peakBreaker

#!/usr/bin/env bash

# GET ARGS
while getopts ":r:c:o:w:hs:hd" o; do case "${o}" in
	h) 
    echo -e "Optional arguments for custom use:"
    echo -e "  -r: Repository (local file or url)"
    echo -e "  -c: Config file"
    echo -e "  -o: Output file"
    echo -e "  -w: Worker program to call"

peakBreaker

# Program to enable avahi service discovery of printers and CUPS
# In Arch Linux
#
## First install the needed programs
sudo pacman -S cups nss-mdns
## Add user to cups group
sudo usermod -a -G cups <USER>

## Alter the hosts line to be like this
# hosts: ... mdns_minimal [NOTFOUND=return] resolve [!UNAVAIL=return] dns ...

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%% 
%% Full page table test
%% 

\documentclass[12pt]{article}
\pagenumbering{gobble}
\usepackage{tabularx}
\newcolumntype{C}{>{\hsize=.5\hsize}X}
\usepackage[left=1cm, right=1cm, top=1cm, bottom=1cm]{geometry}

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# Get filename, whcih can be used for script identification
filename_no_ext = path.splitext(path.basename(__file__))[0]

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import datetime

# Set the config for the date iterator
start_date = datetime.datetime(2018, 9, 10)
end_date = datetime.datetime(2018, 11, 20)
d = start_date
delta = datetime.timedelta(days=1)

# Iterate from start date, adding delta with every iteration
while d <= end_date:

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def ecdf(data):
    """Compute ECDF for a one-dimensional array of measurements.
    
    Very useful for graphical EDA
    """
    # Number of data points: n
    n = len(data)

    # x-data for the ECDF: x
    x = np.sort(data)

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# Pred and prob arrays are numpy array outputs from a sklearn model:
#  - pred_array = model.predict(X).astype(int)
#  - prob_arr = model.predict_proba(X)
#
# Here we run the inital data through multiple models and structure the
# model output into a multilevel dataframe for probabilities and predictions
# 
# Typically the next stage would be to enhance the labels of numerical results
# to string/categories or similar basaed on whatever we want, aswell as providing
# the results to a database or something like that

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# Import TSNE
from sklearn.manifold import TSNE


def run_tsne(samples):
    # Create a TSNE instance: model
    model = TSNE(learning_rate=200)

    # Apply fit_transform to samples: tsne_features
    tsne_features = model.fit_transform(samples)

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"""
From documents to clusters

This script will run through a list of docs and process out the groups the docs may belong to using
cluster analysis, NMF and TF*IDF for preprocessing.  These are some basic techniques for unsupervised NLP
which may be very handy.

"""

# For creating the data structure to process

peakBreaker

FROM archlinux/base:latest

# Basic dependencies
RUN pacman -Syu --noconfirm
RUN pacman -S tar curl sudo grep base-devel --noconfirm
RUN mkdir -p /opt/yay/
WORKDIR /opt/yay/

# Add the builduser (cant install yay as root)
RUN useradd builduser -m # Create the builduser