def getPathData(data, param):
path_data = []
start = time.time()
# tree = Grid_standard(data, param)
tree = Quad_standard(data, param)
# tree = Kd_standard(data, param)
tree.buildIndex()
print "time ", time.time() - start
print "data points: ", tree.checkCorrectness(tree.root)
leaf_boxes = getLeafNode(tree, 2)
for data in leaf_boxes:
# [[x_min,y_min],[x_max,y_max]]
if data[1] > 10:
print (data[0][0][0]+data[0][1][0])/2 , "\t" , (data[0][0][1]+data[0][1][1])/2 , "\t" , data[1]
path = []
box = data[0]
# (x_min, y_min) --> (x_min, y_max) --> (x_max, y_max) --> (x_max, y_min) --> (x_min, y_min)
path.append((mpath.Path.MOVETO, (box[0][0], box[0][1])))
path.append((mpath.Path.LINETO, (box[0][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[0][1])))
path.append((mpath.Path.CLOSEPOLY, (box[0][0], box[0][1])))
path_data.append((path, data[1]))
return path_data
def getPathData(data, param):
path_data = []
start = time.time()
# tree = Grid_standard(data, param)
tree = Quad_standard(data, param)
# tree = Kd_standard(data, param)
tree.buildIndex()
print "time ", time.time() - start
print "data points: ", tree.checkCorrectness(tree.root)
leaf_boxes = getLeafNode(tree, 2)
for data in leaf_boxes:
# [[x_min,y_min],[x_max,y_max]]
if data[1] > 10:
print(data[0][0][0] + data[0][1][0]) / 2, "\t", (
data[0][0][1] + data[0][1][1]) / 2, "\t", data[1]
path = []
box = data[0]
# (x_min, y_min) --> (x_min, y_max) --> (x_max, y_max) --> (x_max, y_min) --> (x_min, y_min)
path.append((mpath.Path.MOVETO, (box[0][0], box[0][1])))
path.append((mpath.Path.LINETO, (box[0][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[1][1])))
path.append((mpath.Path.LINETO, (box[1][0], box[0][1])))
path.append((mpath.Path.CLOSEPOLY, (box[0][0], box[0][1])))
path_data.append((path, data[1]))
return path_data
def run_Quad_standard(self, param):
logging.debug('building Quad_baseline...')
tree = Quad_standard(self.data, param)
start = time.clock()
tree.buildIndex()
end = time.clock()
logging.info('[T] Quad_standard building time: %.2f' % (end - start))
return self.query(tree)
def eval_partition(data, param):
# tree = Grid_standard(data, param)
tree = Quad_standard(data, param)
# tree = Kd_standard(data, param)
tree.buildIndex()
seed = 1000
fov_count = 20
print optimization(tree, fov_count, seed, param)
def run_Quad_geo(self, param):
logging.debug('building Quad_geo...')
param.geoBudget = 'optimal'
tree = Quad_standard(self.data, param)
tree.buildIndex()
return self.query(tree, "Quad_geo")
def run_Quad_baseline(self, param):
logging.debug('building Quad_baseline...')
param.geoBudget = 'none'
tree = Quad_standard(self.data, param)
tree.buildIndex()
return self.query(tree)
def evalPSD(data, param):
global method_list, exp_name
exp_name = 'evalPSD'
method_list = ['Kd_standard']
# Params.maxHeight = 10
res_cube_abs = np.zeros((len(eps_list), len(seed_list), len(method_list)))
res_cube_rel = np.zeros((len(eps_list), len(seed_list), len(method_list)))
for j in range(len(seed_list)):
for i in range(len(eps_list)):
param.Eps = eps_list[i]
for k in range(len(method_list)):
param.Seed = seed_list[j]
if method_list[k] == 'Quad_standard':
tree = Quad_standard(data, param)
elif method_list[k] == 'Kd_standard':
tree = Kd_standard(data, param)
else:
logging.error('No such index structure!')
sys.exit(1)
tree.buildIndex()
with open(param.dataset, 'r') as ins:
for row in ins:
row = row.split()
user_id = int(row[0])
# loc_id = int(row[4])
leaf = tree.leafCover((float(row[2]), float(row[3])))
if leaf == None:
print (float(row[2]), float(row[3]))
continue
# update the number of times user_id visits this location
if leaf.users.has_key(user_id):
leaf.users[user_id] = leaf.users[user_id] + 1
else:
leaf.users[user_id] = 1
loc_users = tree.loc_users()
print len(loc_users), np.mean([len(loc_users[i]) for i in loc_users.keys()])
user_locs = transform(loc_users)
print len(user_locs), np.mean([len(user_locs[k]) for k in user_locs.keys()])
# sampling
L = 3
sampled_user_locs = samplingL(user_locs, L)
print len(user_locs), np.mean([len(sampled_user_locs[k]) for k in sampled_user_locs.keys()])
sampled_loc_users = transform(sampled_user_locs)
print sampled_loc_users
print len(sampled_loc_users), np.mean([len(sampled_loc_users[i]) for i in sampled_loc_users.keys()])
E_actual = shannonEntropy(sampled_loc_users, param.K)
print E_actual
print len(E_actual)
# evalLimitCM2((param, sampled_loc_users, 3, E_actual, L))
pool = Pool(processes=len(eps_list))
params = []
for C in C_list:
params.append((param, sampled_loc_users, C, E_actual, L))
pool.map(evalLimitCM2, params)
pool.join()
def __init__(self, data, param):
Quad_standard.__init__(self, data, param)
def __init__(self, data, param):
Quad_standard.__init__(self, data, param)
