# Found on the internet - online calculator powered by mXparser v.2.4.0

Dear All,

Recently I found on the internet online calculator powered by mXparser. This is one possible usage scenario of mXparser engine, here only part of functionalities are utilized. (no user defined functions, recursion, only one argument), yet calculator is nice 🙂

Expect mXparser v.3.0.0 release soon 🙂

Best regards,

# mXparser 3.0.0 is very close to release

Dear All,

I am happy to announce that mXparser 3.0.0 is very close to release. Change will bring a lot of new features 🙂 including:

• Random numbers
• Probability distributions
• Random variables
• Double precision rounding
• Intelligent automatic ULP rounding to minimize issues similar to  0.1 + 0.1 + 0.1 = 0.30000000000000004
• Epsilon comparison from binary relations
• New special functions (non-elementary)
• Significant code reorganization

Best regards

I am happy to announce that first milestone was achieved – as of today mXparser has 1000 unique downloads, and the number is growing! This is very exciting, especially considering questions you asked and feedback I gained.  mXparser seems fits your needs providing the same API for JAVA, Android and .NET (including MONO). Single framework / technology days have  gone away, and are not  coming back, meaning cross-platform apps  are with us for good.

Best regards,

# mXparser on nuget.org

The mXparser package is now available on the nuget.org site.

### MathParser.org-mXparser on nuget.org


PM> Install-Package MathParser.org-mXparser



Best regards,

# mXparser - v.2.4.0 - Average, Variance, Standard deviation, New iterated operators

Dear All,

I am happy to announce that new version of mXparser has just been released. Update delivers below functionalities

##### - Mean value: mean(a1, a2, ..., an)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("mean(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 5.5

##### - Bias-corrected sample variance: var(a1, a2, ..., an)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("var(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 9.166666666666666

##### - Bias-corrected sample standard deviation: std(a1, a2, ..., an)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("std(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 3.0276503540974917


### New iterated operators

##### - Minimum from sample function values: mini(i, from, to, f(i), <by>)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("mini(x, -pi, pi, sin(x), 2*pi/1000)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: -1.0

##### - Maximum from sample function values: maxi(i, from, to, f(i), <by>)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("maxi(x, -pi, pi, sin(x), 2*pi/1000)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 1.0

##### - Average from sample function values: avg(i, from, to, f(i), <by>)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("avg(x, -pi, pi, sin(x), 2*pi/1000)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 4.8615748597837905E-17

##### - Bias-corrected variance from sample function values: vari(i, from, to, f(i), <by>)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("vari(x, -pi, pi, sin(x), 2*pi/1000)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 0.4999999999999962

##### - Bias-corrected standard deviation from sample function values: stdi(i, from, to, f(i), <by>)
import org.mariuszgromada.math.mxparser.*;
...

Expression e = new Expression("stdi(x, -pi, pi, sin(x), 2*pi/1000)");
mXparser.consolePrintln("Res: " + e.calculate());

[mXparser-v.2.4.0] Res: 0.7071067811865449


# Stirling numbers of the second kind and sampling with replacement

### Sampling with replacement

Some time ago I considered the problem of sampling with replacement exactly n-elements from n-element set. As a result of such an operation output set may contains duplicates - let us here assume  that we received exactly k unique elements (of course 1 ≤ k ≤ n).  Then the question came:

### Number of ways with k-unique outputs

What is the number of ways to obtain exactly k unique elements sampling with replacement n-elements from n-element set?

Above question relates to bootstrap estimation, as the answer gives distribution of unique elements (its number) in bootstrap samples.

Let B(n,k) be the function representing the number of such ways.

### 1st step - partial permutation (k-permutation of n aka variation without repetition)

Pretty standard - the number of ways to choose k different elements of n paying attention to the order - this is a variation without repetition V(n,k).

$V(n,k)=\frac{n!}{(n-k)!}$

### 2nd step - Stirling numbers of the second kind

Let us think form the other perspective, forgetting for a moment about just sampled k unique elements and the need of additional n-k (though it's true). Instead let's imagine that we have n items, including k unique. The trick is now to understand that having k different elements in a set of n elements is generating division of original set into k non-empty disjunctive subsets. How many ways to divide n-element set into k-subsets? This is the Stirling number of the second kind marked S2 (n, k). Finally

$B(n,k)=V(n,k)\times S_2(n,k)$

### mXparser - Math Parser - definition

import org.mariuszgromada.math.mxparser.*;
...

Function V = new Function("V(n,k) = n! / (n-k)!");
Function B = new Function("B(n,k) = V(n,k) * Stirl2(n,k)", V);
int n = 5;
for (int k = 0; k <= n; k++)
mXparser.consolePrintln("B(" + n + "," + k + ") = " + B.calculate(n,k) );


Result


[mXparser-v.2.3.1] B(5,0) = 0.0
[mXparser-v.2.3.1] B(5,1) = 5.0
[mXparser-v.2.3.1] B(5,2) = 300.0
[mXparser-v.2.3.1] B(5,3) = 1500.0
[mXparser-v.2.3.1] B(5,4) = 1200.0
[mXparser-v.2.3.1] B(5,5) = 120.0



Best regards,

# Indirect recursion using mXparser

Hello,

Today I will present how flexible mXparser really is. As an example we will approximate $\sin(x)$ and $\cos(x)$ using indirect recursion steps, which means two functions depending on each other. Let us start with a bit of theory starting with basic trigonometric identities:

$\sin(2x)=2\sin(x)\cos(x)$
$\cos(2x)=\cos^2(x)-\sin^2(x)$

Above formals can be equivalently written as

$\sin(x)=2\sin\big(\frac{x}{2}\big)\cos\big(\frac{x}{2}\big)$
$\cos(x)=\cos^2\big(\frac{x}{2}\big)-\sin^2\big(\frac{x}{2}\big)$

Please notice that knowing the solution for smaller value $\frac{x}{2}$ it is possible to get solution for the original one $x$.  This simply means that mentioned trigonometric identities are in fact example of recursion - here indirect recursion as $\sin(x)$ function is using $\cos(x)$, and $\cos(x)$ is using $\sin(x)$. The complete recursion definition requires base case definition (stop condition).

For $x$ near to $0$ function $\sin(x)$ can be well approximated exactly by $x$, while in case of $\cos(x)$ constant $1$ is pretty good approximation. This gives good stop condition.

For small $a>0$ let us define two recursive functions:

$\text{s}(x)=\begin{cases}x&\text{dla}\quad |x|
$\text{c}(x)=\begin{cases}1&\text{dla}\quad |x|

Once again please notice that $\text{s}$ (and $\text{c}$ separately) is using both $\text{s}\big(\frac{x}{2}\big)$ and $\text{c}\big(\frac{x}{2}\big)$.

We expect that smaller parameter $a$ is giving better approximations - below you will find functions graphs separately for $a = 0.5$ and $a=0.01$.

import org.mariuszgromada.math.mxparser.*;
...
/* Recursive functions definition */
Constant a = new Constant("a", 0.1);
Function s = new Function("s(x) =  if( abs(x) < a, x, 2*s(x/2)*c(x/2) )", a);
Function c = new Function("c(x) =  if( abs(x) < a, 1, c(x/2)^2-s(x/2)^2 )", a);

/* Pointing that 's' is using 'c', and 'c' is using 's' */



Best regards,

# mXparser v.2.3.0: Extensive prime numbers support, Multi-threading performance tests, New built-in functions and constants!

## Major update of the library was released on Jan,  17th 2016.

### Extensive support for the prime numbers:

• new class PrimesCache in mathcollection
• MathFunctions extended with primality test
• ispr(n) - Primality test function supported in expressions
• Pi(n) - Prime Counting function supported in expressions
• mXparser.initPrimesCache() methods (and others) to initialize prime numbers cache

### Some special functions supported

• Ei(x) - Exponential integral function supported in expressions
• li(x) - Logarithmic integral function supported in expressions
• Li(x) - Offset logarithmic integral function supported in expressions

### New constants

• [G] - Gompertz Constant OEIS A073003 supported in expressions
• [li2] - li(2) A069284 - supported in expressions

• Default number of cores taken from the environment
• Possibility to change number of default threads

Enjoy 🙂

# mXparser v.2.2.0: Android is coming

### mXparser and Android

Since mXparser-v.2.2.0 library is being always tested also on the Android platform. I can confirm that all regression tests were passed without any problems. Recommended library to use directly in the Android project is mXparser built with JDK 1.7.

### Console output available in String

In terms of System.out.println() Android behavior is different than JVM causing that all data passed to Console on JVM is printed in Log.cat by Android Dalvik. If you will use mXparser.consolePrintln()/Print() methods instead of System.out. equivalents  you will also get access to the console output string containing printed data. Please refer to the API specification of the mXparser class.

# mXparser v.2.1.1-1: binaries for older version of JAVA and .NET

No changes to API, but library tested and compiled for:

• JAVA: 1.5, 1.6, 1.7, 1.8
• .NET: 2.0, 3.0, 3.5, 4.0, 4.5, 4.6