syhw

from sklearn.datasets import fetch_20newsgroups, load_digits
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn import metrics

newsgroups_train = fetch_20newsgroups(subset='train')
vectorizer = TfidfVectorizer(encoding='latin-1', max_features=10000)

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"""
A deep neural network with or w/o dropout in one file.
"""

import numpy
import theano
import sys
import math
from theano import tensor as T
from theano import shared

syhw

"""
A deep neural network with or w/o dropout in one file.
"""

import numpy
import theano
import sys
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

syhw

# -*- coding: utf-8 -*- 
import os, re, random, math
from collections import Counter

""" Naive Bayes with Gibbs sampling, so it can deal with unlabeled data """
# as from "Gibbs Sampling for the Uninitiated" Philip Resnik, Eric Hardisty

def Dirichlet(v):
    """ takes a vector of counts v and returns a Multinomial ~ Dirichlet(v) """
    y = []

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{
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  "name": ""
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

syhw

import numpy as np
import matplotlib.pyplot as plt
from sklearn.neural_network import BernoulliRBM
from sklearn import linear_model, metrics
from sklearn.pipeline import Pipeline

X = np.array([[0,1,0,1,0,1,0,1,
              1,0,1,0,1,0,1,0,
              0,1,0,1,0,1,0,1,
              1,0,1,0,1,0,1,0,

syhw

from sklearn.gaussian_process import GaussianProcess
import numpy as np
import copy
from matplotlib import pyplot as pl
np.random.seed(1)


def f(x, alpha=5., beta=10.):
    """The function to predict: Weibull centered on 5, ranging from 1 to 2."""
    #return x * np.sin(x)

syhw

# -*- coding: utf-8 -*-
import itertools, random

import numpy as np
from scipy import linalg
import pylab as pl
import matplotlib as mpl
import math

epsilon = 10e-8