[LON-CAPA-cvs] cvs: loncom /html/adm/help/tex CAPA_LON-CAPA_function_diffs.tex Problem_LON-CAPA_Functions.tex
bowersj2
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Tue, 10 Jun 2003 15:24:24 -0000
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bowersj2 Tue Jun 10 11:24:24 2003 EDT
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Problem_LON-CAPA_Functions.tex
Log:
.tex tables for LON-CAPA functions available in problems
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Index: loncom/html/adm/help/tex/CAPA_LON-CAPA_function_diffs.tex
+++ loncom/html/adm/help/tex/CAPA_LON-CAPA_function_diffs.tex
\label{CAPA_LON-CAPA_function_diffs}
\begin{longtable}{|p{5cm}|p{6.5cm}|p{5.5cm}|}
\hline
\textbf{CAPA Functions }
&\textbf{LON-CAPA }
&\textbf{Differences (if any) }
\endhead
\hline
sin(x), cos(x), tan(x) & \&sin(\$x), \&cos(\$x), \&tan(\$x) & \\
\hline
asin(x), acos(x), atan(x), atan2(y,x) & \&asin(\$x), \&acos(\$x), \&atan(\$x), \&atan2(\$y,\$x) & \\
\hline
log(x), log10(x) & \&log(\$x), \&log10(\$x) & \\
\hline
exp(x), pow(x,y), sqrt(x) & \&exp(\$x), \&pow(\$x,\$y), \&sqrt(\$x) & \\
\hline
abs(x), sgn(x) & \&abs(\$x), \&sgn(\$x) & \\
\hline
erf(x), erfc(x) & \&erf(\$x), \&erfc(\$x) & \\
\hline
ceil(x), floor(x) & \&ceil(\$x), \&floor(\$x) & \\
\hline
min(...), max(...) & \&min(...), \&max(...) & \\
\hline
factorial(n) & \&factorial(\$n) & \\
\hline
N\%M & \$N\%\$M & \\
\hline
sinh(x), cosh(x), tanh(x) & \&sinh(\$x), \&cosh(\$x), \&tanh(\$x) & \\
\hline
asinh(x), acosh(x), atanh(x) & \&asinh(\$x), \&acosh(\$x), \&atanh(\$x) & \\
\hline
/DIS(\$x,''nn'') & \&format(\$x,''nn'') & The difference is obvious. \\
\hline
Not in CAPA & \&prettyprint(\$x,''nn'') & \\
\hline
Not in CAPA & \&dollarformat(\$x & \\
\hline
roundto(x,n) & \&roundto(\$x,\$n) & \\
\hline
web(``a'',''b'',''c'') or web(a,b,c) & \&web(``a'',''b'',''c'') or \&web(\$a,\$b,\$c) & \\
\hline
html(``a'') or html(a) & \&html(``a'') or \&html(\$a) & \\
\hline
jn(m,x) & \&j0(\$x), \&j1(\$x), \&jn(\$m,\$x), \&jv(\$y,\$x) & In CAPA, j0, j1 and jn are contained in one function, jn(m,x) where m takes the value of 0, 1 or 2. jv(y,x) is new to LON-CAPA. \\
\hline
yn(m,x) & \&y0(\$x), \&y1(\$x), \&yn(\$m,\$x), \&yv(\$y,\$x) & In CAPA, y0, y1 and yn are contained in one function, yn(m,x) where m takes the value of 0, 1 or 2. yv(y,x) is new to LON-CAPA. \\
\hline
random(l,u,d) & \&random(\$l,\$u,\$d) & In CAPA, all the 3 arguments must be of the same type. However, now you can mix the type \\
\hline
choose(i,...) & \&choose(\$i,...) & \\
\hline
/MAP(seed;w,x,y,z;a,b,c,d) & \parbox{6.49cm}{
Option 1 - \&map(\$seed,[$\backslash$\$w,$\backslash$\$x,$\backslash$\$y,$\backslash$\$z],[\$a,\$b,\$c,\$d]) or !!
Option 2 - \&map(\$seed,$\backslash$@mappedArray,[\$a,\$b,\$c,\$d]) !!
Option 3 - @mappedArray = \&map(\$seed,[\$a,\$b,\$c,\$d]) !!
Option 4 - (\$w,\$x,\$y,\$z) = \&map(\$seed,$\backslash$@a) !!
where \$a='A'!!
\$b='B'!!
\$c='B'!!
\$d='B'!!
\$w, \$x, \$y, and \$z are variables } & In CAPA, the arguments are divided into three groups separated by a semicolon ;. In LON-CAPA, the separation is done by using [] brackets or using an array @a. Note the backslash ($\backslash$) before the arguments in the second and third groups. \\
\hline
rmap(seed;a,b,c,d;w,x,y,z) & \parbox{6.49cm}{Option 1 - \&rmap(\$seed,[$\backslash$\$w,$\backslash$\$x,$\backslash$\$y,$\backslash$\$z],[\$a,\$b,\$c,\$d]) or !!
Option 2 - \&rmap(\$seed,$\backslash$@rmappedArray,[\$a,\$b,\$c,\$d]) !!
Option 3 - @rmapped\_array = \&rmap(\$seed,[\$a,\$b,\$c,\$d]) !!
Option 4 - (\$w,\$x,\$y,\$z) = \&rmap(\$seed,$\backslash$@a) !!
where \$a='A'!!
\$b='B'!!
\$c='B'!!
\$d='B'!!
\$w, \$x, \$y, and \$z are variables } & In CAPA, the arguments are divided into three groups separated by a semicolon ;. In LON-CAPA, the separation is done by using [] brackets (with create an unamed vector reference) or using an array @a. Note the backslash ($\backslash$) before the arguments in the second and third groups (Which cause Perl to send to variable locations rather than the variable values, similar to a C pointer). \\
\hline
NOT IMPLEMENTED IN CAPA & \$a=\&xmlparse(\$string) & New to LON-CAPA \\
\hline
tex(a,b), tex(``a'',''b'') & \&tex(\$a,\$b), \&tex(``a'',''b'') & \\
\hline
var\_in\_tex(a) & \&var\_in\_tex(\$a) & \\
\hline
to\_string(x), to\_string(x,y) & \&to\_string(\$x), \&to\_string(\$x,\$y) & \\
\hline
capa\_id(), class(), section(), set(), problem() & \&class(), \§ion() & capa\_id(), set() and problem() are no longer used. Currently, they return a null value. \\
\hline
name(), student\_number() & \&name(), \&student\_number() & \\
\hline
open\_date(), due\_date(), answer\_date() & \&open\_date(), \&due\_date(), \&answer\_date() & Output format for time is changed slightly. If pass noon, it displays ..pm else it displays ..am. So 23:59 is displayed as 11:59 pm. \\
\hline
get\_seed(), set\_seed() & Not implemented & \\
\hline
sub\_string(a,b,c) & \&sub\_string(\$a,\$b,\$c)
perl substr function. However, note the differences & Perl intrinsic function, substr(string,b,c) starts counting from 0 (as opposed to 1). In the example to the left, substr(\$a,4,4) returns ``ome ``. \\
\hline
array[xx] & @arrayname
Array is intrinsic in perl. To access a specific element use \$arrayname[\$n] where \$n is the \$n+1 element since the array count starts from 0 & In LON-CAPA, an array is defined by @arrayname. It is not necessary to specify the dimension of the array. \\
\hline
array\_moments(B,A) & @B=\&array\_moments(@A) & In CAPA, the moments are passed as an array in the first argument whereas in LON-CAPA, the array containing the moments are set equal to the function. \\
\hline
array\_max(Name), array\_min(Name) & \&min(@Name), \&max(@Name) & Combined with the min and max functions defined earlier. \\
\hline
init\_array(Name) & undef @name & Use perl intrinsic undef function. \\
\hline
random\_normal (return\_array,item\_cnt,seed,av,std\_dev) & @return\_array=\&random\_normal (\$item\_cnt,\$seed,\$av,\$std\_dev) & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
random\_beta (return\_array,item\_cnt,seed,aa,bb) & @return\_array=\&random\_beta (\$item\_cnt,\$seed,\$aa,\$bb)
NOTE: Both \$aa and \$bb MUST be greater than 1.0E-37. & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
random\_gamma (return\_array,item\_cnt,seed,a,r) & @return\_array=\&random\_gamma (\$item\_cnt,\$seed,\$a,\$r)
NOTE: Both \$a and \$r MUST be positive. & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
random\_exponential (return\_array,item\_cnt,seed,av) & @return\_array=\&random\_exponential (\$item\_cnt,\$seed,\$av)
NOTE: \$av MUST be non-negative. & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
random\_poisson (return\_array,item\_cnt,seed,mu) & @return\_array=\&random\_poisson (\$item\_cnt,\$seed,\$mu)
NOTE: \$mu MUST be non-negative. & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
random\_chi (return\_array,item\_cnt,seed,df) & @return\_array=\&random\_chi (\$item\_cnt,\$seed,\$df)
NOTE: \$df MUST be positive. & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
random\_noncentral\_chi (return\_array,item\_cnt,seed,df,nonc) & @return\_array=\&random\_noncentral\_chi (\$item\_cnt,\$seed,\$df,\$nonc)
NOTE: \$df MUST be at least 1 and \$nonc MUST be non-negative. & In CAPA the results are passed as the first argument whereas in LON-CAPA the results are set equal to the function. \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_f (\$item\_cnt,\$seed,\$dfn,\$dfd)
NOTE: Both \$dfn and \$dfd MUST be positive. & New to LON-CAPA \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_noncentral\_f (\$item\_cnt,\$seed,\$dfn,\$dfd,\$nonc)
NOTE: \$dfn must be at least 1, \$dfd MUST be positive, and \$nonc must be non-negative. & New to LON-CAPA \\
\hline
NOT DOCUMENTED IN CAPA & @return\_array=\&random\_multivariate\_normal (\$item\_cnt,\$seed,$\backslash$@mean,$\backslash$@covar)
NOTE: @mean should be of length p array of real numbers. @covar should be a length p array of references to length p arrays of real numbers (i.e. a p by p matrix. & Note the backslash before the @mean and @covar arrays. \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_multinomial (\$item\_cnt,\$seed,@p)
NOTE: \$item\_cnt is rounded with int() and the result must be non-negative. The number of elements in @p must be at least 2. & New to LON-CAPA \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_permutation (\$seed,@array) & New to LON-CAPA \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_uniform (\$item\_cnt,\$seed,\$low,\$high)
NOTE: \$low must be less than or equal to \$high. & New to LON-CAPA \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_uniform\_integer (\$item\_cnt,\$seed,\$low,\$high)
NOTE: \$low and \$high are both passed through int(). \$low must be less than or equal to \$high. & New to LON-CAPA \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_binomial (\$item\_cnt,\$seed,\$nt,\$p)
NOTE: \$nt is rounded using int() and the result must be non-negative. \$p must be between 0 and 1 inclusive. & New to LON-CAPA \\
\hline
NOT IMPLEMENTED IN CAPA & @return\_array=\&random\_negative\_binomial (\$item\_cnt,\$seed,\$ne,\$p)
NOTE: \$ne is rounded using int() and the result must be positive. \$p must be between 0 and 1 exclusive. & New to LON-CAPA \\
\hline
\end{longtable}
Index: loncom/html/adm/help/tex/Problem_LON-CAPA_Functions.tex
+++ loncom/html/adm/help/tex/Problem_LON-CAPA_Functions.tex
\label{Problem_LON-CAPA_Functions}
\begin{longtable}{|p{8.5cm}|p{8.5cm}|}
\hline
\textbf{LON-CAPA Function }
&\textbf{Description }
\endhead
\hline
\&sin(\$x), \&cos(\$x), \&tan(\$x) & Trigonometric functions where x is in radians. \$x can be a pure number, i.e., you can call \&sin(3.1415) \\
\hline
\&asin(\$x), \&acos(\$x), \&atan(\$x), \&atan2(\$y,\$x) & Inverse trigonometric functions. Return value is in radians. For asin and acos the value of x must be between -1 and 1. The atan2 returns a value between -pi and pi the sign of which is determined by y. \$x and \$y can be pure numbers \\
\hline
\&log(\$x), \&log10(\$x) & Natural and base-10 logarithm. \$x can be a pure number \\
\hline
\&exp(\$x), \&pow(\$x,\$y), \&sqrt(\$x) & Exponential, power and square root, i.e.,ex, xy and /x. \$x and \$y can be pure numbers \\
\hline
\&abs(\$x), \&sgn(\$x) & Abs takes the absolute value of x while sgn(x) returns 1, 0 or -1 depending on the value of x. For x$>$0, sgn(x) = 1, for x=0, sgn(x) = 0 and for x$<$0, sgn(x) = -1. \$x can be a pure number \\
\hline
\&erf(\$x), \&erfc(\$x) & Error function. erf = 2/sqrt(pi) integral (0,x) et-sq and \emph{ erfx(x)}
= 1.0 - \emph{erf(x)}
. \$x can be a pure number \\
\hline
\&ceil(\$x), \&floor(\$x) & Ceil function returns an integer rounded up whereas floor function returns and integer rounded down. If x is an integer than it returns the value of the integer. \$x can be a pure number \\
\hline
\&min(...), \&max(...) & Returns the minimum/ maximum value of a list of arguments if the arguments are numbers. If the arguments are strings then it returns a string sorted according to the ASCII codes \\
\hline
\&factorial(\$n) & Argument (n) must be an integer else it will round down. The largest value for n is 170. \$n can be a pure number \\
\hline
\$N\%\$M & N and M are integers and returns the remainder (in integer) of N/M. \$N and \$M can be pure numbers \\
\hline
\&sinh(\$x), \&cosh(\$x), \&tanh(\$x) & Hyperbolic functions. \$x can be a pure number \\
\hline
\&asinh(\$x), \&acosh(\$x), \&atanh(\$x) & Inverse hyperbolic functions. \$x can be a pure number \\
\hline
\&format(\$x,''nn'') & Display or format \$x as nn where nn is nF or nE and n is an integer. Also supports the first character being a \$, it thjen will format the result with a call to \&dollarformat() described below. \\
\hline
\&prettyprint(\$x,''nn'') & Display or format \$x as nn where nn is nF or nE and n is an integer. Also supports the first character being a \$, it then will format the result with a a call to \&dollarformat() described below. In E mode it will attempt to generate a pretty x10\^{}3 rather than a E3 following the number \\
\hline
\&dollarformat(\$x & Reformats \$x to have a \$ (or $\backslash$\$ if in tex mode) and to have , grouping thousands. \\
\hline
\&roundto(\$x,\$n) & Rounds a real number to n decimal points. \$x and \$n can be pure numbers \\
\hline
\&web(``a'',''b'',''c'') or \&web(\$a,\$b,\$c) & Returns either a, b or c depending on the output medium. a is for plain ASCII, b for tex output and c for html output \\
\hline
\&html(``a'') or \&html(\$a) & Output only if the output mode chosen is in html format \\
\hline
\&j0(\$x), \&j1(\$x), \&jn(\$m,\$x), \&jv(\$y,\$x) & Bessel functions of the first kind with orders 0, 1 and m respectively. For jn(m,x), m must be an integer whereas for jv(y,x), y is real. \$x can be a pure number. \$m must be an integer and can be a pure integer number. \$y can be a pure real number \\
\hline
\&y0(\$x), \&y1(\$x), \&yn(\$m,\$x), \&yv(\$y,\$x) & Bessel functions of the second kind with orders 0, 1 and m respectively. For yn(m,x), m must be an integer whereas for yv(y,x), y is real. \$x can be a pure number. \$m must be an integer and can be a pure integer number. \$y can be a pure real number \\
\hline
\&random(\$l,\$u,\$d) & Returns a uniformly distributed random number between the lower bound, l and upper bound, u in steps of d. \$l, \$u and \$d can be pure numbers \\
\hline
\&choose(\$i,...) & Choose the ith item from the argument list. i must be an integer greater than 0 and the value of i should not exceed the number of items. \$i can be a pure integer \\
\hline
\parbox{6.49cm}{
Option 1 - \&map(\$seed,[$\backslash$\$w,$\backslash$\$x,$\backslash$\$y,$\backslash$\$z],[\$a,\$b,\$c,\$d]) or !!
Option 2 - \&map(\$seed,$\backslash$@mappedArray,[\$a,\$b,\$c,\$d]) !!
Option 3 - @mappedArray = \&map(\$seed,[\$a,\$b,\$c,\$d]) !!
Option 4 - (\$w,\$x,\$y,\$z) = \&map(\$seed,$\backslash$@a) !!
where \$a='A'!!
\$b='B'!!
\$c='B'!!
\$d='B'!!
\$w, \$x, \$y, and \$z are variables } & Assigns to the variables \$w, \$x, \$y and \$z the values of the \$a, \$b, \$c and \$c (A, B, C and D). The precise value for \$w .. depends on the seed. (Option 1 of calling map). In option 2, the values of \$a, \$b .. are mapped into the array, @mappedArray. The two options illustrate the different grouping. Options 3 and 4 give a consistent way (with other functions) of mapping the items. For each option, the group can be passed as an array, for example, [\$a,\$b,\$c,\$d] =$>$ $\backslash$@a. \\
\hline
\parbox{6.49cm}{Option 1 - \&rmap(\$seed,[$\backslash$\$w,$\backslash$\$x,$\backslash$\$y,$\backslash$\$z],[\$a,\$b,\$c,\$d]) or !!
Option 2 - \&rmap(\$seed,$\backslash$@rmappedArray,[\$a,\$b,\$c,\$d]) !!
Option 3 - @rmapped\_array = \&rmap(\$seed,[\$a,\$b,\$c,\$d]) !!
Option 4 - (\$w,\$x,\$y,\$z) = \&rmap(\$seed,$\backslash$@a) !!
where \$a='A'!!
\$b='B'!!
\$c='B'!!
\$d='B'!!
\$w, \$x, \$y, and \$z are variables } & The rmap functions does the reverse action of map if the same seed is used in calling map and rmap. \\
\hline
\$a=\&xmlparse(\$string) & Runs the internal parser over the argument parsing for display. \textbf{Warning}
This will result in different strings in different targets. Don't use the results of this function as an answer. \\
\hline
\&tex(\$a,\$b), \&tex(``a'',''b'') & Returns a if the output mode is in tex otherwise returns b \\
\hline
\&var\_in\_tex(\$a) & Equivalent to tex(``a'',''``) \\
\hline
\&to\_string(\$x), \&to\_string(\$x,\$y) & If x is an integer, returns a string. If x is real than the output is a string with format given by y. For example, if x = 12.3456, \&to\_string(x,''.3F'') = 12.345 and \&to\_string(x,''.3E'') = 1.234E+01. \\
\hline
\&class(), \§ion() & Returns null string, class descriptive name, section number, set number and null string. \\
\hline
\&name(), \&student\_number() & Return the full name in the following format: lastname, firstname initial. Student\_number returns the student 9-alphanumeric string. If undefined, the functions return null. \\
\hline
\&open\_date(), \&due\_date(), \&answer\_date() & Problem open date, due date and answer date. The time is also included in 24-hr format. \\
\hline
Not implemented & Get and set the random seed. \\
\hline
\&sub\_string(\$a,\$b,\$c)
perl substr function. However, note the differences & Retrieve a portion of string a starting from b and length c. For example, \$a = ``Welcome to LON-CAPA''; \$result=\&sub\_string(\$a,4,4); then \$result is ``come'' \\
\hline
@arrayname
Array is intrinsic in perl. To access a specific element use \$arrayname[\$n] where \$n is the \$n+1 element since the array count starts from 0 & ``xx'' can be a variable or a calculation. \\
\hline
@B=\&array\_moments(@A) & Evaluates the moments of an array A and place the result in array B[i] where i = 0 to 4. The contents of B are as follows: B[0] = number of elements, B[1] = mean, B[2] = variance, B[3] = skewness and B[4] = kurtosis. \\
\hline
\&min(@Name), \&max(@Name) & In LON-CAPA to find the maximum value of an array, use \&max(@arrayname) and to find the minimum value of an array, use \&min(@arrayname) \\
\hline
undef @name & To destroy the contents of an array, use \\
\hline
@return\_array=\&random\_normal (\$item\_cnt,\$seed,\$av,\$std\_dev) & Generate \$item\_cnt deviates of normal distribution of average \$av and standard deviation \$std\_dev. The distribution is generated from seed \$seed \\
\hline
@return\_array=\&random\_beta (\$item\_cnt,\$seed,\$aa,\$bb)
NOTE: Both \$aa and \$bb MUST be greater than 1.0E-37. & Generate \$item\_cnt deviates of beta distribution. The density of beta is: X\^{}(\$aa-1) *(1-X)\^{}(\$bb-1) /B(\$aa,\$bb) for 0$<$X$<$1. \\
\hline
@return\_array=\&random\_gamma (\$item\_cnt,\$seed,\$a,\$r)
NOTE: Both \$a and \$r MUST be positive. & Generate \$item\_cnt deviates of gamma distribution. The density of gamma is: (\$a**\$r)/gamma(\$r) * X**(\$r-1) * exp(-\$a*X). \\
\hline
@return\_array=\&random\_exponential (\$item\_cnt,\$seed,\$av)
NOTE: \$av MUST be non-negative. & Generate \$item\_cnt deviates of exponential distribution. \\
\hline
@return\_array=\&random\_poisson (\$item\_cnt,\$seed,\$mu)
NOTE: \$mu MUST be non-negative. & Generate \$item\_cnt deviates of poisson distribution. \\
\hline
@return\_array=\&random\_chi (\$item\_cnt,\$seed,\$df)
NOTE: \$df MUST be positive. & Generate \$item\_cnt deviates of chi\_square distribution with \$df degrees of freedom. \\
\hline
@return\_array=\&random\_noncentral\_chi (\$item\_cnt,\$seed,\$df,\$nonc)
NOTE: \$df MUST be at least 1 and \$nonc MUST be non-negative. & Generate \$item\_cnt deviates of noncentral\_chi\_square distribution with \$df degrees of freedom and noncentrality parameter \$nonc. \\
\hline
@return\_array=\&random\_f (\$item\_cnt,\$seed,\$dfn,\$dfd)
NOTE: Both \$dfn and \$dfd MUST be positive. & Generate \$item\_cnt deviates of F (variance ratio) distribution with degrees of freedom \$dfn (numerator) and \$dfd (denominator). \\
\hline
@return\_array=\&random\_noncentral\_f (\$item\_cnt,\$seed,\$dfn,\$dfd,\$nonc)
NOTE: \$dfn must be at least 1, \$dfd MUST be positive, and \$nonc must be non-negative. & Generate \$item\_cnt deviates of noncentral F (variance ratio) distribution with degrees of freedom \$dfn (numerator) and \$dfd (denominator). \$nonc is the noncentrality parameter. \\
\hline
@return\_array=\&random\_multivariate\_normal (\$item\_cnt,\$seed,$\backslash$@mean,$\backslash$@covar)
NOTE: @mean should be of length p array of real numbers. @covar should be a length p array of references to length p arrays of real numbers (i.e. a p by p matrix. & Generate \$item\_cnt deviates of multivariate\_normal distribution with mean vector @mean and variance-covariance matrix. \\
\hline
@return\_array=\&random\_multinomial (\$item\_cnt,\$seed,@p)
NOTE: \$item\_cnt is rounded with int() and the result must be non-negative. The number of elements in @p must be at least 2. & Returns single observation from multinomial distribution with \$item\_cnt events classified into as many categories as the length of @p. The probability of an event being classified into category i is given by ith element of @p. The observation is an array with length equal to @p, so when called in a scalar context it returns the length of @p. The sum of the elements of the obervation is equal to \$item\_cnt. \\
\hline
@return\_array=\&random\_permutation (\$seed,@array) & Returns @array randomly permuted. \\
\hline
@return\_array=\&random\_uniform (\$item\_cnt,\$seed,\$low,\$high)
NOTE: \$low must be less than or equal to \$high. & Generate \$item\_cnt deviates from a uniform distribution. \\
\hline
@return\_array=\&random\_uniform\_integer (\$item\_cnt,\$seed,\$low,\$high)
NOTE: \$low and \$high are both passed through int(). \$low must be less than or equal to \$high. & Generate \$item\_cnt deviates from a uniform distribution in integers. \\
\hline
@return\_array=\&random\_binomial (\$item\_cnt,\$seed,\$nt,\$p)
NOTE: \$nt is rounded using int() and the result must be non-negative. \$p must be between 0 and 1 inclusive. & Generate \$item\_cnt deviates from the binomial distribution with \$nt trials and the probabilty of an event in each trial is \$p. \\
\hline
@return\_array=\&random\_negative\_binomial (\$item\_cnt,\$seed,\$ne,\$p)
NOTE: \$ne is rounded using int() and the result must be positive. \$p must be between 0 and 1 exclusive. & Generate an array of \$item\_cnt outcomes generated from negative binomial distribution with \$ne events and the probabilty of an event in each trial is \$p. \\
\hline
\end{longtable}
--bowersj21055258664--