/dports/net/storj/storj-1.45.3/web/storagenode/tests/unit/components/payments/__snapshots__/ |
H A D | EstimationArea.spec.ts.snap | 4 <div class="estimation-container"> 5 <div class="estimation-container__header"> 19 <div class="estimation-table-container"> 142 <div class="estimation-container"> 143 <div class="estimation-container__header"> 157 <div class="estimation-table-container"> 288 <div class="estimation-container"> 303 <div class="estimation-table-container"> 443 <div class="estimation-container"> 468 <div class="estimation-container"> [all …]
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/dports/graphics/opencv/opencv-4.5.3/modules/photo/src/ |
H A D | fast_nlmeans_denoising_invoker_commons.hpp | 320 estimation[0] += (IT)weight * p; in f() 329 estimation[0] += (IT)weight * p[0]; in f() 330 estimation[1] += (IT)weight * p[1]; in f() 339 estimation[0] += (IT)weight * p[0]; in f() 340 estimation[1] += (IT)weight * p[1]; in f() 341 estimation[2] += (IT)weight * p[2]; in f() 350 estimation[0] += (IT)weight * p[0]; in f() 412 estimation[0] = (static_cast<UIT>(estimation[0]) + weights_sum[0]/2) / weights_sum[0]; in f() 421 estimation[i] = (static_cast<UIT>(estimation[i]) + weights_sum[0]/2) / weights_sum[0]; in f() 430 estimation[i] = (static_cast<UIT>(estimation[i]) + weights_sum[i]/2) / weights_sum[i]; in f() [all …]
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/dports/net/storj/storj-1.45.3/web/storagenode/src/app/components/payments/ |
H A D | EstimationArea.vue | 5 <div class="estimation-container"> 6 <div class="estimation-container__header"> 29 <div class="estimation-container__divider" /> 262 public get estimation(): EstimatedPayout { 441 this.estimation.previousMonth.held : 442 this.estimation.currentMonth.held; 448 .estimation-container { 561 .estimation-table-container { 784 .estimation-container { 811 .estimation-container { [all …]
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/dports/math/openturns/openturns-1.18/lib/test/ |
H A D | t_Waarts_noisy_lsf.expout | 109 Pf estimation =0.00833333 110 Pf Variance estimation =1.3912e-05 119 Pf estimation =0.011066 120 Pf Variance estimation =1.22199e-06 128 Pf estimation =0.0101672 129 Pf Variance estimation =1.03011e-06 134 Pf estimation =0.0111345 135 Pf Variance estimation =1.23126e-06 140 Pf estimation =0.011978 150 Pf estimation =0.013092 [all …]
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/dports/math/openturns/openturns-1.18/python/test/ |
H A D | t_Waarts_noisy_lsf.expout | 108 Pf estimation = 0.00833333333333 109 Pf Variance estimation = 1.3912037037e-05 118 Pf estimation = 0.0110659898477 119 Pf Variance estimation = 1.22198947433e-06 127 Pf estimation = 0.0101672062017 128 Pf Variance estimation = 1.03011177529e-06 133 Pf estimation = 0.0111345335107 134 Pf Variance estimation = 1.23126272905e-06 139 Pf estimation = 0.0119779874835 149 Pf estimation = 0.0130919526774 [all …]
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H A D | t_BernsteinCopulaFactory_std.expout | 4 Is estimation a copula ? --> True 10 Is estimation a copula ? --> True 16 Is estimation a copula ? --> True 24 Is estimation a copula ? --> True 30 Is estimation a copula ? --> True 36 Is estimation a copula ? --> True 44 Is estimation a copula ? --> True 50 Is estimation a copula ? --> True 56 Is estimation a copula ? --> True
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H A D | t_Waarts_oblate.expout | 148 Pf estimation = 0.0869565217391 158 Pf estimation = 0.130434782609 168 Pf estimation = 0.134653829611 169 Pf Variance estimation = 0.0 174 Pf estimation = 0.0791728932749 175 Pf Variance estimation = 0.0 180 Pf estimation = 0.195745748137 181 Pf Variance estimation = 0.0 190 Pf estimation = 4.54974078381e-05 191 Pf Variance estimation = 0.0 [all …]
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H A D | t_Waarts_discontinuous_lsf.expout | 113 Pf estimation = 5e-05 114 Pf Variance estimation = 5.00024998e-10 123 Pf estimation = 6.44790602656e-05 124 Pf Variance estimation = 4.15727555848e-11 133 Pf estimation = 6.48513778214e-05 134 Pf Variance estimation = 1.12187135741e-11 139 Pf estimation = 6.58328597209e-05 140 Pf Variance estimation = 1.1732789195e-11 145 Pf estimation = 6.46450266495e-05 146 Pf Variance estimation = 1.08800612532e-11
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H A D | t_SubsetSampling_Waarts_system_series.expout | 5 Pf estimation = 2.21600e-03 6 Pf Variance estimation = 2.21109e-09 16 Pf estimation = 2.10600e-03 17 Pf Variance estimation = 1.78738e-08
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H A D | t_SubsetSampling_R-S.expout | 3 Pf estimation = 2.00000e-04 4 Pf Variance estimation = 1.99960e-10 11 Pf estimation = 2.02700e-04 12 Pf Variance estimation = 3.01140e-10
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/ |
H A D | nonparametric.rst | 10 includes kernel density estimation for univariate and multivariate data, 20 Kernel density estimation 23 The kernel density estimation (KDE) functionality is split between univariate 24 and multivariate estimation, which are implemented in quite different ways. 26 Univariate estimation (as provided by `KDEUnivariate`) uses FFT transforms, 29 bandwidth estimation is done only by a rule of thumb (Scott or Silverman). 31 Multivariate estimation (as provided by `KDEMultivariate`) uses product 33 for bandwidth estimation, as well as estimating mixed continuous, ordered and 48 features (mixed data, cross-validated bandwidth estimation, kernels) as 70 * Liu, R., Yang, L. "Kernel estimation of multivariate [all …]
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/dports/graphics/opencv/opencv-4.5.3/contrib/modules/aruco/tutorials/ |
H A D | table_of_content_aruco.markdown | 4 ArUco markers are binary square fiducial markers that can be used for camera pose estimation. 8 for pose estimation and camera calibration. 12 ChArUco corners and use them for pose estimation and camera calibration. 25 Basic detection and pose estimation from single ArUco markers. 33 Detection and pose estimation using a Board of markers 49 Detection and pose estimation using ChArUco markers
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/dports/finance/R-cran-ccgarch/ccgarch/man/ |
H A D | dcc_estimation.Rd | 1 \name{dcc.estimation} 2 \alias{dcc.estimation} 5 This function carries out the two step estimation of the (E)DCC-GARCH model and returns 10 dcc.estimation(inia, iniA, iniB, ini.dcc, dvar, model, 31 the estimation is completed. If \code{message=0}, the message 42 \item{first}{the results of the first stage estimation} 43 \item{second}{the results of the second stage estimation} 54 The details of the first and second stage estimation are also saved in \code{first} 57 …The switch variable \code{simulation} is useful when one uses \code{dcc.estimation} for simulation… 93 dcc.results <- dcc.estimation(inia=a, iniA=A, iniB=B, ini.dcc=dcc.para,
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/dports/math/gretl/gretl-2021d/share/scripts/misc/ |
H A D | hall_cbapm.inp | 1 # Replicate Alastair Hall's estimation of the Hansen-Singleton 18 # one-step estimation, identity matrix for initial weights 25 # one-step estimation, T(Z'Z)^{-1} for initial weights 32 # iterated estimation, identity matrix for initial weights 39 # iterated estimation, T(Z'Z)^{-1} for initial weights
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/dports/science/dynare/dynare-4.6.4/tests/kalman/likelihood_from_dynare/ |
H A D | fs2000_estimation_check.inc | 3 estimation(kalman_algo=0,mode_compute=4,order=1,datafile=@{data_file_name},smoother,filter_decompos… 7 estimation(kalman_algo=1,mode_file=@{mode_file_name},mode_compute=0,order=1,datafile=@{data_file_na… 15 estimation(kalman_algo=3,mode_file=@{mode_file_name},mode_compute=0,order=1,datafile=@{data_file_na… 23 estimation(kalman_algo=2,mode_file=@{mode_file_name},mode_compute=0,datafile=@{data_file_name},smoo… 31 estimation(kalman_algo=4,mode_file=@{mode_file_name},mode_compute=0,datafile=@{data_file_name},smoo… 40 estimation(kalman_algo=1,fast_kalman_filter,mode_file=@{mode_file_name},mode_compute=0,order=1,data… 48 estimation(kalman_algo=3,fast_kalman_filter,mode_file=@{mode_file_name},mode_compute=0,order=1,data…
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/dports/multimedia/libfame/libfame-0.9.1/ |
H A D | CHANGES | 15 * Fixed bugs in pmvfast motion estimation 28 * Added 8x8 vector estimation 31 * Fixed bugs in fourstep motion estimation 37 * Improved motion estimation/compensation 41 * Added half-pel motion estimation 47 * Added reference frame padding for motion estimation
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/dports/math/R-cran-maxLik/maxLik/tests/ |
H A D | finalHessian.Rout.save | 108 Maximum Likelihood estimation 123 Maximum Likelihood estimation 137 Maximum Likelihood estimation 150 Maximum Likelihood estimation 169 Maximum Likelihood estimation 182 Maximum Likelihood estimation 198 Maximum Likelihood estimation 213 Maximum Likelihood estimation 227 Maximum Likelihood estimation 240 Maximum Likelihood estimation [all …]
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/dports/math/R-cran-Zelig/Zelig/man/ |
H A D | from_zelig_model.Rd | 5 \title{Extract the original fitted model object from a \code{zelig} estimation} 13 Extract the original fitted model object from a \code{zelig} estimation 17 estimation. This can be useful for passing output to non-Zelig 18 post-estimation functions and packages such as texreg and stargazer
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/dports/science/dynare/dynare-4.6.4/matlab/modules/dseries/src/@arima/ |
H A D | estimate.m | 51 % Write *.spc file for estimation. 75 % Run estimation of the ARIMA model 89 % Read and store the estimation results. 135 o.estimation.sigma = linea{2}; 168 o.estimation.number_of_observations = linea{2}; 173 o.estimation.effective_number_of_observations = linea{2}; 178 o.estimation.number_of_estimated_parameters = linea{2}; 184 % o.estimation.log_likelihood = linea{2}; 189 o.estimation.information_criteria.Akaike = linea{2}; 200 o.estimation.information_criteria.HannanQuinn = linea{2}; [all …]
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/dports/misc/openmvg/openMVG-2.0/docs/sphinx/rst/openMVG/multiview/ |
H A D | multiview.rst | 8 - a generic framework "Kernel" that can embed these solvers for robust estimation. 15 openMVG provides solver for the following geometric estimation: 30 N-View geometry estimation 90 Relative pose estimation (Essential matrix) 104 Absolute pose estimation/Camera resection (Pose matrix) 107 Given a list of 3D-2D point correspondences it is possible to compute a camera pose estimation. 117 Resection/Pose estimation from 3D-2D correspondences. 134 In order to use the solver in a generic robust estimation framework, we use them in conjuction with… 138 * the set of correspondences that are used for a robust estimation problem. 149 MINIMUM_SAMPLES: The minimal number of point required for the model estimation,
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/dports/science/dynare/dynare-4.6.4/tests/dates/ |
H A D | dseries_interact.mod | 1 %% Mod-file tests interaction between estimation and shock_decomposition when dseries is used or not 45 estimation(first_obs=2,datafile='data_uav.xlsx', xls_sheet=Tabelle1, xls_range=a1:b54, mh_replic=2,… 47 estimation(first_obs=2,datafile='data_uav.xls', xls_sheet=Tabelle1, xls_range=a1:b54, mh_replic=2, … 53 estimation(first_obs=2,datafile='data_uav.xlsx', xls_sheet=Tabelle1, xls_range=b1:b54, mh_replic=2,… 55 estimation(first_obs=2,datafile='data_uav.xls', xls_sheet=Tabelle1, xls_range=b1:b54, mh_replic=2, …
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/data/testdata/correlation-outlier/ |
H A D | axis-parallel-2d.csv | 12 ## Density correction factor estimation: 1.0869438790346033 43 ## Density correction factor estimation: 0.995718232044199 135 ## Density correction factor estimation: 0.9834254143646409 217 ## Density correction factor estimation: 0.9834254143646409 232 ## Density correction factor estimation: 0.9834254143646409 247 ## Density correction factor estimation: 0.9834254143646409
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H A D | complex-example-2d.csv | 12 ## Density correction factor estimation: 1.0405405405405406 51 ## Density correction factor estimation: 0.9909909909909911 150 ## Density correction factor estimation: 0.9909909909909911 173 ## Density correction factor estimation: 0.9909909909909911 195 ## Density correction factor estimation: 0.9909909909909911
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/data/synthetic/outlier-scenarios/ |
H A D | 6-gaussian-4d.csv | 15 ## Density correction factor estimation: 0.970873786407767 79 ## Density correction factor estimation: 0.970873786407767 133 ## Density correction factor estimation: 0.970873786407767 177 ## Density correction factor estimation: 0.970873786407767 201 ## Density correction factor estimation: 1.0032362459546926 245 ## Density correction factor estimation: 0.970873786407767 278 ## Density correction factor estimation: 1.2135922330097086
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/dports/math/SCIP/scip-7.0.3/scripts/trainEstimation/ |
H A D | periodic_report.set | 3 # report frequency on estimation: -1: never, 0:always, k >= 1: k times evenly during search 5 estimation/reportfreq = 100 9 estimation/treeprofile/enabled = TRUE
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