def main():
user_list = [3, 8, 7, 5]
item_list = [46, 34, 54]
user_list = sorted(user_list)
item_list = sorted(item_list)
user_item_matrix = np.matrix([[3, 0, 3], [5, 4, 0], [1, 2, 4], [2, 2, 0]])
user_similarity_matrix = f.get_similarity(user_item_matrix)
print('用户相似性矩阵如下：')
print(user_similarity_matrix)
Recommender = f.get_recommender(user_item_matrix, user_similarity_matrix,
2)
print('根据相似性得到的推荐矩阵如下：')
print(Recommender)
f.recommend(5, user_list, item_list, Recommender, 2)
def main():
#从数据库中读取所需信息,并做格式化预处理
db = db_cls(db_filename="pj_data.db")
user_list = db.read_Data(sql_query="Select id FROM User_Table")
user_list = [ite[0] for ite in user_list]
#item_list_dict = dict(db.read_Data(sql_query="Select id,song_name FROM Song"))
#item_list = list(item_list_dict)
item_list1 = db.read_Data(sql_query="Select song_id FROM User2Song")
item_list2 = db.read_Data(sql_query="Select song_id FROM SongList2Song")
item_list = list(set(item_list1 + item_list2))
item_list = [ite[0] for ite in item_list]
user_list = sorted(user_list)
item_list = sorted(item_list)
item_list_dict = dict(
db.read_Data(sql_query="Select id,song_name FROM Song"))
for item in item_list:
item_list_dict[item] = item_list_dict.get(item, "歌名未知")
#创建“最近听歌”字典,key为user_id,value为该用户最近听歌的歌曲id组成的列表
recent_listen = db.read_Data(sql_query="Select * FROM User2Song")
recent_listen_dict = dict()
for it in recent_listen:
recent_listen_dict[it[0]] = recent_listen_dict.get(it[0], []) + [it[1]]
for it in user_list:
recent_listen_dict[it] = recent_listen_dict.get(it, [])
#创建“歌单音乐”字典,key为user_id,value为该用户收藏歌单中歌曲id组成的列表
list_music = db.read_Data(sql_query="Select a.user_id,b.song_id \
FROM User2SongList a INNER JOIN SongList2Song b\
ON a.songlist_id = b.songlist_id")
list_music_dict = dict()
for it in list_music:
list_music_dict[it[0]] = list_music_dict.get(it[0], []) + [it[1]]
for it in user_list:
list_music_dict[it] = list_music_dict.get(it, [])
#获取用户-音乐评价矩阵
user_item_matrix = cf.get_matrix(user_list, item_list, recent_listen_dict,
list_music_dict)
#获取用户相似度矩阵
user_similarity_matrix = cf.get_similarity(user_item_matrix)
#获取基于用户相似性得到的推荐矩阵
Recommender = cf.get_recommender(user_item_matrix, user_similarity_matrix,
5)
#为id为393718733的用户推荐5首歌曲
#cf.recommend(393718733, user_list, item_list_dict, Recommender, 20)
cf.recommend(2141581764, user_list, item_list_dict, Recommender, 10)
def main():
parser = argparse.ArgumentParser(
description='Convert a binary logfile to json.')
parser.add_argument('in_path',
metavar='infile',
help='The binary file that is converted to json.')
parser.add_argument('out_path',
metavar='outfile',
nargs='?',
help='The output json file.')
args = parser.parse_args()
in_path = args.in_path
out_path = args.out_path if args.out_path is not None else args.in_path + ".json"
logData = cff.decode(in_path)
for key in logData.keys():
logData[key] = logData[key].tolist()
f = open(out_path, "w")
print("Number of lines written to {}: {}".format(
out_path, str(f.write(json.dumps(logData)))))
f.close()
def process_data(filename, foldername):
logData = cff.decode(filename)
cwd = os.getcwd()
outputdir = cwd + '/' + foldername
if not os.path.exists(outputdir):
os.makedirs(outputdir)
for group, vars in LogGroups.items():
filename = outputdir + '/' + group +'.csv'
names = "tick"
values = [logData["tick"]]
for var in vars:
if var in logData:
names += "," + var
values.append(logData[var])
if len(values) > 1:
values = np.stack(values).T
np.savetxt(filename, values, delimiter=',', header=names)
# -*- coding: utf-8 -*-
"""
example on how to plot decoded sensor data from crazyflie
@author: jsschell
"""
import CF_functions as cff
import matplotlib.pyplot as plt
import re
# decode binary log data
logData = cff.decode("test100")
# set window background to white
plt.rcParams['figure.facecolor'] = 'w'
# number of columns and rows for suplot
plotCols = 1;
plotRows = 0;
# let's see which keys exists in current data set
keys = ""
for k, v in logData.items():
keys += k
# get plot config from user
plotGyro = 0
if re.search('gyr', keys):
inStr = input("plot gyro data? ([Y]es / [n]o): ")
if ((re.search('^[Yy]', inStr)) or (inStr == '')):
plotGyro = 1
plotRows += 1
# -*- coding: utf-8 -*-
"""
example on how to plot decoded sensor data from crazyflie
@author: jsschell
"""
import CF_functions as cff
import matplotlib.pyplot as plt
import re
# decode binary log data
logData = cff.decode("test100")
# set window background to white
plt.rcParams['figure.facecolor'] = 'w'
# number of columns and rows for suplot
plotCols = 1;
plotRows = 0;
# let's see which keys exists in current data set
keys = ""
for k, v in logData.items():
keys += k
# get plot config from user
plotGyro = 0
if re.search('gyr', keys):
inStr = input("plot gyro data? ([Y]es / [n]o): ")
if ((re.search('^[Yy]', inStr)) or (inStr == '')):
plotGyro = 1
plotRows += 1
# -*- coding: utf-8 -*-
"""
example on how to plot decoded sensor data from crazyflie
@author: jsschell
"""
import CF_functions as cff
import matplotlib.pyplot as plt
import re
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()
# decode binary log data
logData = cff.decode(args.filename)
# set window background to white
plt.rcParams['figure.facecolor'] = 'w'
# number of columns and rows for suplot
plotCols = 1;
plotRows = 1;
# let's see which keys exists in current data set
keys = ""
for k, v in logData.items():
keys += k
# get plot config from user
plotGyro = 0
import CF_functions as cff
import csv
from matplotlib import pyplot as plt
# Decode binary log file
log = cff.decode('log_data/log_thrust_est_4')
csv_file = 'csv_data/log_thrust_est_4.csv'
# Begin writing to csv
with open(csv_file, mode='w') as file:
wrtr = csv.writer(file, delimiter=',')
# Extract key names as a list
keys = list(log.keys())
# Write first row with names
wrtr.writerow(keys)
for cols in range(len(list(log['tick']))):
row_list = []
# Extract a value from every key in a row and append it to the list
for key in keys:
row_list.append(log[key][cols])
# Write row to csv file
wrtr.writerow(row_list)
import CF_functions as cff
import matplotlib.pyplot as plt
import re
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Play a pickled policy.')
parser.add_argument("-l", '--log', type=str, help='Logfile')
args = parser.parse_args()
if args.log is not None:
logfile = args.log
else:
logfile = "ctrlNN_feb_1/log01"
# decode binary log data
logData = cff.decode(logfile)
print(logData.keys())
print(logData['ctrlNN.out2'])
OFFSET = 0
# set window background to white
plt.rcParams['figure.facecolor'] = 'w'
# number of columns and rows for suplot
plotCols = 1
plotRows = 1
# let's see which keys exists in current data set
keys = ""
for k, v in logData.items():
keys += k
log_list = os.listdir(arg)
log_dir = arg
multi_log = True
elif os.path.isfile(arg):
log_name = arg
if opt in ('-a', '--all'): plot_all = True
elif opt in ('-k', '--key_group'):
key_to_plot_alone = arg
else:
key_to_plot_alone = None
plot_all = False
if multi_log:
logDataList = []
for log_file in log_list:
logData = cff.decode(os.path.join(log_dir, log_file))
logDataList.append(logData)
printed = []
for data in logDataList:
keys = ""
for k, v in data.items():
keys += k
if k not in printed:
print(k)
printed.append(k)
else:
# decode binary log data
logData = cff.decode(log_name)
# let's see which keys exists in current data set
keys = ""
for k, v in logData.items():