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from sklearn.linear_model import SGDClassifier
from sklearn import cross_validation
from sklearn import metrics
from sklearn.grid_search import GridSearchCV

from sklearn.datasets import load_iris

data = load_iris()
sample_vector = data.data
targets = data.target

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import re
import os
import urllib2
import urlparse
import pycurl
from lxml import html

url = "http://some.remote.org/"
path = "path/to/dir/listing/"
local = '/path/to/local/parent/directory/'

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################## Plot training skies ###################
##
## [email protected]
##
##########################################################

## calculate a vector given
## x,y,e1,e2
gal_line<-function(g,scale=100)
{

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# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
# Christian Jauvin - [email protected]

from collections import defaultdict
import numpy as np

# weights
w_v1_h1 = 2
w_h1_h2 = -1
w_h2_v2 = 1

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# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
# Christian Jauvin - [email protected]

from collections import defaultdict
import numpy as np

# weights
w_v1_h1 = 2
w_h1_h2 = -1
w_h2_v2 = 1

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# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
# Christian Jauvin - [email protected]

from collections import defaultdict
import numpy as np

# weights
w_v1_h1 = 2
w_h1_h2 = -1
w_h2_v2 = 1

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import numpy as np

N = 5000
I_true = np.random.randn(N)
D = I_true[:, None] - I_true[None, :]

I0 = np.zeros_like(I_true)

eta = 1.0 / N  # Learning rate.
tol = 1.25e-11  # Error tolerance.

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import numpy as np
import warnings

from itertools import cycle, izip

from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelBinarizer

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  # Standard scientific Python imports
  import pylab as pl
  import numpy as np
  from time import time
      
  # Import datasets, classifiers and performance metrics
  from sklearn import datasets, svm, pipeline
  from sklearn.kernel_approximation import (RBFSampler,
                                            Nystroem)
  from sklearn.utils import shuffle

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