In :# 多行结果输出支持 from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all"
In :import numpy as np np.set_printoptions(precision=4)
In :# 导入视频 # from IPython.display import YouTubeVideo # YouTubeVideo("8iGzBMboA0I")
In :# 制作切片(tuple 形式) dims = np.index_exp[10:28:1,3:13] dims
Out:(slice(10, 28, 1), slice(3, 13, None))
The SVD algorithm factorizes a matrix into one matrix with orthogonal columns and one with orthogonal rows (along with a diagonal matrix, which contains the relative importance of each factor)
Nonnegative matrix factorization (NMF) is a non-exact factorization that factors into one skinny positive matrix and one short positive matrix. NMF is NP-hard and non-unique. There are a number of variations on it, created by adding different constraints.
Topic Frequency-Inverse Document Frequency (TF-IDF) is a way to normalize term counts by taking into account how often they appear in a document, how long the document is, and how commmon/rare the term is.
TF = (# occurrences of term t in document) / (# of words in documents)
IDF = log(# of documents / # documents with term t in it)
In :x = np.arange(9.).reshape(3, 3) # 有填充的功能 # 条件为真就从 x 选择，否则从 y 选择 np.where(x < 5, x, -1)
Out:array([[ 0., 1., 2.], [ 3., 4., -1.], [-1., -1., -1.]])
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