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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

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# =============
# 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

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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)

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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 = "."):

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"""
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.
"""

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(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})

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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"

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;; 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

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Plotting

Last Update: 12.12.2014
Offline Version

Contents

• Translating
• Basic Plot
Plot Annotation

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# 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