def init_few_fix_evenly_user(): # Scenario : User reports are very few and reports are evenly distributed in fixed-distant interval.
mylist = []
for x in range(0, pr.map_size[0], 5):
for y in range(0, pr.map_size[1], 5):
print x, y
mylist.append( User(getID('user'), x, y) )
return mylist
def init_user():
mylist = []
with open(pr.fn_pos_random, 'rb') as f:
pos = pk.load(f)
pos_um = pos[1]
for i in range(pr.user_no):
x = pos_um[i][0]
y = pos_um[i][1]
mylist.append(User(getID('user'), x, y))
return mylist
def init_cell():
mylist = []
fn_opened = pr.fn_pos_fixed if pr.fix_cell_pos else pr.fn_pos_random
with open(fn_opened, 'rb') as f:
pos = pk.load(f) # include [cm, um]
pos_cm = pos[0]
for i in range(pr.cell_no):
x = pos_cm[i][0]
y = pos_cm[i][1]
power = pos_cm[i][2]
mylist.append(Cell(getID('cell'), x, y, power))
return mylist
def init_nonuniform_user(): # non-uniform spatial distribution of user report
mylist = []
n_room = 4
n_per_room = [120, 120, 120, 119]
center = [[28, 6], [30, 19], [70, 6],
[79,
19]] #room1~4 : left-bottom, left-up, right-bottom, right-up.
variance = [[8, 2.2], [9, 2.2], [9.5, 2.2], [7, 2.2]]
for i in range(n_room):
x = np.random.normal(loc=center[i][0],
scale=variance[i][0],
size=n_per_room[i])
y = np.random.normal(loc=center[i][1],
scale=variance[i][1],
size=n_per_room[i])
for j in range(n_per_room[i]):
mylist.append(User(getID('user'), x[j], y[j]))
return mylist
def init_nonuniform_user(): # non-uniform spatial distribution of user report
mylist = []
n_report = 500
n_room = 4
center = [[27, 6], [30, 19], [70, 6], [79, 19]] #room1~4 : left-bottom, left-up, right-bottom, right-up.
variance = [[9, 2.2], [8, 2.2], [9.5, 2.2], [7, 2.2]]
distribution = 'gaussian' # non-uniform distribution : gaussian/ laplace/ triangular
if distribution == 'gaussian':
# Naive Method:
# x = np.random.normal(loc=rd.uniform(35, 70), scale=rd.uniform(18, 23), size=n_report)
# y = np.random.normal(loc=rd.uniform(8, 17), scale=rd.uniform(4, 8), size=n_report)
for i in range(n_room):
n_per_room = n_report/ n_room
x = np.random.normal(loc=center[i][0], scale=variance[i][0], size=n_per_room)
y = np.random.normal(loc=center[i][1], scale=variance[i][1], size=n_per_room)
for j in range(n_per_room):
mylist.append( User(getID('user'), x[j], y[j]) )
return mylist
