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 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 hci_clicks_example.py
""" | |
Identify users by mouse click timings. | |
Train a POHMM for each user, one sample, and test using the remaining samples. | |
Using the clicks from task 3 (Star Bubbles) in the HCI dataset: | |
https://bitbucket.org/vmonaco/dataset-four-hci-tasks/ | |
$ python hci_clicks_example.py data/task3.click.csv | |
Accuracy (88 samples): 0.375 |
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 |