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// 
// Sparse LU Decomposition (Gilbert-Peierls)
//
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <vector>
#include <functional>
#include <algorithm>
#include <cmath>

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// Two-layer Neural Network
//
// USAGE: 
//   ./a.out in.txt out.txt
//   gnuplot -e "plot 'in.txt', 'out.txt'; pause 2"
//
#include <iostream>
#include <cstdio>
#include <ctime>
#include <cstdlib>

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% Radial Basis Function expansion
%
% f(x) = sum k(x, yi) wi 
%   -> w = k(X,Y) \ f(X) 
% here k(x,y) := rho(|x-y|), e.g., exp(-|x-y2|^2)

% input data
N = 100;
X = rand(N,1);
fX = abs( cos( 2 * 3.14 * (X .* X) ) );

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// Read MNIST Database (handwritten digits)
//
// Usage: 
//   1. download 
//        train-images-idx3-ubyte.gz
//        train-labels-idx2-ubyte.gz
//      from
//        http://yann.lecun.com/exdb/mnist/
//      and extract them.
//

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// 
// Nearlest Neighbour Classification for MNIST dataset
//
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <cmath>
#include <map>
#include <vector>
#include <queue>

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//
// Support Vector Machine 
//   (gradient descent with log-sum-exp approximation)
//
// original:
//   G(w) := min_{+,-} { <w, x_+> - <w,x_-> } --> maximize
// approx:
//   G'(w) := -log sum_{+,-} exp (-<w, x_+> - <w,x_->) --> maximize
// <=>
//   L(w) := sum_{+,-} exp -<w, x_+ - x_-> --> minimize

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// Bellman-Ford / DIjkstra hybrid for shortest path
// 
// D. Yefem and I. Rotem (2010):  
// Hybrid Bellman-Ford-Dijkstra Algorithm, 
// TechnicalReport, Ben-Gurion University of the Negev.
//
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <cmath>

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# coding: utf-8
#
# easy_install tweepy
# easy_install python-dateutil
#
from tweepy import OAuthHandler, Stream
from tweepy.streaming import StreamListener
import tweepy, sys, json
from time import strptime
import datetime, calendar, dateutil.parser

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// Bigram graph G = (V,E)
//   V: set of words
//   E: u->v iff "u v" occurs in the document
//
// Usage: ./a.out < pg11.txt
// ( http://www.gutenberg.org/cache/epub/11/pg11.txt )
//
// the 0.0514497
// and 0.0313457
// to 0.0241298