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In this case, you want to code a function that receives math expressions as input and returns their result. To do this, you write a function called evaluate(): Python 26def evaluate(expression): 27 """Evaluate a math expression.""" 28 # Compile the expression 29 code = compile(...
ROUND_DOWN. 类 java.math.BigDecimal 中的静态变量:从不在舍弃(即截断)的小数之前增加数字。 ROUND_FLOOR. 类 java.math.BigDecimal 中的静态变量:如果 BigDecimal 为正,则作 ROUND_UP ;如果为负,则作 ROUND_DOWN 。 ROUND_HALF_DOWN. 类 java.math.BigDecimal 中的静态变量:若舍弃部分> .5 ,则作 ROUND_...
在Makefile中,foreach循环是一种用于遍历列表的功能。而Evaluate函数是Makefile中的一个内置函数,用于对表达式进行求值。 在foreach循环中,可以使用Evaluate函数来对每个元素进行求值操作。Evaluate函数接受两个参数,第一个参数是一个表达式,第二个参数是一个列表。它会将表达式中的特殊符号(如$())替换为列表...
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def time_since(since): now = time.time() s = now - since m = math.floor(s / 60) s -= m * 60 return '%dm %ds' % (m, s) start = time.time() for epoch in range(1, n_epochs + 1): # 随机选择一个样本 category, line, category_tensor, line_tensor = random_training_pai...
importmath, operator eval_globals = {"Mod":operator.mod} eval_globals.update(vars(math))defeval_sympy_expr(expr, **subs):returneval(str(expr), eval_globals, subs) and foruncertaintiessupport I'd just doimport uncertainties.umath as mathinstead of the default math module. ...
def evaluate(data_loader, model, eval_metric): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. model.eval() total_output = 0.0 with torch.no_grad(): # For all the batches in the dataset. for iteration, batch in enumerate(dat...
( AzureChatCompletion, AzureTextCompletion, ) @tool def my_python_tool( input: str, deployment_type: str, deployment_name: str, AzureOpenAIConnection: AzureOpenAIConnection, ) -> str: # Initialize the kernel kernel = sk.Kernel(log=sk.NullLogger()) # Add the ch...
if isinstance(norm_ans, str) and norm_ans in pred: if not correct: correct = True break else: # it's a float number if pred in norm_answers: if not correct: correct = True break return correct def evaluate(samples): """ Batch evaluation for multiple choice and open questions. """...