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<?php
namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical\Distributions;
use PhpOffice\PhpSpreadsheet\Calculation\Exception;
use PhpOffice\PhpSpreadsheet\Calculation\Functions;
class LogNormal
{
/**
* LOGNORMDIST.
*
* Returns the cumulative lognormal distribution of x, where ln(x) is normally distributed
* with parameters mean and standard_dev.
*
* @param mixed $value Float value for which we want the probability
* @param mixed $mean Mean value as a float
* @param mixed $stdDev Standard Deviation as a float
*
* @return float|string The result, or a string containing an error
*/
public static function cumulative($value, $mean, $stdDev)
{
$value = Functions::flattenSingleValue($value);
$mean = Functions::flattenSingleValue($mean);
$stdDev = Functions::flattenSingleValue($stdDev);
try {
$value = DistributionValidations::validateFloat($value);
$mean = DistributionValidations::validateFloat($mean);
$stdDev = DistributionValidations::validateFloat($stdDev);
} catch (Exception $e) {
return $e->getMessage();
}
if (($value <= 0) || ($stdDev <= 0)) {
return Functions::NAN();
}
return StandardNormal::cumulative((log($value) - $mean) / $stdDev);
}
/**
* LOGNORM.DIST.
*
* Returns the lognormal distribution of x, where ln(x) is normally distributed
* with parameters mean and standard_dev.
*
* @param mixed $value Float value for which we want the probability
* @param mixed $mean Mean value as a float
* @param mixed $stdDev Standard Deviation as a float
* @param mixed $cumulative Boolean value indicating if we want the cdf (true) or the pdf (false)
*
* @return float|string The result, or a string containing an error
*/
public static function distribution($value, $mean, $stdDev, $cumulative = false)
{
$value = Functions::flattenSingleValue($value);
$mean = Functions::flattenSingleValue($mean);
$stdDev = Functions::flattenSingleValue($stdDev);
$cumulative = Functions::flattenSingleValue($cumulative);
try {
$value = DistributionValidations::validateFloat($value);
$mean = DistributionValidations::validateFloat($mean);
$stdDev = DistributionValidations::validateFloat($stdDev);
$cumulative = DistributionValidations::validateBool($cumulative);
} catch (Exception $e) {
return $e->getMessage();
}
if (($value <= 0) || ($stdDev <= 0)) {
return Functions::NAN();
}
if ($cumulative === true) {
return StandardNormal::distribution((log($value) - $mean) / $stdDev, true);
}
return (1 / (sqrt(2 * M_PI) * $stdDev * $value)) *
exp(0 - ((log($value) - $mean) ** 2 / (2 * $stdDev ** 2)));
}
/**
* LOGINV.
*
* Returns the inverse of the lognormal cumulative distribution
*
* @param mixed $probability Float probability for which we want the value
* @param mixed $mean Mean Value as a float
* @param mixed $stdDev Standard Deviation as a float
*
* @return float|string The result, or a string containing an error
*
* @TODO Try implementing P J Acklam's refinement algorithm for greater
* accuracy if I can get my head round the mathematics
* (as described at) http://home.online.no/~pjacklam/notes/invnorm/
*/
public static function inverse($probability, $mean, $stdDev)
{
$probability = Functions::flattenSingleValue($probability);
$mean = Functions::flattenSingleValue($mean);
$stdDev = Functions::flattenSingleValue($stdDev);
try {
$probability = DistributionValidations::validateProbability($probability);
$mean = DistributionValidations::validateFloat($mean);
$stdDev = DistributionValidations::validateFloat($stdDev);
} catch (Exception $e) {
return $e->getMessage();
}
if ($stdDev <= 0) {
return Functions::NAN();
}
return exp($mean + $stdDev * StandardNormal::inverse($probability));
}
}