The cache works as a lookup table, as it stores calculations in a dictionary. You can add it to fibonacci(): Python >>> from decorators import cache, count_calls >>> @cache ... @count_calls ... def fibonacci(num): ... if num < 2: ... return num ... return fibonacci(...
Once PyRun_SimpleStringFlags() has created a module and a dictionary, it calls PyRun_StringFlags(), which creates a fake filename and then calls the Python parser to create an AST from the string and return a module, mod: C PyObject * PyRun_StringFlags(const char *str, int start,...
importlogginga=logging.getLogger('foo')b=logging.getLogger('bar')print(aisb)c=logging.getLogger('foo')print(aisc)# The class in questionclassSpam:def__init__(self,name):self.name=name# Caching supportimportweakref_spam_cache=weakref.WeakValueDictionary()defget_spam(name):ifnamenotin_spam_ca...
Instantiate appropriate class with filename. Returned object acts like a dictionary, with key-value pairs for each piece of metadata. import fileinfo info = fileinfo.MP3FileInfo("/music/ap/mahadeva.mp3") print "\\n".join(["%s=%s" % (k, v) for k, v in info.items()]) Or use ...
class Dataset(object): ''' Class Dataset: Input: numpy values Output: torch variables. ''' def __init__(self, x0, x1, label): self.size = label.shape[0] self.x0 = torch.from_numpy(x0) self.x1 = torch.from_numpy(x1) self.label = torch.from_numpy(label) def __getitem__(...
dictionary = {'item1':10,'item2':20}print(dictionary['item2']) 这将输出20。我们不能使用相同的键创建多个值。这将覆盖重复键的先前值。字典上的操作是唯一的。字典不支持切片。 我们可以使用 update 方法将两个不同的字典合并为一个。此外,如果存在冲突,update 方法将合并现有元素: ...
typedef struct { PyObject_HEAD PyClassObject *in_class;! ! PyObject! *in_dict;! ! PyObject! *in_weakreflist; ! } PyInstanceObject; /* The class object */ /* A dictionary */ /* List of weak references */ 类型和实例各⾃自拥有⾃自⼰己的名字空间. >>> User.__dict__ >>> ...
instantiate with positional args or keywords >>> p[0] + p[1] # indexable like a plain tuple 33 >>> x, y = p # unpack like a regular tuple >>> x, y (11, 22) >>> p.x + p.y # fields also accessible by name 33 >>> d = p._asdict() # convert to a dictionary >>>...
a dictionary holding the data point: (x_data, y_target, class_index) """ row = self._target_df.iloc[index] from_vector, to_vector = \ self._vectorizer.vectorize(row.surname, self._max_seq_length) nationality_index = \ self._vectorizer.nationality_vocab.lookup_token(row.nationality) ...
e = c.new_child() # Child of c, independent from d e.maps[0] # Current context dictionary -- like Python's locals() e.maps[-1] # Root context -- like Python's globals() e.parents # Enclosing context chain -- like Python's nonlocals ...