def init_hidden (self, batch_size): device = "cpu" weights = next(self.parameters()).data h = (weights.new(self.n_layers, batch_size,\ self.n_hidden).zero_().to(device),\ weights.new(self.n_layers, batch_size,\ self.n_hidden).zero_().to(device)) return h 然后,我们通过创建...
#程序文件ex2_11_2.py import string, random, collections #依次加载三个模块 x=string.ascii_letters+string.digits y=''.join([random.choice(x) for i in range(1000)]) count=collections.Counter(y) for k,v in sorted(count.items()): print(k, ':', v) 代码语言:javascript 复制 0 : 28 ...
str1 = 'I am a unicode string' type(str1) # type(str1) => 'str' str2 = b"And I can't be concatenated to a byte string" type(str2) # type(str2) => 'bytes' str3 = str1 + str2 --- Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError...
def translate(model, src, max_len = 80, custom_string=False):model.eval()if custom_sentence == True: src = tokenize_en(src) sentence=\ Variable(torch.LongTensor([[EN_TEXT.vocab.stoi[tok] for tok in sentence]])).cuda()src_mask = (src != input_pad).unsqueeze(-2) e_outputs = ...
StringDtype Timedelta TimedeltaIndex TimestampUInt16Dtype UInt32Dtype UInt64Dtype UInt64Index UInt8Dtypeapi array arrays bdate_range compatconcat core crosstab cut date_rangedescribe_option errors eval factorize get_dummiesget_option infer_freq interval_range io isnaisnull json_normalize lreshape melt ...
pd.concat([df1,df2],axis=0,ignore_index=True) 使用read_csv参数进行选择性读取:使用read_csv中的参数读取文件的特定行、列或块。 df=pd.read_csv('file.csv',usecols=['col1','col2'],nrows=100) 使用fillna处理缺失数据:用特定值或计算值(如列的均值)填充缺失值。
(Stacked Histogram for Continuous Variable) 22、类别变量堆积直方图(Stacked Histogram for Categorical Variable) 23、密度图(Density Plot) 24、带直方图的密度图(Density Curves with Histogram) 25、山峰叠峦图(Joy Plot) 26、分布点图(Distributed Dot Plot) 27、箱图(boxplot) 28、箱图结合点图(Dot + ...
def concat_col_str_condition(df):# concat 2 columns with strings if the last 3 letters of the first column are 'pil' mask = df['col_1'].str.endswith('pil', na=False) col_new = df[mask]['col_1'] + df[mask]['col_2'] col_new.replace('pil', ' ', regex=True,...
"""Creates an age bucket for each participant using the age variable. Meant to be used on a DataFrame with .apply().""" # Convert to an int, in case the data is read in as an "object" (aka string) age = int(age) if age < 30: bucket = '<30' # Age 30 to 39 ('range...
self.h_pool = tf.concat(pooled_outputs, 3) self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total]) # Add dropout with tf.name_scope("dropout"): self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob) ...