View runs_test.py
""" | |
Multivariate Wald-Wolfowitz test for two samples in separate CSV files. | |
See: | |
Friedman, Jerome H., and Lawrence C. Rafsky. | |
"Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests." | |
The Annals of Statistics (1979): 697-717. | |
Given multivariate sample X of length m and sample Y of length n, test the null hypothesis: |
View groupyby_parallel.py
# coding=utf-8 | |
import pandas as pd | |
import itertools | |
import time | |
import multiprocessing | |
from typing import Callable, Tuple, Union | |
def groupby_parallel(groupby_df: pd.core.groupby.DataFrameGroupBy, | |
func: Callable[[Tuple[str, pd.DataFrame]], Union[pd.DataFrame, pd.Series]], |
View rsa.py
#!/usr/bin/env python | |
# This example demonstrates RSA public-key cryptography in an | |
# easy-to-follow manner. It works on integers alone, and uses much smaller numbers | |
# for the sake of clarity. | |
##################################################################### | |
# First we pick our primes. These will determine our keys. | |
##################################################################### |
View cmu_powerlaw.py
''' | |
Created on May 26, 2015 | |
@author: vinnie, vincent@vmonaco.com | |
Power-law results from: | |
"DATA FORENSIC TECHNIQUES USING BENFORD’S LAW AND ZIPF’S LAW FOR KEYSTROKE | |
DYNAMICS", Aamo Iorliam, Anthony T.S. Ho, Norman Poh, Santosh Tirunagari, | |
and Patrick Bours. IWBF 2015. |
View fa.sty
\NeedsTeXFormat{LaTeX2e} | |
\ProvidesPackage{fa} | |
[2018/03/09 v0.3 Construct finite automata and Turing machines] | |
\RequirePackage{tikz} | |
\RequirePackage{etoolbox} | |
\usetikzlibrary{arrows.meta,automata,calc,chains,positioning} | |
% Define \emptystring to \varepsilon |
View segment_motion.py
""" | |
Segment an acceleration or gyroscopic CSV file into motion/non-motion segments | |
using a 2-state HMM and Savitzky–Golay filter as preprocessing | |
""" | |
import sys | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
from hmmlearn.hmm import GaussianHMM | |
from scipy.signal import savgol_filter |