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/dports/net/storj/storj-1.45.3/web/storagenode/tests/unit/components/payments/__snapshots__/
H A DEstimationArea.spec.ts.snap4 <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">
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/dports/graphics/opencv/opencv-4.5.3/modules/photo/src/
H A Dfast_nlmeans_denoising_invoker_commons.hpp320 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()
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/dports/net/storj/storj-1.45.3/web/storagenode/src/app/components/payments/
H A DEstimationArea.vue5 <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 {
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/dports/math/openturns/openturns-1.18/lib/test/
H A Dt_Waarts_noisy_lsf.expout109 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
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/dports/math/openturns/openturns-1.18/python/test/
H A Dt_Waarts_noisy_lsf.expout108 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
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H A Dt_BernsteinCopulaFactory_std.expout4 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
H A Dt_Waarts_oblate.expout148 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
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H A Dt_Waarts_discontinuous_lsf.expout113 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
H A Dt_SubsetSampling_Waarts_system_series.expout5 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
H A Dt_SubsetSampling_R-S.expout3 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
/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/
H A Dnonparametric.rst10 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
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/dports/graphics/opencv/opencv-4.5.3/contrib/modules/aruco/tutorials/
H A Dtable_of_content_aruco.markdown4 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
/dports/finance/R-cran-ccgarch/ccgarch/man/
H A Ddcc_estimation.Rd1 \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,
/dports/math/gretl/gretl-2021d/share/scripts/misc/
H A Dhall_cbapm.inp1 # 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
/dports/science/dynare/dynare-4.6.4/tests/kalman/likelihood_from_dynare/
H A Dfs2000_estimation_check.inc3 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…
/dports/multimedia/libfame/libfame-0.9.1/
H A DCHANGES15 * 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
/dports/math/R-cran-maxLik/maxLik/tests/
H A DfinalHessian.Rout.save108 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
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/dports/math/R-cran-Zelig/Zelig/man/
H A Dfrom_zelig_model.Rd5 \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
/dports/science/dynare/dynare-4.6.4/matlab/modules/dseries/src/@arima/
H A Destimate.m51 % 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};
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/dports/misc/openmvg/openMVG-2.0/docs/sphinx/rst/openMVG/multiview/
H A Dmultiview.rst8 - 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,
/dports/science/dynare/dynare-4.6.4/tests/dates/
H A Ddseries_interact.mod1 %% 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, …
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/data/testdata/correlation-outlier/
H A Daxis-parallel-2d.csv12 ## 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
H A Dcomplex-example-2d.csv12 ## 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
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/data/synthetic/outlier-scenarios/
H A D6-gaussian-4d.csv15 ## 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
/dports/math/SCIP/scip-7.0.3/scripts/trainEstimation/
H A Dperiodic_report.set3 # 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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