Handtuned MagicDraw UML
19.0 v9
probabilityDensity =
(shape/time) * Math.pow((time-location)*scale, shape) *
Math.exp(-1.0 * Math.pow((time-location)*scale, shape))
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probability = Math.exp(-1.0 * Math.pow((time-location)*scale, shape))
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function LanczosApproximatedGamma(x) {
// from wikipedia
// coefficients for g=7 n=9
const g = 7;
const coefficients = [0.99999999999980993,
676.5203681218851,-1259.1392167224028,771.32342877765313,-176.61502916214059,
12.507343278686905,-0.13857109526572012,9.9843695780195716e-6,1.5056327351493116e-7];
//Euler's reflection formula
if (x < 0.5)
return Math.PI / (Math.sin(Math.PI * x) * LanczosApproximatedGamma(1.0 - x));
// adjust the argument - Laczos formula calculates Gamma(z+1)
// therefore we are setting x = z+1
const z = x - 1.0;
// Lanczos approximation
var result = coefficients[0];
for (var i = 1; i < coefficients.length; ++i) {
result += coefficients[i] / (z + i);
}
return (Math.sqrt(2.0 * Math.PI) * Math.pow((z + g + 0.5), (z + 0.5))) * Math.exp(-1.0*(z + g + 0.5)) * result;
}
meanTimeToEvent = (1.0/scale) * LanczosApproximatedGamma(1.0 + 1.0/shape)
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y=1.0-x
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probabilityDensity =
1.0/(shape * (time-location) * Math.sqrt(2.0 * Math.PI))
* Math.exp(-0.5 * Math.pow(Math.log((time-location)*scale)/shape, 2))
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function AbramowitzStegunApproximatedGaussErrorFunction(x) {
// from wikipedia, originally from Abramowitz and Stegun
// (maximum error: 1.5×10−7)
// \operatorname {erf} x\approx 1-\left(a_{1}t+a_{2}t^{2}+\cdots +a_{5}t^{5}\right)e^{-x^{2}},\quad t={\frac {1}{1+px}}
// where p = 0.3275911, a1 = 0.254829592, a2 = −0.284496736, a3 = 1.421413741, a4 = −1.453152027, a5 = 1.061405429
if(0==x)
return 0;
var sign_ = (x>0)?1.0:-1.0;
x = sign_*x;// absolute of x
const coefficientsA = [0.254829592,-0.284496736,1.421413741,-1.453152027,1.061405429];
const t = 1.0/(1.0 + 0.3275911 * x);//p = 0.3275911;
var tPow = t;
var result = 0.0;
for (var i = 0; i < coefficientsA.length; ++i) {
result += coefficientsA[i] * tPow;
tPow *= t;
}
return sign_ * (1 - result * Math.exp(-1.0*x*x));
}
probability = (1.0 - AbramowitzStegunApproximatedGaussErrorFunction(Math.log((time-location)*scale)/shape/Math.sqrt(2.0)))/2.0
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meanTimeToEvent = (1.0/scale) * Math.exp(0.5 * shape * shape)
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probabilityDensity = scale*Math.exp(-1.0*scale*(time-location))
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probability = Math.exp(-1.0*scale*(time-location))
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meanTimeToEvent = 1.0/scale
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eventRate = shape * scale * Math.pow((time-location)*scale, shape - 1.0)
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function AbramowitzStegunApproximatedGaussErrorFunction(x) {
// from wikipedia, originally from Abramowitz and Stegun
// (maximum error: 1.5×10−7)
// \operatorname {erf} x\approx 1-\left(a_{1}t+a_{2}t^{2}+\cdots +a_{5}t^{5}\right)e^{-x^{2}},\quad t={\frac {1}{1+px}}
// where p = 0.3275911, a1 = 0.254829592, a2 = −0.284496736, a3 = 1.421413741, a4 = −1.453152027, a5 = 1.061405429
if(0==x)
return 0;
var sign_ = (x>0)?1.0:-1.0;
x = sign_*x;// absolute of x
const coefficientsA = [0.254829592,-0.284496736,1.421413741,-1.453152027,1.061405429];
const t = 1.0/(1.0 + 0.3275911 * x);//p = 0.3275911;
var tPow = t;
var result = 0.0;
for (var i = 0; i < coefficientsA.length; ++i) {
result += coefficientsA[i] * tPow;
tPow *= t;
}
return sign_ * (1 - result * Math.exp(-1.0*x*x));
}
var density =
1.0/(shape * (time-location) * Math.sqrt(2.0 * Math.PI))
* Math.exp(-0.5 * Math.pow(Math.log((time-location)*scale)/shape, 2));
var probability = (1.0 - AbramowitzStegunApproximatedGaussErrorFunction(Math.log((time-location)*scale)/shape/Math.sqrt(2.0)))/2.0;
eventRate = density/probability
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eventRate = scale
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ratio = x/(x+y)
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y=1.0/x
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if (input && !input.isEmpty()) {
output = 1.0;
}
for (var i = input.iterator(); i.hasNext();) {
output = i.next() * output;
}
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function factorial (n) {
if (n <2)
return 1;
return factorial(n-1)*n;
}
function C_k_of_n (k, n) {
return (factorial(n)/factorial(k))/factorial(n-k);
}
var R = input;
var result = 0.0;
for (var i = k; i <=n; ++i) {
result += C_k_of_n(i, n) * Math.pow(R, i) * Math.pow(1.0-R, n-i);
}
output = result;
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if (input && !input.isEmpty()) {
output = 1.0;
}
for (var i = input.iterator(); i.hasNext();) {
output = (1.0 - i.next()) * output;
}
output = 1.0 - output;
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function getCombinations(array, min) {
var res = [];
function iter(i, temp, count) {
if (i === array.length) {
if (count >= min) {
res.push(temp);
}
return;
}
iter(i + 1, temp.concat(array[i]), count + 1);
iter(i + 1, temp.concat(1.0 - array[i]), count);
}
iter(0, [], 0);
return res;
}
function and(arrayOfProbabilities) {
var output = 1.0;
for (i = 0; i < arrayOfProbabilities.length; i++) {
output = arrayOfProbabilities[i] * output;
}
return output;
}
function sum(arrayOfProbabilities) {
var output = 0.0;
for (i = 0; i < arrayOfProbabilities.length; i++) {
output = output + arrayOfProbabilities[i];
}
return output;
}
var allCombinations = getCombinations(input.toArray(), k);
var andsArray = [];
for (j = 0; j < allCombinations.length; j++) {
andsArray.push(and(allCombinations[j]));
}
output = sum(andsArray);
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