def main():
parser = optparse.OptionParser()
parser.add_option("-r", "--runtime", dest="runtime", default=None)
parser.add_option("-o", "--output", dest="output", default=None)
(options, args) = parser.parse_args()
if not options.runtime:
print >> sys.stderr, "Error: --runtime is required"
sys.exit(1)
runtime = options.runtime
bindings = loadBindings()
b = bootstrap.Bootstrap(runtime,
bindings,
moduleId="titanium",
moduleName="Titanium")
jsTemplate = open(os.path.join(thisDir, "bootstrap.js")).read()
gperfTemplate = open(os.path.join(thisDir, "bootstrap.gperf")).read()
outDir = genDir
if options.output != None:
outDir = options.output
b.generateJS(jsTemplate, gperfTemplate, outDir)
def file_list_to_df(climb_files, control_files, sym_files):
"""
Loads the likelihoods of all runs as a dict of DataFrames,
and collapses symmetrical inds.
"""
result_paths = zip(climb_files, control_files, sym_files)
for i, (climb_file, control_file, sym_file) in enumerate(result_paths):
try:
B = bootstrap.Bootstrap(climb_file, control_file, sym_file)
except tables.NoSuchNodeError:
continue
yield i, B.likdata
data_field1 = data_field1.reset_index()
data_field1.columns=['X','Y']
num_childtraingtest =4000 #子训练集个数
print('训练组数',num_childtraingtest)
# 3 结合6个仿真和4个现场 是10行数据，从10行数据中提取分布
childtest = ChidTraingTest.TraingTest(data_field1, num_childtraingtest)
# 4 bootstrap 采样
loops = 100
percetion_m_childTest = pd.DataFrame()
for i in range(0, num_childtraingtest * 10, 10) :
data2 = childtest[i:i+10]
# print( bootstrap.Bootstrap(data2) )
percetion_m_childTest = percetion_m_childTest.append( bootstrap.Bootstrap(data2 , loops).ix[0] )
percetion_m_childTest = percetion_m_childTest.reset_index(drop= True)
percetion_m_childTest.columns = ['mux', 'muy', 'sigmax', 'sigmay']
# print(percetion_m_childTest)
percetion_m_childTest.to_excel('3.xlsx')
s3 = [np.mean(percetion_m_childTest['mux']), np.mean(percetion_m_childTest['muy']),
np.mean(percetion_m_childTest['sigmax']), np.mean(percetion_m_childTest['sigmay'])]
print('所有的平均值，直接平均 :')
print(s3 )
# 4 聚类
s4 = k_means.k_means(percetion_m_childTest)
from oslo_config import cfg from oslo_log import log import bootstrap import os import service conf = cfg.CONF log.register_options(conf) conf(project='pythontest', prog='pythontest-user', args=[]) #conf(defualt_config_files='/etc/pythontest.conf') log.setup(conf, 'pythontest') boot = bootstrap.Bootstrap(conf) conf.drivers.transport = 'wsgi' application = boot.transport() app = application.app #periodic_task = service.Periodic_Task(conf, boot._storage) import time, threading #def run_periodic_task(): #threading.Thread.daemon = True #if conf.periodic_task_interval is None: #interval = 60*60*24 #periodic_task.list_boss_pe_endpointt() #periodic_task.format_pe_list_result() #periodic_task.pe_contrast_to_local_db() #periodic_task.list_boss_ce_endpoint() #periodic_task.format_ce_list_result() #periodic_task.ce_contrast_to_local_db() #interval = conf.periodic_task_interval #t = threading.Timer(interval, run_periodic_task)
num_childtraingtest = 2
for j in range(100, 1000, 25):
H.append(j)
loops = j #bootstrap采样循环次数,修改
# print('训练组数',num_childtraingtest)
# D =pd.DataFrame( index= num_childtraingtest)
# 3 结合6个仿真和4个现场 是10行数据，从10行数据中提取分布
childtest = ChidTraingTest.TraingTest(data_field1, num_childtraingtest)
# 4 bootstrap 采样
percetion_m_childTest = pd.DataFrame()
for i in range(0, num_childtraingtest * 10, 10):
data2 = childtest[i:i + 10]
# print( bootstrap.Bootstrap(data2) )
percetion_m_childTest = percetion_m_childTest.append(
bootstrap.Bootstrap(data2, loops).ix[0])
percetion_m_childTest = percetion_m_childTest.reset_index(drop=True)
percetion_m_childTest.columns = ['mux', 'muy', 'sigmax', 'sigmay']
s3 = [
np.mean(percetion_m_childTest['mux']),
np.mean(percetion_m_childTest['muy']),
np.mean(percetion_m_childTest['sigmax']),
np.mean(percetion_m_childTest['sigmay'])
]
# print(s3)
# 6误差平方和
aa = [100, 120, 10, 15]
d1 = 0
for i in range(4):
def main():
bs = bootstrap.Bootstrap('rhd.csv')
conf_interval = bs.getBootstrapCIs(0.05, bs.X.iloc[[0]])
print('confidence interval:', conf_interval)
print('actual execution time:', bs.y.iloc[[0]])
