Eugene Scherba escherba

escherba / cities.json

Created May 15, 2019 21:34 — forked from joshgeller/cities.json

1000 Largest US Cities By Population With Geographic Coordinates and Timezone, in JSON

escherba / install.sh

Created December 24, 2016 21:14 — forked from wdullaer/install.sh

Install Latest Docker and Docker-compose on Ubuntu

	# Ask for the user password
	# Script only works if sudo caches the password for a few minutes
	sudo true

	# Install kernel extra's to enable docker aufs support
	# sudo apt-get -y install linux-image-extra-$(uname -r)

	# Add Docker PPA and install latest version
	# sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9
	# sudo sh -c "echo deb https://get.docker.io/ubuntu docker main > /etc/apt/sources.list.d/docker.list"

escherba / letor_metrics.py

Created June 1, 2016 19:13 — forked from mblondel/letor_metrics.py

Learning to rank metrics.

	# (C) Mathieu Blondel, November 2013
	# License: BSD 3 clause

	import numpy as np


	def ranking_precision_score(y_true, y_score, k=10):
	"""Precision at rank k

	Parameters

escherba / rank_metrics.py

Created May 31, 2016 22:34 — forked from bwhite/rank_metrics.py

Ranking Metrics

	"""Information Retrieval metrics

	Useful Resources:
	http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
	http://www.nii.ac.jp/TechReports/05-014E.pdf
	http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
	http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
	Learning to Rank for Information Retrieval (Tie-Yan Liu)
	"""
	import numpy as np

escherba / hellinger.py

Last active March 31, 2016 21:29 — forked from larsmans/hellinger.py

Hellinger distance for discrete probability distributions in Python

	"""
	Three ways of computing the Hellinger distance between two discrete
	probability distributions using NumPy and SciPy.
	"""

	import numpy as np
	from scipy.linalg import norm
	from scipy.spatial.distance import euclidean