def __init__(self, settings_file):
self.settings_file = settings_file
self.json_file = read_json(settings_file)
self.todo_size, self.history_size, self.delta_size = -1, -1, -1
self.todo_list, self.history_list, self.delta_list = [], [], []
self.get_queue_size()
def __init__(self, name, settings_file, parent=None):
self.name = name
self.media_type = ""
self.settings = read_json(settings_file)
self.parent = parent
self.get_suggested_metadata()
reply_kb_university = ReplyKeyboardMarkup(
[["City University of Hong Kong"], ["Hong Kong Baptist University"],
["Lingnan University"], ["The Chinese University of Hong Kong"],
["The Education University of Hong Kong"],
["The Hong Kong Polytechnic University"],
["The Hong Kong University of Science and Technology"],
["The University of Hong Kong"], ["Hang Seng University of Hong Kong"],
["Hong Kong Shue Yan University"], ["The Open University of Hong Kong"],
["Others"]],
one_time_keyboard=True)
# reply_kb_example = ReplyKeyboardMarkup([['Where is the library?'],['Tell me the contact of KEC']], one_time_keyboard=True)
# initial the nltk parts
default_tagger = nltk.tag.DefaultTagger('NN')
model = read_json('json/models.json')
tl_MWs = read_multiwords_json('json/multiwords.json')
tags_dict = {}
tagger = nltk.tag.UnigramTagger(model=model, backoff=default_tagger)
tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
mwtknzr = MWETokenizer(tl_MWs)
stop_words = set(stopwords.words('english'))
# initialize the list of information for class schedule
clList = readClSchedule('schedule/MTT_2021S2_Custom.xls')
def createKeyBoardLayout(btnStringList: list) -> ReplyKeyboardMarkup:
for btsString in btnStringList:
pl.clf()
pl.plot(x, y, label='Raw Data')
for i in range(len(smoothy)):
pl.plot(x, smoothy[i], label='Smooth('+str(f[i])+')')
pl.legend()
pl.savefig('./imgs/lowess')
print "Successfully created picture file lowess.png"
if __name__ == '__main__':
#pdb.set_trace()
import numpy as np
from readjson import read_json
cpu, cpu_time, mem, mem_time, load, load_time = read_json("./host2.json")
#x = np.asarray(cpu_time)
y=[]
for i in xrange(30):
y = y+cpu
x = np.arange(0.0, len(y))
print "----------------- LOWESS ---------------------"
def chunks(l, n):
for i in xrange(0, len(l), n):
yield l[i:i+n]
chunk_size = 1000
from ewma import ewma_with_window
ave_y = ewma_with_window(y, 0.8, 50)
pl.clf()
pl.plot(x, y, label="raw data")
pl.plot(x, ave_y, label="EWMAverage")
pl.legend()
pl.savefig("./imgs/emwa_window")
print "Successfully created picture file emwa_window.png"
if __name__ == '__main__':
#pdb.set_trace()
import numpy as np
from readjson import read_json
cpu, cpu_time, mem, mem_time, load, load_time = read_json("./host2.json")
x = np.asarray(cpu_time)
y = cpu
#x = np.array([[1,2,3,4,5,6],[1,232,3,41,5,6]]).reshape(2,6)
#y = np.array([[1,2,2,3,4,5]])
print(x.shape)
print(y.shape)
print "----------------- LOWESS ---------------------"
testLowess(x, y)
print "------------ Liner Regression ----------------"
testLinerRegression(x, y)
print "------------- Poly Regression ----------------"
