| /dports/graphics/dssim/dssim-3.1.2/cargo-crates/libaom-sys-0.9.1/vendor/aom_dsp/ |
| H A D | noise_util.c | 159 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
165 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
170 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
179 mean_y[y] += d; in aom_noise_data_validate()
188 mean_y[y] /= h; in aom_noise_data_validate()
189 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
195 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
196 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
218 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/www/firefox-esr/firefox-91.8.0/third_party/aom/aom_dsp/ |
| H A D | noise_util.c | 157 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
163 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
168 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
177 mean_y[y] += d; in aom_noise_data_validate()
186 mean_y[y] /= h; in aom_noise_data_validate()
187 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
193 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
194 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
216 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/third_party/libaom/source/libaom/aom_dsp/ |
| H A D | noise_util.c | 159 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
165 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
170 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
179 mean_y[y] += d; in aom_noise_data_validate()
188 mean_y[y] /= h; in aom_noise_data_validate()
189 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
195 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
196 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
218 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/www/firefox/firefox-99.0/third_party/aom/aom_dsp/ |
| H A D | noise_util.c | 157 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
163 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
168 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
177 mean_y[y] += d; in aom_noise_data_validate()
186 mean_y[y] /= h; in aom_noise_data_validate()
187 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
193 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
194 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
216 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/mail/thunderbird/thunderbird-91.8.0/third_party/aom/aom_dsp/ |
| H A D | noise_util.c | 157 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
163 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
168 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
177 mean_y[y] += d; in aom_noise_data_validate()
186 mean_y[y] /= h; in aom_noise_data_validate()
187 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
193 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
194 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
216 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/www/chromium-legacy/chromium-88.0.4324.182/third_party/libaom/source/libaom/aom_dsp/ |
| H A D | noise_util.c | 159 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
165 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
170 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
179 mean_y[y] += d; in aom_noise_data_validate()
188 mean_y[y] /= h; in aom_noise_data_validate()
189 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
195 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
196 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
218 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/multimedia/aom/aom-3.2.0/aom_dsp/ |
| H A D | noise_util.c | 159 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
165 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
170 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
179 mean_y[y] += d; in aom_noise_data_validate()
188 mean_y[y] /= h; in aom_noise_data_validate()
189 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
195 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
196 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
218 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/lang/spidermonkey78/firefox-78.9.0/third_party/aom/aom_dsp/ |
| H A D | noise_util.c | 157 double *mean_x, *mean_y, *var_x, *var_y; in aom_noise_data_validate() local
163 mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); in aom_noise_data_validate()
168 memset(mean_y, 0, sizeof(*mean_y) * h); in aom_noise_data_validate()
177 mean_y[y] += d; in aom_noise_data_validate()
186 mean_y[y] /= h; in aom_noise_data_validate()
187 var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y]; in aom_noise_data_validate()
193 if (fabs(mean_y[y] - mean) >= kMeanThreshold) { in aom_noise_data_validate()
194 fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean); in aom_noise_data_validate()
216 aom_free(mean_y); in aom_noise_data_validate()
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| /dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/bls/ |
| H A D | bls.c | 136 double* mean_y = mean_y_0 + block; in run_bls() local
144 mean_y[n] = 0.0; in run_bls()
149 mean_y[ind] += y[n] * ivar[n]; in run_bls()
156 mean_y[ind] = mean_y[n]; in run_bls()
166 mean_y[n] += mean_y[n-1]; in run_bls()
181 double y_in = mean_y[n+dur] - mean_y[n]; in run_bls()
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| /dports/lang/rust/rustc-1.58.1-src/src/etc/ |
| H A D | cpu-usage-over-time-plot.sh | 36 f(x) = mean_y
37 fit f(x) 'cpu-$builder.csv' using 1:(100-\$2) via mean_y
39 set label 1 gprintf("Average = %g%%", mean_y) center font ",18"
46 mean_y with lines linetype 1 linecolor rgb "#ff0000" title "average", \\
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| /dports/astro/oskar/OSKAR-2.8.0/oskar/math/src/ |
| H A D | oskar_fit_ellipse.c | 27 float mean_x = 0.0f, mean_y = 0.0f, sumX[5]; in oskar_fit_ellipse_f() local
44 mean_y += y[i]; in oskar_fit_ellipse_f()
47 mean_y /= (float)num_points; in oskar_fit_ellipse_f()
55 const float y_ = y[i] - mean_y; in oskar_fit_ellipse_f()
142 double mean_x = 0.0, mean_y = 0.0, sumX[5]; in oskar_fit_ellipse_d() local
159 mean_y += y[i]; in oskar_fit_ellipse_d()
162 mean_y /= (double)num_points; in oskar_fit_ellipse_d()
170 const double y_ = y[i] - mean_y; in oskar_fit_ellipse_d()
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| /dports/biology/fasta/fasta/ |
| H A D | scalesws.c | 277 double mean_x, mean_y, var_x, var_y, covar_xy; in fit_llen() local
336 mean_x = mean_y = 0.0; in fit_llen()
345 mean_y += (double)score_sums[j]; in fit_llen()
350 mean_x /= n; mean_y /= n; in fit_llen()
352 var_y = var_y / (float)n - mean_y * mean_y; in fit_llen()
354 covar_xy = covar_xy / (float)n - mean_x * mean_y; in fit_llen()
440 double mean_x, mean_y, var_x, var_y, covar_xy; in fit_llens() local
483 mean_x = mean_y = mean_y2 = 0.0; in fit_llens()
492 mean_y += (double)score_sums[j]; in fit_llens()
497 mean_x /= n; mean_y /= n; in fit_llens()
[all …]
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| H A D | scalesw2.c | 278 double mean_x, mean_y, var_x, var_y, covar_xy; in fit_llen() local
339 mean_x = mean_y = 0.0; in fit_llen()
348 mean_y += (double)score_sums[j]; in fit_llen()
353 mean_x /= n; mean_y /= n; in fit_llen()
355 var_y = var_y / (float)n - mean_y * mean_y; in fit_llen()
357 covar_xy = covar_xy / (float)n - mean_x * mean_y; in fit_llen()
443 double mean_x, mean_y, var_x, var_y, covar_xy; in fit_llens() local
486 mean_x = mean_y = mean_y2 = 0.0; in fit_llens()
495 mean_y += (double)score_sums[j]; in fit_llens()
500 mean_x /= n; mean_y /= n; in fit_llens()
[all …]
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| /dports/audio/praat/praat-6.2.03/external/gsl/ |
| H A D | gsl_statistics__covariance_source.c | 107 long double mean_x, mean_y;
119 mean_y = data2[0 * stride2];
125 delta_y = data2[i * stride2] - mean_y;
130 mean_y += delta_y / (i + 1.0);
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| /dports/misc/vxl/vxl-3.3.2/core/vgl/algo/ |
| H A D | vgl_fit_cylinder_3d.hxx | 185 T mean_x = T(0), mean_y = T(0), mean_z = T(0); in fit() local
188 mean_x += p.x(); mean_y += p.y(); mean_z += p.z(); in fit()
190 mean_x/=n; mean_y/=n; mean_z/=n; in fit()
198 sxx += (x-mean_x)*(x-mean_x); sxy += (x-mean_x)*(y-mean_y); sxz += (x-mean_x)*(z-mean_z); in fit()
199 syy += (y-mean_y)*(y-mean_y); syz += (y-mean_y)*(z-mean_z); in fit()
200 szz += (z-mean_y)*(z-mean_z); in fit()
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| /dports/misc/vxl/vxl-3.3.2/contrib/mul/msm/tools/ |
| H A D | msm_compare_markups.cxx | 343 double mean_y() const { return sum2/n; } in mean_y() function in mbm_covar_stats_2d
344 vgl_point_2d<double> mean() const { return {mean_x(),mean_y()}; } in mean()
347 double var12() const { return sum12/n-mean_x()*mean_y(); } in var12()
348 double var22() const { return sum22/n-mean_y()*mean_y(); } in var22()
387 ref_shape[k].y()+pt_stats[k].mean_y()); in write_ellipses()
410 ref_shape[k].y()+pt_stats[k].mean_y()); in write_ellipses()
444 ref_shape[k].y()+pt_stats[k].mean_y()); in write_centre_points()
461 ref_shape[k].y()+pt_stats[k].mean_y()); in write_centre_points()
512 W_sum[0][1]*c_stats[j].mean_y(); in compute_stats()
514 W_sum[1][1]*c_stats[j].mean_y(); in compute_stats()
[all …]
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| /dports/math/py-spreg/spreg-1.2.4/spreg/ |
| H A D | diagnostics.py | 77 mean_y = reg.mean_y # (scalar) mean of dependent observations
79 U = np.sum((predy - mean_y) ** 2)
211 mean_y = reg.mean_y # (scalar) mean of dep observations
213 ss_tot = ((y - mean_y) ** 2).sum(0)
1220 mean_y = aux.mean_y
1222 ss_tot = sum((y - mean_y) ** 2)
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| /dports/devel/efl/efl-1.25.1/src/modules/evas/image_savers/tgv/ |
| H A D | evas_image_save_tgv.c | 188 double mean_x = 0, mean_y = 0, var_x = 0, var_y = 0, cov_xy = 0; in evas_image_save_file_tgv() local
462 const double delta_y = (double)done[k] - mean_y; in evas_image_save_file_tgv()
464 mean_y = mean_y + (double)(delta_y / (pixels_count + 1)); in evas_image_save_file_tgv()
466 var_y = var_y + ((double)(done[k] - mean_y) * delta_y); in evas_image_save_file_tgv()
467 … cov_xy = cov_xy + ((double)(todo[k] - mean_x) * (double)(done[k] - mean_y)); in evas_image_save_file_tgv()
521 … double temp = (mean_x * mean_x + mean_y * mean_y + c1) * (var_x * var_x + var_y * var_y + c2); in evas_image_save_file_tgv()
522 double ssim = (2 * mean_x * mean_y + c1) * ( 2 * cov_xy + c2) / temp; in evas_image_save_file_tgv()
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| /dports/math/gsl/gsl-2.7/statistics/ |
| H A D | covariance_source.c | 107 long double mean_x, mean_y; in FUNCTION() local
119 mean_y = data2[0 * stride2]; in FUNCTION()
125 delta_y = data2[i * stride2] - mean_y; in FUNCTION()
130 mean_y += delta_y / (i + 1.0); in FUNCTION()
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| /dports/misc/vxl/vxl-3.3.2/contrib/brl/bbas/bsta/ |
| H A D | bsta_spherical_histogram.hxx | 399 double mean_x = 0.0, mean_y = 0.0, mean_z = 0.0; in mean() local
407 mean_x += pc*x; mean_y += pc*y; mean_z += pc*z; in mean()
410 double length = std::sqrt(mean_x*mean_x + mean_y*mean_y + mean_z*mean_z); in mean()
416 mean_x /= length; mean_y /= length; mean_z /= length; in mean()
417 convert_to_spherical(static_cast<T>(mean_x), static_cast<T>(mean_y), in mean()
424 double mean_x = T(0), mean_y = T(0) , mean_z = T(0); in covariance_matrix() local
436 mean_x += pc*x; mean_y += pc*y; mean_z += pc*z; in covariance_matrix()
439 double length = std::sqrt(mean_x*mean_x + mean_y*mean_y + mean_z*mean_z); in covariance_matrix()
442 mean_x /= length; mean_y /= length; mean_z /= length; in covariance_matrix()
444 convert_to_spherical(static_cast<T>(mean_x), static_cast<T>(mean_y), in covariance_matrix()
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| /dports/net/chrony-lite/chrony-4.2/ |
| H A D | samplefilt.c | 286 double mean_x, mean_y, disp, var, prev_avg_var; in combine_selected_samples() local
302 for (i = 0, mean_x = mean_y = 0.0; i < n; i++) { in combine_selected_samples()
304 mean_y += filter->y_data[i]; in combine_selected_samples()
307 mean_y /= n; in combine_selected_samples()
325 disp += (filter->y_data[i] - mean_y) * (filter->y_data[i] - mean_y); in combine_selected_samples()
384 result->offset = mean_y; in combine_selected_samples()
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| /dports/net/chrony/chrony-4.2/ |
| H A D | samplefilt.c | 286 double mean_x, mean_y, disp, var, prev_avg_var; in combine_selected_samples() local
302 for (i = 0, mean_x = mean_y = 0.0; i < n; i++) { in combine_selected_samples()
304 mean_y += filter->y_data[i]; in combine_selected_samples()
307 mean_y /= n; in combine_selected_samples()
325 disp += (filter->y_data[i] - mean_y) * (filter->y_data[i] - mean_y); in combine_selected_samples()
384 result->offset = mean_y; in combine_selected_samples()
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| /dports/science/kst2/kst-plot-38eddb5322a1d557f9d86ea95d456d76272941e3/src/plugins/fits/lorentzian_unweighted/ |
| H A D | fitlorentzian_unweighted.cpp | 169 double mean_y = 0.0; in function_initial_estimate() local
185 mean_y += Y[i]; in function_initial_estimate()
195 mean_y /= double(npts); in function_initial_estimate()
202 if (max_y - mean_y > mean_y - min_y) { // positive going gaussian in function_initial_estimate()
207 A = min_y - mean_y; in function_initial_estimate()
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| /dports/devel/rust-analyzer/rust-analyzer-2021-12-20/crates/test_utils/src/ |
| H A D | assert_linear.rs | 72 let mean_y = mean(&ys); in finish() localVariable
78 num += (x - mean_x) * (y - mean_y); in finish()
84 let a = mean_y - b * mean_x; in finish()
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| /dports/lang/rust/rustc-1.58.1-src/src/tools/rust-analyzer/crates/test_utils/src/ |
| H A D | assert_linear.rs | 72 let mean_y = mean(&ys); in finish() localVariable
78 num += (x - mean_x) * (y - mean_y); in finish()
84 let a = mean_y - b * mean_x; in finish()
|