低通滤波器(Low-pass Filter):允许低频信号通过,抑制高频信号。 高通滤波器(High-pass Filter):允许高频信号通过,抑制低频信号。 带通滤波器(Band-pass Filter):允许特定频率范围内的信号通过,抑制其他频率的信号。 带阻滤波器(Band-stop Filter):抑制特定频率范围内的信号,允许其他频率的信号通过。 滤波器的设计和...
傅里叶变换(Fourier Transform,FT)是一种将时域信号转换为频域信号的数学工具,而快速傅里叶变换(Fast Fourier Transform,FFT)则是实现傅里叶变换的一种高效算法。FFT 算法通过递归分治的方法,将一个大问题分解为多个小问题,从而显著减少计算复杂度。 傅里叶变换的基本概念:时域信号和频域信号的概念。 FFT算法的数学...
根据参数选择是否转换为小写、过滤停用词和标点符号 words = tokenize_words( line, lowercase=lowercase, filter_stopwords=filter_stopwords, filter_punctuation=filter_punctuation, **kwargs, ) # 将单词转换为字节列表,每个字节用空格分隔 words
在本文中,我们将深入探讨NumPy中的zeros函数以及如何在NumPy中处理复数。这两个主题对于数值计算、信号处理和科学模拟等领域都非常重要。 1. NumPy中的zeros函数 zeros函数是NumPy库中最常用的函数之一,它用于创建一个填充零的数组。这个函数在初始化数组、创建占位符数组以及在各种数学和科学计算中都有广泛的应用。 1.1...
""" # 调用父类的初始化方法,传入参数 N, unk, filter_stopwords, filter_punctuation super().__init__(N, unk, filter_stopwords, filter_punctuation) # 设置超参数字典中的 id 键值对为 "GoodTuringNGram" self.hyperparameters["id"] = "GoodTuringNGram" # 设置超参数字典中的 conf 键值对为传入...
a.p/loops_autovec.dispatch.c during GIMPLE pass: vect In file included from ../numpy/_core/src/common/npy_cpu_dispatch.h:41, from ../numpy/_core/src/common/npy_config.h:5, from ../numpy/_core/include/numpy/npy_common.h:10,...
Data Science Operations: Filter, Order, Aggregate That wraps up a section that was heavy in theory but a little light on practical, real-world examples. In this section, you’ll work through some examples of real, useful data science operations: filtering, sorting, and aggregating data. Indexi...
Changes from all commits File filter Conversations doc release 1.11.0-notes.rst source/reference routines.other.rst numpy add_newdocs.py core _internal.py bento.info bscript function_base.py numeric.py setup.py src multiarray array_assign.c arrayobject.c common.c common...
# we can now select just those elements which meet that criteria print(an_array[filter]) # For short, we could have just used the approach below without the need for the separate filter array. an_array[(an_array % 2 == 0)]
There are more genes in our gene length data than were actually measured in the experiment. Let’s filter so we only get the relevant genes, and we want to make sure they are in the same order as in our count data. This is where pandas indexing comes in handy! We can get the inter...