| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/ |
| LeastSquaresConverter.java | 34 * reference observations. The observations may be obtained for example from
41 * (i.e. the output of the vectorial objective function) and the observations. The
63 /** Observations to be compared to objective function to compute residuals. */
64 private final double[] observations; field in class:LeastSquaresConverter
74 * @param observations observations to be compared to objective function to compute residuals
77 final double[] observations) {
79 this.observations = observations.clone()
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/fitting/ |
| HarmonicCoefficientsGuesser.java | 129 /** Sampled observations. */
130 private final WeightedObservedPoint[] observations; field in class:HarmonicCoefficientsGuesser
142 * @param observations sampled observations
144 public HarmonicCoefficientsGuesser(WeightedObservedPoint[] observations) {
145 this.observations = observations.clone();
161 /** Sort the observations with respect to the abscissa.
168 WeightedObservedPoint curr = observations[0];
169 for (int j = 1; j < observations.length; ++j)
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| CurveFitter.java | 49 private final List<WeightedObservedPoint> observations; field in class:CurveFitter
56 observations = new ArrayList<WeightedObservedPoint>();
83 observations.add(new WeightedObservedPoint(weight, x, y));
93 observations.add(observed);
103 return observations.toArray(new WeightedObservedPoint[observations.size()]);
107 * Remove all observations.
110 observations.clear();
130 double[] target = new double[observations.size()];
131 double[] weights = new double[observations.size()]
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| GaussianFitter.java | 107 * instance initialized with the specified observations.
109 * @param observations points used to initialize the created
114 protected GaussianParametersGuesser createParametersGuesser(WeightedObservedPoint[] observations) {
115 return new GaussianParametersGuesser(observations);
|
| HarmonicFitter.java | 83 final WeightedObservedPoint[] observations = fitter.getObservations(); local
84 if (observations.length < 4) {
86 observations.length, 4);
89 HarmonicCoefficientsGuesser guesser = new HarmonicCoefficientsGuesser(observations);
|
| GaussianParametersGuesser.java | 40 private final WeightedObservedPoint[] observations; field in class:GaussianParametersGuesser
48 * @param observations observed points upon which should base guess
50 public GaussianParametersGuesser(WeightedObservedPoint[] observations) {
51 if (observations == null) {
54 if (observations.length < 3) {
55 throw new NumberIsTooSmallException(observations.length, 3, true);
57 this.observations = observations.clone();
67 parameters = basicGuess(observations);
|
| /external/v8/test/mjsunit/harmony/ |
| proxies-set.js | 105 var observations = [];
109 observations.push(arguments);
114 observations.push(arguments);
120 assertEquals(2, observations.length)
121 assertArrayEquals([target, toKey(p)], observations[0]);
122 assertSame(target, observations[0][0]);
123 assertArrayEquals([target, toKey(p), dataDescriptor(42)], observations[1]);
124 assertSame(target, observations[1][0]);
125 observations = [];
128 assertEquals(2, observations.length
282 var observations = []; variable
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| /external/opencv3/modules/photo/src/ |
| denoise_tvl1.cpp | 74 void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters){
76 CV_Assert(observations.size()>0 && niters>0 && lambda>0);
83 int i, x, y, rows=observations[0].rows, cols=observations[0].cols,count;
84 for(i=1;i<(int)observations.size();i++){
85 CV_Assert(observations[i].rows==rows && observations[i].cols==cols);
89 observations[0].convertTo(X, workdepth, 1./255);
90 std::vector< Mat_<double> > Rs(observations.size());
125 Rs[count].begin(),Rs[count].end(),observations[count].begin<uchar>()
[all...] |
| /external/ceres-solver/examples/ |
| simple_bundle_adjuster.cc | 56 const double* observations() const { return observations_; } function in class:BALProblem
188 const double* observations = bal_problem.observations(); local
199 SnavelyReprojectionError::Create(observations[2 * i + 0],
200 observations[2 * i + 1]);
|
| bal_problem.h | 75 const double* observations() const { return observations_; } function in class:ceres::examples::BALProblem
|
| bundle_adjuster.cc | 256 // Observations is 2*num_observations long array observations =
259 const double* observations = bal_problem->observations(); local
268 observations[2 * i + 0],
269 observations[2 * i + 1])
271 observations[2 * i + 0],
272 observations[2 * i + 1]);
|
| /external/opencv3/modules/java/src/ |
| photo+Photo.java | 56 // C++: void denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30)
59 //javadoc: denoise_TVL1(observations, result, lambda, niters)
60 public static void denoise_TVL1(List<Mat> observations, Mat result, double lambda, int niters)
62 Mat observations_mat = Converters.vector_Mat_to_Mat(observations);
68 //javadoc: denoise_TVL1(observations, result)
69 public static void denoise_TVL1(List<Mat> observations, Mat result)
71 Mat observations_mat = Converters.vector_Mat_to_Mat(observations);
639 // C++: void denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30)
|
| photo.cpp | 892 // void denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30)
903 std::vector<Mat> observations; local
905 Mat_to_vector_Mat( observations_mat, observations );
907 cv::denoise_TVL1( observations, result, (double)lambda, (int)niters );
927 std::vector<Mat> observations; local
929 Mat_to_vector_Mat( observations_mat, observations );
931 cv::denoise_TVL1( observations, result );
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| /external/opencv3/modules/photo/include/opencv2/ |
| photo.hpp | 300 we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
303 @param observations This array should contain one or more noised versions of the image that is to
315 CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
[all...] |
| /external/ceres-solver/internal/ceres/ |
| system_test.cc | 341 const double* observations() const { return observations_; } function in class:ceres::internal::BundleAdjustmentProblem
|
| /external/chromium-trace/catapult/third_party/gsutil/third_party/boto/boto/machinelearning/ |
| layer1.py | 73 Generates predictions for a group of observations. The
74 observations to process exist in one or more data files
102 predictions for the group of observations.
106 points to the group of observations to predict.
249 The observations should exist in the database hosted on an
423 evaluated on a set of observations associated to a
544 the observations to build the `MLModel`. The value is an integer
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| /cts/apps/CtsVerifier/libs/ |
| opencv3-android.jar | |
