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(PECL stats >= 1.0.0)
stats_stat_correlation — Not documented
$arr1
, array $arr2
)本函数还未编写文档,仅有参数列表。
arr1
arr2
[#1] non at dot com [2015-01-19 11:30:40]
undefined for me, thus I've implemented my own correlation which is much faster and simpler than the one provided above.
function Corr($x, $y){
$length= count($x);
$mean1=array_sum($x) / $length;
$mean2=array_sum($y) / $length;
$a=0;
$b=0;
$axb=0;
$a2=0;
$b2=0;
for($i=0;$i<$length;$i++)
{
$a=$x[$i]-$mean1;
$b=$y[$i]-$mean2;
$axb=$axb+($a*$b);
$a2=$a2+ pow($a,2);
$b2=$b2+ pow($b,2);
}
$corr= $axb / sqrt($a2*$b2);
return $corr;
}
[#2] admin at maychu dot net (Le Cong) [2009-06-14 21:58:44]
Please note that this function is reserved for two arrays with continued numbers inside (just integers).
I tested this function and found that it calculate the Pearson's Correlation Coefficient of two arrays.
---
Here's suggested documentation:
stats_stat_correlation ?? Calculate the Pearson's Correlation Coefficient of two arrays of continued numbers.
Parameters:
arr1 = array (integer1a, interger2a ...)
arr2 = array (integer1b, interger2b ...))
(Note that the count of elements in two arrays must be equal)
Return value: Pearson's Correlation Coefficient in decimal format (ex. 0.934399822094)
Code examples:
<?php
// Provided by admin@maychu.net
$array_x = array(5,3,6,7,4,2,9,5);
$array_y = array(4,3,4,8,3,2,10,5);
$pearson = stats_stat_correlation($array_x,$array_y);
echo $pearson;
?>
[#3] umar dot anjum at ymail dot com [2009-03-04 22:05:51]
I tried to use this function, but got a not-defined error. Anyway, I have created a set of functions to replace this:
<?php
//Since Correlation needs two arrays, I am hardcoding them
$array1[0] = 59.3;
$array1[1] = 61.2;
$array1[2] = 56.8
$array1[3] = 97.55;
$array2[0] = 565.82;
$array2[1] = 54.568;
$array2[2] = 84.22;
$array2[3] = 483.55;
//To find the correlation of the two arrays, simply call the
//function Correlation that takes two arrays:
$correlation = Correlation($array1, $array2);
//Displaying the calculated Correlation:
print $correlation;
//The functions that work behind the scene to calculate the
//correlation
function Correlation($arr1, $arr2)
{
$correlation = 0;
$k = SumProductMeanDeviation($arr1, $arr2);
$ssmd1 = SumSquareMeanDeviation($arr1);
$ssmd2 = SumSquareMeanDeviation($arr2);
$product = $ssmd1 * $ssmd2;
$res = sqrt($product);
$correlation = $k / $res;
return $correlation;
}
function SumProductMeanDeviation($arr1, $arr2)
{
$sum = 0;
$num = count($arr1);
for($i=0; $i<$num; $i++)
{
$sum = $sum + ProductMeanDeviation($arr1, $arr2, $i);
}
return $sum;
}
function ProductMeanDeviation($arr1, $arr2, $item)
{
return (MeanDeviation($arr1, $item) * MeanDeviation($arr2, $item));
}
function SumSquareMeanDeviation($arr)
{
$sum = 0;
$num = count($arr);
for($i=0; $i<$num; $i++)
{
$sum = $sum + SquareMeanDeviation($arr, $i);
}
return $sum;
}
function SquareMeanDeviation($arr, $item)
{
return MeanDeviation($arr, $item) * MeanDeviation($arr, $item);
}
function SumMeanDeviation($arr)
{
$sum = 0;
$num = count($arr);
for($i=0; $i<$num; $i++)
{
$sum = $sum + MeanDeviation($arr, $i);
}
return $sum;
}
function MeanDeviation($arr, $item)
{
$average = Average($arr);
return $arr[$item] - $average;
}
function Average($arr)
{
$sum = Sum($arr);
$num = count($arr);
return $sum/$num;
}
function Sum($arr)
{
return array_sum($arr);
}
?>