WMJi’s gists

WMJi / PSI.R

Created December 6, 2017 10:02 — forked from agabrielsen/PSI.R

Population Distribution Index (PSI) Studies on the Validation of Internal Rating Systems

	PSI = function(X,Y,saveplot,showplot,ratings,psinames) {
	# ---------------------------------------------------------------
	# PURPOSE:
	# Population Distribution Index (PSI)
	# Basel Committee on Banking Supervision
	# Working Paper No. 14
	# Studies on the Validation of Internal Rating Systems
	# ---------------------------------------------------------------
	# INPUTS:
	# X Array of (%) of Frequency

WMJi / GBT_CaliforniaHousing.py

Created November 6, 2017 08:58 — forked from tonicebrian/GBT_CaliforniaHousing.py

Gradient Boosting Trees using Python

	# =============
	# Introduction
	# =============
	# I've been doing some data mining lately and specially looking into `Gradient
	# Boosting Trees <http://en.wikipedia.org/wiki/Gradient_boosting>`_ since it is
	# claimed that this is one of the techniques with best performance out of the
	# box. In order to have a better understanding of the technique I've reproduced
	# the example of section 10.14.1 California Housing in the book `The Elements of Statistical Learning <http://www-stat.stanford.edu/~tibs/ElemStatLearn/>`_.
	# Each point of this dataset represents the house value of a property with some
	# attributes of that house. You can get the data and the description of those

WMJi / hist.py

Created July 24, 2017 14:16 — forked from bistaumanga/hist.py

Histogram Equalization in python

	import numpy as np

	def imhist(im):
	# calculates normalized histogram of an image
	m, n = im.shape
	h = [0.0] * 256
	for i in range(m):
	for j in range(n):
	h[im[i, j]]+=1
	return np.array(h)/(m*n)

WMJi / mnist.py

Created July 23, 2017 16:35 — forked from akesling/mnist.py

	import os
	import struct
	import numpy as np

	"""
	Loosely inspired by http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py
	which is GPL licensed.
	"""

	def read(dataset = "training", path = "."):

WMJi / partial_corr.py

Created April 1, 2017 21:36 — forked from wizzeh/partial_corr.py

Partial Correlation in Python

	"""
	Partial Correlation in Python

	Based on Fabian Pedregosa-Izquierdo's implementation at:
	https://gist.github.com/fabianp/9396204419c7b638d38f

	This version of the algorithm calculates the partial correlation coefficient controlling for Z.
	I use row vectors here, for whatever reason.
	"""

WMJi / clojure-match.clj

Created June 14, 2016 13:19 — forked from ckirkendall/clojure-match.clj

Language Compare F#, Ocaml, Scala, Clojure, Ruby and Haskell - Simple AST example

	(use '[clojure.core.match :only [match]])

	(defn evaluate [env [sym x y]]
	(match [sym]
	['Number] x
	['Add] (+ (evaluate env x) (evaluate env y))
	['Multiply] (* (evaluate env x) (evaluate env y))
	['Variable] (env x)))

	(def environment {"a" 3, "b" 4, "c" 5})

WMJi / gist:02cf13d2626208d1e8ceef45426ea5ac

Created May 16, 2016 13:35 — forked from oconnor663/gist:9aeb4ed56394cb013a20

TOML vs YAML

Here's the canonical TOML example from the TOML README, and a YAML version of the same. Which looks nicer?

title = "TOML Example"
[owner]
name = "Tom Preston-Werner"

WMJi / gist:1f4f384d9327f779b06bcbee046860ac

Created May 2, 2016 03:39 — forked from cataska/gist:2717986

Why is Lisp so great? or Why so many parenthesis?

	;; Why is Lisp so great? or Why so many parenthesis?

	;; The funny thing about Lisp is that everybody asks why it has so may parenthesis. Quite a few friends of mine who have studied Lisp in college don’t like it that much. I couldn’t really understand why, until I realized they usually take a class that uses the book Concepts of Programming Languages by Robert W. Sebesta as a textbook. I’m in no position to review this book because I haven’t read it. But from what I’ve skimmed, Lisp is not very well represented in this book, to put it very nicely. He describes Lisp only as a functional programming language, tells a little bit about cons cells, and that’s pretty much it! No object orientation in lisp, no syntactic abstraction, no meta-programming, and so on. My feeling is that if I didn’t know Lisp and read this book I wouldn’t be very impressed by Lisp.

	;; So why is Lisp so great and why so many parenthesis? These two different questions have the same answer; because Lisp have syntactic abstraction trough t

WMJi / Plot_Examples.md

Created February 11, 2016 07:52 — forked from gizmaa/Plot_Examples.md

Various Julia plotting examples using PyPlot

Plotting

Last Update: 12.12.2014
Offline Version

	# 0 is too far from ` ;)
	set -g base-index 1

	# Automatically set window title
	set-window-option -g automatic-rename on
	set-option -g set-titles on

	#set -g default-terminal screen-256color
	set -g status-keys vi
	set -g history-limit 10000