def generate_request_peaks(id, t, maxX, maxY):
probstartx = [0.100202429, 0.402496626, 0.761133603, 0.906882591, 0.979419703, 1]
probstarty = [0.082321188, 0.472334683, 0.840755735, 0.978744939, 0.997300945, 1]
probendx = [0.127278866, 0.310600945, 0.538825118, 0.753882512, 0.917960837, 1]
probendy = [0.082321188, 0.472334683, 0.840755735, 0.978744939, 0.997300945, 1]
return Request(id, Location(generate_value(probstartx, maxX), generate_value(probstarty, maxY)),
Location(generate_value(probendx, maxX), generate_value(probendy, maxY)),
np.random.uniform(high=t))
def generate_uniform(n, t, seed, maxX, maxY):
random.seed(seed)
requests = [
Request(i, Location(random.random() * maxX,
random.random() * maxY),
Location(random.random() * maxX,
random.random() * maxY),
random.random() * t) for i in range(n)
]
return requests
def extractRequests(filename):
requests = []
with open(filename) as csv_file:
i = 1
csv_reader = csv.reader(csv_file)
for row in csv_reader:
requests.append(Request(i, Location(float(row[1]), float(row[2])),
Location(float(row[3]), float(row[4])),
float(row[0])))
return requests
def extractRequests(fileName):
f = open(filename, 'w')
with open(src) as csv_file:
csv_reader = csv.reader(csv_file)
requests = [
Request(Location(float(row[1]), float(row[2])),
Location(float(row[3]), float(row[4])), float(row[0]))
for row in csv_reader
]
return requests
def generate_peaks(n, t, seed, maxX, maxY):
np.random.seed(seed)
requests = [
Request(i, Location(random.random() * maxX,
random.random() * maxY),
Location(random.random() * maxX,
random.random() * maxY),
random.random() * t) for i in range(n)
]
requests = [generate_request_peaks(i, t, maxX, maxY) for i in range(n)]
return requests
def generate_uniform(n, seed, maxX, maxY):
random.seed(seed)
workers = [
Worker(i, Location(random.random() * maxX,
random.random() * maxY), 0.0) for i in range(n)
]
return workers
def generate_request_peaks_normal(id, t, maxX, maxY):
mean1 = [0.25 * maxX, 0.25 * maxY]
mean2 = [0.75 * maxX, 0.75 * maxY]
cov = [[0.0625 * maxX, 0], [0, 0.0625 * maxY]]
rand = np.random.uniform()
if rand < 0.6:
return Request(id, Location(np.random.uniform(high=maxX), np.random.uniform(high=maxY)),
Location(np.random.uniform(high=maxX), np.random.uniform(high=maxY)),
np.random.uniform(high=t))
elif rand < 0.8:
return Request(id, normal_location_in_range(maxX, maxY, mean1, cov),
normal_location_in_range(maxX, maxY, mean2, cov),
np.random.uniform(low=t*0.2, high=t*0.4))
else:
return Request(id, normal_location_in_range(maxX, maxY, mean2, cov),
normal_location_in_range(maxX, maxY, mean1, cov),
np.random.uniform(low=t*0.6, high=t*0.8))
def normal_location_in_range(maxX, maxY, mean, cov):
while (True):
location = np.random.multivariate_normal(mean, cov)
if location[0] >= 0 and location[0] <= maxX and location[
1] >= 0 and location[1] <= maxY:
return Location(location[0], location[1])
def generate_request_peakt(id, t, maxX, maxY):
probt = [0.276071549, 0.522443469, 0.718191023, 0.830239622, 0.907863652, 0.951400607, 0.979075262, 1]
return Request(id, Location(np.random.uniform(high=maxX), np.random.uniform(high=maxY)),
Location(np.random.uniform(high=maxX), np.random.uniform(high=maxY)),
generate_value(probt, t))