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/dports/x11-fonts/roboto-fonts-ttf/roboto-2.134/third_party/spiro/curves/
H A Dmecsolve.py574 def trymec(sm, sp): argument
577 if sm < 0:
602 guess_avth = .5 * (sp + sm) * (sp - sm)
622 sm = i * tic
643 sm = i * tic
653 if sm < 0:
674 sm = 5.
677 sm = 3.
679 params = [sm, sp]
682 sm, sp = params
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/
H A Dgee_score_test_simulation.py31 import statsmodels.api as sm namespace
45 def negbinom(u, mu, scale): argument
46 p = (scale - 1) / scale
87 scale = 10 variable
126 y = negbinom(u, mu=mu[hyp], scale=scale)
129 m0 = sm.GEE(y,
133 family=sm.families.Poisson())
134 r0 = m0.fit(scale='X2')
138 m1 = sm.GEE(y,
142 family=sm.families.Poisson())
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H A Drobust_models_1.py18 import statsmodels.api as sm namespace
34 norms = sm.robust.norms
149 sm.robust.scale.mad(x)
164 sm.robust.scale.iqr(x)
186 sm.robust.scale.qn_scale(x)
206 huber = sm.robust.scale.Huber()
207 loc, scale = huber(fat_tails) variable
208 print(loc, scale)
210 sm.robust.mad(fat_tails)
214 sm.robust.scale.mad(fat_tails)
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H A Dstatespace_concentrated_scale.py16 import statsmodels.api as sm
18 dta = sm.datasets.macrodata.load_pandas().data
79 class LocalLevel(sm.tsa.statespace.MLEModel):
152 class LocalLevelConcentrated(sm.tsa.statespace.MLEModel):
211 print('scale = %.5f' % res_conc.scale)
212 print('h * scale = %.5f' % (res_conc.params[0] * res_conc.scale))
221 mod_ar = sm.tsa.SARIMAX(dta.cpi, order=(1, 0, 0), trend='ct')
225 mod_ar_conc = sm.tsa.SARIMAX(dta.cpi,
242 (tuple(res_ar_conc.params) + (res_ar_conc.scale, )))
H A Dquasibinomial.py25 import statsmodels.api as sm namespace
60 model1 = sm.GLM.from_formula("blotch ~ 0 + C(variety) + C(site)",
61 family=sm.families.Binomial(),
63 result1 = model1.fit(scale="X2")
79 class vf(sm.families.varfuncs.VarianceFunction):
90 bin = sm.families.Binomial()
92 model2 = sm.GLM.from_formula("blotch ~ 0 + C(variety) + C(site)",
95 result2 = model2.fit(scale="X2")
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/distributions/examples/
H A Dmatchdist.py24 def plothist(x,distfn, args, loc, scale, right=1):
39 yt = distfn.pdf( bins, loc=loc, scale=scale, *args)
42 ys = stats.t.pdf( bins, 10,scale=10,)*right
181 sm = rvs.mean()
186 par0 = (sm-2*sstd,sm+2*sstd)
187 par_est = tuple(distfn.fit(rvs,loc=sm,scale=sstd,*par0))
189 par_est = tuple(distfn.fit(rvs,loc=sm,scale=sstd))
191 par_est = tuple(distfn.fit(rvs,-5,loc=sm,scale=sstd))
198 sm = rvs.mean()
202 sm = rvs.mean()
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/dports/lang/yorick/yorick-y_2_2_04/i/
H A Delliptic.i203 scale= sqrt(m); in ell_f()
239 phi/= scale; in ell_f()
331 sm= 1./(1.-m(list)); in ellip_k()
332 m(list)*= -sm; in ellip_k()
333 sm= 0.5*pi*sqrt(sm); in ellip_k()
335 scale= merge(scale,sm,mask); in ellip_k()
347 return scale/a; in ellip_k()
369 sm= 1.-m(list); in ellip_e()
370 m(list)/= -sm; in ellip_e()
371 sm= 0.5*pi*sqrt(sm); in ellip_e()
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/dports/games/spring/spring_98.0/rts/Game/Camera/
H A DFreeController.cpp172 const float scale = (dotVal * slide * -dGrav); in Update() local
173 vel.x += (gndNormal.x * scale); in Update()
174 vel.z += (gndNormal.z * scale); in Update()
194 const float scale = (newDist / dist); in Update() local
195 pos = trackPos + (diff * scale); in Update()
204 const float scale = (newDist / dist); in Update() local
205 pos.x = trackPos.x + (scale * diff.x); in Update()
206 pos.z = trackPos.z + (scale * diff.z); in Update()
429 sm["vx"] = prevVel.x; in GetState()
430 sm["vy"] = prevVel.y; in GetState()
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/genmod/tests/
H A Dtest_glm.py148 assert_almost_equal(self.res1.scale, self.res2.scale,
1286 assert_allclose(rslt_gradient.scale, rslt_irls.scale,
1406 assert_allclose(rslt_gradient.scale, rslt_irls.scale,
1500 assert_allclose(self.res1.scale, self.res2.scale, atol=1e-6, rtol=1e-6)
2179 result1 = model1.fit(method="newton", scale=scale)
2183 result2 = model2.fit(method="newton", scale=scale)
2208 result1 = model1.fit(method="newton", scale=scale)
2489 res = mod.fit(scale=1)
2568 scale = 2.412699
2569 qaic = r.info_criteria(crit="qaic", scale=scale)
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H A Dtest_glm_weights.py330 scale = self.res1.deviance / self.res1._iweights.sum()
334 scale=scale)
366 scale = res1.scale * model.df_resid / model.wnobs
371 scale=scale)
374 adj_sm = -1 / 2 * ((model.endog - res1.mu) ** 2).sum() / scale
385 fam = sm.families
412 scale = mu / (rate * shape)
413 endog = (np.random.poisson(rate, size=scale.shape[0]) *
414 np.random.gamma(shape * scale))
427 fam = sm.families
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/dports/math/R-cran-prodlim/prodlim/R/
H A DsurvModel.R15 sm <- lava::lvm(~eventtime+censtime) functionVar
16 lava::distribution(sm,"eventtime") <- lava::coxWeibull.lvm(scale=1/100) functionVar
17 lava::distribution(sm,"censtime") <- lava::coxWeibull.lvm(scale=1/100)
18 sm <- lava::eventTime(sm,time~min(eventtime=1,censtime=0),"event")
19 sm
/dports/science/py-pymatgen/pymatgen-2022.0.15/pymatgen/analysis/tests/
H A Dtest_structure_matcher.py386 scale=True,
409 scale=True,
433 scale=True,
456 scale=True,
466 scale=True,
559 scale=True,
591 scale=True,
616 scale=True,
630 scale=True,
672 scale=True,
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/dports/math/R-cran-VGAM/VGAM/man/
H A Dsmartpred.Rd3 \alias{sm.bs}
4 \alias{sm.ns}
5 \alias{sm.scale}
6 \alias{sm.scale.default}
7 \alias{sm.poly}
29 sm.scale(x, center = TRUE, scale = TRUE)
40 %scale()
132 \code{scale(scale(x))}.
275 # ns is changed to sm.ns and scale is changed to sm.scale:
286 fit2 <- vglm(y ~ sm.ns(sm.scale(x2), df = 5), uninormal, data = ldata)
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/dports/graphics/inkscape/inkscape-1.1_2021-05-24_c4e8f9ed74/src/
H A Dpure-transform.h28 …virtual SnappedPoint snap(::SnapManager *sm, SnapCandidatePoint const &p, Geom::Point pt_orig, Geo…
39 …void snap(::SnapManager *sm, std::vector<Inkscape::SnapCandidatePoint> const &points, Geom::Point …
50 …SnappedPoint snap(::SnapManager *sm, SnapCandidatePoint const &p, Geom::Point pt_orig, Geom::OptRe…
69 …SnappedPoint snap(::SnapManager *sm, SnapCandidatePoint const &p, Geom::Point pt_orig, Geom::OptRe…
102 PureScale(Geom::Scale scale, Geom::Point origin, bool uniform) : in PureScale() argument
103 _scale (scale), in PureScale()
104 _scale_snapped (scale), in PureScale()
120 PureScaleConstrained(Geom::Scale scale, Geom::Point origin): in PureScaleConstrained() argument
121 PureScale(scale, origin, true) {}; // Non-uniform constrained scaling is not supported in PureScaleConstrained()
181 …PureSkewConstrained(Geom::Coord skew, Geom::Coord scale, Geom::Point origin, Geom::Dim2 direction)… in PureSkewConstrained() argument
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/dports/editors/yudit/yudit-3.0.7/swindow/
H A DSFont.cpp178 double sc = fallbackFont.scale(); in setSize()
188 im.scale (fontScale, fontScale); in setSize()
294 sm.x0 = -sm.x0; in draw()
295 sm.t0 = sm.t0 + currw; in draw()
539 sm.scale (fallbackScale * sc, fallbackScale * sc); in draw()
736 double sc = fallbackFont.scale(); in draw()
740 SS_Matrix2D mo = sm; in draw()
904 sm.scale (fallbackScale * sc, fallbackScale * sc); in width()
957 SS_Matrix2D sm; in width() local
958 double sc = fallbackFont.scale(); in width()
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/dports/x11/xpr/xpr-1.0.5/
H A Dx2jet.c556 RGBshiftmask sm; member
562 RGBshiftmask *sm, in setup_RGBshiftmask() argument
565 sm->Rmask = rmask; sm->Gmask = gmask; sm->Bmask = bmask; in setup_RGBshiftmask()
566 sm->Rshift = 0; sm->Gshift = 0; sm->Bshift = 0; in setup_RGBshiftmask()
575 for (; !(rmask & 1); sm->Rshift++) in setup_RGBshiftmask()
577 for (; !(gmask & 1); sm->Gshift++) in setup_RGBshiftmask()
579 for (; !(bmask & 1); sm->Bshift++) in setup_RGBshiftmask()
658 setup_RGBshiftmask(&color.sm, xwd_header.red_mask, in prepare_color_mapping()
820 xred = xwd_colors[((index & color.sm.Rmask) >> color.sm.Rshift)].red; in select_printer_color()
821 xgreen = xwd_colors[((index & color.sm.Gmask) >> color.sm.Gshift)].green; in select_printer_color()
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/dports/x11-wm/phoc/phoc-f26fa0123742ba95d303ad552fc1f8d2d0117288/helpers/
H A Dscale-to-fit23 gsettings set sm.puri.phoc.application:/sm/puri/phoc/application/"$APP_ID"/ scale-to-fit "${val}"
26 G_MESSAGES_DEBUG= gsettings get sm.puri.phoc.application:/sm/puri/phoc/application/"$APP_ID"/ scale…
/dports/misc/openvdb/openvdb-9.0.0/openvdb/openvdb/unittest/
H A DTestPrePostAPI.cc171 ScaleMap sm; in TEST_F() local
203 ScaleMap sm; in TEST_F() local
234 ScaleMap sm; in TEST_F() local
240 correct.preScale(scale); in TEST_F()
250 const Mat4d result = sm.preScale(scale)->getAffineMap()->getConstMat4(); in TEST_F()
265 ScaleMap sm; in TEST_F() local
281 const Mat4d result = sm.postScale(scale)->getAffineMap()->getConstMat4(); in TEST_F()
296 ScaleMap sm; in TEST_F() local
326 ScaleMap sm; in TEST_F() local
358 ScaleMap sm; in TEST_F() local
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/dports/www/gitlab-workhorse/gitlab-foss-0a901d60f8ae4a60c04ae82e6e9c3a03e9321417/app/assets/stylesheets/
H A Dutilities.scss20 @each $index, $size in $type-scale {
55 .gl-children-ml-sm-3 > * {
56 @include media-breakpoint-up(sm) {
164 @media (max-width: $breakpoint-sm) {
171 @media (max-width: $breakpoint-sm) {
177 .gl-sm-pr-3 {
178 @media (min-width: $breakpoint-sm) {
184 .gl-sm-w-half {
185 @media (min-width: $breakpoint-sm) {
190 .gl-sm-mr-3 {
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/dports/graphics/graphviz/graphviz-2.44.1/lib/neatogen/
H A Doverlap.c295 static void scale_coord(int dim, int m, real *x, real scale){ in scale_coord() argument
298 x[i] *= scale; in scale_coord()
313 real scale = -1, scale_best = -1; in overlap_scaling() local
358 scale = 0.5*(scale_sta + scale_sto); in overlap_scaling()
359 scale_coord(dim, m, x, scale); in overlap_scaling()
361 scale_coord(dim, m, x, 1./scale);/* unscale */ in overlap_scaling()
365 scale_sta = scale; in overlap_scaling()
367 scale_best = scale_sto = scale; in overlap_scaling()
420 sm->Lwd = SparseMatrix_copy(sm->Lw); in OverlapSmoother_new()
431 if (!(sm->Lw) || !(sm->Lwd)) { in OverlapSmoother_new()
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/dports/math/R-cran-sm/sm/man/
H A Dsm.survival.Rd1 \name{sm.survival}
2 \alias{sm.survival}
10 is obtained by smoothing across the covariate scale. A small amount
11 of smoothing is then also applied across the survival time scale in
15 sm.survival(x, y, status, h , hv = 0.05, p = 0.5, status.code = 1, \dots)
29 the smoothing parameter applied to the covariate scale. A normal kernel
34 estimate derived from the smoothing procedure in the covariate scale.
44 other optional parameters are passed to the \code{sm.options}
54 see the documentation of \code{\link{sm.options}} for their
76 \code{\link{sm.regression}}, \code{\link{sm.options}}
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H A Dsm.variogram.Rd1 \name{sm.variogram}
2 \alias{sm.variogram}
49 \item{original.scale}{
51 original variogram scale (the default) or on the square-root absolute value scale
135 \code{\link{sm.regression}}, \code{\link{sm.options}}
146 sm.variogram(Position, Percent, original.scale = FALSE, se = FALSE)
147 sm.variogram(Position, Percent, original.scale = FALSE)
148 sm.variogram(Position, Percent, original.scale = FALSE, model = "independent")
149 sm.variogram(East, Percent, original.scale = FALSE, model = "independent")
158 vgm.m <- sm.variogram(loc.m, Co.m, nbins = nbins, original.scale = TRUE,
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/plots/
H A Dgraphics_gofplots_qqplot.py13 import statsmodels.api as sm namespace
16 data = sm.datasets.longley.load()
17 data.exog = sm.add_constant(data.exog, prepend=True)
18 mod_fit = sm.OLS(data.endog, data.exog).fit()
26 sm.graphics.qqplot(res, ax=ax)
32 sm.graphics.qqplot(res, line='s', ax=ax)
38 sm.graphics.qqplot(res, line='45', fit=True, ax=ax)
57 x = np.random.normal(loc=8.25, scale=3.5, size=37)
58 y = np.random.normal(loc=8.00, scale=3.25, size=37)
59 pp_x = sm.ProbPlot(x, fit=True)
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H A Dgraphics_gofplots_qqplot_2samples.py9 import statsmodels.api as sm namespace
12 x = np.random.normal(loc=8.5, scale=2.5, size=37)
13 y = np.random.normal(loc=8.0, scale=3.0, size=37)
14 pp_x = sm.ProbPlot(x)
15 pp_y = sm.ProbPlot(y)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/graphics/tests/
H A Dtest_gofplots.py7 import statsmodels.api as sm namespace
180 self.data = sm.datasets.longley.load()
193 self.data = sm.datasets.longley.load()
247 ProbPlot(self.data, dist=stats.norm(loc=8.5, scale=3.0), scale=3.0)
280 assert self.prbplt.scale == 3
291 self.data = sm.datasets.longley.load()
332 data = sm.datasets.longley.load()
333 data.exog = sm.add_constant(data.exog, prepend=False)
334 mod_fit = sm.OLS(data.endog, data.exog).fit()
644 pp_x = sm.ProbPlot(x)
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