/dports/math/cado-nfs/cado-nfs-f4284e2391121b2bfb97bc4880b6273c7250dc2f/parameters/polynomials/ |
H A D | c145.poly | 12 # lognorm 45.09
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H A D | rsa704.poly | 12 # lognorm: 60.65, alpha: -9.46, (alpha_proj: -1.90) E: 51.19, nr: 4
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H A D | rsa210.poly | 14 # lognorm: 63.15, alpha: -11.18 (proj: -2.13), E: 51.97, nr: 6
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H A D | c210.poly | 14 # lognorm: 56.48, alpha: -6.95 (proj: -1.48), E: 49.52, nr: 4
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H A D | c200.poly | 13 # lognorm: 61.03, alpha: -6.73 (proj: -1.50), E: 54.29, nr: 3
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H A D | c220.poly | 14 # lognorm: 62.47, alpha: -9.94 (proj: -2.33), E: 52.53, nr: 6
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H A D | rsa200.poly | 15 # lognorm: 63.46, alpha: -6.76 (proj: -2.33), E: 56.70, nr: 3
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H A D | rsa180.poly | 4 # lognorm: 56.24, alpha: -5.64 (proj: -1.95), E: 50.60, nr: 5
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/dports/devel/liblognorm/liblognorm-2.0.6/ |
H A D | lognorm.pc.in | 6 Name: lognorm
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H A D | Makefile.in | 107 CONFIG_CLEAN_FILES = lognorm.pc 196 $(srcdir)/lognorm.pc.in AUTHORS COPYING ChangeLog INSTALL NEWS \ 380 pkgconfig_DATA = lognorm.pc 434 lognorm.pc: $(top_builddir)/config.status $(srcdir)/lognorm.pc.in
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/dports/math/R-cran-gss/gss/R/ |
H A D | gssanova.R | 191 lognorm=mkdata.lognorm(y,eta,wt,offset,nu), 449 lognorm=dev0.resid.lognorm(y,eta,wt,nu[[1]]), 468 lognorm=dev0.resid.lognorm(y,eta.new,wt,nu[[1]]), 485 lognorm=mkdata.lognorm(y,eta,wt,offset,nu), 516 lognorm=dev0.resid.lognorm(y,eta,wt,nu[[1]]), 572 lognorm=dev0.resid.lognorm(y,eta,wt,nu[[1]]), 601 lognorm=mkdata.lognorm(y,eta,wt,offset,nu), 626 lognorm=cv.lognorm(y,eta,wt,z$hat[1:nobs],nu[[1]],alpha),
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H A D | summary.gssanova0.R | 25 lognorm=dev.resid.lognorm(y,object$eta,wt,object$nu), 35 lognorm=dev.null.lognorm(y,wt,offset,object$nu),
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H A D | gssanova1.R | 49 lognorm="u", 170 lognorm=mkdata.lognorm(y,eta,wt,offset,nu), 194 lognorm=mkdata.lognorm(y,eta,wt,offset,nu),
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki-core-math/src/test/resources/de/lmu/ifi/dbs/elki/math/statistics/distribution/ |
H A D | distribution-gen-testdata.py | 62 ("lognorm", "0_1", scipy.stats.lognorm(1, 0, math.exp(0)), "lnorm(x, 0, 1 %s)", 43), 63 ("lognorm", "1_3", scipy.stats.lognorm(3, 0, math.exp(1)), "lnorm(x, 1, 3 %s)", 44), 64 ("lognorm", "01_01", scipy.stats.lognorm(.1, 0, math.exp(.1)), "lnorm(x, .1, .1 %s)", 45),
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/dports/math/cado-nfs/cado-nfs-f4284e2391121b2bfb97bc4880b6273c7250dc2f/polyselect/ |
H A D | rootsieve1.c | 598 double lognorm = L2_lognorm (poly->pols[ALG_SIDE], poly->skew); in rotate_v() local 607 u, v, mod * w + modw, lognorm, A[j], E); in rotate_v() 620 double lognorm = L2_lognorm (poly->pols[ALG_SIDE], poly->skew); in rotate_v() local 639 u, v, bestw, lognorm, best_alpha, E); in rotate_v() 1022 double lognorm = L2_lognorm (poly->pols[ALG_SIDE], poly->skew); in main() local 1023 double maxlognorm = lognorm + margin; in main() 1024 printf ("initial lognorm %.2f, maxlognorm %.2f\n", lognorm, maxlognorm); in main()
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H A D | rotation_bound.sage | 203 # returns index, logskew, skew, lognorm 438 # return the skewness optimizing the lognorm sum 662 return float(lognorm+alpha[0]+deviation*alpha[1]) 673 lognorm=min([flog(normfunc(RP(f)+e*RP(g))) for e in E]) 675 return float(lognorm+alpha[0]+deviation*alpha[1]) 690 return float(lognorm+alpha[0]+deviation*alpha[1]) 709 return float(lognorm+alpha[0]+deviation*alpha[1]) 728 lognorm=min([flog(normfunc(RP(f)+e*RP(g))) for e in E]) 730 return float(lognorm+alpha[0]+deviation*alpha[1]) 755 lognorm=min([flog(normfunc(RP(f)+e*RP(g))) for e in E]) [all …]
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/dports/math/R-cran-gss/gss/man/ |
H A D | gssanova0.Rd | 26 families \code{"weibull"}, \code{"lognorm"}, and 86 \code{"weibull"}, \code{"lognorm"}, and \code{"loglogis"}, it is the 92 \code{"weibull"}, \code{"lognorm"}, and \code{"loglogis"}, the score 118 For \code{family="weibull"}, \code{"lognorm"}, or \code{"loglogis"},
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H A D | rkpk.Rd | 60 \code{"lognorm"}, and \code{"loglogis"}.} 74 weibull, lognorm, and loglogis families.}
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H A D | gssanova.Rd | 21 families \code{"weibull"}, \code{"lognorm"}, and 72 \code{\link{NegBinomial}}. For \code{"weibull"}, \code{"lognorm"}, 98 For \code{family="weibull"}, \code{"lognorm"}, or \code{"loglogis"},
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/dports/textproc/highlight/highlight-4.1/langDefs/ |
H A D | excel.lang | 13 …, "gamma.dist", "gamma.inv", "hypgeom.dist", "ceiling.precise", "lognorm.dist", "lognorm.inv", "mo…
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/distributions/ |
H A D | estimators.py | 616 x = stats.lognorm.rvs(sh,loc=100, scale=10,size=200) 619 print(stats.lognorm.fit(x, 1.,loc=x.min()-1,scale=1)) 624 args = (xsorted, stats.lognorm)
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H A D | transformed.py | 206 print(stats.lognorm.cdf(1,1)) 208 print(stats.lognorm.stats(1))
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/dports/misc/thrill/thrill-12c5b59bca66df93b66628b3829027bd0f110dd9/extlib/tlx/tests/ |
H A D | sort_strings_test.hpp | 52 std::lognormal_distribution<double> lognorm(0.0, 1.0); in fill_random_lognormal() local 56 lognorm(rng) * letters.size() / 2.0); in fill_random_lognormal()
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/dports/devel/tlx/tlx-0.5.20200222-5-g8982a9d/tests/ |
H A D | sort_strings_test.hpp | 52 std::lognormal_distribution<double> lognorm(0.0, 1.0); in fill_random_lognormal() local 56 lognorm(rng) * letters.size() / 2.0); in fill_random_lognormal()
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/dports/math/R-cran-Zelig/Zelig/ |
H A D | NAMESPACE | 38 "Zelig-lognorm",
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