solve polynomial using Newton method
Polynomial Regression
Robust polynomial regression using LMedS
Evaluate a polynomial using double-precision floating-point arithmetic.
Polynomial Regression 2D
The polynomial kernel
A class of simple polynomial functionality including root finding
A library to find JS RegExp with super-linear worst-case time complexity for attack strings that repeat a single character.
pipe streams together and close all of them if one of them closes
Savitzky–Golay filter in Javascript
A practical, root-focused JavaScript polynomial utility library.
A factory for kernel functions
Next.js plugin to transpile code from node_modules
The minimal Signals implementation based on https://github.com/vuejs/core/pull/5912.
Evaluates a polynomial.
JavaScript/TypeScript library for multivariate polynomial regression.
Universal Drag-and-Drop Component Supporting both Vue 3 and Vue 2
This allows you to add regression lines to any series. Supports: linear, polynomial, logarithmic, exponential and loess. Calculates the r-value
Fuzzy filtering and string similarity scoring - compatible with fuzzaldrin
A time-lock puzzle generator
Core libraries that every NodeJS toolchain project should use
General purpose node utilities
Provide data formatters (data model builder & json builder) to work with JSON API specification v1.0 in your JavaScript / TypeScript code
Evaluates a polynomial.
Provides a class (CongruenceSolver) for finding the modular zeros of a polynomial (given the coefficients and modulus) and a binary (csolve) to to solve your congruences at the command line.
This is a gem dedicated to parsing, manipulating and finding roots,extrema and inflection points of polynomials. It can solve polynomial equations with a degree up to 4 by implementing formulas for solving quadratic, cubic and quartic functions.
This gem contains command-line utility for solving 0/1 knapsack problem using branch-and-bound method, dynamic programming, simple heuristic (weight/price) and fully polynomial time approximation scheme. It can measure CPU and wall-clock time spent by solving a problem, compute relative error of the result and generate graphs from those values.
== DESCRIPTION: Charlie is a library for genetic algorithms (GA) and genetic programming (GP). == FEATURES: - Quickly develop GAs by combining several parts (genotype, selection, crossover, mutation) provided by the library. - Sensible defaults are provided with any genotype, so often you only need to define a fitness function. - Easily replace any of the parts by your own code. - Test different strategies in GA, and generate reports comparing them. Example report: http://charlie.rubyforge.org/example_report.html == INSTALL: * sudo gem install charlie == EXAMPLES: This example solves a TSP problem (also quiz #142): N=5 CITIES = (0...N).map{|i| (0...N).map{|j| [i,j] } }.inject{|a,b|a+b} class TSP < PermutationGenotype(CITIES.size) def fitness d=0 (genes + [genes[0]]).each_cons(2){|a,b| a,b=CITIES[a],CITIES[b] d += Math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 ) } -d # lower distance -> higher fitness. end use EdgeRecombinationCrossover, InversionMutator end Population.new(TSP,20).evolve_on_console(50) This example finds a polynomial which approximates cos(x) class Cos < TreeGenotype([proc{3*rand-1.5},:x], [:-@], [:+,:*,:-]) def fitness -[0,0.33,0.66,1].map{|x| (eval_genes(:x=>x) - Math.cos(x)).abs }.max end use TournamentSelection(4) end Population.new(Cos).evolve_on_console(500)