List[List[int]]: lists = [] ls1 = [ls[0]] for i in range(1, len(ls)): if ls[i - 1] + 1 == ls[i]: (续下页) 46 Chapter 1. 文档 Qwen (接上页) ls1.append(ls[i]) else: lists.append(ls1) ls1 = [ls[i]] lists.append(ls1) append(ls1) return lists class FAISSWrapper(FAISS): chunk_size = 250 chunk_conent = True score_threshold = 0 def similarity_search_with_score_by_vector( self, embedding: List[float], k: int = 4 ) -> List[Tuple[Document and self.score_threshold > 0: return [] id_list = sorted(list(id_set)) id_lists = separate_list(id_list) for id_seq in id_lists: (续下页) 1.16. Langchain 47 Qwen (接上页) for id in id_seq: if id == id_seq[0]:

文本方面的词袋模型、词嵌入模型等 3. 特征提取 18 许永洪,吴林颖.中国各地区人口特征和房价波动的动态关系[J].统计研究,2019,36(01) 1.PCA(Principal Component Analysis，主成分分析) PCA 是降维最经典的方法，它旨在是找到数据中的主成分，并利 用这些主成分来表征原始数据，从而达到降维的目的。 PCA 的思想是通过坐标轴转换，寻找数据分布的最优子空间。 的样本降低到? 维 步骤 3. 特征提取 降维 19 许永洪,吴林颖.中国各地区人口特征和房价波动的动态关系[J].统计研究,2019,36(01) 2. ICA(Independent Component Analysis，独立成分分析) ICA独立成分分析，获得的是相互独立的属性。ICA算法本质寻找一 个线性变换 ? = ??，使得 ? 的各个特征分量之间的独立性最大。 PCA 对数据

30 3.PCA(主成分分析) 01 降维概述 02 SVD(奇异值分解) 03 PCA(主成分分析) 31 3.PCA(主成分分析) 主成分分析（Principal Component Analysis,PCA）是一种降维方法， 通过将一个大的特征集转换成一个较小的特征集，这个特征集仍然包含 了原始数据中的大部分信息，从而降低了原始数据的维数。 减少一个数据集的特征数 Dimensionality of Data with Neural Networks.[J]. Science, 2006. [3] Jolliffe I T . Principal Component Analysis[J]. Journal of Marketing Research, 2002, 87(4):513. [4] 李航. 统计学习方法[M]. 北京: 清华大学出版社,2019

4-15. The encoder RNN transforms the english sequence to a latent representation . The decoder component receives and outputs spanish language sequence . Figure 4-15: RNN Encoder-Decoder This basic idea are grouped under the Sparse group. After input sequence and the attention parameters, the next component to attack is the softmax computation. The Low Rank methods project the keys and the values to a

Assuming , and as usual, the compression ratio using the above formula computes to: . Table 5-1 lists the compression ratios for different values of , using the above values of and . Number of Centroids

Linear Regression September 13, 2023 16 / 31 GD Algorithm (Contd.) In more details, we update each component of θ according to the fol- lowing rule θj ← θj − α∂J(θ) ∂θj , ∀j = 0, 1, · · · , n Calculating

replace these with fewer ones, without loss of information. On simple way is to use PCA (Principal Component Analysis) Suppose that all data are in a space, we first find the direction of high- est variance

the unsupervised learning algorithms too can be kernelized (e.g., K-means clustering, Principal Component Analysis, etc.) Feng Li (SDU) SVM December 28, 2021 53 / 82 Kernelized SVM Training SVM dual

regression, etc. Many of the unsupervised learning algorithms (e.g., K-means clustering, Principal Component Analysis, etc.) can be kernelized too. Recall that, the dual problem of SVM can be formulated as

training data are denoted by {x(i), y(i)}i=1,··· ,m, where x(i) is a n-dimensional vector with each component x(i) j ∈ {0, 1} (j = 1, · · · , n), and y(i) ∈ {1, · · · , k}. For brevity, we use [k] to denote