A-M baby
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Ingy döt Net
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19005 changed files with 197040 additions and 7 deletions
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USING: math math.statistics ;
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: arithmetic-mean ( seq -- n )
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[ 0 ] [ mean ] if-empty ;
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( scratchpad ) { 2 3 5 } arithmetic-mean >float
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3.333333333333333
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class Main
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{
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static Float average (Float[] nums)
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{
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if (nums.size == 0) return 0.0f
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Float sum := 0f
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nums.each |num| { sum += num }
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return sum / nums.size.toFloat
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}
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public static Void main ()
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{
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[[,], [1f], [1f,2f,3f,4f]].each |Float[] i|
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{
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echo ("Average of $i is: " + average(i))
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}
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}
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}
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!vl0=?vl1=?vl&!
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v< +<>0n; >n;
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>l1)?^&,n;
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Mean := function(v)
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local n;
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n := Length(v);
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if n = 0 then
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return 0;
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else
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return Sum(v)/n;
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fi;
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end;
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Mean([3, 1, 4, 1, 5, 9]);
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# 23/6
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def avg = { list -> list == [] ? 0 : list.sum() / list.size() }
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println avg(0..9)
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println avg([2,2,2,4,2])
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println avg ([])
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REAL :: vec(100) ! no zero-length arrays in HicEst
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vec = $ - 1/2 ! 0.5 ... 99.5
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mean = SUM(vec) / LEN(vec) ! 50
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END
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x = [3,1,4,1,5,9]
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print,mean(x)
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print,moment(x)
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; ==>
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3.83333 8.96667 0.580037 -1.25081
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procedure main(args)
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every (s := 0) +:= !args
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write((real(s)/(0 ~= *args)) | 0)
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end
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mean=: +/ % #
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mean 3 1 4 1 5 9
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3.83333
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mean $0 NB. $0 is a zero-length vector
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0
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x=: 20 4 ?@$ 0 NB. a 20-by-4 table of random (0,1) numbers
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mean x
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0.58243 0.402948 0.477066 0.511155
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11
Task/Averages-Arithmetic-mean/J/averages-arithmetic-mean-3.j
Normal file
11
Task/Averages-Arithmetic-mean/J/averages-arithmetic-mean-3.j
Normal file
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mean1=: 3 : 0
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z=. 0
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for_i. i.#y do. z=. z+i{y end.
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z % #y
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)
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mean1 3 1 4 1 5 9
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3.83333
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mean1 $0
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0
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mean1 x
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0.58243 0.402948 0.477066 0.511155
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julia> mean([1,2,3])
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2.0
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julia> mean([])
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ERROR: mean of empty collection undefined: []
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mean: {(+/x)%#x}
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mean 1 2 3 5 7
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3.6
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mean@!0 / empty array
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0.0
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integer MAX_ELEMENTS = 10;
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integer MAX_VALUE = 100;
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default {
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state_entry() {
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list lst = [];
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integer x = 0;
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for(x=0 ; x<MAX_ELEMENTS ; x++) {
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lst += llFrand(MAX_VALUE);
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}
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llOwnerSay("lst=["+llList2CSV(lst)+"]");
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llOwnerSay("Geometric Mean: "+(string)llListStatistics(LIST_STAT_GEOMETRIC_MEAN, lst));
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llOwnerSay(" Max: "+(string)llListStatistics(LIST_STAT_MAX, lst));
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llOwnerSay(" Mean: "+(string)llListStatistics(LIST_STAT_MEAN, lst));
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llOwnerSay(" Median: "+(string)llListStatistics(LIST_STAT_MEDIAN, lst));
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llOwnerSay(" Min: "+(string)llListStatistics(LIST_STAT_MIN, lst));
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llOwnerSay(" Num Count: "+(string)llListStatistics(LIST_STAT_NUM_COUNT, lst));
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llOwnerSay(" Range: "+(string)llListStatistics(LIST_STAT_RANGE, lst));
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llOwnerSay(" Std Dev: "+(string)llListStatistics(LIST_STAT_STD_DEV, lst));
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llOwnerSay(" Sum: "+(string)llListStatistics(LIST_STAT_SUM, lst));
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llOwnerSay(" Sum Squares: "+(string)llListStatistics(LIST_STAT_SUM_SQUARES, lst));
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}
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}
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total=17
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dim nums(total)
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for i = 1 to total
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nums(i)=i-1
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next
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for j = 1 to total
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sum=sum+nums(j)
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next
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if total=0 then mean=0 else mean=sum/total
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print "Arithmetic mean: ";mean
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to average :l
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if empty? :l [output 0]
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output quotient apply "sum :l count :l
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end
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print average [1 2 3 4] ; 2.5
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avg(x)
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where
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sum = first(x) fby sum + next(x);
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n = 1 fby n + 1;
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avg = sum / n;
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end
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define(`extractdec', `ifelse(eval(`$1%100 < 10'),1,`0',`')eval($1%100)')dnl
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define(`fmean', `eval(`($2/$1)/100').extractdec(eval(`$2/$1'))')dnl
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define(`mean', `rmean(`$#', $@)')dnl
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define(`rmean', `ifelse(`$3', `', `fmean($1,$2)',dnl
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`rmean($1, eval($2+$3), shift(shift(shift($@))))')')dnl
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mean(0,100,200,300,400,500,600,700,800,900,1000)
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fn mean data =
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(
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total = 0
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for i in data do
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(
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total += i
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)
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if data.count == 0 then 0 else total as float/data.count
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)
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print (mean #(3, 1, 4, 1, 5, 9))
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MEAN(X)
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;X is assumed to be a list of numbers separated by "^"
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QUIT:'$DATA(X) "No data"
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QUIT:X="" "Empty Set"
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NEW S,I
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SET S=0,I=1
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FOR QUIT:I>$L(X,"^") SET S=S+$P(X,"^",I),I=I+1
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QUIT (S/$L(X,"^"))
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mean := proc( a :: indexable )
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local i;
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Normalizer( add( i, i in a ) / numelems( a ) )
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end proc:
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> mean( { 1/2, 2/3, 3/4, 4/5, 5/6 } ); # set
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71
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---
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100
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> mean( [ a, 2, c, 2.3, e ] ); # list
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0.8600000000 + a/5 + c/5 + e/5
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> mean( Array( [ 1, sin( s ), 3, exp( I*t ), 5 ] ) ); # array
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9/5 + 1/5 sin(s) + 1/5 exp(t I)
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> mean( [ sin(s)^2, cos(s)^2 ] );
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2 2
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1/2 sin(s) + 1/2 cos(s)
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> Normalizer := simplify: # use a stronger normalizer than the default
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> mean( [ sin(s)^2, cos(s)^2 ] );
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1/2
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> mean([]); # empty argument causes an exception to be raised.
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Error, (in mean) numeric exception: division by zero
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mean := () -> Normalizer( `+`( args ) / nargs ):
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> mean( 1, 2, 3, 4, 5 );
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3
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> mean( a + b, b + c, c + d, d + e, e + a );
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2 a 2 b 2 c 2 d 2 e
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--- + --- + --- + --- + ---
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5 5 5 5 5
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> mean(); # again, an exception is raised
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Error, (in mean) numeric exception: division by zero
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mean := ( s :: seq(algebraic) ) -> Normalizer( `+`( args ) / nargs ):
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Unprotect[Mean];
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Mean[{}] := 0
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Mean[{3,4,5}]
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Mean[{3.2,4.5,5.9}]
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Mean[{-4, 1.233}]
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Mean[{}]
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Mean[{1/2,1/3,1/4,1/5}]
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Mean[{a,c,Pi,-3,a}]
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4
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4.53333
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-1.3835
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0
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77/240
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1/5 (-3+2 a+c+Pi)
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/*Arithmetic Mean of a large number of Integers
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- or - solve a very large constraint matrix
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over 1 million rows and columns
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Nigel_Galloway
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March 18th., 2008.
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*/
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param e := 20;
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set Sample := {1..2**e-1};
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var Mean;
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var E{z in Sample};
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/* sum of variances is zero */
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zumVariance: sum{z in Sample} E[z] = 0;
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/* Mean + variance[n] = Sample[n] */
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variances{z in Sample}: Mean + E[z] = z;
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solve;
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printf "The arithmetic mean of the integers from 1 to %d is %f\n", 2**e-1, Mean;
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end;
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GLPSOL: GLPK LP/MIP Solver, v4.47
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Parameter(s) specified in the command line:
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--nopresol --math AM.mprog
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Reading model section from AM.mprog...
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24 lines were read
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Generating zumVariance...
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Generating variances...
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Model has been successfully generated
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Scaling...
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A: min|aij| = 1.000e+000 max|aij| = 1.000e+000 ratio = 1.000e+000
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Problem data seem to be well scaled
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Constructing initial basis...
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Size of triangular part = 1048575
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GLPK Simplex Optimizer, v4.47
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1048576 rows, 1048576 columns, 3145725 non-zeros
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0: obj = 0.000000000e+000 infeas = 5.498e+011 (1)
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* 1: obj = 0.000000000e+000 infeas = 0.000e+000 (0)
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OPTIMAL SOLUTION FOUND
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Time used: 2.0 secs
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Memory used: 1393.8 Mb (1461484590 bytes)
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The arithmetic mean of the integers from 1 to 1048575 is 524288.000000
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Model has been successfully processed
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mean([2, 7, 11, 17]);
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PROCEDURE Avg;
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VAR avg : REAL;
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BEGIN
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avg := sx / n;
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InOut.WriteString ("Average = ");
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InOut.WriteReal (avg, 8, 2);
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InOut.WriteLn
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END Avg;
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PROCEDURE Average (Data : ARRAY OF REAL; Samples : CARDINAL) : REAL;
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(* Calculate the average over 'Samples' values, stored in array 'Data'. *)
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VAR sum : REAL;
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n : CARDINAL;
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BEGIN
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sum := 0.0;
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FOR n := 0 TO Samples - 1 DO
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sum := sum + Data [n]
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END;
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RETURN sum / FLOAT(Samples)
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END Average;
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