Averages/Simple moving average
You are encouraged to solve this task according to the task description, using any language you may know.
Computing the simple moving average of a series of numbers.
Create a stateful function/class/instance that takes a number as argument and returns a simple moving average of its arguments so far.
See also: Standard Deviation
AutoHotkey
ahk forum: discussion For Integers: <lang AutoHotkey>MsgBox % MovingAverage(5,3) ; 5, averaging length <- 3 MsgBox % MovingAverage(1) ; 3 MsgBox % MovingAverage(-3) ; 1 MsgBox % MovingAverage(8) ; 2 MsgBox % MovingAverage(7) ; 4
MovingAverage(x,len="") { ; for integers (faster)
Static Static sum:=0, n:=0, m:=10 ; default averaging length = 10 If (len>"") ; non-blank 2nd parameter: set length, reset sum := n := i := 0, m := len If (n < m) ; until the buffer is not full sum += x, n++ ; keep summing data Else ; when buffer is full sum += x-v%i% ; add new, subtract oldest v%i% := x, i := mod(i+1,m) ; remember last m inputs, cycle insertion point Return sum/n
}</lang> For floating point numbers: <lang AutoHotkey> MovingAverage(x,len="") { ; for floating point numbers
Static Static n:=0, m:=10 ; default averaging length = 10 If (len>"") ; non-blank 2nd parameter: set length, reset n := i := 0, m := len n += n < m, sum := 0 v%i% := x, i := mod(i+1,m) ; remember last m inputs, cycle insertion point Loop %n% ; recompute sum to avoid error accumulation j := A_Index-1, sum += v%j% Return sum/n
}</lang>
C
<lang c>#include <stdio.h>
- include <stdlib.h>
- include <stdarg.h>
typedef struct sma_obj {
double sma; double sum; int period; double *values; int lv;
} sma_obj_t;
typedef union sma_result {
sma_obj_t *handle; double sma; double *values;
} sma_result_t;
enum Action { SMA_NEW, SMA_FREE, SMA_VALUES, SMA_ADD, SMA_MEAN }; sma_result_t sma(enum Action action, ...) {
va_list vl; sma_result_t r; sma_obj_t *o; double v;
va_start(vl, action); switch(action) { case SMA_NEW: // args: int period r.handle = malloc(sizeof(sma_obj_t)); r.handle->sma = 0.0; r.handle->period = va_arg(vl, int); r.handle->values = malloc(r.handle->period * sizeof(double)); r.handle->lv = 0; r.handle->sum = 0.0; break; case SMA_FREE: // args: sma_obj_t *handle r.handle = va_arg(vl, sma_obj_t *); free(r.handle->values); free(r.handle); r.handle = NULL; break; case SMA_VALUES: // args: sma_obj_t *handle o = va_arg(vl, sma_obj_t *); r.values = o->values; break; case SMA_MEAN: // args: sma_obj_t *handle o = va_arg(vl, sma_obj_t *); r.sma = o->sma; break; case SMA_ADD: // args: sma_obj_t *handle, double value o = va_arg(vl, sma_obj_t *); v = va_arg(vl, double); if ( o->lv < o->period ) { o->values[o->lv++] = v; o->sum += v; o->sma = o->sum / o->lv; } else { o->sum -= o->values[ o->lv % o->period]; o->sum += v; o->sma = o->sum / o->period; o->values[ o->lv % o->period ] = v; o->lv++; } r.sma = o->sma; break; } va_end(vl); return r;
}</lang>
<lang c>double v[] = { 1, 2, 3, 4, 5, 5, 4, 3, 2, 1 };
int main() {
int i;
sma_obj_t *h3 = sma(SMA_NEW, 3).handle; sma_obj_t *h5 = sma(SMA_NEW, 5).handle;
for(i=0; i < sizeof(v)/sizeof(double) ; i++) { printf("next number %lf, SMA_3 = %lf, SMA_5 = %lf\n",
v[i], sma(SMA_ADD, h3, v[i]).sma, sma(SMA_ADD, h5, v[i]).sma);
}
sma(SMA_FREE, h3); sma(SMA_FREE, h5); return 0;
}</lang>
Common Lisp
This implementation uses a circular list to store the numbers within the window; at the beginning of each iteration pointer refers to the list cell which holds the value just moving out of the window and to be replaced with the just-added value.
<lang lisp>(defun simple-moving-average (period &aux
(sum 0) (count 0) (values (make-list period)) (pointer values)) (setf (rest (last values)) values) ; construct circularity (lambda (n) (when (first pointer) (decf sum (first pointer))) ; subtract old value (incf sum n) ; add new value (incf count) (setf (first pointer) n) (setf pointer (rest pointer)) ; advance pointer (/ sum (min count period))))</lang>
E
This implementation produces two (function) objects sharing state. It is idiomatic in E to separate input from output (read from write) rather than combining them into one object.
The structure is the same as the implementation of Standard Deviation#E.
<lang e>pragma.enable("accumulator") def makeMovingAverage(period) {
def values := ([null] * period).diverge() var index := 0 var count := 0 def insert(v) { values[index] := v index := (index + 1) %% period count += 1 } /** Returns the simple moving average of the inputs so far, or null if there have been no inputs. */ def average() { if (count > 0) { return accum 0 for x :notNull in values { _ + x } / count.min(period) } } return [insert, average]
}</lang>
<lang e>? for period in [3, 5] { > def [insert, average] := makeMovingAverage(period) > println(`Period $period:`) > for value in [1,2,3,4,5,5,4,3,2,1] { > insert(value) > println(value, "\t", average()) > } > println() > } 0.0 1.0 0.9428090415820626 0.8660254037844386 0.9797958971132716 1.0 1.3997084244475297 2.0</lang>
Forth
<lang forth>
- f+! ( f addr -- ) dup f@ f+ f! ;
- ,f0s ( n -- ) falign 0 do 0e f, loop ;
- period @ ;
- used cell+ ;
- head 2 cells + ;
- sum 3 cells + faligned ;
- ring ( addr -- faddr )
dup sum float+ swap head @ floats + ;
- update ( fvalue addr -- addr )
dup ring f@ fnegate dup sum f+! fdup dup ring f! dup sum f+! dup head @ 1+ over period mod over head ! ;
- moving-average
create ( period -- ) dup , 0 , 0 , 1+ ,f0s does> ( fvalue -- avg ) update dup used @ over period < if 1 over used +! then dup sum f@ used @ 0 d>f f/ ;
3 moving-average sma 1e sma f. \ 1. 2e sma f. \ 1.5 3e sma f. \ 2. 4e sma f. \ 3. </lang>
Fortran
<lang fortran>program Movavg
implicit none
integer :: i
write (*, "(a)") "SIMPLE MOVING AVERAGE: PERIOD = 3"
do i = 1, 5 write (*, "(a, i2, a, f8.6)") "Next number:", i, " sma = ", sma(real(i)) end do do i = 5, 1, -1 write (*, "(a, i2, a, f8.6)") "Next number:", i, " sma = ", sma(real(i)) end do
contains
function sma(n)
real :: sma real, intent(in) :: n real, save :: a(3) = 0 integer, save :: count = 0
if (count < 3) then count = count + 1 a(count) = n else a = eoshift(a, 1, n) end if
sma = sum(a(1:count)) / real(count)
end function
end program Movavg </lang>
J
Note: J is block-oriented, not stream oriented. That is, J expresses algorithms with the semantics that all the data is available at once (rather than maintaining state and waiting for the next item).
In that context, moving average is expressed very concisely in J as (+/%#)\.
In the context of the task, we need to produce a stateful function to consume streams. Since J does not have native lexical closure, we need to implement it. Thus the streaming solution is more complex:
lex =: 1 :'(a[n__a=.m#_.[a=.18!:3$~0)&(4 :''(+/%#)(#~1-128!:5)n__x=.1|.!.y n__x'')'
an example:
sma =: 5 lex sma&> 1 2 3 4 5 5 4 3 2 1 1 1.5 2 2.5 3 3.8 4.2 4.2 3.8 3
Here, the &> is analogous to the "for each" of other languages.
Java
<lang java5>import java.util.LinkedList; public class MovingAverage {
LinkedList<Double> window; private int size; public MovingAverage(int size) { window = new LinkedList<Double>(); this.size = size; } public static void main(String[] args) { double[] testData = {1,2,3,4,5,5,4,3,2,1}; int[] windowSizes = {3,5}; for(int windSize : windowSizes){ MovingAverage ma = new MovingAverage(windSize); for (double x : testData) { ma.newNum(x); System.out.println(ma.getAvg()); } System.out.println(); } }
public void newNum(double num) { window.add(num); if (window.size() > size) { window.removeFirst(); } }
public double getAvg() { if (window.isEmpty()) return 0; double ret = 0; double sum = 0; for (double num : window) { sum += num; } return sum / Math.min(window.size(), size); }
}</lang> Output:
1.0 1.5 2.0 3.0 4.0 4.666666666666667 4.666666666666667 4.0 3.0 2.0 1.0 1.5 2.0 2.5 3.0 3.8 4.2 4.2 3.8 3.0
Perl
<lang perl>sub sma ($)
{my ($period, $sum, @a) = shift, 0; return sub {unshift @a, shift; $sum += $a[0]; @a > $period and $sum -= pop @a; return $sum / @a;}}</lang>
Python
<lang python>def simplemovingaverage(period):
assert period == int(period) and period > 0, "Period must be an integer >0" summ = n = 0.0 values = [0.0] * period # old value queue
def sma(x): nonlocal summ, n, values n += 1 values.insert(0, x) summ += x - values.pop() n = n if n <= period else period return summ / n
return sma
if __name__ == '__main__':
for period in [3, 5]: print ("\nSIMPLE MOVING AVERAGE: PERIOD =", period) sma = simplemovingaverage(period) for i in range(1,6): print (" Next number = %-2g, SMA = %g " % (i, sma(i))) for i in range(5, 0, -1): print (" Next number = %-2g, SMA = %g " % (i, sma(i)))</lang>
Sample output
SIMPLE MOVING AVERAGE: PERIOD = 3 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 3 Next number = 5 , SMA = 4 Next number = 5 , SMA = 4.66667 Next number = 4 , SMA = 4.66667 Next number = 3 , SMA = 4 Next number = 2 , SMA = 3 Next number = 1 , SMA = 2 SIMPLE MOVING AVERAGE: PERIOD = 5 Next number = 1 , SMA = 1 Next number = 2 , SMA = 1.5 Next number = 3 , SMA = 2 Next number = 4 , SMA = 2.5 Next number = 5 , SMA = 3 Next number = 5 , SMA = 3.8 Next number = 4 , SMA = 4.2 Next number = 3 , SMA = 4.2 Next number = 2 , SMA = 3.8 Next number = 1 , SMA = 3
R
This is easiest done with two functions: one to handle the state (i.e. the numbers already entered), and one to calculate the average. <lang R>
- concat concatenates the new values to the existing vector of values, then discards any values that are too old.
lastvalues <- local( {
values <- c(); function(x, len) { values <<- c(values, x); lenv <- length(values); if(lenv > len) values <<- values[(len-lenv):-1] values }
})
- moving.average accepts a numeric scalars input (and optionally a length, i.e. the number of values to retain) and calculates the stateful moving average.
moving.average <- function(latestvalue, len=3) {
#Check that all inputs are numeric scalars is.numeric.scalar <- function(x) is.numeric(x) && length(x)==1L if(!is.numeric.scalar(latestvalue) || !is.numeric.scalar(len)) { stop("all arguments must be numeric scalars") } #Calculate mean of variables so far mean(lastvalues(latestvalue, len))
} moving.average(5) # 5 moving.average(1) # 3 moving.average(-3) # 1 moving.average(8) # 2 moving.average(7) # 4 </lang>
Ruby
A closure: <lang ruby>def simple_moving_average(size)
nums = [] sum = 0.0 lambda do |hello| nums << hello goodbye = nums.length > size ? nums.shift : 0 sum += hello - goodbye sum / nums.length end
end
ma3 = simple_moving_average(3) ma5 = simple_moving_average(5)
(1.upto(5).to_a + 5.downto(1).to_a).each do |num|
printf "Next number = %d, SMA_3 = %.3f, SMA_5 = %.1f\n", num, ma3.call(num), ma5.call(num)
end</lang>
A class <lang ruby>class MovingAverager
def initialize(size) @size = size @nums = [] @sum = 0.0 end def <<(hello) @nums << hello goodbye = @nums.length > @size ? @nums.shift : 0 @sum += hello - goodbye self end def average @sum / @nums.length end alias to_f average def to_s average.to_s end
end
ma3 = MovingAverager.new(3) ma5 = MovingAverager.new(5)
(1.upto(5).to_a + 5.downto(1).to_a).each do |num|
printf "Next number = %d, SMA_3 = %.3f, SMA_5 = %.1f\n", num, ma3 << num, ma5 <<num
end</lang>
Smalltalk
<lang smalltalk>Object subclass: MovingAverage [
|valueCollection period collectedNumber sum| MovingAverage class >> newWithPeriod: thePeriod [
|r| r := super basicNew. ^ r initWithPeriod: thePeriod
] initWithPeriod: thePeriod [ valueCollection := OrderedCollection new: thePeriod.
period := thePeriod. collectedNumber := 0. sum := 0
] sma [ collectedNumber < period ifTrue: [ ^ sum / collectedNumber ] ifFalse: [ ^ sum / period ] ] add: value [ collectedNumber < period ifTrue: [
sum := sum + value. valueCollection add: value. collectedNumber := collectedNumber + 1. ] ifFalse: [ sum := sum - (valueCollection removeFirst). sum := sum + value. valueCollection add: value ]. ^ self sma
]
].</lang>
<lang smalltalk>|sma3 sma5|
sma3 := MovingAverage newWithPeriod: 3. sma5 := MovingAverage newWithPeriod: 5.
- ( 1 2 3 4 5 5 4 3 2 1 ) do: [ :v |
('Next number %1, SMA_3 = %2, SMA_5 = %3' % { v . (sma3 add: v) asFloat . (sma5 add: v) asFloat }) displayNl
]</lang>
Tcl
<lang tcl>oo::class create SimpleMovingAverage {
variable vals idx constructor Template:Period 3 { set idx end-[expr {$period-1}] set vals {} } method val x { set vals [lrange [list {*}$vals $x] $idx end] expr {[tcl::mathop::+ {*}$vals]/double([llength $vals])} }
}</lang> Demonstration: <lang tcl>SimpleMovingAverage create averager3 SimpleMovingAverage create averager5 5 foreach n {1 2 3 4 5 5 4 3 2 1} {
puts "Next number = $n, SMA_3 = [averager3 val $n], SMA_5 = [averager5 val $n]"
}</lang> Output:
Next number = 1, SMA_3 = 1.0, SMA_5 = 1.0 Next number = 2, SMA_3 = 1.5, SMA_5 = 1.5 Next number = 3, SMA_3 = 2.0, SMA_5 = 2.0 Next number = 4, SMA_3 = 3.0, SMA_5 = 2.5 Next number = 5, SMA_3 = 4.0, SMA_5 = 3.0 Next number = 5, SMA_3 = 4.666666666666667, SMA_5 = 3.8 Next number = 4, SMA_3 = 4.666666666666667, SMA_5 = 4.2 Next number = 3, SMA_3 = 4.0, SMA_5 = 4.2 Next number = 2, SMA_3 = 3.0, SMA_5 = 3.8 Next number = 1, SMA_3 = 2.0, SMA_5 = 3.0
TI-83 BASIC
Prompts for a source list A and the length K of the moving average. The 'L' in "LB" and "LB" is found in "List"/"OPS".
:Prompt LA,K : :For(I,1,dim(LA)) :0→S :For(J,I-K+1,I) :If J≥1 :S+LA(J)→S :End :S/K→LB(I) :End
TI-89 BASIC
movinavg(la,k) Func Local lb,s,i,j For i,1,dim(la) 0→s For j,i-k+1,i If j≥1 Then s+la[j]→s EndIf EndFor s/k→lb[i] EndFor Return lb EndFunc