First of all, thank you jalvesaq for the great package! 🙏🙏
If you'd like to colorize R terminal output you can use {jalvesaq/colorout}
I customize extra color patterns for
- base R: class for
str
- {data.table} : row id, column class
#!/bin/sh | |
# Speed up boot slightly | |
sysrc background_dhclient=YES | |
sysrc -f /boot/loader.conf autoboot_delay=3 | |
# Quieten boot messages | |
sysrc rc_startmsgs=NO | |
sysrc -f /boot/loader.conf boot_mute=YES | |
sysrc -f /boot/loader.conf beastie_disable=YES |
#!/usr/bin/python3 | |
from functools import partial | |
def colors16(): | |
for bold in [0, 1]: | |
for i in range(30, 38): | |
for j in range(40, 48): | |
print(f'\x1b[{bold};{i};{j}m {bold};{i};{j} |\x1b[0m', end='') | |
print() |
First of all, thank you jalvesaq for the great package! 🙏🙏
If you'd like to colorize R terminal output you can use {jalvesaq/colorout}
I customize extra color patterns for
str
Name: Olivier Nguyen
Mentors: Lea Goetz, Heiko Strathmann
Organization: Shogun Machine Learning Toolbox
Abstract
/* | |
* Copyright (C) 2017 Alberts Muktupāvels | |
* | |
* This program is free software: you can redistribute it and/or modify | |
* it under the terms of the GNU General Public License as published by | |
* the Free Software Foundation, either version 3 of the License, or | |
* (at your option) any later version. | |
* | |
* This program is distributed in the hope that it will be useful, | |
* but WITHOUT ANY WARRANTY; without even the implied warranty of |
# Learn Gradient Boosting Model by coding | |
# good slide | |
# http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf | |
# http://cran.r-project.org/web/packages/gbm/gbm.pdf | |
#1 Gradient Boosting for Regression | |
# generate data | |
generate_func = function(n=1000,size=0.5){ |