def main():
# Tests first a random input, then a sorted input
for testType in ["Random", "Sorted"]:
# Displays if random or sorted
print(testType.title().center(25 + (12 * (3)) + 1, "=") + "\n")
# Uses HOF to determine the testSet
if testType == "Random":
testSet = getRandomList
else:
testSet = lambda x: list(range(x))
# Displays all three metrics as described for this lab
for test in ["time", "swaps", "comparisons"]:
# Invokes compare for all sorts
compare([
"Selection",
"Bubble",
"Quick",
"Merge",
], [selectionSort, bubbleSort, quickSort, mergeSort],
[10, 100, 1000, 10000],
dataSet=testSet,
counter={
"comparisons": 0,
"swaps": 0
},
compareType=test)
print()
def similarity(self):
sizeM = len(self.sequenceS)
sizeN = len(self.sequenceT)
for i in range(1, sizeM + 1):
for j in range(1, sizeN + 1):
maxValue = max(
self.matrix[i - 1, j] + values["GAP"],
self.matrix[i - 1, j - 1] +
tls.compare(self.sequenceS[i - 1], self.sequenceT[j - 1]),
self.matrix[i, j - 1] + values["GAP"], 0)
self.matrix[i, j] = maxValue
if (maxValue > self.optimalValue):
self.optimalValue = maxValue
self.positionOptimalValue = [i, j]
return self.matrix
def check_new_chapters(url):
"""check if there is new chapter and
save new chapters in json form into noval.json
"""
html, chapters = request_get(url, a_xpath)
# 获取章节
chpt_infos = []
for chapter in chapters:
chpt_url = chapter.xpath("./@href")
chpt_name = chapter.xpath("./text()")
chpt_infos.append([chpt_url, chpt_name])
# 对比并保存新的 小说章节
title = html.xpath("//div[@class='title']")
if title:
save_filename = title[0] + '.json'
else:
title = url.split('/')[-1]
# 查询新章节
if os.path.exists(save_filename):
with open(save_filename, 'r') as info_f:
old_chpt_infos = json.loads(info_f.read())
new_chpt_infos = compare(old=old_chpt_infos, new=chpt_infos)
# todo: download chapter according to new_chpt_infos
if new_chpt_infos:
for chpt_url, chpt_name in new_chpt_infos:
# 查看url 是否以 '/' 结尾
base_url = url.split('index.html')[0]
if base_url[-1] == '/':
chapter_link = base_url + chpt_url
else:
chapter_link = base_url + '/' + chpt_url
noval_filename = download_chapter(title, chapter_link)
# send email to user
send_email(noval_filename)
# 删除
os.remove(noval_filename)
with open(save_filename, 'w') as f:
chpt_json = json.dumps(chpt_infos)
f.write(chpt_json)
def align(self):
sequenceS = self.sequenceS
sequenceT = self.sequenceT
alignS = ""
alignT = ""
lenS = self.positionOptimalValue[0]
lenT = self.positionOptimalValue[1]
while (lenS >= 0 and lenT >= 0):
if (lenT - 1 < 0 and lenS - 1 < 0):
break
if (lenS - 1 < 0):
scoreUp = None
scoreDiag = None
if (lenT - 1 < 0):
scoreLeft = None
scoreDiag = None
score = self.matrix[lenS, lenT]
scoreDiag = self.matrix[lenS - 1, lenT - 1]
scoreUp = self.matrix[lenS - 1, lenT]
scoreLeft = self.matrix[lenS, lenT - 1]
if (scoreDiag != None and score == scoreDiag +
tls.compare(sequenceS[lenS - 1], sequenceT[lenT - 1])):
alignS = sequenceS[lenS - 1] + alignS
alignT = sequenceT[lenT - 1] + alignT
lenS = lenS - 1
lenT = lenT - 1
continue
if (scoreLeft != None and score == scoreLeft + values["GAP"]):
alignS = "-" + alignS
alignT = sequenceT[lenT - 1] + alignT
lenT = lenT - 1
continue
else:
alignS = sequenceS[lenS - 1] + alignS
alignT = "-" + alignT
lenS = lenS - 1
continue
return (alignS, alignT)
def _check_state(self, test_outputs, ins):
val_l = [test_outputs[k] for k in self.out_names]
return compare(val_l, ins, False)
def _check_state(self, test_outputs, ins):
val_l = [test_outputs[k] for k in self.out_names]
return compare(val_l, ins, False)
def main():
compare(["Brute", "Fancy"],
[lambda x: nQueensBrute(x, True), lambda x: nQueensFancy(x, True)],
[4, 5, 6, 7, 8, 9])
def advance_state(self, o):
s = self.cur_state
next_state = [
# S*0
(
(compare(s, "0000")) or
(compare(s, "0001") and compare(o, "000010")) or
(compare(s, "0010")) or
(compare(s, "0110"))),
# S*1
(
(compare(s, "0001") and compare(o, "10-011")) or
(compare(s, "0001") and compare(o, "000000")) or
(compare(s, "0010") and not o[3]) or
(compare(s, "0110"))),
# S*2
(
(compare(s, "0001") and compare(o, "000000")) or
(compare(s, "0010") and o[3]) or
(compare(s, "0011")) or
(compare(s, "0110"))),
# S*3
(compare(s, "0001") and compare(o, "000--0") and (o[2] ^ o[1]))
]
self.cur_state = next_state
def get_output(self):
s = self.cur_state
return {
'RegDst': True,
'RegWrite': compare(s, "-111") or compare(s, "-100"),
'ALUSrcA': s[3] or s[1],
'MemRead': compare(s, "-011") or compare(s, "0000"),
'MemWrite': compare(s, "-101"),
'MemToReg': False,
'lorD': s[0],
'IRWrite': compare(s, "0000"),
'PCWrite': compare(s, "1--1") or compare(s, "0000"),
'PCWriteCond': compare(s, "1--0"),
'AluOP1': s[2],
'AluOP0': s[3],
'AluSrcB1': s[0] or compare(s, "-01-"),
'AluSrcB0': compare(s, "0-0-"),
'PCSrc1': s[0],
'PCSrc0': compare(s, "1--0")
}
def similarity(self):
sizeM = len(self.sequenceS)
sizeN = len(self.sequenceT)
for i in range(0, sizeM+1):
self.matrix[i, 0] = i*values["GAP"]
for j in range(0, sizeN+1):
self.matrix[0, j] = j*values["GAP"]
for i in range(1, sizeM+1):
for j in range(1, sizeN+1):
self.matrix[i, j] = max(self.matrix[i-1, j] + values["GAP"], self.matrix[i-1, j-1] + tls.compare(self.sequenceS[i-1], self.sequenceT[j-1]), self.matrix[i, j-1] + values["GAP"])
return self.matrix