Vishal Goklani vgoklani

## cvdiff.py
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

## download_remote_dir.py
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/'

## plot_skies.R
################## Plot training skies ###################
##
## corey.chivers@mail.mcgill.ca
##
##########################################################

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

## boltzmann.py
# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
# Christian Jauvin - cjauvin@gmail.com

from collections import defaultdict
import numpy as np

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

## boltzmann.py
# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
# Christian Jauvin - cjauvin@gmail.com

from collections import defaultdict
import numpy as np

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

## boltzmann.py
# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
# Christian Jauvin - cjauvin@gmail.com

from collections import defaultdict
import numpy as np

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

## astrofrog.py
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.

## mlp.py
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

## mnist_kernel_approximation.py
  # 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

## ml_with_sklearn_notebook.ipynb

      
        
          
            
              
              5 files
            
          
          
            
              
              0 forks
            
          
          
            
              
              0 comments
            
          
          
            
              
              0 stars
            
          
        
        
          
              
          
          
            
                vgoklani
                / ml_with_sklearn_notebook.ipynb
            
            
              Created
              December 1, 2012 06:29
                — forked from amueller/ml_with_sklearn_notebook.ipynb
            
              
                Teaser on machine learning with scikit-learn
              
          
        
      
        
  
    
    

          
    
      
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
	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
	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/'
	################## Plot training skies ###################
	##
	## corey.chivers@mail.mcgill.ca
	##
	##########################################################

	## calculate a vector given
	## x,y,e1,e2
	gal_line<-function(g,scale=100)
	{
	# Simulator for the simple Boltzmann machine of Coursera NN Lecture 11e
	# Christian Jauvin - cjauvin@gmail.com

	from collections import defaultdict
	import numpy as np

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