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from multiprocessing import Pool

# parallelize function
def product(a,b):
	print a*b

# auxiliary funciton to make it work
def product_helper(args):
	return product(*args)

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import numpy as np
import theano
import theano.tensor as T
from theano import function,sandbox
from theano import ProfileMode
import warnings
warnings.filterwarnings("ignore")

# Dummy Data
theano.config.floatX = 'float32' # Theano needs this type of data for GPU use

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import numpy as np
from scipy import linalg
from sklearn.utils import array2d, as_float_array
from sklearn.base import TransformerMixin, BaseEstimator


class ZCA(BaseEstimator, TransformerMixin):

    def __init__(self, regularization=10**-5, copy=False):
        self.regularization = regularization

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tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

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if __name__ == '__main__':
    from sklearn.cross_validation import StratifiedShuffleSplit
    # svc = LinearSVC(class_weight='auto', penalty='l1', dual=False, verbose = 15, C=0.1)
    svc = Perceptron(penalty='l1', alpha=0.05, fit_intercept=True, n_iter = 100, shuffle=True, n_jobs=-1, class_weight='auto', verbose=15)
    sf = StratifiedShuffleSplit(y,n_iter = 2, train_size =0.9, test_size=0.1);
    for train,test in sf:
        X_train, X_test = X[train], X[test]
        y_train, y_test = y[train], y[test]
        print 'Train set size: ', X_train.shape
        print 'Test set size: ', X_test.shape

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#!/bin/bash
from bs4 import BeautifulSoup
import requests
import urllib2
import os
import re, urlparse
import time
import pdb
from interruptingcow import timeout

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import pandas as pd
from sqlalchemy import *

engine = create_engine('mysql://username:pass@server_address', pool_recycle=60)
DF = pd.io.parsers.read_csv('csv/file/path.csv')
DF = DF.dropna()
DF.to_sql('table_name', con=engine, if_exists='replace', flavor='mysql') # replace truncates the existing table and creates a new one

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def svd_whiten(X):

    U, s, Vt = np.linalg.svd(X)

    # U and Vt are the singular matrices, and s contains the singular values.
    # Since the rows of both U and Vt are orthonormal vectors, then U * Vt
    # will be white
    X_white = np.dot(U, Vt)

    return X_white

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name: "sentio_full_train"
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html

layers{
  name: "data"
  type: HDF5_DATA
  top: "data"
  top: "label"
  hdf5_data_param {

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{
 "metadata": {
  "name": "",
  "signature": "sha256:7278f115593dc43b248938b9210d9794dad8be21e1221b4b92f49716c2b633b8"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [