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enable bbrplus on debian 9 and above
git clone https://github.com/Xaster/bbrplus-debian.git cd bbrplus-debian # run as root make && make install sysctl -w net.core.default_qdisc=fq
sysctl -w net.ipv4.tcp_congestion_control=bbrplus
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| dataset = pd.read_csv('https://raw.githubusercontent.com/mk-gurucharan/Regression/master/IceCreamData.csv') | |
| X = dataset['Temperature'].values | |
| y = dataset['Revenue'].values | |
| dataset.head(5) |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| dataset = pd.read_csv('https://raw.githubusercontent.com/mk-gurucharan/Classification/master/DMVWrittenTests.csv') | |
| X = dataset.iloc[:, [0, 1]].values | |
| y = dataset.iloc[:, 2].values | |
| from sklearn.model_selection import train_test_split | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| dataset = pd.read_csv('https://raw.githubusercontent.com/mk-gurucharan/Regression/master/IceCreamData.csv') | |
| X = dataset['Temperature'].values | |
| y = dataset['Revenue'].values | |
| dataset.head(5) |
enable bbrplus on debian 9 and above
git clone https://github.com/Xaster/bbrplus-debian.git
cd bbrplus-debian
# run as root
make && make install
sysctl -w net.core.default_qdisc=fqsysctl -w net.ipv4.tcp_congestion_control=bbrplus
| #!/bin/bash | |
| # This file is designed to spin up a Wireguard VPN quickly and easily, | |
| # including configuring a recursive local DNS server using Unbound | |
| # | |
| # Make sure to change the public/private keys before running the script | |
| # Also change the IPs, IP ranges, and listening port if desired | |
| # iptables-persistent currently requires user input | |
| # add wireguard repo | |
| sudo add-apt-repository ppa:wireguard/wireguard -y |
| #!/bin/bash | |
| # Install Shadowsocks on CentOS 7 | |
| echo "Installing Shadowsocks..." | |
| random-string() | |
| { | |
| cat /dev/urandom | tr -dc 'a-zA-Z0-9' | fold -w ${1:-32} | head -n 1 | |
| } |
| library(mnormt) | |
| mycols <- topo.colors(100,0.5) | |
| xhat <- c(0.2, -0.2) | |
| Sigma <- matrix(c(0.4, 0.3, | |
| 0.3, 0.45), ncol=2) | |
| x1 <- seq(-2, 4,length=151) | |
| x2 <- seq(-4, 2,length=151) | |
| f <- function(x1,x2, mean=xhat, varcov=Sigma) | |
| dmnorm(cbind(x1,x2), mean,varcov) | |
| z <- outer(x1,x2, f) |
| garchAutoTryFit = function( | |
| ll, | |
| data, | |
| trace=FALSE, | |
| forecast.length=1, | |
| with.forecast=TRUE, | |
| ic="AIC", | |
| garch.model="garch" ) | |
| { | |
| formula = as.formula( paste( sep="", |