-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathinputs.py
246 lines (204 loc) · 7.9 KB
/
inputs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
# IMPORTS
import numpy as np
import pandas as pd
import sympy as sp
import matplotlib.pyplot as plt
import itertools as it
import math
import scipy.stats as stats
import pickle
# - GLOBAL VARIABLES -
N_POINTS = [10, 15, 20, 25, 50, 100, 150, 200]
# N_POINTS = [15, 25]
H_DRONE = [20] # m
RAY_DRONE = 50 # m
E = [2500000, 5000000, 10000000] # J
S = [2000, 4000, 8000, 16000] # MB
HEIGHTS = (-5, 5) # m
WEIGHTS = (100, 1000) # MB
ZIPF_PARAM = [0]
ALFA = 200 # J/m
BETA = 700 # J/s
GAMMA = 9 # MB/s
MAX_REWARD = 10
ITERATIONS = 13
MAX_DOWNLOAD_E = (WEIGHTS[1] / GAMMA) * BETA
# MAX_DISTANCE = (E[0] - MAX_DOWNLOAD_E) / alfa
MAX_DISTANCE = 500
# - INPUT FUNCTIONS -
# - START -
# np.random.seed(0)
def build_circle(sensor, height):
global RAY_DRONE
ray = math.sqrt(pow(RAY_DRONE, 2) - pow((height - sensor.z), 2))
sensor_2D = sp.Point(sensor.x, sensor.y, evaluate=False)
# circle = sp.Circle(sensor_2D, ray)
return [sensor_2D, ray], sensor_2D
def get_intersections(x0, y0, r0, x1, y1, r1):
# circle 1: (x0, y0), radius r0
# circle 2: (x1, y1), radius r1
d = math.sqrt((x1 - x0) ** 2 + (y1 - y0) ** 2)
# non intersecting
if d > r0 + r1:
return None, None
# One circle within other
if d < abs(r0 - r1):
return None, None
# coincident circles
if d == 0 and r0 == r1:
return sp.Point(x0, y0, evaluate=False), sp.Point(x1, y1, evaluate=False)
else:
a = (r0 ** 2 - r1 ** 2 + d ** 2) / (2 * d)
h = math.sqrt(r0 ** 2 - a ** 2)
x2 = x0 + a * (x1 - x0) / d
y2 = y0 + a * (y1 - y0) / d
x3 = x2 + h * (y1 - y0) / d
y3 = y2 - h * (x1 - x0) / d
x4 = x2 - h * (y1 - y0) / d
y4 = y2 + h * (x1 - x0) / d
return sp.Point(x3, y3, evaluate=False), sp.Point(x4, y4, evaluate=False)
def generate_rewards(n_points, theta):
global MAX_REWARD
x = np.arange(1, MAX_REWARD + 1)
prob = x ** (-theta)
prob = prob / prob.sum()
bounded_zipf = stats.rv_discrete(name='bounded_zipf', values=(x, prob))
rewards = bounded_zipf.rvs(size=n_points)
return rewards
def generate_storages(n_points, theta):
global WEIGHTS
if theta < 0:
theta = abs(theta)
x = np.arange(WEIGHTS[0], WEIGHTS[1] + 1)
prob = x ** (-theta)
prob = prob / prob.sum()
bounded_zipf = stats.rv_discrete(name='bounded_zipf', values=(x, prob))
weights = bounded_zipf.rvs(size=n_points)
weights = [WEIGHTS[1] - i for i in weights]
else:
x = np.arange(WEIGHTS[0], WEIGHTS[1] + 1)
prob = x ** (-theta)
prob = prob / prob.sum()
bounded_zipf = stats.rv_discrete(name='bounded_zipf', values=(x, prob))
weights = bounded_zipf.rvs(size=n_points)
return weights
def get_point_inside_circle(p, circle):
if math.sqrt((p.x - circle[0].x) ** 2 + (p.y - circle[0].y) ** 2) <= circle[1]:
return True
else:
return False
def generate_3D_points(n_points, theta, height, same_set=False, plot=False):
list_3D_points = []
list_circles = []
list_2D_points = []
list_intersection_point = []
global RAY_DRONE, MAX_DISTANCE, HEIGHTS, ALFA, BETA, GAMMA
if same_set:
np.random.seed(3)
xs = ((-MAX_DISTANCE) - MAX_DISTANCE) * np.random.random_sample((n_points,)) + MAX_DISTANCE
ys = ((-MAX_DISTANCE) - MAX_DISTANCE) * np.random.random_sample((n_points,)) + MAX_DISTANCE
zs = (HEIGHTS[0] - HEIGHTS[1]) * np.random.random_sample((n_points,)) + HEIGHTS[1]
rewards = generate_rewards(n_points, 0)
weights = generate_storages(n_points, theta)
rewards = np.insert(rewards, 0, 0)
weights = np.insert(weights, 0, 0)
# # Controllo feasibility qua
# for i in range(n_points):
# point = sp.Point3D(xs[i], ys[i], zs[i], evaluate=False)
# depot = sp.Point3D(0, 0, 0, evaluate=False)
#
# # controllo traveling + hovering <= E
for i in range(n_points):
point = sp.Point3D(xs[i], ys[i], zs[i], evaluate=False)
circle, point_2D = build_circle(point, height)
list_3D_points.append([point, {i + 1}])
list_circles.append([circle, i + 1])
list_2D_points.append([point_2D, {i + 1}])
if plot:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs, ys, zs)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
# EVERY PAIRS
for c1, c2 in it.combinations(list_circles, 2):
i1, i2 = get_intersections(c1[0][0].x, c1[0][0].y, c1[0][1], c2[0][0].x, c2[0][0].y, c2[0][1])
# print("CIRCLES: ", c1[1], " ",c2[1])
if i1 is not None and i1 != i2:
# print("SECANTE")
list_intersection_point.append([i1, {c1[1], c2[1]}])
list_intersection_point.append([i2, {c1[1], c2[1]}])
if i1 is not None and i1 == i2:
# print("TANGENTE")
list_intersection_point.append([i1, {c1[1], c2[1]}])
# print("INTERSEZIONI TROVATE: ", len(list_intersection_point))
for c1 in list_intersection_point:
others = set()
# print("-HEY")
for c2 in list_circles:
if get_point_inside_circle(c1[0], c2[0]):
others.add(c2[1])
# print("--CIAO")
c1[1] = c1[1].union(others)
waypoints = [[sp.Point(0, 0, evaluate=False), {0}]]
waypoints = waypoints + list_2D_points + list_intersection_point
adj_distances = get_adj_matrix_distance(waypoints, ALFA)
hovering_cost = get_hovering_cost(weights, BETA, GAMMA)
return [waypoints, list_2D_points, list_intersection_point, rewards, weights, adj_distances, hovering_cost]
def get_distance(p1, p2, alfa):
return sp.N(p1.distance(p2)) * alfa
def get_hovering_cost(weights, beta, gamma):
N = len(weights)
hovering = np.zeros(N)
for i in range(N):
hovering[i] = (weights[i] / gamma) * beta
return hovering
def get_adj_matrix_distance(waypoints, alfa):
N = len(waypoints)
matrix = np.zeros((N, N))
for i in range(N):
for j in range(N):
matrix[i][j] = get_distance(waypoints[i][0], waypoints[j][0], alfa)
return matrix
def generate_problem_instance(dump=False):
global N_POINTS, ZIPF_PARAM, H_DRONE
instances = []
full_instances = []
for n_point in N_POINTS:
for theta in ZIPF_PARAM:
for h in H_DRONE:
instances = []
for i in range(ITERATIONS):
instances.append(generate_3D_points(n_point, theta, h))
full_instances.append(instances)
if dump:
name = "problems/exaustive_test/problem_n" + str(n_point) + "_t" + str(theta) + "_h" + str(h) + ".dat"
outputFile = open(name, 'wb')
pickle.dump(instances, outputFile)
print("DUMP OF ", name, " DONE.")
return full_instances
def generate_problem_instance_altitude(same_set=False, dump=False):
global ZIPF_PARAM
N_POINTS = [10, 15, 20, 25]
H_DRONE = [5, 10, 15, 20, 25, 30, 35, 40, 45]
instances = []
full_instances = []
for n_point in N_POINTS:
for theta in ZIPF_PARAM:
for h in H_DRONE:
instances = []
for i in range(ITERATIONS):
instances.append(generate_3D_points(n_point, theta, h, same_set))
full_instances.append(instances)
if dump:
name = "problems/altitude_test/problem_alt_n" + str(n_point) + "_t" + str(theta) + "_h" + str(h) + ".dat"
if same_set:
name = "problems/altitude_same_set_test/problem_alt_n" + str(n_point) + "_t" + str(theta) + "_h" + str(
h) + ".dat"
outputFile = open(name, 'wb')
pickle.dump(instances, outputFile)
print("DUMP OF ", name, " DONE.")
return full_instances
# - END -