Please comment below if you have an update, e.g., with another networking-related dataset.
A commit should be a wrapper for related changes. For example, fixing two different bugs should produce two separate commits. Small commits make it easier for other developers to understand the changes and roll them back if something went wrong. With tools like the staging area and the ability to stage only parts of a file, Git makes it easy to create very granular commits.
Committing often keeps your commits small and, again, helps you commit only related changes. Moreover, it allows you to share your code more frequently with others. That way it‘s easier for everyone to integrate changes regularly and avoid having merge conflicts. Having large commits and sharing them infrequently, in contrast, makes it hard to solve conflicts.
| # Detecting correlation | |
| # Defines three functions using base R to illustrate techniques for identifying correlations | |
| # between continuous random variables, then tests against different types of data | |
| # Pearsons r, distance correlation, Maximal Information Coefficient (approximated) | |
| # A simple bootstrap function to estimate confidence intervals | |
| bootstrap <- function(x,y,func,reps,alpha){ | |
| estimates <- c() |
学部の授業でターミナルを触ったことはあるけどよく分からないという人が，言語処理100本ノックを進めるにあたって黒い画面で躓かないレベルにまで到達するための資料：
書籍で体系的に学ぶのであれば，大角『新しいLinuxの教科書』が良いらしい．
| from __future__ import division, print_function, absolute_import | |
| import numpy as np | |
| import scipy | |
| from spectral import check_COLA # https://github.com/scipy/scipy/pull/6058 | |
| windows = np.unique(scipy.signal.windows._win_equiv.values()) | |
| windows = sorted(windows, key=lambda x: x.__name__) | |
| for window in windows: | |
| try: |
| %matplotlib inline | |
| import sys | |
| import matplotlib.pyplot as plt | |
| from matplotlib import animation as ani | |
| import numpy as np | |
| import pandas as pd | |
| import scipy.stats as st | |
| from scipy.special import ndtri | |
| # Data Import |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
| def fft_hann(t,pt,M): | |
| """ | |
| Perform FFT with Hanning windowing, | |
| 50% overlap of 2^M+1 windows. | |
| The resulting spectra are averaged. | |
| """ | |
| # Amplitude correction of Hanning | |
| ampl_corr = 2. | |
| # Signal length |