NOTE: The following functions, or at least algorithms corresponding to this functionality, now exists in Tcllib's math library module.
The min and max calculations are taken from the Tcl core tcllib package [L1 ]. The rest of the functions have been submitted to Scriptics, and will, I hope, be added to the tcllib math module soon.
Until then, here they are! -PSE
DKF - A few optimisations, the key one of which is to take notice of the fact that:
__
\ _ 2
2 /_ ( x - x )
s = ---------------- (Definition of variance)
N N
__ __
\ 2 \
/_ x _ /_ x _ 2
= ------- - 2 x ----- + x (Binomial expansion)
N N
__
\ 2
/_ x _ 2
= ------- - x (Apply definition of mean)
N
________
/ 2
/ N s
s = / N
N-1 / --------- (We're using the other variance)
\/ N - 1I happened to know about this because I remembered that my calculator can work out means and std.dev.s of data on the fly, despite not having sufficient storage to keep all the intermediate results. Which meant that you had to be able to work this out in a single loop (corresponding to the data-entry stage on my calculator.) This technique has one minor disadvantage in that it is not as well-behaved when working with very large values. It's tricky to code round that while keeping the code well-behaved when dealing with very small values too...
Just fixed a typo, expt -> expr. -PSE
# math.tcl --
#
# Collection of math functions.
#
package provide math 1.0
namespace eval ::math {
}
# ::math::min --
#
# Return the minimum of two or more values
#
# Arguments:
# val first value
# args other values
#
# Results:
# min minimum value
proc ::math::min {val args} {
set min $val
foreach val $args {
if { $val < $min } {
set min $val
}
}
set min
}
# ::math::max --
#
# Return the maximum of two or more values
#
# Arguments:
# val first value
# args other values
#
# Results:
# max maximum value
proc ::math::max {val args} {
set max $val
foreach val $args {
if { $val > $max } {
set max $val
}
}
set max
}
# ::math::sum --
#
# Return the sum of one or more values
#
# Arguments:
# val first value
# args all other values
#
# Results:
# sum arithmetic sum of all values in args
proc ::math::sum {val args} {
set sum $val
foreach val $args {
set sum [ expr { $sum+$val } ]
}
set sum
}
# ::math::mean --
#
# Return the mean of two or more values
#
# Arguments:
# val first value
# args other values
#
# Results:
# mean arithmetic mean value
proc ::math::mean {val args} {
set sum $val
set N [ expr { [ llength $args ] + 1 } ]
foreach val $args {
set sum [ expr { $sum+$val } ]
}
set mean [ expr { $sum/$N } ]
set mean
}
## A helper function...
## Note that this is the heart of why the improved versions are better
## since it allows you to only make a single pass through the data.
proc ::math::SumSum2 {list} {
set sum 0.0
set sum2 0.0
foreach x $list {
set sum [expr {$sum + $x}]
set sum2 [expr {$sum2 + $x*$x}]
}
list $sum $sum2
}
# ::math::sigma --
#
# Return the standard deviation of three or more values
#
# Arguments:
# val1 first value
# val2 second value
# args other values
#
# Results:
# sigma population standard deviation value
proc ::math::sigma {val1 val2 args} {
# More efficient to build list before using concat...
set args [concat [list $val1 $val2] $args]
foreach {sum sum2} [SumSum2 $args] {}
set N [llength $args]
set mean [expr {$sum/$N}]
set sigma2 [expr {($sum2 - $mean*$mean*$N)/($N-1)}]
return [expr {sqrt($sigma2)}]
### Old Algorithm Follows
#set sum [ expr { $val1+$val2 } ]
#set N [ expr { [ llength $args ] + 2 } ]
#foreach val $args {
# set sum [ expr { $sum+$val } ]
#}
#set mean [ expr { $sum/$N } ]
#set sigma_sq 0
#foreach val [ concat $val1 $val2 $args ] {
# set sigma_sq [ expr { $sigma_sq+pow(($val-$mean),2) } ]
#}
#set sigma_sq [ expr { $sigma_sq/($N-1) } ]
#set sigma [ expr { sqrt($sigma_sq) } ]
#set sigma
}
# ::math::cov --
#
# Return the coefficient of variation of three or more values
#
# Arguments:
# val1 first value
# val2 second value
# args other values
#
# Results:
# cov coefficient of variation expressed as percent value
proc ::math::cov {val1 val2 args} {
set args [concat [list $val1 $val2] $args]
foreach {sum sum2} [SumSum2 $args] {}
set N [llength $args]
set mean [expr {$sum/$N}]
set sigma2 [expr {($sum2 - $N*$mean*$mean)/($N-1)}]
set sigma [expr {sqrt($sigma2)}]
return [expr {($sigma/$mean)*100}]
### Old Algorithm
#set sum [ expr { $val1+$val2 } ]
#set N [ expr { [ llength $args ] + 2 } ]
#foreach val $args {
# set sum [ expr { $sum+$val } ]
#}
#set mean [ expr { $sum/$N } ]
#set sigma_sq 0
#foreach val [ concat $val1 $val2 $args ] {
# set sigma_sq [ expr { $sigma_sq+pow(($val-$mean),2) } ]
#}
#set sigma_sq [ expr { $sigma_sq/($N-1) } ]
#set sigma [ expr { sqrt($sigma_sq) } ]
#set cov [ expr { ($sigma/$mean)*100 } ]
#set cov
}
proc ::math::stats {val1 val2 args} {
set args [concat [list $val1 $val2] $args]
foreach {sum sum2} [SumSum2 $args] {}
set N [llength $args]
set mean [expr {$sum/$N}]
set sigma2 [expr {($sum2 - $N*$mean*$mean)/($N-1)}]
set sigma [expr {sqrt($sigma2)}]
return [list $mean $sigma [expr {($sigma/$mean)*100}]]
### Old Algorithm
#set sum [ expr { $val1+$val2 } ]
#set N [ expr { [ llength $args ] + 2 } ]
#foreach val $args {
# set sum [ expr { $sum+$val } ]
#}
#set mean [ expr { $sum/$N } ]
#set sigma_sq 0
#foreach val [ concat $val1 $val2 $args ] {
# set sigma_sq [ expr { $sigma_sq+pow(($val-$mean),2) } ]
#}
#set sigma_sq [ expr { $sigma_sq/($N-1) } ]
#set sigma [ expr { sqrt($sigma_sq) } ]
#set cov [ expr { ($sigma/$mean)*100 } ]
#return [ list $mean $sigma $cov ]
}
# ::math::prod --
#
# Return the product of one or more values
#
# Arguments:
# val first value
# args other values
#
# Results:
# prod product of multiplying all values in the list
proc ::math::prod {val args} {
set prod $val
foreach val $args {
set prod [ expr { $prod*$val } ]
}
set prod
}RS: Let me take the last proc as an example why I still often prefer to "roll my own". math::prod expects its input as flat args, so if you have them in a list already, you'd have to
eval math::prod $myList
Also, reduction of a list can be so helpful - avoiding intermediate variable, reducing required runtime - that I'd always write a product like this:
expr [join $myList *]
Even as the expr argument cannot be braced, this is still faster and, if you can "read" such join reductions, clearer than the above - and no proc needed at all...
Larry Kurfis I found this useful; puts "mean of [join $dlist +] = [expr [expr [join $dlist +]]] / [llength $dlist]]]"
JPS: For all your statistical median needs:
proc median {l} {
if {[set len [llength $l]] % 2} then {
return [lindex [lsort -real $l] [expr {($len - 1) / 2}]]
} else {
return [expr {([lindex [set sl [lsort -real $l]] [expr {($len / 2) - 1}]] \
+ [lindex $sl [expr {$len / 2}]]) / 2.0}]
}
}AM I added this to the math::statistics module, with an extra provision for missing values (not unimportant for statistical software), like this:
# median
# Determine the median from a list of data
#
# Arguments:
# data (Unsorted) list of data
#
# Result:
# Median (either the middle value or the mean of two values in the
# middle)
#
# Note:
# Adapted from the Wiki page "Stats", code provided by JPS
#
proc ::math::statistics::median { data } {
set org_data $data
set data {}
foreach value $org_data {
if { $value != {} } {
lappend data $value
}
}
set len [llength $data]
set data [lsort -real $data]
if { $len % 2 } {
lindex $data [expr {($len-1)/2}]
} else {
expr {([lindex $data [expr ($len / 2) - 1]] \
+ [lindex $data [expr $len / 2]]) / 2.0}
}
}