def exp4():
logging.basicConfig(level=logging.DEBUG, filename='log/debug.log')
logging.info(time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime()) + " START")
param = Params(1000)
data = data_readin(param)
# print "Loc"
# for lid in param.locs.keys():
# print len(param.locs[lid])
print "var"
for lid in param.locs.keys():
users = param.locs[lid]
print max(users.values())
# print "User"
# for uid in param.users.keys():
# print len(param.users[uid])
param.NDIM, param.NDATA = data.shape[0], data.shape[1]
param.LOW, param.HIGH = np.amin(data, axis=1), np.amax(data, axis=1)
evalPSD(data, param)
tree = Kd_standard(data, param)
else:
logging.error("No such index structure!")
sys.exit(1)
tree.buildIndex()
res_cube_value[i, j, k] = optimization(tree, fov_count, seed_list[j], param)
res_value_summary = np.average(res_cube_value, axis=1)
np.savetxt(param.resdir + exp_name + dataset_identifier, res_value_summary, fmt="%.4f\t")
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG, filename="../../log/debug.log")
logging.info(time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime()) + " START")
param = Params(1000)
data = data_readin(param)
param.NDIM, param.NDATA = data.shape[0], data.shape[1]
param.LOW, param.HIGH = np.amin(data, axis=1), np.amax(data, axis=1)
print data
# eval_partition(data, param)
eval_analyst(data, param)
# eval_bandwidth(data, param)
# eval_skewness(data, param)
logging.info(time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime()) + " END")
logging.basicConfig(level=logging.DEBUG, filename='../log/debug.log')
logging.info(time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime()) + " START")
# eps_list = [0.001, 0.004, 0.007, 0.01]
# dataset_list = ['yelp', 'foursquare', 'gowallasf', 'gowallala']
eps_list = [0.05, 0.45]
dataset_list = ['gowallasf']
for dataset in dataset_list:
for eps in eps_list:
param = Params(1000)
all_workers = data_readin(param)
param.NDIM, param.NDATA = all_workers.shape[0], all_workers.shape[1]
param.LOW, param.HIGH = np.amin(all_workers, axis=1), np.amax(all_workers, axis=1)
param.DATASET = dataset
param.select_dataset()
param.Eps = eps
param.debug()
path_data = getPathData(all_workers, param)
# max_count = 0
# for data in path_data:
# if data[1] > max_count:
# max_count = data[1]
fig, ax = plt.subplots()
# img = imread("background.png")
leaf_boxes.append((curr.n_box, curr.n_count))
return leaf_boxes
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG, filename='log/debug.log')
# dataset_list = ['yelp', 'foursquare', 'gowallasf', 'gowallala']
dataset_list = ['mediaq']
for dataset in dataset_list:
param = Params(1000)
data = data_readin(param)
param.NDIM, param.NDATA = data.shape[0], data.shape[1]
param.LOW, param.HIGH = np.amin(data, axis=1), np.amax(data, axis=1)
param.DATASET = dataset
param.select_dataset()
param.debug()
path_data = getPathData(data, param)
fig, ax = plt.subplots()
# img = imread("background.png")
for data in path_data:
path = data[0]
codes, verts = zip(*path)
path = mpath.Path(verts, codes)
# weight = min(1, (data[1] + 0.0) / 500)
weight = 1
time.strftime("%a, %d %b %Y %H:%M:%S", time.localtime()) + " START")
# eps_list = [0.001, 0.004, 0.007, 0.01]
# dataset_list = ['yelp', 'foursquare', 'gowallasf', 'gowallala']
eps_list = [0.05, 0.45]
dataset_list = ['gowallasf']
for dataset in dataset_list:
for eps in eps_list:
param = Params(1000)
all_workers = data_readin(param)
param.NDIM, param.NDATA = all_workers.shape[0], all_workers.shape[
1]
param.LOW, param.HIGH = np.amin(all_workers,
axis=1), np.amax(all_workers,
axis=1)
param.DATASET = dataset
param.select_dataset()
param.Eps = eps
param.debug()
path_data = getPathData(all_workers, param)
# max_count = 0
# for data in path_data:
# if data[1] > max_count:
# max_count = data[1]
fig, ax = plt.subplots()
