stochastic

What is the difference between stochastic hill climbing and first-choice hill climbing algorithms? Gusti Ahmad Fanshuri Alfarisy Hill Climbing Search Algorithm is one of the family of local searches that move based on the better states of its neighbors. Stochastic Hill Climbing chooses a random better state from all better states in the neighbors while first-choice Hill Climbing chooses the first better state from randomly generated neighbors. First-Choice Hill Climbing will become a good strategy if the current state has a lot of neighbors. I am quoting from Artificial Intelligence: A Modern

I have a system of stochastic differential equations that I would like to solve. I was hoping that this issue was already address. I am a bit concerned about constructing my own solver because I fear my solver would be too slow, and there could be the issues with numerical stability. Is there a python module for such problems? If not, is there a standard approach for solving such systems. There is one: http://diffusion.cgu.edu.tw/ftp/sde/ Example from the site: """ add required Python packages """ from pysde import * from sympy import * """ Variables acclaimed """ x,dx=symbols('x dx') r,G,e,d

my target is to have a plot that shows Stochastic oscillator on forex market, and in order to validate which parameter is the best one to setup it, I would use a slider to modify it and show updated result on plot. I have my historical data, for a defined pair (let say AUDUSD) and after loading it, I calculate Stocastic oscillator: function [stoch, fk, dk] = stochastic(n, k, d) X=csvread("AUDUSD_2017.csv"); C=X(2:length(X),5); L=X(2:length(X),4); H=X(2:length(X),3); O=X(2:length(X),2); for m=n:length(C)-n stoch(m)=((C(m)-min(L(m-n+1:m)))/(max(H(m-n+1:m))-min(L(m-n+1:m))))*100; endfor for m=n