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@oliverangelil
oliverangelil / maize_yield.py
Created June 17, 2018 04:42
Maize Yield for Siya
import os
import pandas as pd
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
path_maize = '/home/siyabusa/terra/Forecast_ProcessedFiles/Outputs/Maize/'
files_maize = sorted([i for i in os.listdir(path_maize) if i.endswith('ec1.csv')])
names = []
@oliverangelil
oliverangelil / for_henri.txt
Created June 14, 2018 08:06
log file of luatex breaking in AUCTEX
This is LuaTeX, Version beta-0.80.0 (TeX Live 2015) (rev 5238) (format=lualatex 2015.11.17) 14 JUN 2018 18:00
restricted \write18 enabled.
file:line:error style messages enabled.
**\input .tex
(./.tex)
! Emergency stop.
<*> \input .tex
*** (job aborted, no legal \end found)
@oliverangelil
oliverangelil / for_henri.tex
Last active June 14, 2018 08:10
simple luatex
\documentclass{article}
\usepackage{fontawesome}
\begin{document}
\begin{center}
\textbf{\faEnvelope John Smith}\\
\end{center}
\end{document}
@oliverangelil
oliverangelil / note.txt
Created June 4, 2018 20:40
port install mysql57 note
mysql57 has the following notes:
On activation if no /opt/local/etc/mysql57/my.cnf file exists one
will be created which loads
/opt/local/etc/mysql57/macports-default.cnf.
If a /opt/local/etc/mysql57/my.cnf file exists MacPorts does not
touch it and any changes you make to /opt/local/etc/mysql57/my.cnf
will be preserved (e.g., during port upgrades, deactivations or
activations). /opt/local/etc/mysql57/my.cnf is a good place to
customize your mysql57 installation.
@oliverangelil
oliverangelil / ridge_bias_variance.py
Last active October 13, 2021 09:15
generate dummy data and apply ridge with varying levels of regularisation. Plots train and test results. This code goes with my blog post: https://oliverangelil.github.io/posts/bias-variance
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
import numpy as np
import matplotlib.pyplot as plt
# generate data
X, y, w = make_regression(n_samples=1000, n_features=200, coef=True,
random_state=1, bias=0, noise=3, tail_strength=0.9, effective_rank=10)