代码语言:txt 复制 import numpy as np from sklearn.preprocessing import OneHotEncoder # 创建一个二维Numpy数组 data = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]]) # 创建OneHotEncoder对象 encoder = OneHotEncoder(sparse=False) # 对数据进行热编码 encoded_data = encoder.fit_transf...
Examples --- >>> B = BytePairEncoder(max_merges=100).fit("./example.txt") >>> encoded_tokens = B.transform("Hello! How are you 😁 ?") >>> encoded_tokens [[72, 879, 474, ...]] """ # 如果输入是字符串,则转换为列表 if isinstance(text, str): text = [text] # 对文本中...
one_hot_encoder = np.eye(len(np.unique(arr))) #将原数组转换为one-hot编码 one_hot_arr = one_hot_encoder.dot(arr) print(one_hot_arr) 输出结果应该类似于: [[1. 0. 0.] [0. 1. 0.] [1. 0. 0.] [0. 0. 1.]] 在上面的示例中,我们首先定义了一个包含一些字符串的NumPy数组。然...
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"""A byte-pair encoderforsub-word embeddings. Notes --- Byte-pair encoding [1][2]isa compression algorithm that iteratively replaces the most frequently ocurring byte pairsinasetof documentswitha new, single token. It has gained popularityasa preprocessing step...
onehot.py import numpy as npfrom .core import Callableclass OneHotEncoder(Callable):"""One-Hot Encodes labels. First takes in a candidate set to figure out what elements itneeds to consider, and then one-hot encodes subsequent input datasets in theforward pass.SIMPLIFICATIONS:- Implementation ...
one-hot 编码的列数n_cols = np.max(labels) +1ifn_classesisNoneelsen_classes# 创建一个全零矩阵,用于存储 one-hot 编码one_hot = np.zeros((N, n_cols))# 将对应位置的值设为 1.0,实现 one-hot 编码one_hot[np.arange(N), labels] =1.0returnone_hot# 定义一个梯度提升决策树的类classGradient...
Bernoulli variational autoencoder Wasserstein GAN with gradient penalty word2vec encoder with skip-gram and CBOW architectures ReLU Tanh Affine Sigmoid Leaky ReLU ELU SELU GELU Exponential Hard Sigmoid Softplus Cross entropy Squared error Bernoulli VAE loss ...
Even if thecategory_encoders.one_hot.OneHotEncoderdoesn't encode any features, we would expect it to convert a pd.DataFrame into a numpy.ndarray if we set the parameter : return_df=False Actual Behavior When thecategory_encoders.one_hot.OneHotEncoderdeals with a dataframe with only numerical...
One-hot encoding / decoding Huffman coding / decoding Byte pair encoding / decoding Term frequency-inverse document frequency (TF-IDF) encoding MFCC encoding Utilities Similarity kernels Distance metrics Priority queue Ball tree Discrete sampler