| /dports/math/R-cran-numbers/numbers/man/ |
| H A D | cf2num.Rd | 7 Evaluate a generalized continuous fraction as an alternating sum.
10 cf2num(a, b = 1, a0 = 0, finite = FALSE)
16 \item{finite}{logical; shall Algorithm 1 be applied.}
22 by converting it into an alternating sum and then applying the
28 With \code{finite=TRUE} the accelleration is turned off.
55 cf2num(a, b, a0, finite = FALSE) # 1.525135276161128
56 cf2num(a, b, a0, finite = TRUE) # 1.525135259240266
|
| /dports/devel/boost-python-libs/boost_1_72_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each PDF (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
83 { // cdf is sum of pdfs.
90 This is exactly the same finite sum as used by `gamma_p/gamma_q` internally.
96 The slight danger when using the sum directly like this, is that if
100 rapidly converging) sum, so that danger isn't present since you always
108 For the incomplete beta function, there are no simple finite sums
110 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/devel/boost-docs/boost_1_72_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each PDF (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
83 { // cdf is sum of pdfs.
90 This is exactly the same finite sum as used by `gamma_p/gamma_q` internally.
96 The slight danger when using the sum directly like this, is that if
100 rapidly converging) sum, so that danger isn't present since you always
108 For the incomplete beta function, there are no simple finite sums
110 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/databases/percona57-pam-for-mysql/boost_1_59_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each pdf (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
84 { // cdf is sum of pdfs.
91 This is exactly the same finite sum as used by gamma_p/gamma_q internally.
97 The slight danger when using the sum directly like this, is that if
101 rapidly converging) sum, so that danger isn't present since you always
109 For the incomplete beta function, there are no simple finite sums
111 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/databases/mysqlwsrep57-server/boost_1_59_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each pdf (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
84 { // cdf is sum of pdfs.
91 This is exactly the same finite sum as used by gamma_p/gamma_q internally.
97 The slight danger when using the sum directly like this, is that if
101 rapidly converging) sum, so that danger isn't present since you always
109 For the incomplete beta function, there are no simple finite sums
111 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/databases/percona57-server/boost_1_59_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each pdf (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
84 { // cdf is sum of pdfs.
91 This is exactly the same finite sum as used by gamma_p/gamma_q internally.
97 The slight danger when using the sum directly like this, is that if
101 rapidly converging) sum, so that danger isn't present since you always
109 For the incomplete beta function, there are no simple finite sums
111 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/databases/xtrabackup/boost_1_59_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each pdf (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
84 { // cdf is sum of pdfs.
91 This is exactly the same finite sum as used by gamma_p/gamma_q internally.
97 The slight danger when using the sum directly like this, is that if
101 rapidly converging) sum, so that danger isn't present since you always
109 For the incomplete beta function, there are no simple finite sums
111 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/databases/percona57-client/boost_1_59_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each pdf (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
84 { // cdf is sum of pdfs.
91 This is exactly the same finite sum as used by gamma_p/gamma_q internally.
97 The slight danger when using the sum directly like this, is that if
101 rapidly converging) sum, so that danger isn't present since you always
109 For the incomplete beta function, there are no simple finite sums
111 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/devel/boost-libs/boost_1_72_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each PDF (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
83 { // cdf is sum of pdfs.
90 This is exactly the same finite sum as used by `gamma_p/gamma_q` internally.
96 The slight danger when using the sum directly like this, is that if
100 rapidly converging) sum, so that danger isn't present since you always
108 For the incomplete beta function, there are no simple finite sums
110 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/devel/hyperscan/boost_1_75_0/libs/math/doc/sf/ |
| H A D | poisson_optimisation.qbk | 11 The first obvious step is to use a finite sum of each PDF (Probability *density* function)
70 cdf = sum from 0 to k of C[k]
83 { // cdf is sum of pdfs.
90 This is exactly the same finite sum as used by `gamma_p/gamma_q` internally.
96 The slight danger when using the sum directly like this, is that if
100 rapidly converging) sum, so that danger isn't present since you always
108 For the incomplete beta function, there are no simple finite sums
110 finite sum of the PDF and an incomplete beta call, the finite sum may indeed
|
| /dports/math/R-cran-nloptr/nloptr/man/ |
| H A D | check.derivatives.Rd | 5 \title{Check analytic gradients of a function using finite difference
19 analytic gradient and its finite difference approximation are flagged as an
33 finite difference approximation, the relative errors, and vector comparing
37 This function compares the analytic gradients of a function with a finite
46 return( sum( ( x - a )^2 ) )
66 return( c( sum(x-a), sum( (x-a)^2 ) ) )
|
| /dports/math/R-cran-Rmpfr/Rmpfr/R/ |
| H A D | Summary.R | 38 sum(x, na.rm=na.rm, ...) / length(x)
74 qq <- c(qq, "NA's" = sum(nas))
89 finite <- is.finite(xx) functionVar
90 xx[finite] <- zapsmall(xx[finite])
|
| /dports/math/R-cran-wk/wk/R/ |
| H A D | plot.R | 124 coord_x <- rep(NA_real_, length(geom_id) + sum(new_geom) - 1L)
125 coord_y <- rep(NA_real_, length(geom_id) + sum(new_geom) - 1L)
195 xlim = range(x_bare$x, finite = TRUE),
196 ylim = range(x_bare$y, finite = TRUE),
214 xlim_min <- range(x_bare$xmin, finite = TRUE)
215 xlim_max <- range(x_bare$xmax, finite = TRUE)
216 ylim_min <- range(x_bare$ymin, finite = TRUE)
217 ylim_max <- range(x_bare$ymax, finite = TRUE)
221 xlim = range(c(xlim_min, xlim_max), finite = TRUE), nameattr
222 ylim = range(c(ylim_min, ylim_max), finite = TRUE), nameattr
[all …]
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| /dports/math/R-cran-VGAM/VGAM/R/ |
| H A D | print.vlm.q | 24 rank <- sum(!is.na(coef))
35 is.finite(deviance(object)))
38 is.finite(object@ResSS))
69 rank <- sum(!is.na(coef))
80 is.finite(deviance(x)))
83 is.finite(x@ResSS))
|
| /dports/math/R/R-4.1.2/src/library/base/R/ |
| H A D | summary.R | 47 c(qq, "NA's" = sum(nas))
73 finite <- is.finite(x) functionVar
74 xx[finite] <- zapsmall(x[finite])
91 finite <- is.finite(x) functionVar
92 xx[finite] <- zapsmall(x[finite])
119 tt <- c(tt[o[ - drop]], "(Other)" = sum(tt[o[drop]]))
121 if(ana) c(tt, "NA's" = sum(nas)) else tt
|
| /dports/math/libRmath/R-4.1.1/src/library/base/R/ |
| H A D | summary.R | 47 c(qq, "NA's" = sum(nas))
73 finite <- is.finite(x) functionVar
74 xx[finite] <- zapsmall(x[finite])
91 finite <- is.finite(x) functionVar
92 xx[finite] <- zapsmall(x[finite])
119 tt <- c(tt[o[ - drop]], "(Other)" = sum(tt[o[drop]]))
121 if(ana) c(tt, "NA's" = sum(nas)) else tt
|
| /dports/lang/swi-pl/swipl-8.2.3/man/lib/summaries.d/ |
| H A D | clpfd.tex | 23 \predicatesummary{automaton}{3}{Describes a list of finite domain variables with a finite automaton…
24 \predicatesummary{automaton}{8}{Describes a list of finite domain variables with a finite automaton…
26 \predicatesummary{circuit}{1}{True iff the list Vs of finite domain variables induces a Hamiltonian…
30 \predicatesummary{element}{3}{The N-th element of the list of finite domain variables Vs is V.}
46 \predicatesummary{sum}{3}{The sum of elements of the list Vars is in relation Rel to Expr.}
49 \predicatesummary{zcompare}{3}{Analogous to compare/3, with finite domain variables A and B.}
|
| /dports/sysutils/istio/istio-1.6.7/common-protos/google/api/servicecontrol/v1/ |
| H A D | distribution.proto | 31 // - the sum-squared-deviation of the samples, used to compute variance
36 // The number of finite buckets. With the underflow and overflow buckets,
79 // The i'th finite bucket covers the interval
82 // finite buckets at all if 'bound' only contains a single element; in
106 // The sum of squared deviations from the mean:
113 // optional. If present, they must sum to the `count` value.
118 // in each of the finite buckets. And `bucket_counts[N] is the number
134 // upper bound of +inf. All other buckets (if any) are called "finite"
136 // below, there are three ways to define the finite buckets.
145 // lower bound of the smallest finite bucket; the lower bound of the
[all …]
|
| /dports/sysutils/istio/istio-1.6.7/vendor/istio.io/api/common-protos/google/api/servicecontrol/v1/ |
| H A D | distribution.proto | 31 // - the sum-squared-deviation of the samples, used to compute variance
36 // The number of finite buckets. With the underflow and overflow buckets,
79 // The i'th finite bucket covers the interval
82 // finite buckets at all if 'bound' only contains a single element; in
106 // The sum of squared deviations from the mean:
113 // optional. If present, they must sum to the `count` value.
118 // in each of the finite buckets. And `bucket_counts[N] is the number
134 // upper bound of +inf. All other buckets (if any) are called "finite"
136 // below, there are three ways to define the finite buckets.
145 // lower bound of the smallest finite bucket; the lower bound of the
[all …]
|
| /dports/sysutils/istio/istio-1.6.7/vendor/istio.io/gogo-genproto/common-protos/google/api/servicecontrol/v1/ |
| H A D | distribution.proto | 31 // - the sum-squared-deviation of the samples, used to compute variance
36 // The number of finite buckets. With the underflow and overflow buckets,
79 // The i'th finite bucket covers the interval
82 // finite buckets at all if 'bound' only contains a single element; in
106 // The sum of squared deviations from the mean:
113 // optional. If present, they must sum to the `count` value.
118 // in each of the finite buckets. And `bucket_counts[N] is the number
134 // upper bound of +inf. All other buckets (if any) are called "finite"
136 // below, there are three ways to define the finite buckets.
145 // lower bound of the smallest finite bucket; the lower bound of the
[all …]
|
| /dports/science/healpix/Healpix_3.50/data/ |
| H A D | README | 8 over finite-size pixels, affecting the angular spectral products:
16 accuracy of finite sum over pixels approximating integrals over the sphere.
29 accuracy of finite sum over pixels approximating integrals over the sphere.
|
| /dports/math/freefem++/FreeFem-sources-4.6/examples/plugin/ |
| H A D | cmaes-VarIneq.edp | 38 //two finite element functions
47 ssp[kX+In[].sum] = fef2[i];
54 //two finite element functions with it
63 fef2[i] = ssp[kX+In[].sum];
78 real[int] constraints(In[].sum);
79 for(int i=0;i<In[].sum;++i)
81 constraints[i] = X[i] - X[i+In[].sum];
105 real[int] start(2*In[].sum);
109 start(0:In[].sum-1) = 0.;
110 start(In[].sum:2*In[].sum-1) = 0.1;
|
| /dports/math/cocoalib/CoCoALib-0.99712/src/CoCoA-5/packages/ |
| H A D | monomial_ideals.cpkg5 | 71 if characteristic(P) <> 0 then error("Not yet implemented for finite characteristic");endif;
123 L := interreduced(support(sum([ $.BorelPP0(PP) | PP in PPList ])));
125 error("Not yet implemented for finite characteristic");
127 Return ideal(support(sum(L))); // To sort the list
141 error("Not yet implemented for finite characteristic");
143 return ideal(support(sum(L))); // To sort the list
167 error("Not yet implemented for finite characteristic");
181 error("Not yet implemented for finite characteristic");
193 error("Not yet implemented for finite characteristic");
204 S := [ sum([ random(-range,range)*Y | Y in first(indets(P),i)])
[all …]
|
| /dports/math/giacxcas/CoCoALib-0.99700/src/CoCoA-5/packages/ |
| H A D | monomial_ideals.cpkg5 | 71 if characteristic(P) <> 0 then error("Not yet implemented for finite characteristic");endif;
123 L := interreduced(support(sum([ $.BorelPP0(PP) | PP in PPList ])));
125 error("Not yet implemented for finite characteristic");
127 Return ideal(support(sum(L))); // To sort the list
141 error("Not yet implemented for finite characteristic");
143 return ideal(support(sum(L))); // To sort the list
167 error("Not yet implemented for finite characteristic");
181 error("Not yet implemented for finite characteristic");
193 error("Not yet implemented for finite characteristic");
204 S := [ sum([ random(-range,range)*Y | Y in first(indets(P),i)])
[all …]
|
| /dports/math/octave/octave-6.4.0/scripts/plot/draw/ |
| H A D | pie3.m | 36 ## element @var{x}i represents of the total sum of @var{x}:
37 ## @code{pct = @var{x}(i) / sum (@var{x})}.
51 ## Note: If @code{sum (@var{x}) @leq{} 1} then the elements of @var{x} are
52 ## interpreted as percentages directly and are not normalized by @code{sum
53 ## (x)}. Furthermore, if the sum is less than 1 then there will be a missing
71 error ("pie3: all data in X must be finite");
116 %!error <all data in X must be finite> pie3 ([1 2 Inf])
117 %!error <all data in X must be finite> pie3 ([1 2 NaN])
|