/dports/science/py-GPy/GPy-1.10.0/GPy/testing/ |
H A D | state_space_main_tests.py | 251 (f_mean, f_var, loglikelhood, g_loglikelhood, \ 259 f_mean_squeezed = np.squeeze(f_mean[1:,:]) # exclude initial value 273 return f_mean, f_var 292 (f_mean, f_var, loglikelhood, g_loglikelhood, \ 301 f_mean_squeezed = np.squeeze(f_mean[1:,:]) # exclude initial value 310 return f_mean, f_var 394 plt.plot( f_mean, 'r.-',label='Kalman filter estimates') 397 plt.plot( f_mean + 2*np.sqrt(f_var), 'r.--') 398 plt.plot( f_mean - 2*np.sqrt(f_var), 'r.--') 461 plt.plot( np.squeeze(f_mean[1:]+H*f_var[1:]*H), 'b--') [all …]
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/dports/graphics/rawtherapee/rawtherapee-5.8/rtengine/ |
H A D | guidedfilter.cc | 123 const auto f_mean = in guidedFilter() local 147 f_mean(p1, I1, r1); in guidedFilter() 151 f_mean(I1, I1, r1); in guidedFilter() 161 f_mean(meanI, I1, r1); in guidedFilter() 164 f_mean(meanp, p1, r1); in guidedFilter() 168 f_mean(p1, p1, r1); in guidedFilter() 172 f_mean(I1, I1, r1); in guidedFilter() 180 f_mean(I1, I1, r1); in guidedFilter() 181 f_mean(p1, p1, r1); in guidedFilter()
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/dports/graphics/art/ART-1.9.3/rtengine/ |
H A D | guidedfilter.cc | 162 const auto f_mean = in guidedFilter() local 183 f_mean(meanI, I1, r1); in guidedFilter() 187 f_mean(meanp, p1, r1); in guidedFilter() 192 f_mean(corrIp, corrIp, r1); in guidedFilter() 197 f_mean(corrI, corrI, r1); in guidedFilter() 217 f_mean(meana, a, r1); in guidedFilter() 221 f_mean(meanb, b, r1); in guidedFilter()
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/dports/graphics/cimg/CImg-v.2.9.7/examples/ |
H A D | gaussian_fit1d.cpp | 82 float f_amp = 1, f_mean = 1, f_std = 1, f_lambda = f_lambda0; in main() local 83 if (f_params) std::sscanf(f_params,"%f%*c%f%*c%f",&f_amp,&f_mean,&f_std); in main() 87 f_mean = samples((int)cmax,0); in main() 91 CImg<> beta = CImg<>::vector(f_amp,f_mean,f_std); in main() 142 const float f_amp, const float f_mean, const float f_std, in draw_gaussfit() argument 164 yf0 = f_fact*std::exp(-cimg::sqr(x0 - f_mean)/f_std2); in draw_gaussfit()
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/dports/graphics/gmic-qt/CImg-v.2.9.8/examples/ |
H A D | gaussian_fit1d.cpp | 82 float f_amp = 1, f_mean = 1, f_std = 1, f_lambda = f_lambda0; in main() local 83 if (f_params) std::sscanf(f_params,"%f%*c%f%*c%f",&f_amp,&f_mean,&f_std); in main() 87 f_mean = samples((int)cmax,0); in main() 91 CImg<> beta = CImg<>::vector(f_amp,f_mean,f_std); in main() 142 const float f_amp, const float f_mean, const float f_std, in draw_gaussfit() argument 164 yf0 = f_fact*std::exp(-cimg::sqr(x0 - f_mean)/f_std2); in draw_gaussfit()
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/dports/graphics/photoflow/PhotoFlow-8472024f/src/operations/ |
H A D | guided_filter.cc | 280 const auto f_mean = f_mean2; in guidedFilter() local 322 f_mean(meanI, I1, r1, 0); in guidedFilter() 329 f_mean(meanp, p1, r1, 0); in guidedFilter() 351 f_mean(corrI, corrI, r1, 0); in guidedFilter() 389 f_mean(meana, a, r1, r1); in guidedFilter() 393 f_mean(meanb, b, r1, r1); in guidedFilter() 515 const auto f_mean = f_mean2; in guidedFilter() local 553 f_mean(meanp, p1, r1, 0); in guidedFilter() 563 f_mean(corrI, corrI, r1, 0); in guidedFilter() 594 f_mean(meana, a, r1, r1); in guidedFilter() [all …]
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H A D | enhanced_usm.cc | 238 const auto f_mean = f_mean2; in eusm_gf() local 264 f_mean(meanI, I1, r1, 0); in eusm_gf() 276 f_mean(corrI, corrI, r1, 0); in eusm_gf() 317 f_mean(meana, a, r1, r1); in eusm_gf() 328 f_mean(meanb, b, r1, r1); in eusm_gf()
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/dports/graphics/mirtk/MIRTK-2.0.0-122-g38210fa/Applications/src/ |
H A D | average-measure.py | 157 f_mean = None variable 164 f_mean = open_table(args.mean_table, args.append, default=sys.stdout) 169 f_out = [f_mean, f_var, f_sdev, f_se] # order must be same as expected by write_stats! 259 close_table(f_mean)
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/dports/devel/hyperscan/boost_1_75_0/libs/math/test/ |
H A D | test_kolmogorov_smirnov.cpp | 70 auto f_mean = [&, dist](RealType t) { return pdf(dist, t) * t; }; in test_spots() local 71 BOOST_CHECK_CLOSE_FRACTION(integrator.integrate(f_mean, eps), mean, tol); in test_spots()
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/mkldnn/tests/benchdnn/rnn/ |
H A D | rnn.cpp | 372 return fill_memory(prb, kind, mem_dt, mem_fp, c.dt, c.f_mean, c.f_stddev, in fill_memory() 445 float expect_gemm_output = (1.f / prb.n_gates()) * prb.cfg[SRC_LAYER].f_mean in fill_src_iter_c() 446 + (1.f / prb.n_gates()) * prb.cfg[SRC_ITER].f_mean in fill_src_iter_c() 447 + prb.cfg[BIAS].f_mean; in fill_src_iter_c() 454 const bool need_adjust = expect_src_iter_c_mean < prb.cfg[SRC_ITER_C].f_mean in fill_src_iter_c() 455 && prb.cfg[SRC_ITER_C].f_mean != 0; in fill_src_iter_c() 457 adjust_factor = expect_src_iter_c_mean / prb.cfg[SRC_ITER_C].f_mean; in fill_src_iter_c() 461 c.f_mean * adjust_factor, c.f_stddev * adjust_factor, in fill_src_iter_c() 535 prb.cfg[kind].f_mean, prb.cfg[kind].f_stddev); in fill_bias()
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H A D | rnn.hpp | 185 float f_mean, f_stddev; // parameters of normal distribution member
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H A D | rnn_aux.cpp | 742 data_shift = cfg.is_s8() ? 0 : cfg[SRC_LAYER].f_mean; in set_qparams()
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/dports/math/onednn/oneDNN-2.5.1/tests/benchdnn/rnn/ |
H A D | rnn.cpp | 372 return fill_memory(prb, kind, mem_dt, mem_fp, c.dt, c.f_mean, c.f_stddev, in fill_memory() 445 float expect_gemm_output = (1.f / prb.n_gates()) * prb.cfg[SRC_LAYER].f_mean in fill_src_iter_c() 446 + (1.f / prb.n_gates()) * prb.cfg[SRC_ITER].f_mean in fill_src_iter_c() 447 + prb.cfg[BIAS].f_mean; in fill_src_iter_c() 454 const bool need_adjust = expect_src_iter_c_mean < prb.cfg[SRC_ITER_C].f_mean in fill_src_iter_c() 455 && prb.cfg[SRC_ITER_C].f_mean != 0; in fill_src_iter_c() 457 adjust_factor = expect_src_iter_c_mean / prb.cfg[SRC_ITER_C].f_mean; in fill_src_iter_c() 461 c.f_mean * adjust_factor, c.f_stddev * adjust_factor, in fill_src_iter_c() 535 prb.cfg[kind].f_mean, prb.cfg[kind].f_stddev); in fill_bias()
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H A D | rnn.hpp | 185 float f_mean, f_stddev; // parameters of normal distribution member
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H A D | rnn_aux.cpp | 742 data_shift = cfg.is_s8() ? 0 : cfg[SRC_LAYER].f_mean; in set_qparams()
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/dports/misc/vxl/vxl-3.3.2/core/vil/tests/ |
H A D | test_image_view_maths.cxx | 230 double f_mean, f_var; in test_image_view_maths_byte() local 231 vil_math_mean_and_variance(f_mean, f_var, f_image, 0); in test_image_view_maths_byte() 232 TEST_NEAR("Mean", f_mean, 0, test_tolerance); in test_image_view_maths_byte()
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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/src/unit_test/ |
H A D | dakota_hdf5_mixed_sampling.in | 36 descriptors 'f_mean'
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/dports/science/code_saturne/code_saturne-7.1.0/src/alge/ |
H A D | cs_benchmark_matrix.c | 1400 double f_min, f_max, f_mean; in _matrix_time_spmv_stats_ops() local 1402 MPI_Allreduce(&f_loc, &f_mean, 1, MPI_DOUBLE, MPI_SUM, cs_glob_mpi_comm); in _matrix_time_spmv_stats_ops() 1406 f_mean /= cs_glob_n_ranks; in _matrix_time_spmv_stats_ops() 1412 f_mean, f_min, f_max); in _matrix_time_spmv_stats_ops()
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/dports/science/code_saturne/code_saturne-7.1.0/src/lagr/ |
H A D | cs_lagr_stat.c | 2717 cs_field_t *f_mean = cs_field_by_id(mt_mean->f_id); in _cs_lagr_stat_update_mesh_moment() local 2718 cs_real_t *restrict m = f_mean->val; in _cs_lagr_stat_update_mesh_moment() 2914 cs_field_t *f_mean = cs_field_by_id(mt_mean->f_id); in _cs_lagr_stat_update_all() local 2915 mean_val = f_mean->val; in _cs_lagr_stat_update_all() 4833 cs_field_t *f_mean = cs_field_by_id(mt_mean->f_id); in cs_lagr_stat_update_event() local 4834 mean_val = f_mean->val; in cs_lagr_stat_update_event()
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/dports/math/py-Pyomo/Pyomo-6.1.2/pyomo/contrib/sensitivity_toolbox/ |
H A D | sens.py | 348 f_mean = value(o)
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/dports/graphics/opencv/opencv-4.5.3/contrib/modules/bioinspired/src/ |
H A D | retina_ocl.cpp | 722 float f_mean = static_cast<float>(mean[0]); in centerReductImageLuminance() local 729 (float)f_mean, (float)f_stddev); in centerReductImageLuminance()
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/dports/science/code_saturne/code_saturne-7.1.0/src/base/ |
H A D | cs_time_moment.c | 2085 cs_field_t *f_mean = cs_field_by_id(mt_mean->f_id); in cs_time_moment_update_all() local 2086 m = f_mean->val; in cs_time_moment_update_all()
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