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

February 28, 2016Release notesMariusz Gromada

Dear All,

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

New variadic functions

– 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

Enjoy 🙂

Mariusz Gromada

Stirling numbers of the second kind and sampling with replacement

February 23, 2016Combinatorics, Mathematics, Probability theoryMariusz Gromada

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,

Mariusz Gromada

Indirect recursion using mXparser

February 21, 2016MathematicsMariusz Gromada

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:

$$\text{s}(x)=\begin{cases}x&\text{dla}\quad |x|<a\\2\text{s}\big(\frac{x}{2}\big)\text{c}\big(\frac{x}{2}\big)&\text{dla}\quad |x|\geq a\end{cases}$$
$$\text{c}(x)=\begin{cases}1&\text{dla}\quad |x|<a\\\text{c}^2\big(\frac{x}{2}\big)-\text{s}^2\big(\frac{x}{2}\big)&\text{dla}\quad |x|\geq a\end{cases}$$

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' */
s.addDefinitions(c);
c.addDefinitions(s);

Best regards,

Mariusz Gromada

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

January 20, 2016Release notesMariusz Gromada

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

Multithreading performance tests

Default number of cores taken from the environment
Possibility to change number of default threads
PerformanceTests.start(int threadsNum)
mXparser.setThreadsNumber(int threadsNumber)

New regression tests to cover new functionalities

Download mXparser-v.2.3.0

Enjoy 🙂

Mariusz Gromada

mXparser v.2.2.0: Android is coming

January 17, 2016Release notesmXparser releaseMariusz Gromada

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.

Enjoy 🙂

Mariusz Gromada

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

January 7, 2016Release notesmXparser releaseMariusz Gromada

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

Please follow below link to download mXparser v.2.1.1-1

mXparser-v.2.1.1-1 on GitHub

Enjoy 🙂

Mariusz Gromada

mXparser – user defined functions applied to Fundamental theorem of calculus

January 5, 2016Calculus, MathematicsMath Parser, User defined functionsMariusz Gromada

Fundamental Theorem of Calculus is a kind of a link between two most important calculus concepts: derivative and integral.

Fundamental Theorem of Calculus – formal statement

For continuous real-valued function $$f:[a,b]\to\mathbb{R}$$ defined on closed interval $$[a,b]$$ let $$F:[a,b]\to\mathbb{R}$$ be the function given by

$$F(x)=\int_a^x f(t)\text{d}t$$

The $$F$$ is uniformly continuous on $$[a, b]$$, differentiable on the open interval $$(a, b)$$, and

$$F'(x)=f(x)$$

Fundamental Theorem of Calculus – mXparser test

import org.mariuszgromada.math.mxparser.*;
...
/* Function */
Function f = new Function("f(x) = sin(x)");
		
/* Antiderivative */
Function F = new Function("F(x) = int(f(t), t, 0, x)", f);
		
/* function = derivative ( antiderivative ) */
Argument x = new Argument("x = pi");
Expression e = new Expression("f(x) - der(F(x), x)", x, f, F);
mXparser.consolePrintln("Res : " + e.getExpressionString() + " = " + e.calculate());
mXparser.consolePrintln("Computing time = " + e.getComputingTime() + " s.");

Res : f(x) - der(F(x), x) = 6.237833817291525E-8
Computing time = 0.411 s.

Best regards,
Mariusz Gromada