| /dports/math/openturns/openturns-1.18/lib/test/ |
| H A D | t_DistFunc_spearman.expout | 1 size=5, ties=true, tail=true, rho=-1, p=1
2 size=5, ties=true, tail=true, rho=-0.9, p=0.981307
3 size=5, ties=true, tail=true, rho=-0.8, p=0.947956
4 size=5, ties=true, tail=true, rho=-0.7, p=0.90594
5 size=5, ties=true, tail=true, rho=-0.6, p=0.857622
11 size=5, ties=true, tail=true, rho=0, p=0.5
21 size=5, ties=true, tail=true, rho=1, p=0
22 size=5, ties=true, tail=false, rho=-1, p=0
32 size=5, ties=true, tail=false, rho=0, p=0.5
42 size=5, ties=true, tail=false, rho=1, p=1
[all …]
|
| /dports/math/openturns/openturns-1.18/python/test/ |
| H A D | t_DistFunc_spearman.expout | 1 size= 5 ties= True tail= True rho=-1 p=1
2 size= 5 ties= True tail= True rho=-0.9 p=0.981307
3 size= 5 ties= True tail= True rho=-0.8 p=0.947956
4 size= 5 ties= True tail= True rho=-0.7 p=0.90594
5 size= 5 ties= True tail= True rho=-0.6 p=0.857622
11 size= 5 ties= True tail= True rho=0 p=0.5
21 size= 5 ties= True tail= True rho=1 p=0
22 size= 5 ties= True tail= False rho=-1 p=0
32 size= 5 ties= True tail= False rho=0 p=0.5
42 size= 5 ties= True tail= False rho=1 p=1
[all …]
|
| /dports/math/gecode/gecode-0916a1a/gecode/kernel/branch/ |
| H A D | view-sel.hpp | 469 return ties[0]; in select()
654 Val b = m(home,x[ties[0]],ties[0]); in brk()
658 Val mxi = m(home,x[ties[i]],ties[i]); in brk()
661 b=mxi; j=0; ties[j++]=ties[i]; in brk()
664 ties[j++]=ties[i]; in brk()
677 Val b_m = m(home,x[ties[0]],ties[0]); in select()
679 Val mxi = m(home,x[ties[i]],ties[i]); in select()
804 Val w = m(home,x[ties[0]],ties[0]); in brk()
807 Val mxi = m(home,x[ties[i]],ties[i]); in brk()
825 if (!c(l,static_cast<double>(m(home,x[ties[i]],ties[i])))) in brk()
[all …]
|
| /dports/math/R-cran-intervals/intervals/R/ |
| H A D | size-methods.R | 14 ties <- x[,2] == x[,1] functionVar
15 ties[ is.na( ties ) ] <- FALSE
16 result[ ties ] <- ifelse( all( closed(x) ), 1, 0 )
17 result[ !ties ] <- result[ !ties ] + sum( closed(x) ) - 1 # NAs just stay NA
29 ties <- x[,2] == x[,1] functionVar
30 ties[ is.na( ties ) ] <- FALSE
32 result[ ties ] <- ifelse( rs[ ties ] == 2, 1, 0 )
33 result[ !ties ] <- result[ !ties ] + rs[ !ties ] - 1
|
| /dports/math/R-cran-matrixStats/matrixStats/tests/ |
| H A D | rowRanks.R | 72 cat(sprintf("ties.method = %s\n", ties))
74 y1 <- matrixStats::rowRanks(x, ties.method = ties)
75 if (ties != "last" || getRversion() >= "3.3.0") {
76 y2 <- rowRanks_R(x, ties.method = ties)
85 if (ties != "last" || getRversion() >= "3.3.0") {
86 y2 <- colRanks_R(x, ties.method = ties)
105 for (ties in c("random")) { globalVar
106 cat(sprintf("ties.method = %s\n", ties))
109 y0 <- rowRanks_R(x, ties.method = ties)
127 y0 <- colRanks_R(x, ties.method = ties)
[all …]
|
| H A D | weightedMedian.R | 22 y2a <- weightedMedian(x, ties = "weighted") # 5.5 (default)
23 y2b <- weightedMedian(x, ties = "min") # 5
24 y2c <- weightedMedian(x, ties = "max") # 6
58 for (ties in c("weighted", "mean", "min", "max")) { globalVar
59 cat(sprintf("ties = %s\n", ties))
60 y <- weightedMedian(x, w, ties = ties)
82 for (ties in c("weighted", "mean", "min", "max")) { globalVar
83 y <- weightedMedian(x, w, ties = ties)
84 cat(sprintf("mode = %s, ties = %s, result = %g\n", mode, ties, y))
95 y <- weightedMedian(x, w, ties = "min")
|
| /dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki-core-util/src/main/java/de/lmu/ifi/dbs/elki/utilities/datastructures/heap/ |
| H A D | TiedTopBoundedUpdatableHeap.java | 67 return super.size() + ties.size(); in size()
73 ties.clear(); in clear()
92 if(pos >= 0 && !ties.isEmpty() && compare(e, ties.get(0)) < 0) { in offerAt()
97 final E e2 = ties.remove(ties.size() - 1); in offerAt()
107 if(ties.isEmpty()) { in peek()
111 return ties.get(ties.size() - 1); in peek()
117 if(ties.isEmpty()) { in poll()
121 E e = ties.remove(ties.size() - 1); in poll()
143 ties.add(e); in handleOverflow()
149 for(E e2 : ties) { in handleOverflow()
[all …]
|
| H A D | TiedTopBoundedHeap.java | 43 private List<E> ties = new ArrayList<>(); field in TiedTopBoundedHeap
66 return super.size() + ties.size(); in size()
72 ties.clear(); in clear()
77 if (ties.isEmpty()) { in peek()
80 return ties.get(ties.size() - 1); in peek()
86 if (ties.isEmpty()) { in poll()
89 return ties.remove(ties.size() - 1); in poll()
95 if (ties.isEmpty()) { in replaceTopElement()
119 ties.add(e); in handleOverflow()
122 ties.clear(); in handleOverflow()
[all …]
|
| /dports/math/R-cran-raster/raster/R/ |
| H A D | modal.R | 9 function(x, ..., ties='random', na.rm=FALSE, freq=FALSE) { argument
41 ties <- match(ties[1], c('lowest', 'highest', 'first', 'random', 'NA')) - 1
42 if (is.na(ties)) {
49 w <- .getMode(z, ties=ties)
51 w <- as.logical(.getMode(z, ties=ties))
53 w <- .getMode(z, ties=ties)
58 w <- .getMode(z, ties=ties)
|
| H A D | approxNA.R | 8 function(x, filename="", method="linear", yleft, yright, rule=1, f=0, ties=mean, z=NULL, NArule=1, … argument
46 …1, function(x) stats::approx(x=xout, y=x, xout=xout, method=method, rule=rule, f=f, ties=ties)$y )) functionVar
48 …ts::approx(x=xout, y=x, xout=xout, method=method, yright=yright, rule=rule, f=f, ties=ties)$y )) functionVar
50 …s::approx(x=xout, y=x, xout=xout, method=method, yleft=yleft, rule=rule, f=f, ties=ties)$y )) functionVar
52 …(x=xout, y=x, xout=xout, method=method, yright=yright, yleft=yleft, rule=rule, f=f, ties=ties)$y )) functionVar
81 …, function(x) stats::approx(x=xout, y=x, xout=xout, method=method, rule=rule, f=f, ties=ties)$y ) ) functionVar
83 …tats::approx(x=xout, y=x, xout=xout, method=method, yright=yright, rule=rule, f=f, ties=ties)$y ) ) functionVar
85 … stats::approx(x=xout, y=x, xout=xout, method=method, yleft=yleft, rule=rule, f=f, ties=ties)$y ) ) functionVar
87 …x=xout, y=x, xout=xout, method=method, yright=yright, yleft=yleft, rule=rule, f=f, ties=ties)$y ) ) functionVar
|
| /dports/math/R/R-4.1.2/src/library/base/man/ |
| H A D | rank.Rd | 23 \item{ties.method}{a character string specifying how ties are treated,
34 (called \sQuote{ties}), the argument \code{ties.method} determines the
59 (whether or not there are any ties).
69 (r2 <- rank(x2)) # ties are averaged
75 rank(x2, ties.method= "first") # first occurrence wins
77 rank(x2, ties.method= "random") # ties broken at random
78 rank(x2, ties.method= "random") # and again
80 ## keep ties ties, no average
82 (rmi <- rank(x2, ties.method= "min")) # as in Sports
86 tMeth <- eval(formals(rank)$ties.method)
[all …]
|
| /dports/math/libRmath/R-4.1.1/src/library/base/man/ |
| H A D | rank.Rd | 23 \item{ties.method}{a character string specifying how ties are treated,
34 (called \sQuote{ties}), the argument \code{ties.method} determines the
59 (whether or not there are any ties).
69 (r2 <- rank(x2)) # ties are averaged
75 rank(x2, ties.method= "first") # first occurrence wins
77 rank(x2, ties.method= "random") # ties broken at random
78 rank(x2, ties.method= "random") # and again
80 ## keep ties ties, no average
82 (rmi <- rank(x2, ties.method= "min")) # as in Sports
86 tMeth <- eval(formals(rank)$ties.method)
[all …]
|
| /dports/devel/p5-Heap-Simple-Perl/Heap-Simple-Perl-0.14/t/ |
| H A D | Ties.pm | 5 my $ties = 0;
8 return $ties;
12 die "You still have ties hanging" if $ties;
20 $ties++;
30 $ties--;
43 $ties++;
58 $ties--;
71 $ties++;
96 $ties--;
|
| /dports/devel/p5-Heap-Simple-XS/Heap-Simple-XS-0.10/t/ |
| H A D | Ties.pm | 5 my $ties = 0;
8 return $ties;
12 die "You still have ties hanging" if $ties;
20 $ties++;
30 $ties--;
43 $ties++;
58 $ties--;
71 $ties++;
96 $ties--;
|
| /dports/print/lilypond-devel/lilypond-2.23.5/lily/ |
| H A D | tie-formatting-problem.cc | 312 vector<Grob *> ties; in from_tie() local
313 ties.push_back (tie); in from_tie()
314 from_ties (ties); in from_tie()
328 if (ties.empty ()) in from_ties()
331 x_refpoint_ = ties[0]; in from_ties()
332 y_refpoint_ = ties[0]; in from_ties()
804 score_aptitude (&(*ties)[i], specifications_[i], ties, i); in score_ties_aptitude()
810 if (ties->is_scored ()) in score_ties()
815 ties->set_scored (); in score_ties()
824 ties->add_tie_score ((*ties)[i].score (), i, "conf"); in score_ties_configuration()
[all …]
|
| /dports/print/lilypond/lilypond-2.22.1/lily/ |
| H A D | tie-formatting-problem.cc | 312 vector<Grob *> ties; in from_tie() local
313 ties.push_back (tie); in from_tie()
314 from_ties (ties); in from_tie()
328 if (ties.empty ()) in from_ties()
331 x_refpoint_ = ties[0]; in from_ties()
332 y_refpoint_ = ties[0]; in from_ties()
800 score_aptitude (&(*ties)[i], specifications_[i], ties, i); in score_ties_aptitude()
806 if (ties->scored_) in score_ties()
811 ties->scored_ = true; in score_ties()
820 ties->add_tie_score ((*ties)[i].score (), i, "conf"); in score_ties_configuration()
[all …]
|
| /dports/math/R-cran-matrixStats/matrixStats/R/ |
| H A D | weightedMedian.R | 73 interpolate = is.null(ties), ties = NULL, ...) { argument
90 if (is.null(ties)) {
93 if (ties == "weighted") {
95 } else if (ties == "min") {
97 } else if (ties == "max") {
99 } else if (ties == "mean") {
102 stop(sprintf("Unknown value of argument '%s': %s", "ties", ties))
|
| /dports/math/R/R-4.1.2/src/library/stats/R/ |
| H A D | approx.R | 25 regularize.values <- function(x, y, ties, warn.collapsing = TRUE, na.rm = TRUE) { argument
44 if (!identical(ties, "ordered")) {
46 if(is.function(ties) || is.character(ties))# fn or name of one
48 else if(is.list(ties) && length(T <- ties) == 2L && is.function(T[[2]])) {
50 ties <- T[[2]]
64 y <- as.vector(tapply(y, match(x,x), ties))# as.v: drop dim & dimn.
74 yleft, yright, rule = 1, f = 0, ties = mean, na.rm = TRUE) argument
80 r <- regularize.values(x, y, ties, missing(ties), na.rm=na.rm)
118 yleft, yright, rule = 1, f = 0, ties = mean, na.rm = TRUE) argument
124 x <- regularize.values(x, y, ties, missing(ties), na.rm=na.rm)
[all …]
|
| /dports/math/libRmath/R-4.1.1/src/library/stats/R/ |
| H A D | approx.R | 25 regularize.values <- function(x, y, ties, warn.collapsing = TRUE, na.rm = TRUE) { argument
44 if (!identical(ties, "ordered")) {
46 if(is.function(ties) || is.character(ties))# fn or name of one
48 else if(is.list(ties) && length(T <- ties) == 2L && is.function(T[[2]])) {
50 ties <- T[[2]]
64 y <- as.vector(tapply(y, match(x,x), ties))# as.v: drop dim & dimn.
74 yleft, yright, rule = 1, f = 0, ties = mean, na.rm = TRUE) argument
80 r <- regularize.values(x, y, ties, missing(ties), na.rm=na.rm)
118 yleft, yright, rule = 1, f = 0, ties = mean, na.rm = TRUE) argument
124 x <- regularize.values(x, y, ties, missing(ties), na.rm=na.rm)
[all …]
|
| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/duration/tests/results/ |
| H A D | survival.R | 23 for (ties in c("breslow", "efron")) { globalVar
25 ti = substr(ties, 1, 3)
29 md = coxph(surv ~ exog, ties=ties)
41 md = coxph(surv ~ exog, ties=ties)
50 md = coxph(surv ~ exog + strata(strata), ties=ties)
59 md = coxph(surv ~ exog + strata(strata), ties=ties)
|
| /dports/devel/R-cran-data.table/data.table/man/ |
| H A D | frank.Rd | 15 frank(x, \dots, na.last=TRUE, ties.method=c("average",
19 ties.method=c("average", "first", "last", "random",
29 \item{ties.method}{ A character string specifying how ties are treated, see \code{Details}. }
50 # ties.method = min
51 frankv(x, ties.method="min")
52 # ties.method = dense
53 frankv(x, ties.method="dense")
59 frankv(DT, cols="x", ties.method="dense", na.last=NA)
62 frankv(DT, ties.method="first", na.last="keep")
66 frank(DT, x, -y, ties.method="first")
[all …]
|
| /dports/games/fmars/fmars-0.0.207/ |
| H A D | pymars.py | 19 w1wins, w2wins, ties = mars.fightn (w1, w2, 2000, 4000) # rounds = 2000, fnumber = 4000 variable
20 print w1wins, ties
21 print w2wins, ties
38 w1wins = w2wins = ties = 0
47 ties += 1
53 print w1wins, ties
54 print w2wins, ties
|
| /dports/devel/R-cran-gtools/gtools/R/ |
| H A D | stat_mode.R | 59 ties = c("all", "first", "last", "missing"), argument
61 ties <- match.arg(ties)
74 if (ties == "first") {
76 } else if (ties == "last") {
78 } else if (ties == "all") {
|
| /dports/print/lilypond-devel/lilypond-2.23.5/input/regression/ |
| H A D | tie-single.ly | 3 texidoc = "Formatting for isolated ties.
6 @item short ties are in spaces
7 @item long ties cross staff lines
8 @item ties avoid flags of left stems.
9 @item ties avoid dots of left notes.
11 @item short ties are vertically centered in the space, as well those
14 @item extremely short ties are put over the noteheads, instead of between.
|
| /dports/print/lilypond/lilypond-2.22.1/input/regression/ |
| H A D | tie-single.ly | 3 texidoc = "Formatting for isolated ties.
6 @item short ties are in spaces
7 @item long ties cross staff lines
8 @item ties avoid flags of left stems.
9 @item ties avoid dots of left notes.
11 @item short ties are vertically centered in the space, as well those
14 @item extremely short ties are put over the noteheads, instead of between.
|