def generate():
output_dir = "/tmp/" + str(random.randint(1, sys.maxint))
output_file_name = "/tmp/" + str(random.randint(1, sys.maxint))
try:
data = {
"app_name": request.form["app_name"],
"language": request.form["language"],
"company_name": request.form["company_name"],
"app_package_name_prefix": request.form["app_package_name_prefix"],
"app_class_prefix": request.form["app_class_prefix"],
"application_id": request.form["application_id"],
"compile_sdk_version": int(request.form["compile_sdk_version"]),
"build_tools_version": request.form["build_tools_version"],
"min_sdk_version": int(request.form["min_sdk_version"]),
"target_sdk_version": int(request.form["target_sdk_version"]),
"crashlytics_api_key": request.form["crashlytics_api_key"],
"output_dir": output_dir
}
grow.grow(data)
shutil.make_archive(output_file_name, 'zip', output_dir)
return send_file(output_file_name + ".zip",
mimetype="application/zip",
as_attachment=True,
attachment_filename=request.form["app_class_prefix"] +
".zip")
finally:
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
if os.path.exists(output_file_name):
os.remove(output_file_name)
def generate():
output_dir = "/tmp/" + str(random.randint(1, sys.maxint))
output_file_name = "/tmp/" + str(random.randint(1, sys.maxint))
try:
data = {
"app_name" : request.form["app_name"],
"language" : request.form["language"],
"company_name" : request.form["company_name"],
"app_package_name_prefix" : request.form["app_package_name_prefix"],
"app_class_prefix": request.form["app_class_prefix"],
"application_id" : request.form["application_id"],
"compile_sdk_version" : int(request.form["compile_sdk_version"]),
"build_tools_version" : request.form["build_tools_version"],
"min_sdk_version" : int(request.form["min_sdk_version"]),
"target_sdk_version" : int(request.form["target_sdk_version"]),
"crashlytics_api_key" : request.form["crashlytics_api_key"],
"output_dir" : output_dir
}
grow.grow(data)
shutil.make_archive(output_file_name, 'zip', output_dir)
return send_file(output_file_name + ".zip",
mimetype="application/zip",
as_attachment=True,
attachment_filename=request.form["app_class_prefix"] + ".zip")
finally:
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
if os.path.exists(output_file_name):
os.remove(output_file_name)
def test(self):
tests = [
[6, [1, 2, 3]],
[16, [4, 1, 1, 1, 4]],
[64, [2, 2, 2, 2, 2, 2]],
]
for exp, inp in tests:
self.assertEqual(grow(inp), exp)
def test_rand(self):
from random import randint
from operator import mul
reference = lambda a: len(a) and int(0 not in a) and __import__(
"functools").reduce(mul, a)
def test_case():
return [randint(0, 1000) for _ in range(randint(1, 1000))]
for _ in range(100):
t = test_case()
self.assertEqual(grow(t[:]), reference(t))
def getfitness(templateFiles):
"""Automated generation of FEM models and execuation of simulations"""
global switch, valid_ind, indList, err1, err2, result_GEN
while switch:
try:
job = indList.get(block=False)
except Empty:
break
else:
# define the name in the form "index of generation"_"index of individual":
flag = 1
name = job[0]
GEN = int(name.split('_')[0])
phenotype = decoder(job[1])
if Multi:
for x in result_GEN:
for y in x:
if y[1] == phenotype:
job[1].fitness.values = y[2]
result_GEN[GEN].append(
[job[0], phenotype, job[1].fitness.values])
flag = 0
break
if flag == 0:
break
else:
for y in result_GEN:
same = 0
for key in y[1].keys():
if y[1][key] != phenotype[key]:
same = 0
break
same = 1
if same:
print("%s is the same as %s." % (name, y[0]))
job[1].fitness.values = (y[2], )
flag = 0
break
if flag:
print("starting the evaluation of %s at %s." %
(name, datetime.now()))
stdout.flush()
for n, templateFile in enumerate(templateFiles):
replacedFile = str(templateFile[1])
#replacedFile= replacedFile.replace('NAME', name)
for key in phenotype.keys():
replacedFile = replacedFile.replace(
key, str(phenotype[key]))
f1 = open('T%s_%s' % (name, templateFile[0]), 'w')
f1.write(replacedFile + '\n')
f1.close()
print("start growing %s" % name)
stdout.flush()
try:
grow(name, phenotype)
except:
switch = 0
err1 = traceback.format_exc()
stdout.flush()
else:
try:
fitness = objective(name, phenotype)
except:
switch = 0
err2 = traceback.format_exc()
stdout.flush()
else:
files = glob("%s*" % name)
for file in files:
remove(file)
job[1].fitness.values = fitness
if Multi:
result_GEN[GEN].append([
job[0],
decoder(job[1]), job[1].fitness.values
])
else:
result_GEN.append([
job[0],
decoder(job[1]), job[1].fitness.values[0]
])
valid_ind.append(job[1])
print("The evaluation of %s bas completed at %s" %
(name, datetime.now()))
stdout.flush()
def test_grow():
for exp, inp in tests:
assert grow(inp) == exp
# 读取文件并填充到数组中
f = open("testDigits/" + file)
for line in f.readlines():
# 去掉末尾的换行符
line = line.strip("\n").strip("\r")
# 每一行的数组
row = []
for letter in line:
row.append(letter)
arr.append(row)
f.close()
struct = gs.clearStructure(struct)
# 填充结构
fs.fillStructure(arr, struct)
# 生长
struct = grow.grow(struct)
# 评估结果
X_train.append(assess.do(struct))
# 归一化
print "训练完毕，已训练样本" + str(len(X_train)) + "个"
print "开始进行归一化..."
X_train, normPara = norm.doAll(X_train)
print "归一化完毕，开始进行数据测试..."
# 查看精确度
# 正确和错误数量
right = 0