zcliang97

  
from numpy import np
import random

class GeneticAlgorithm:
    def __init__(self, createIndividual, mutateIndividual, fitnessFunction, crossoverFunction, population_size=1000, crossoverProb=0.6, mutateProb=0.4, alpha=0.5):
        self.create = createIndividual
        self.mutate = mutateIndividual
        self.fitness = fitnessFunction
        self.populationSize = population_size

zcliang97

"""
Tabu Search Class
"""
class TabuSearch:
    def __init__(self, initialSolution, solutionEvaluator, neighborOperator, aspirationCriteria, acceptableScoreThreshold, tabuTenure):
        self.currSolution = initialSolution
        self.bestSolution = initialSolution
        self.evaluate = solutionEvaluator
        self.aspirationCriteria = aspirationCriteria
        self.neighborOperator = neighborOperator

zcliang97

"""
Simulated Annealing Class
"""
import random
import math

class SimulatedAnnealing:
    def __init__(self, initialSolution, solutionEvaluator, initialTemp, finalTemp, tempReduction, neighborOperator, iterationPerTemp=100, alpha=10, beta=5):
        self.solution = initialSolution
        self.evaluate = solutionEvaluator

zcliang97

import numpy as np

t = 10
# compute kalman filter over t iterations
for k in range(1, len(t)):  # Start at 1 because we have initial prediction from ground truth.

    delta_t = t[k] - t[k - 1]  # time step (difference between timestamps)
    px = np.cos(x_est[k-1][2])
    py = np.sin(x_est[k-1][2])
    

zcliang97

import numpy as np

input_angles = [0, np.pi/2, np.pi, (3/2) * np.pi, 2 * np.pi]

print('Input: ', input_angles)

sin_output = np.sin(input_angles)
cos_output = np.cos(input_angles)

print('Sine values: ', sin_output)

zcliang97

#include "Simulation.h"
#include "Constants.h"
#include "Particle.h"

#include <cassert>
#include <cmath>
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>