Namir Shammas

ID:2295
First Name:Namir
Last Name:Shammas
Last Change:2024-12-29
Number of Files:10 (149th most prolific)
Number of Downloads:6,081 (1,637th most downloaded)

BESTFIT3   (details) Prime ENG   3 KB / 5 KB
Inspired by Best Regression Fit, this calculates the best fit, returning the best Rsqr value, the power of best Y transformation (0 means ln(y)), the power of best X transformation (0 means ln(x)), the best slope, the best intercept, the minimum X value, the maximum X value, the minimum Y value, and the maximum Y value.
By Namir Shammas. 2017-12-28

Bisection 1.0.1   (details) Prime ENG   585 KB / 1 KB
There are numerous algorithms that calculate the roots of single-variable nonlinear functions. The most popular of such algorithms is Newton's method. The slowest and simplest root seeking algorithm is the Bisection method. This method has the user select an interval that contains the sought root. The method iteratively shrinks the root-bracketing interval to zoom in on the sought root.
By Namir Shammas. 2014-01-11

Console Programmable RPN Calculator   (details) Other ENG   4,592 KB
A command-line based programmable RPN calculator. Includes full VB source code; written for Windows with the .NET framework installed. Has advanced functionality, including solving and integration as well as linear/multiple regression. Documentation in PDF and HTML format.
By Namir Shammas. 2017-04-19

Dec2Frac   (details) Prime ENG   3 KB / 1 KB
Converts decimals to fractions.
By Namir Shammas. 2014-10-22

Enhanced Random Search Optimization 2.0   (details) Prime ENG   8 KB / 6-12 KB
Two variations of a routine for random search optimizations, with lower and upper bounds specified for each variable. When each improved optimum point is found, the program performs a secondary (a.k.a. 'local') random search in the region around the improved optimum point. After half of the number of iterations are performed, the program narrows the search region around the current best point. The second variation makes sure that the reduced bounds do not cross the original bounds. Use this new version if crossing the initial bounds is prohibited by the nature of the problem you are dealing with. Otherwise, you can use either version of the program.
By Namir Shammas. 2024-06-08

Implicit Trapezoidal Integration Method for ODE   (details) Prime ENG   3 KB / 4 KB
Solves ordinary differential equations using the Implicit Trapezoidal Integration method.
By Namir Shammas. 2024-06-08

Newton's Method with Strings   (details) Prime ENG   4 KB / 2 KB
A version of Newton's method where you specify a string representing f(x), along with the initial guess for the root, and the tolerance value. Thus you can solve for different functions without having to hard code these functions in the PPL code. Also includes an enhanced version.
By Namir Shammas. 2018-01-07

Reducing the Number of Slope Evaluations for Newton's Method   (details) Prime ENG   7 KB / 6 KB
Two programs that solve the roots of nonlinear functions using Newton’s method. What is different about these programs is that they reduce the number of function calls used in calculating the slope. The HP Prime programs calculate the slope using the forward divided difference method.
By Namir Shammas. 2024-12-29

Shammas Polynomial Curve Fitting   (details) Prime ENG   22 KB / 7-34 KB
A set of four programs for working with four classes of quasi-polynomials dubbed Shammas Polynomials, used for regression. They are the Shammas Polynomial, in the form Pn(x) = a0 + a1*x^p(1) + a2*x^p(2) + ... + an*x^p(n), the Quantum Shammas Polynomial, in the form of Qn(x) = c0 + c1*x^a(1) + c2*x^a(2) + ... + cn*x^a(n), the Symmetric Shammas Polynomial (which is an even-ordered polyterm), in the general form Pn(x) = bn/x^p(n) + ... + b1/x^p(1) + a0 + ... + + a2*x^p(1) + an*x^p(n), and the Symmetric Quantum Shammas Polynomial (which is an even-ordered polyterm), in the general form Pn(x) = cn/x^a(1) + ... + c1/a^a(n/2) + d0 + ... + + d1*x^a(n/2+1) + dn*x^a(n).
By Namir Shammas. 2024-06-08

The Dormand–Prince Method for ODEs 2.0   (details) Prime ENG   8 KB / 5-10 KB
Two variations of an implementation of the Dormand–Prince method for ODEs. The Dormand–Prince method is more accurate than the Runge–Kutta–Fehlberg method and it is used by the MATLAB ode45 solver. The second version will alter the integration steps between a user-specified range of steps, given a range of min/max tolerance error values for y.
By Namir Shammas. 2024-06-08

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