def read_problem(file):
global numSites
global numDemands
global sites
try:
if (file[-3:].lower() == "dat"):
sites = readDataFiles.readDat(file)
elif (file[-3:].lower() == "tsp"):
sites = readDataFiles.readTSP(file)
except IOError:
print('Error reading file')
raise
numSites = sites.shape[0]
numDemands = numSites
plot.plotData(sites)
print('%d locations' % numSites)
print('Finished Reading File!')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from plot import plotData
from data import loadData
import GradientDescent as gd
import numpy as np
data = loadData('../data/ex1data1.txt')
(m, features) = data.shape
X = data[:, 0]
y = data[:, 1]
# Plotting the data
plotData(X, y)
plt.show()
# Gradient Descent
print("Running Gradient Descent ...")
X = np.append(np.ones((m, 1)), X, 1) # add a column of ones to X
theta = np.zeros((2, 1)) # initialize fitting parameters
# some gradient descent settings
iterations = 1500
alpha = 0.01
# compute and display initial cost (expected: 32.0727338775)
cost = gd.computeCost(X, y, theta)
print("initial cost: ", cost)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from plot import plotData
from data import loadData
import GradientDescent as gd
import numpy as np
data = loadData('../data/ex1data1.txt')
(m, features) = data.shape
X = data[:, 0]
y = data[:, 1]
# Plotting the data
plotData(X, y)
plt.show()
# Gradient Descent
print("Running Gradient Descent ...")
X = np.append(np.ones((m, 1)), X, 1) # add a column of ones to X
theta = np.zeros((2, 1)) # initialize fitting parameters
# some gradient descent settings
iterations = 1500
alpha = 0.01
# compute and display initial cost (expected: 32.0727338775)
cost = gd.computeCost(X, y, theta)
print("initial cost: ", cost)
from request import getData from plot import plotData #Poznan, Polanka getData(6531) #Cordoba getData(8461) #Liverpool getData(3187) #Nijmegen getData(6477) #Toulouse getData(3835) #Hanover getData(6205) #Gent getData(8904) plotData()
def geneticAlgorithm(pop,
iter_max,
pcross,
corss_type,
pmut,
mut_type,
save_plot=False,
file_name="figure"):
count = 0
print(f"Starting generation {count}! Initial population is {pop}")
print(f"Function parameters are:\n\titer_max = {iter_max}"
# f"\n\tpsel = {psel}"
f"\n\tpcross = {pcross}"
f"\n\tcorss_type = {corss_type}"
f"\n\tpmut = {pmut}"
f"\n\tmut_type = {mut_type}")
# Generate initial population
population = generateNotSoRandomPopulation(pop)
# check hard constraints
hard_constraint_check = checkHardConstraints(population)
population = population[np.argwhere(
hard_constraint_check == True).flatten()]
print(
f"Count of initial population going to the next generation: {np.count_nonzero(hard_constraint_check)}"
)
# Fitness calculation for every chromosome.
penalties = penaltyFunction(population)
fitness = fitnessFunction(population, penalties)
meanPenaltiesList = [penalties.mean()]
elitePenaltiesList = [penalties.min()]
print(
f"Average penalty = {meanPenaltiesList[-1]:.3f},"
f" Min penalty = {penalties.min():.3f}, Max penalty = {penalties.max():.3f}"
)
print(
f"Min fitness = {fitness.min():.3f}, Max fitness = {fitness.max():.3f}"
)
while True:
# Generate next generation
count += 1
print(f"---------- Moving to generation {count} ----------")
print("Selecting...")
population = selectWorthyChromosomes(population, fitness)
print("Crossing over...")
population = crossover(population, corss_type, pcross)
print("Mutating ...")
population = mutate(population, mut_type, pmut, .33)
hard_constraint_check = checkHardConstraints(population)
population = population[np.argwhere(
hard_constraint_check == True).flatten()]
print(
f"Count of population that survived: {np.count_nonzero(hard_constraint_check)}"
)
penalties = penaltyFunction(population)
fitness = fitnessFunction(population, penalties)
meanPenaltiesList.append(penalties.mean())
elitePenaltiesList.append(penalties.min())
print(
f"Average penalty = {meanPenaltiesList[-1]:.3f},"
f" Min penalty = {penalties.min():.3f}, Max penalty = {penalties.max():.3f}"
)
print(
f"Min fitness = {fitness.min():.3f}, Max fitness = {fitness.max():.3f}"
)
if terminationCriteria(count, iter_max, meanPenaltiesList,
np.count_nonzero(hard_constraint_check)):
# Finished is true!
plotData(
count, meanPenaltiesList, elitePenaltiesList,
f"Results with pop={pop} for pcorss={pcross},"
f" corss_type={corss_type}, pmut={pmut}, mut_type={mut_type}",
save_plot, file_name)
break
return meanPenaltiesList, elitePenaltiesList
sim.setVoltage(24.0) #V
sim.setLoadTorque(0.0) #Nm
sim.setLoadInertia(0.0) #kg*m^2
sim.position_controller_inputs['setpoint'] = 0.0
sim.PositionController.Kp = 20.0
sim.PositionController.Ki = 0.0
sim.VelocityController.Kp = 2.0
sim.VelocityController.Ki = 1.0
sim.CurrentController.PID_id.Kp = 2.0
sim.CurrentController.PID_iq.Kp = 2.0
sim.CurrentController.PID_id.Ki = 1.0
sim.CurrentController.PID_iq.Ki = 1.0
TIME = 1.0 #sec
AMPLITUDE = np.pi / 4.0 #rad
FREQUNCY = 5.0 #Hz
def call_back(t, dt):
sim.position_controller_inputs['setpoint'] = np.sin(2 * np.pi * t * FREQUNCY) * AMPLITUDE
#sim.position_controller_inputs['setpoint'] = signal.square(2 * np.pi * t * 1.0) * AMPLITUDE
result = sim.simpleAnalysis(TIME, call_back)
plot.plotData(result)
import matplotlib.pyplot as plt
import plot
## =============== Part 1: Loading and Visualizing Data ================
# We start the exercise by first loading and visualizing the dataset.
# The following code will load the dataset into your environment and plot the data.
print('Loading and Visualizing Data ...')
# Load from ex6data1,You will have X, y in your environment:
data = sco.loadmat('ex6data1.mat')
X,y = data["X"],data['y']
print(np.shape(X),np.shape(y))
# Plot training data
f1 = plot.plotData(X,y)
f1.show()
input('Program paused. Press enter to continue.')
## ==================== Part 2: Training Linear SVM ====================
# The following code will train a linear SVM on the dataset and plot the decision boundary learned.
# Load from ex6data1. You will have X, y in your environment:
data1 = sco.loadmat('ex6data1.mat')
# fprintf('\nTraining Linear SVM ...\n')
#
# % You should try to change the C value below and see how the decision
# % boundary varies (e.g., try C = 1000)
# C = 1;
# model = svmTrain(X, y, C, @linearKernel, 1e-3, 20);