def start_evaluator():
print("Starting Evaluator...")
#Read Data
gd = getData()
rec_tmp = []
print("Got first item")
#Loop until nothing else to read
while (gd.empty == False):
global rec_db1
#Call Algorithm and get Recommendations
rec_tmp = callback(gd)
if (len(rec_tmp) != 0):
if (delay == True):
rec_db1 = rec_db1.append(rec_tmp)
else:
rec_db1 = rec_tmp
#Read Data Again
gd = getData()
# If the session and item matches a recommendation
# then provide a reward
if ((len(rec_db1) != 0) and (gd.loc[gd['SessionID'].isin(
rec_db1.iloc[:, 0])]['Event'].values == 3)):
#Generate Reward
algoreward(gd)
if ((delay == False) and (len(rec_db1) != 0)):
rec_db1.drop(rec_db1.index[0], inplace=True)
#Calculate and plot metrics
calc_metrics(rec_tmp, gd)
print('Evaluator Run Complete')
# print rec_db1;
get_metrics()
save_dir = os.path.join(THIS_FOLDER, "results")
glove_path = os.path.join(THIS_FOLDER, "glove")
################### Input ####################################################
option = input(
"Please select what you would like to do:\n 1. Train and chat 2. Load model and chat \n"
)
embedding = '2'
if option == '1':
embedding = input(
"Please select what kind of embedding would you like to use:\n 1. GloVe 2. Embedding layer \n"
)
################### READ, NORMALIZE, CREATE PAIRS ############################
MAX_LENGTH = 15 # maximum words in a sentence
sentences_lengths = getData(DATA_PATH, PAIRS_PATH) # read_data.py
# if the trimmed lines are already saved skip getTestData
if path.exists(LINES_PATH) == False or os.stat(LINES_PATH).st_size == 0:
getTestData(DATA_TEST_PATH, LINES_PATH, MAX_LENGTH) # read_data.py
#################### CREATE VOCABULARY AND NEW PAIRS #########################
voc, pairs = prepareData(PAIRS_PATH, MAX_LENGTH) # vocabulary.py
print("total dialogues " + str(len(pairs)))
print("total words " + str(voc.__len__()))
lines = trimLines(LINES_PATH, voc)
#################### PREPARE DATA IN BATCHES #################################
import read_data as rd
import matplotlib.pyplot as plt
data = rd.getData("example.in")
#data = rd.getData("mother_of_all_warehouses.in")
#data = rd.getData("redundancy.in")
drones = data[3]
print drones[0].position
environment = data[0]
warehouses = data[1]
orders = data[2]
plt.figure(1)
plt.margins(0.1,0.1)
j=0
for w in warehouses:
if j == 0:
plt.plot(w.position[0], w.position[1],'bo',markersize=20)
j=1
else:
plt.plot(w.position[0], w.position[1],'go')
for o in orders:
plt.plot(o.location[0], o.location[1],'ro',alpha=0.3)
plt.show()
Calculates the total weight of an order.
:param order:
:return:
"""
total_weight = 0
for product_id, num_items in enumerate(order.products):
total_weight += num_items * product_weights[product_id]
return total_weight
def get_current_weight(drone, product_weights):
return calculate_total_weight(drone, product_weights)
if __name__ == '__main__':
environment, warehouses, orders, drones = getData('busy_day.in')
commands = []
# sort order by total weight of items in order
order_total_weights = [(order, calculate_total_weight(order, environment.product_weights)) for order in orders]
orders = [order for order in sorted(order_total_weights, key=operator.itemgetter(1))]
current_drone = drones[0]
for order in orders:
fulfilled = False
product_id = 0
# check if items are left in the order
while order.products[product_id] > 0:
"""
# 定义超参
D = 55
p = 0.95
ff = 0.2
CL_i = {} # 存放的是一个片段，不是一个点
CL_j = {}
CL_i_index = {}
CL_j_index = {}
outlying = [] # 存放异常值
outlying_fine = [] # 存放异常值的整条线段
start_time = time.time()
# 数据划分
data = getData()
# 在粗糙路径数据集中两两配对寻找边缘路径
print("---------------------------------数据读取完成，进入划分模块---------------------------------")
for number, item in enumerate(data):
for i in range(len(item[0])-1):
L_i = [item[0][i], item[0][i + 1]] # 取粗线段中的线段
CL_j_index[number] = L_i
for j in range(len(item[0])-1):
L_j = [item[0][j], item[0][j + 1]] # 取粗线段中的线段
CL_i_index[number] = L_j
if (L_i[0] == L_j[0]) and L_i[1] == L_j[1]: # 寻找两个data几何中不相同的两条路径 L_i_l,L_j_l中存放的是粗线段的片段和对应的精细化片段
pass
else:
# relu 1
if lower_bounds_dist(L_i, L_j, item[1]) > D:
