print("1. Three-hump camel function.")
print("2. Booth function.")
print("3. Beale function.")
print("4. Exit")
chooseFunction = int(input("Choose: "))
while chooseFunction != 4:
if chooseFunction == 1:
print("=== Three-hump camel function ===")
alh = SwarmPackagePy.pso(50,
tf.three_hump_camel_function,
-5,
5,
20,
100,
w=0.5,
c1=1,
c2=1)
print(alh.get_Gbest())
animation3D(alh.get_agents(), tf.three_hump_camel_function, -5, 5)
elif chooseFunction == 2:
print("=== Booth function ===")
alh = SwarmPackagePy.pso(50,
tf.booth_function,
-10,
10,
20,
100,
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 27 10:12:26 2017
@author: MichelMacSDD
"""
import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf
from SwarmPackagePy import animation, animation3D
alh = SwarmPackagePy.pso(50,
tf.easom_function,
-10,
10,
2,
20,
w=0.5,
c1=1,
c2=1)
animation(alh.get_agents(), tf.easom_function, -10, 10)
animation3D(alh.get_agents(), tf.easom_function, -10, 10)
Yd_train = distance_to_erbs(Y_train, erbs)
X_test = testmatrix[:,2:8]
Y_test = testmatrix[:,0:2]
Yd_test = distance_to_erbs(Y_test, erbs)
tup = X_test.shape
inter_step = numpy.zeros(X_test.shape[1])
results = numpy.zeros(Y_test.shape)
nbrs = NearestNeighbors(n_neighbors=k, algorithm='kd_tree', leaf_size=60).fit(X_train)
for i in range(0, tup[0]):
distances, indices = nbrs.kneighbors(X_test[i,:].reshape(1,6))
inter_step = kNN_estimator(indices, distances, Y_train, erbs)
# print(inter_step)
# results[i,:] = SwarmPackagePy.aba(200, fitness, [min_lat, min_long], [max_lat, max_long], 2, 50).get_Gbest()
results[i,:] = SwarmPackagePy.pso(100, fitness, [min_lat, min_long], [max_lat, max_long], 2, 50, 0.1, 1, 1).get_Gbest()
err = distance_to_reference(Y_test, results)
# for i in range(0, err.size):
# err[i] = vincenty((Y_test[i,0], Y_test[i,1]), (results[i,0], results[i,1])).meters
k_measures = [sqrt((err**2).mean()), err.std(), err.max(), err.min()]
print(err)
print(k_measures)
f = open('fichier-test.csv', 'w+')
f.write("lat,lon\n")
for i in range(0,results.shape[0]):
f.write("%.6f, %.6f\n" % (results[i,0], results[i,1]))
# print(results)
# d = distance_to_reference(results, Y_test)