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vk_v4_0.py
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vk_v4_0.py
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#!/usr/bin/env python
# coding: utf-8
# In[3]:
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import gamma as gamma_function
# In[ ]:
# In[162]:
class Cuckoo():
X_min = 0
X_max = 100
alpha_min = 0.9
alpha_max = 1.0
pa_min = 0.05
pa_max = 0.25
N_nest = 25
N_itertotal = 100
gamma = 0.1 # noise factor
lamda = 1.5 # constant used in le'vy flight
total_nodes = 0
anchor_percent = 0
t_range = 0 # transmission range
X_anchor = []
X_unknown = []
X_loc = []
def __init__(self, total_nodes=100, anchor_percent = 0.20, t_range = 25):
self.total_nodes = total_nodes
self.anchor_percent = anchor_percent
self.t_range = t_range
self.M = int(self.anchor_percent * self.total_nodes) # no. of anchor nodes
self.N = self.total_nodes - self.M # no. of unknown nodes
var1 = 10
for i in range(self.M):
x_anchor = np.random.randint(100)
y_anchor = np.random.randint(100)
self.X_anchor.append([x_anchor, y_anchor])
for i in range(self.N):
x_unknown = np.random.randint(100)
y_unknown = np.random.randint(100)
self.X_unknown.append([x_unknown, y_unknown])
self.X_unknown = np.array(self.X_unknown)
self.X_anchor = np.array(self.X_anchor)
self.X_anchor_og = self.X_anchor.copy()
self.X_unknown_og = self.X_unknown.copy()
def show_field(self):
plt.figure(figsize=(8, 8))
plt.plot(self.X_unknown[:, 0], self.X_unknown[:, 1], 'ro', label="Unknown Node")
plt.plot(self.X_anchor[:, 0], self.X_anchor[:, 1], 'go', label="Anchor Node")
plt.legend()
plt.grid()
plt.show()
# step size (alpha)
def alpha(self, n_iter):
return self.alpha_max - ((n_iter/ self.N_itertotal) * (self.alpha_max - self.alpha_min)) # returns step size value
# Le'vy flight function
def levy(self):
temp = np.power(((gamma_function(1 + self.lamda) * np.sin(np.pi * (self.lamda /2))) / (gamma_function((1 + self.lamda)/2) * self.lamda * np.power(2, ((self.lamda - 1)/2)) )), 1/self.lamda)
u = np.random.normal(0, temp)
v = np.random.normal(0,1)
r = u / (np.power(abs(v), (1/self.lamda)))
return r # random walk value
# location limit tester
def limiter(self, point):
x = point[0]
y = point[1]
if x > self.X_max and y > self.X_max:
x,y = self.X_max, self.X_max
# X_j = X_rand
elif x > self.X_max and self.X_min < y < self.X_max:
x,y = self.X_max, y
# X_j = X_rand
elif x > self.X_max and y < self.X_min:
x,y = self.X_max, self.X_min
# X_j = X_rand
elif self.X_min < x < self.X_max and y < self.X_min:
x,y = x, self.X_min
# X_j = X_rand
elif x < self.X_min and y < self.X_min:
x,y = self.X_min, self.X_min
# X_j = X_rand
elif x < self.X_min and self.X_min < y < self.X_max:
x,y = self.X_min, y
# X_j = X_rand
elif x < self.X_min and y > self.X_max:
x,y = self.X_min, self.X_max
# X_j = X_rand
elif self.X_min < x < self.X_max and y > self.X_max:
x,y = x, self.X_max
# X_j = X_rand
return [x,y]
def neighbours(self, node, anchors):
x = node[0]
y = node[1]
X_anchor = anchors
l = []
for j in range(len(X_anchor)): # for every anchor nodes
dist_real = np.power((np.power((x - X_anchor[j][0]), 2) + np.power((y - X_anchor[j][1]), 2)), 0.5)
dist_err = dist_real + np.random.normal(0, (self.gamma*dist_real))
if dist_err < self.t_range:
l.append(X_anchor[j])
return l # neighbouring anchors coordinates
def objective(self, node, n_anchors):
"""objective function (to minimize)"""
x = node[0]
y = node[1]
l = self.neighbours(node, n_anchors)
m = len(l)
rerror = []
if len(l) >= 3:
for ancn in l:
dist_real = np.power((np.power((x - ancn[0]), 2) + np.power((y - ancn[1]), 2)), 0.5)
dist_err = dist_real + np.random.normal(0, (self.gamma*dist_real))
rerror.append(np.power(dist_real - dist_err,2))
return np.sum(rerror)/m or None # mean of square of ranging error
def mod_cs(self, N_anchor):
X_nest = []
for i in range(self.N_nest):
x_nest = np.random.randint(100)
y_nest = np.random.randint(100)
X_nest.append([x_nest, y_nest])
Obj_X_nest = []
for i in range(len(X_nest)):
Obj_X_nest.append(self.objective(X_nest[i], N_anchor))
# print(Obj_X_nest)
N_iter = 0
fmins = []
while(N_iter < self.N_itertotal):
N_iter += 1
X_js = []
for i in range(len(X_nest)):
X_j = X_nest[i].copy()
X_j[0] = X_j[0] + self.alpha(N_iter) * self.levy()
X_j[1] = X_j[1] + self.alpha(N_iter) * self.levy()
X_j = self.limiter(X_j)
F_j = self.objective(X_j, N_anchor) or np.inf
rand_k = np.random.randint(0, len(X_nest))
F_k = self.objective(X_nest[rand_k], N_anchor) or np.inf
if F_j > F_k:
X_j[0] = X_nest[rand_k][0]
X_j[1] = X_nest[rand_k][1]
F_j = F_k
X_js.append(X_j)
Obj_X_js = []
for i in range(len(X_js)):
Obj_X_js.append(self.objective(X_js[i], N_anchor))
Obj_X_js = np.array([np.inf if i is None else i for i in Obj_X_js])
F_min = Obj_X_js[np.argmin(Obj_X_js)]
# print(F_min)
best_sol = X_js[np.argmin(Obj_X_js)]
Pa_j = []
for i in Obj_X_js:
K = i - F_min
if K < 1:
Pa_j.append(self.pa_min + (self.pa_max - self.pa_min) * K)
else:
Pa_j.append(self.pa_max / N_iter)
for i in range(len(Pa_j)):
rand_temp = np.random.uniform(0, 1)
if rand_temp < Pa_j[i]:
my_x = np.random.randint(100)
my_y = np.random.randint(100)
if (self.objective(X_js[i], N_anchor) or np.inf) > (self.objective([my_x, my_y], N_anchor) or np.inf):
X_js[i] = [my_x, my_y]
Obj_X_js = []
for i in range(len(X_js)):
Obj_X_js.append(self.objective(X_js[i], N_anchor))
Obj_X_js = np.array([np.inf if i is None else i for i in Obj_X_js])
F_min = Obj_X_js[np.argmin(Obj_X_js)]
# print(F_min)
fmins.append(F_min)
best_sol = X_js[np.argmin(Obj_X_js)]
X_nest = X_js.copy()
return best_sol
plt.plot(fmins)
plt.show()
def update_Unknown(self, indexes):
"""Updating Unknown List"""
X_unknown_temp = []
for j in range(len(self.X_unknown)):
if j in indexes:
pass
else:
X_unknown_temp.append(X_unknown[j])
self.X_unknown = np.array(X_unknown_temp)
def main(self):
localised_indexes = []
for i in range(len(self.X_unknown)):
nn = self.neighbours(self.X_unknown[i], self.X_anchor)
if len(nn) >= 3:
updated_node_location = self.mod_cs(nn)
self.X_loc.append([updated_node_location, self.X_unknown[i]])
# Updated
X_anchor_temp = list(self.X_anchor)
X_anchor_temp.append(updated_node_location)
self.X_anchor = np.array(X_anchor_temp)
localised_indexes.append(i)
self.update_Unknown(localised_indexes)
# In[ ]:
# In[163]:
coco = Cuckoo(anchor_percent=0.1, t_range=25)
# In[ ]:
# In[164]:
coco.main()
# In[165]:
coco.X_loc
# In[ ]:
# In[129]:
x = [0.5586032962287517,0.6017524859504793,0.7813999837819767,0.46606363790524846,1.0247709434914343,1.252005912323164,0.7495009374755108,0.561087541248166,0.6173482423311187,0.7023584934592145,0.5555991502481207,0.2347329658872088,0.7689648691600328,0.42170816132227973,0.25106692688462434,1.015593603834578,0.409359463643792,0.23540883134280907,0.5913839305269655,0.600203577388256,0.5000388726258884,0.47597790189065975,0.3658604050896738,0.49870120762058423,1.0246665774750374,0.22224505802313932,0.48748272031646006,0.2721460419888341,0.38653619800686356,0.2615371220932912,0.6958970282936564,0.36899753381356065,0.2039305516189484,0.17151067125529823,0.48919234541387874,0.39500189054510565,0.3530413033536565,0.5591781993082433,0.7749257166059389,0.3368538823421103,0.11406348593214892,0.1550569530750901,0.1974787800028229,0.3971344522252228,0.5281760365228262,0.27339861412968086,0.21166372780804943,0.5555694651180548,0.24039256191025846,0.2507917463528349,0.26165577892959974,0.2902138425532703,0.09274393957755404,0.14342988579925614,0.09482794773206088,0.10777099278167086,0.2167607678878698,0.08407868156026704,0.4162032579761468,0.19392965695433126,0.1531513524666788,0.10250629296185206,0.10944110289909664,0.3372574640392889,0.09181169924020706,0.16634369946602098,0.461380034902016,0.10096995500968431,0.5163841659356835,0.4100570120780608,0.3305394441703243,0.12650693376928016,0.4605351112395287,0.4704711004480148,0.39490322471822437,0.18080358326668763,0.40822830771771684,0.23356350554663777,0.22729262817308193,0.24627316532161503,0.16842430253528257,0.4886667259598101,0.17134357949552495,0.3237412112321077,0.21311484660525568,0.40680583941819976,0.2780304862894268,0.31027772973891987,0.07095677497515178,0.2835606346307872,0.19350661176946501,0.2259702437046537,0.23069606587795344,0.10291948260618343,0.3505927932052159,0.520609285578258,0.552334894422106,0.34428465409389686,0.20672654684411254,0.1638661522394548,]
# In[131]:
plt.plot(x)
plt.show()
# In[160]:
coco.X_anchor
# In[161]:
# In[ ]: