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#!/usr/bin/env python


'''
Query on GoogleImageSearch and install resulted images by scraping.

To use this script install mechanize and BeautifulSoup packages as
easy_install mechanize
easy_install Beautiful

erogol

#!/usr/bin/env python


'''
Query on GoogleImageSearch and install resulted images by scraping.

To use this script install mechanize and BeautifulSoup packages as
easy_install mechanize
easy_install Beautiful

erogol

function [] = sample_neg_examples(ROOT_PATH, num_sample, OUTPUT_PATH)
SEARCH_PATH = fullfile(ROOT_PATH,'**','*');
SAVE_PATH = 'neg_examples'
paths = rdir(SEARCH_PATH);


if exist('OUTPUT_PATH','var')
    SAVE_PATH = OUTPUT_PATH;
end

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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if ~exist('vlfeat', 'dir')
  from = 'http://www.vlfeat.org/download/vlfeat-0.9.13-bin.tar.gz' ;
  fprintf('Downloading vlfeat from %s\n', from) ;
  untar(from, 'data') ;
  movefile('data/vlfeat-0.9.13', 'vlfeat') ;
end

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function [ res ] = ie505_hw1( n,a,b )
%İE505_HW1 Summary of this function goes here
%   Detailed explanation goes here

[~,bins]= hist([a,b],1000);

r =unique( round( n*bins));

res = arrayfun(@(x)nchoosek(n,x)*double(1/(2^n)),r);

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'''
split a file into two randomly, line by line. 
Usage: split.py <input file> <output file 1> <output file 2> [<probability of writing to the first file>] [<random seed>]'
'''

import csv
import sys
import random

input_file = sys.argv[1]

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def linear_model_ensemble(X, y, X_test, fold_num, fold_num_sec, grid_search_range, oobe=True, x_val=True ):

      '''
      X         - Train set 
    
      y         - Train set labels with. Labels are 1 for pos instances and -1 for neg instances
    
      fold_num1 - Fold size for the first step X-validation to set the hyper-params
                and feature selectors
    

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from HTMLParser import HTMLParser

class MLStripper(HTMLParser):
    def __init__(self):
        self.reset()
        self.fed = []
    def handle_data(self, d):
        self.fed.append(d)
    def get_data(self):
        return ''.join(self.fed)

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import numpy as np
import numpy
import theano
import theano.tensor as T
from theano import function, config, shared, sandbox
from theano import ProfileMode
from sklearn import cluster, datasets
import matplotlib.pyplot as plt

def rsom(data, cluster_num, alpha, epochs = -1, batch = 1, verbose = False):