我有5个字符串变量。 然后我将它们列入清单。
如果有一种方法可以将列表循环到字符串变量的实际名称吗？
答案 0 :(得分：1)
您可以创建Dictionary<string,string>并按照以下方式迭代：
var dict = new Dictionary<string, string>();
dict.Add("var1", var1);
... //do it for all variables
foreach (var variable in dict.Keys)
{
var varname = variable;
var varvalue = dict[variable];
}
键是变量名，值是变量值
答案 1 :(得分：0)
不，这是不可能的。将字符串变量添加到import httplib2, argparse, os, sys, json
from oauth2client import tools, file, client
from googleapiclient import discovery
from googleapiclient.errors import HttpError
#Project and model configuration
project_id = '132567073760'
model_id = 'HAR-model'
#activity labels
labels = {
'1': 'walking', '2': 'walking upstairs',
'3': 'walking downstairs', '4': 'sitting',
'5': 'standing', '6': 'laying'
}
def main():
""" Simple logic: train and make prediction """
try:
make_prediction()
except HttpError as e:
if e.resp.status == 404: #model does not exist
print("Model does not exist yet.")
train_model()
make_prediction()
else: #real error
print(e)
def make_prediction():
""" Use trained model to generate a new prediction """
api = get_prediction_api() //error here
print("Fetching model.")
model = api.trainedmodels().get(project=project_id, id=model_id).execute()
if model.get('trainingStatus') != 'DONE':
print("Model is (still) training. \nPlease wait and run me again!") #no polling
exit()
print("Model is ready.")
"""
#Optionally analyze model stats (big json!)
analysis = api.trainedmodels().analyze(project=project_id, id=model_id).execute()
print(analysis)
exit()
"""
#read new record from local file
with open('record.csv') as f:
record = f.readline().split(',') #csv
#obtain new prediction
prediction = api.trainedmodels().predict(project=project_id, id=model_id, body={
'input': {
'csvInstance': record
},
}).execute()
#retrieve classified label and reliability measures for each class
label = prediction.get('outputLabel')
stats = prediction.get('outputMulti')
#show results
print("You are currently %s (class %s)." % (labels[label], label) )
print(stats)
def train_model():
""" Create new classification model """
api = get_prediction_api()
print("Creating new Model.")
api.trainedmodels().insert(project=project_id, body={
'id': model_id,
'storageDataLocation': 'machine-learning-dataset/dataset.csv',
'modelType': 'CLASSIFICATION'
}).execute()
def get_prediction_api(service_account=True):
scope = [
'https://www.googleapis.com/auth/prediction',
'https://www.googleapis.com/auth/devstorage.read_only'
]
return get_api('prediction', scope, service_account)
def get_api(api, scope, service_account=True):
""" Build API client based on oAuth2 authentication """
STORAGE = file.Storage('oAuth2.json') #local storage of oAuth tokens
credentials = STORAGE.get()
if credentials is None or credentials.invalid: #check if new oAuth flow is needed
if service_account: #server 2 server flow
with open('service_account.json') as f:
account = json.loads(f.read())
email = account['client_email']
key = account['private_key']
credentials = client.SignedJwtAssertionCredentials(email, key, scope=scope)
STORAGE.put(credentials)
else: #normal oAuth2 flow
CLIENT_SECRETS = os.path.join(os.path.dirname(__file__), 'client_secrets.json')
FLOW = client.flow_from_clientsecrets(CLIENT_SECRETS, scope=scope)
PARSER = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, parents=[tools.argparser])
FLAGS = PARSER.parse_args(sys.argv[1:])
credentials = tools.run_flow(FLOW, STORAGE, FLAGS)
#wrap http with credentials
http = credentials.authorize(httplib2.Http())
return discovery.build(api, "v1.6", http=http)
if __name__ == '__main__':
main()
时，只显示存储在列表中的变量的值，而不是用于更早存储值的变量名。
答案 2 :(得分：0)
修改
您可以使用表达式树并将变量提升为闭包：
static string GetVariableName<T>(Expression<Func<T>> expr)
{
var body = (MemberExpression)expr.Body;
return body.Member.Name;
}
您可以使用以下方法：
static void Main()
{
var someVar = 3;
Console.Write(GetVariableName(() => someVar));
}
最后，你可以用C＃6这样做：
static void Main()
{
var someVar = 3;
Console.Write(nameof(someVar));
}
但是，我不知道这样做的原因是什么。如果您需要将变量名称保留为键，请执行@wudzik sugested。