Vinnie Monaco vmonaco

## 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

## 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:

## 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

## 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.
	"""
	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
	"""
	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:
	"""
	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
	'''
	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.