSquares.h | 122 * 2 - compute the covariance matrix
123 * 3 - pick the eigenvector corresponding to the smallest eigenvalue of the covariance matrix
148 // compute the covariance matrix
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| /external/opencv/cv/include/ |
| cvtypes.h | 300 CvMat* process_noise_cov; /* process noise covariance matrix (Q) */
301 CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */
302 CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)):
306 CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)):
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| /cts/tests/tests/uirendering/src/android/uirendering/cts/bitmapcomparers/ |
| MSSIMComparer.java | 152 * Finds the variance of the two sets of pixels, as well as the covariance of the windows. The
154 * the second is the variance of the second set of pixels, and the third is the covariance.
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| /external/chromium_org/third_party/webrtc/modules/video_coding/main/source/ |
| jitter_estimator.h | 125 double _thetaCov[2][2]; // Estimate covariance
126 double _Qcov[2][2]; // Process noise covariance
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| /external/clang/test/ARCMT/ |
| checking.m | 198 - (id) init03; // covariance
199 - (id) init04; // covariance
217 - (Test8_super*) init30; // id exception to covariance
221 - (Test8_super*) init34; // covariance
224 - (Test8*) init40; // id exception to covariance
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| /external/chromium_org/ui/gfx/ |
| color_analysis.cc | 417 gfx::Matrix3F covariance = gfx::Matrix3F::Zeros(); local
419 return covariance;
454 // Covariance (not normalized) is E(X*X.t) - m * m.t and this is how it
459 covariance.set(
478 return covariance;
564 gfx::Matrix3F covariance = ComputeColorCovariance(source_bitmap); local
566 gfx::Vector3dF eigenvals = covariance.SolveEigenproblem(&eigenvectors);
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