scipy

python十大机器学习框架

由 我只是一个虾纸丫 提交于 2021-01-08 17:30:11
随着人工智能技术的发展与普及，Python超越了许多其他编程语言，成为了机器学习领域中最热门最常用的编程语言之一。有许多原因致使Python在众多开发者中如此受追捧，其中之一便是其拥有大量的与机器学习相关的开源框架以及工具库。根据 http:// builtwith.com 的数据显示，45%的科技公司都倾向于使用Python作为人工智能与机器学习领域的编程语言。 使Python如此受欢迎主要由于： Python从设计之初就是为效率而生，以使项目从开发到部署再在运维都能保持较高的生产力； 坊间有大量的基于Python的开源框架及工具库； Python易于上手，可以说是编程小白的福音； 相比起C、Java、C++来讲，Python的语法更简单，更高级，只需要更少行数的代码便能实现其他编程语言同样的功能； Python的跨平台能力； 正是由于Python简单易用以及高开发效率，吸引了大量的开发者为其创建更多新的机器学习工具库；而又因为大量的机器学习工具库的出现，使得Python在机器学习领域变得如此流行。 下面我们就来探索一下机器学习领域中最受欢迎的十大框架或工具库： Tensorflow 如果你正在使用Python来从事机器学习项目，那么你一定听说过其中一个著名的框架——Tensorflow。Tensorflow框架主要由Google大脑团队开发，主要用于深度学习计算

Python - solving Bernoulli's beam equation with scipy

由 家住魔仙堡 提交于 2021-01-07 06:38:34
问题 The process of answering the question has already started in the question on the link bellow, but that topic was specifically about integrating a function, which was answered. So I added a new question. Python - Integrating a function and plotting results THE PROBLEM: how to solve a beam equation y''(x) = M(x) / (E*I) using scipy integrate. SOLUTION, courtesy of gboffi: #---------- DESCRIPTION # cantilever beam with point load P at the free end # original beam equation: y''(x) = M(x)/(E*I) #

Python - solving Bernoulli's beam equation with scipy

由 こ雲淡風輕ζ 提交于 2021-01-07 06:36:02
问题 The process of answering the question has already started in the question on the link bellow, but that topic was specifically about integrating a function, which was answered. So I added a new question. Python - Integrating a function and plotting results THE PROBLEM: how to solve a beam equation y''(x) = M(x) / (E*I) using scipy integrate. SOLUTION, courtesy of gboffi: #---------- DESCRIPTION # cantilever beam with point load P at the free end # original beam equation: y''(x) = M(x)/(E*I) #

Python audio: Removing noise from a signal

由 ≯℡__Kan透↙ 提交于 2021-01-07 02:52:39
问题 In the code, first I'm opening wav file called output_test.wav. I then filter the noise from the signal using fftpack. Problem : I'm trying to convert the filtered signal i.e. filtered_sig array into wav file properly. Currently when i open TestFiltered.wav I get the error: "The item was encoded into a format not supported: 0xc00d5212" Upon further investigation it seems i'm not filtering noise correctly? I think the error comes from the last 2 lines: filteredwrite = np.fft.irfft(filtered_sig

Python audio: Removing noise from a signal

由 心不动则不痛 提交于 2021-01-07 02:52:36
问题 In the code, first I'm opening wav file called output_test.wav. I then filter the noise from the signal using fftpack. Problem : I'm trying to convert the filtered signal i.e. filtered_sig array into wav file properly. Currently when i open TestFiltered.wav I get the error: "The item was encoded into a format not supported: 0xc00d5212" Upon further investigation it seems i'm not filtering noise correctly? I think the error comes from the last 2 lines: filteredwrite = np.fft.irfft(filtered_sig

Python audio: Removing noise from a signal

由 主宰稳场 提交于 2021-01-07 02:51:45
问题 In the code, first I'm opening wav file called output_test.wav. I then filter the noise from the signal using fftpack. Problem : I'm trying to convert the filtered signal i.e. filtered_sig array into wav file properly. Currently when i open TestFiltered.wav I get the error: "The item was encoded into a format not supported: 0xc00d5212" Upon further investigation it seems i'm not filtering noise correctly? I think the error comes from the last 2 lines: filteredwrite = np.fft.irfft(filtered_sig

大邓强力推荐-jupyter notebook使用小技巧

由 人盡茶涼 提交于 2021-01-06 12:09:02
1. 快捷键 在jupyter notebook菜单栏有Help按钮，可以查看jupyter的快捷键 2. 将多个变量输出 一般jupyter notebook默认只打印最后一个变量的结果。比如 通过设置InteractiveShell.astnodeinteractivity参数为all，就可以让所有的变量或者声明都能显示出来 3. 问号？ 除了Help菜单能让我们快读查看numpy、pandas、scipy和matplotlib库，其实在cell中使用 ?可以查看库、函数、方法和变量的信息。 4. 在notebook中画图 作图最常用的就是matplotlib，记得在cell中写上这句 5. IPython魔法命令 查看当前工作目录 % pwd 执行上面的代码，得到 '/Users/suosuo/Desktop/20180820 jupyter notebook技巧' 更改当前工作目录 查看目录文件列表 6. 执行shell命令 命令行的命令前面加个 !即可在notebook中进行。 比如我们想要安装jieba库,需要打开终端输入 7. markdown标记语言 一级标题 # 一级标题 二级标题 ## 二级标题 三级标题 ### 三级标题 有序列表 元素1 元素2 元素3 会被MathJax渲染成 而在.ipynb文件中增加了下图的这个按钮

使用树状图做层次聚类分析

由 ￡可爱￡侵袭症＋ 提交于 2021-01-05 13:27:26
一、实验目的 如果您以前从未使用过树状图，那么使用树状图是查看多维数据如何聚集在一起的好方法。 在这本笔记本中，我将简单探索通过层次分析，借助树状图将其可视化。 二、层次分析 层次分析是聚类分析的一种，scipy有这方面的封装包。 linkage函数从字面意思是链接，层次分析就是不断链接的过程，最终从n条数据，经过不断链接，最终聚合成一类，算法就此停止。 dendrogram是用来绘制树形图的函数。 三、实验数据 grain_variety是标签，其他列为多种属性的值（特征）。 from scipy.cluster.hierarchy import linkage, dendrogram import matplotlib.pyplot as plt import pandas as pd seeds_df = pd.read_csv('seeds-less-rows.csv') seeds_df.head() #移除grain_variety varieties = list(seeds_df.pop('grain_variety')) varieties ['Kama wheat', 'Kama wheat', 'Kama wheat', 'Rosa wheat', 'Rosa wheat', 'Rosa wheat', 'Rosa wheat', 'Rosa wheat',

Calculate very large number using python

由 ＊爱你＆永不变心＊ 提交于 2021-01-05 09:07:13
问题 I'm trying to calculate (3e28 choose 2e28)/2^(3e28). I tried scipy.misc.comb to calculate 3e28 choose 2e28 but it gave me inf. When I calculate 2^(3e28), it raised OverflowError: (34, 'Result too large'). How can I compute or estimate (3e28 choose 2e28)/2^(3e28)? 回答1: Use Stirling's approximation (which is very accurate in the 1e10+ range), combined with logarithms: (3e28 choose 2e28) / 2^(3e28) = 3e28! / [(3e28 - 2e28)! * 2e28!] / 2^(3e28) = e^ [log (3e28!) - log((3e28-2e28)!) - log(2e28!) -

Calculate very large number using python

由 蹲街弑〆低调 提交于 2021-01-05 09:04:51
问题 I'm trying to calculate (3e28 choose 2e28)/2^(3e28). I tried scipy.misc.comb to calculate 3e28 choose 2e28 but it gave me inf. When I calculate 2^(3e28), it raised OverflowError: (34, 'Result too large'). How can I compute or estimate (3e28 choose 2e28)/2^(3e28)? 回答1: Use Stirling's approximation (which is very accurate in the 1e10+ range), combined with logarithms: (3e28 choose 2e28) / 2^(3e28) = 3e28! / [(3e28 - 2e28)! * 2e28!] / 2^(3e28) = e^ [log (3e28!) - log((3e28-2e28)!) - log(2e28!) -
订阅 scipy