| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/fitting/ |
| D | HarmonicCoefficientsGuesser.java | 130 private final WeightedObservedPoint[] observations; field in HarmonicCoefficientsGuesser
144 public HarmonicCoefficientsGuesser(WeightedObservedPoint[] observations) { in HarmonicCoefficientsGuesser() argument
145 this.observations = observations.clone(); in HarmonicCoefficientsGuesser()
168 WeightedObservedPoint curr = observations[0]; in sortObservations()
169 for (int j = 1; j < observations.length; ++j) { in sortObservations()
171 curr = observations[j]; in sortObservations()
175 WeightedObservedPoint mI = observations[i]; in sortObservations()
177 observations[i + 1] = mI; in sortObservations()
179 mI = observations[i]; in sortObservations()
182 observations[i + 1] = curr; in sortObservations()
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| D | CurveFitter.java | 49 private final List<WeightedObservedPoint> observations; field in CurveFitter
56 observations = new ArrayList<WeightedObservedPoint>(); in CurveFitter()
83 observations.add(new WeightedObservedPoint(weight, x, y)); in addObservedPoint()
93 observations.add(observed); in addObservedPoint()
103 return observations.toArray(new WeightedObservedPoint[observations.size()]); in getObservations()
110 observations.clear(); in clearObservations()
130 double[] target = new double[observations.size()]; in fit()
131 double[] weights = new double[observations.size()]; in fit()
133 for (WeightedObservedPoint point : observations) { in fit()
168 final double[][] jacobian = new double[observations.size()][]; in jacobian()
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| D | GaussianParametersGuesser.java | 40 private final WeightedObservedPoint[] observations; field in GaussianParametersGuesser
50 public GaussianParametersGuesser(WeightedObservedPoint[] observations) { in GaussianParametersGuesser() argument
51 if (observations == null) { in GaussianParametersGuesser()
54 if (observations.length < 3) { in GaussianParametersGuesser()
55 throw new NumberIsTooSmallException(observations.length, 3, true); in GaussianParametersGuesser()
57 this.observations = observations.clone(); in GaussianParametersGuesser()
67 parameters = basicGuess(observations); in guess()
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| D | HarmonicFitter.java | 83 final WeightedObservedPoint[] observations = fitter.getObservations(); in fit() local
84 if (observations.length < 4) { in fit()
86 observations.length, 4); in fit()
89 HarmonicCoefficientsGuesser guesser = new HarmonicCoefficientsGuesser(observations); in fit()
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| D | GaussianFitter.java | 114 …protected GaussianParametersGuesser createParametersGuesser(WeightedObservedPoint[] observations) { in createParametersGuesser() argument
115 return new GaussianParametersGuesser(observations); in createParametersGuesser()
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
| D | LeastSquaresConverter.java | 64 private final double[] observations; field in LeastSquaresConverter
77 final double[] observations) { in LeastSquaresConverter() argument
79 this.observations = observations.clone(); in LeastSquaresConverter()
113 final double[] observations, final double[] weights) in LeastSquaresConverter() argument
115 if (observations.length != weights.length) { in LeastSquaresConverter()
118 observations.length, weights.length); in LeastSquaresConverter()
121 this.observations = observations.clone(); in LeastSquaresConverter()
146 final double[] observations, final RealMatrix scale) in LeastSquaresConverter() argument
148 if (observations.length != scale.getColumnDimension()) { in LeastSquaresConverter()
151 observations.length, scale.getColumnDimension()); in LeastSquaresConverter()
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| /external/ceres-solver/examples/ |
| D | simple_bundle_adjuster.cc | 56 const double* observations() const { return observations_; } in observations() function in BALProblem
188 const double* observations = bal_problem.observations(); in main() local
199 SnavelyReprojectionError::Create(observations[2 * i + 0], in main()
200 observations[2 * i + 1]); in main()
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| D | bundle_adjuster.cc | 259 const double* observations = bal_problem->observations(); in BuildProblem() local
268 observations[2 * i + 0], in BuildProblem()
269 observations[2 * i + 1]) in BuildProblem()
271 observations[2 * i + 0], in BuildProblem()
272 observations[2 * i + 1]); in BuildProblem()
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| D | bal_problem.h | 75 const double* observations() const { return observations_; } in observations() function
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| /external/ceres-solver/docs/source/ |
| D | history.rst | 22 observations of the newly discovered asteroid `Ceres
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| D | tutorial.rst | 484 Assuming the observations are in a :math:`2n` sized array called
697 bal_problem.observations()[2 * i + 0],
698 bal_problem.observations()[2 * i + 1]);
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| D | modeling.rst | 589 IntrinsicProjection(const double* observations);
650 new CameraProjection(observations));
677 IntrinsicProjection(const double* observations);
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| D | solving.rst | 2167 observations :math:`y` is the solution to the non-linear least squares
2200 So, if it is the case that the observations being fitted to have a
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| /external/ceres-solver/internal/ceres/ |
| D | system_test.cc | 341 const double* observations() const { return observations_; } in observations() function in ceres::internal::BundleAdjustmentProblem
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| /external/zlib/src/doc/ |
| D | txtvsbin.txt | 57 The idea behind this algorithm relies on two observations.
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| /external/llvm/docs/tutorial/ |
| D | LangImpl9.rst | 188 I'll make a few observations:
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| D | OCamlLangImpl8.rst | 193 I'll make a few observations:
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| /external/chromium-trace/trace-viewer/third_party/webapp2/ |
| D | CHANGES | 508 - Review of Response based on observations from
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| /external/pcre/dist/doc/ |
| D | pcre.txt | 9399 observations about PCRE.
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