In [31]:

    

from sklearn import preprocessing
from sklearn import linear_model, datasets
import numpy as np
import matplotlib.pyplot as plt


    

In [32]:

    

%matplotlib inline


    

In [33]:

    

X = np.array([[ 1., -1.,  2.],
              [ 2.,  0.,  0.],
              [ 0.,  1., -1.]])


    

In [34]:

    

X_scaled = preprocessing.scale(X)


    

In [35]:

    

X_scaled


    

    
Out[35]:





array([[ 0.        , -1.22474487,  1.33630621],
       [ 1.22474487,  0.        , -0.26726124],
       [-1.22474487,  1.22474487, -1.06904497]])

In [36]:

    

iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target
print type(X), X.shape, X.size
print type(Y), Y.shape, Y.size


    

    




<type 'numpy.ndarray'> (150L, 2L) 300
<type 'numpy.ndarray'> (150L,) 150

In [37]:

    

h = .02


    

In [38]:

    

logreg = linear_model.LogisticRegression(C=1e5)


    

In [39]:

    

logreg.fit(X, Y)


    

    
Out[39]:





LogisticRegression(C=100000.0, class_weight=None, dual=False,
          fit_intercept=True, intercept_scaling=1, max_iter=100,
          multi_class='ovr', penalty='l2', random_state=None,
          solver='liblinear', tol=0.0001, verbose=0)

In [40]:

    

x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])


    

In [41]:

    

Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())


    

    
Out[41]:





([], <a list of 0 Text yticklabel objects>)






    

In [42]:

    

iris


    

    
Out[42]:





{'DESCR': 'Iris Plants Database\n\nNotes\n-----\nData Set Characteristics:\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n    :Summary Statistics:\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...\n',
 'data': array([[ 5.1,  3.5,  1.4,  0.2],
        [ 4.9,  3. ,  1.4,  0.2],
        [ 4.7,  3.2,  1.3,  0.2],
        [ 4.6,  3.1,  1.5,  0.2],
        [ 5. ,  3.6,  1.4,  0.2],
        [ 5.4,  3.9,  1.7,  0.4],
        [ 4.6,  3.4,  1.4,  0.3],
        [ 5. ,  3.4,  1.5,  0.2],
        [ 4.4,  2.9,  1.4,  0.2],
        [ 4.9,  3.1,  1.5,  0.1],
        [ 5.4,  3.7,  1.5,  0.2],
        [ 4.8,  3.4,  1.6,  0.2],
        [ 4.8,  3. ,  1.4,  0.1],
        [ 4.3,  3. ,  1.1,  0.1],
        [ 5.8,  4. ,  1.2,  0.2],
        [ 5.7,  4.4,  1.5,  0.4],
        [ 5.4,  3.9,  1.3,  0.4],
        [ 5.1,  3.5,  1.4,  0.3],
        [ 5.7,  3.8,  1.7,  0.3],
        [ 5.1,  3.8,  1.5,  0.3],
        [ 5.4,  3.4,  1.7,  0.2],
        [ 5.1,  3.7,  1.5,  0.4],
        [ 4.6,  3.6,  1. ,  0.2],
        [ 5.1,  3.3,  1.7,  0.5],
        [ 4.8,  3.4,  1.9,  0.2],
        [ 5. ,  3. ,  1.6,  0.2],
        [ 5. ,  3.4,  1.6,  0.4],
        [ 5.2,  3.5,  1.5,  0.2],
        [ 5.2,  3.4,  1.4,  0.2],
        [ 4.7,  3.2,  1.6,  0.2],
        [ 4.8,  3.1,  1.6,  0.2],
        [ 5.4,  3.4,  1.5,  0.4],
        [ 5.2,  4.1,  1.5,  0.1],
        [ 5.5,  4.2,  1.4,  0.2],
        [ 4.9,  3.1,  1.5,  0.1],
        [ 5. ,  3.2,  1.2,  0.2],
        [ 5.5,  3.5,  1.3,  0.2],
        [ 4.9,  3.1,  1.5,  0.1],
        [ 4.4,  3. ,  1.3,  0.2],
        [ 5.1,  3.4,  1.5,  0.2],
        [ 5. ,  3.5,  1.3,  0.3],
        [ 4.5,  2.3,  1.3,  0.3],
        [ 4.4,  3.2,  1.3,  0.2],
        [ 5. ,  3.5,  1.6,  0.6],
        [ 5.1,  3.8,  1.9,  0.4],
        [ 4.8,  3. ,  1.4,  0.3],
        [ 5.1,  3.8,  1.6,  0.2],
        [ 4.6,  3.2,  1.4,  0.2],
        [ 5.3,  3.7,  1.5,  0.2],
        [ 5. ,  3.3,  1.4,  0.2],
        [ 7. ,  3.2,  4.7,  1.4],
        [ 6.4,  3.2,  4.5,  1.5],
        [ 6.9,  3.1,  4.9,  1.5],
        [ 5.5,  2.3,  4. ,  1.3],
        [ 6.5,  2.8,  4.6,  1.5],
        [ 5.7,  2.8,  4.5,  1.3],
        [ 6.3,  3.3,  4.7,  1.6],
        [ 4.9,  2.4,  3.3,  1. ],
        [ 6.6,  2.9,  4.6,  1.3],
        [ 5.2,  2.7,  3.9,  1.4],
        [ 5. ,  2. ,  3.5,  1. ],
        [ 5.9,  3. ,  4.2,  1.5],
        [ 6. ,  2.2,  4. ,  1. ],
        [ 6.1,  2.9,  4.7,  1.4],
        [ 5.6,  2.9,  3.6,  1.3],
        [ 6.7,  3.1,  4.4,  1.4],
        [ 5.6,  3. ,  4.5,  1.5],
        [ 5.8,  2.7,  4.1,  1. ],
        [ 6.2,  2.2,  4.5,  1.5],
        [ 5.6,  2.5,  3.9,  1.1],
        [ 5.9,  3.2,  4.8,  1.8],
        [ 6.1,  2.8,  4. ,  1.3],
        [ 6.3,  2.5,  4.9,  1.5],
        [ 6.1,  2.8,  4.7,  1.2],
        [ 6.4,  2.9,  4.3,  1.3],
        [ 6.6,  3. ,  4.4,  1.4],
        [ 6.8,  2.8,  4.8,  1.4],
        [ 6.7,  3. ,  5. ,  1.7],
        [ 6. ,  2.9,  4.5,  1.5],
        [ 5.7,  2.6,  3.5,  1. ],
        [ 5.5,  2.4,  3.8,  1.1],
        [ 5.5,  2.4,  3.7,  1. ],
        [ 5.8,  2.7,  3.9,  1.2],
        [ 6. ,  2.7,  5.1,  1.6],
        [ 5.4,  3. ,  4.5,  1.5],
        [ 6. ,  3.4,  4.5,  1.6],
        [ 6.7,  3.1,  4.7,  1.5],
        [ 6.3,  2.3,  4.4,  1.3],
        [ 5.6,  3. ,  4.1,  1.3],
        [ 5.5,  2.5,  4. ,  1.3],
        [ 5.5,  2.6,  4.4,  1.2],
        [ 6.1,  3. ,  4.6,  1.4],
        [ 5.8,  2.6,  4. ,  1.2],
        [ 5. ,  2.3,  3.3,  1. ],
        [ 5.6,  2.7,  4.2,  1.3],
        [ 5.7,  3. ,  4.2,  1.2],
        [ 5.7,  2.9,  4.2,  1.3],
        [ 6.2,  2.9,  4.3,  1.3],
        [ 5.1,  2.5,  3. ,  1.1],
        [ 5.7,  2.8,  4.1,  1.3],
        [ 6.3,  3.3,  6. ,  2.5],
        [ 5.8,  2.7,  5.1,  1.9],
        [ 7.1,  3. ,  5.9,  2.1],
        [ 6.3,  2.9,  5.6,  1.8],
        [ 6.5,  3. ,  5.8,  2.2],
        [ 7.6,  3. ,  6.6,  2.1],
        [ 4.9,  2.5,  4.5,  1.7],
        [ 7.3,  2.9,  6.3,  1.8],
        [ 6.7,  2.5,  5.8,  1.8],
        [ 7.2,  3.6,  6.1,  2.5],
        [ 6.5,  3.2,  5.1,  2. ],
        [ 6.4,  2.7,  5.3,  1.9],
        [ 6.8,  3. ,  5.5,  2.1],
        [ 5.7,  2.5,  5. ,  2. ],
        [ 5.8,  2.8,  5.1,  2.4],
        [ 6.4,  3.2,  5.3,  2.3],
        [ 6.5,  3. ,  5.5,  1.8],
        [ 7.7,  3.8,  6.7,  2.2],
        [ 7.7,  2.6,  6.9,  2.3],
        [ 6. ,  2.2,  5. ,  1.5],
        [ 6.9,  3.2,  5.7,  2.3],
        [ 5.6,  2.8,  4.9,  2. ],
        [ 7.7,  2.8,  6.7,  2. ],
        [ 6.3,  2.7,  4.9,  1.8],
        [ 6.7,  3.3,  5.7,  2.1],
        [ 7.2,  3.2,  6. ,  1.8],
        [ 6.2,  2.8,  4.8,  1.8],
        [ 6.1,  3. ,  4.9,  1.8],
        [ 6.4,  2.8,  5.6,  2.1],
        [ 7.2,  3. ,  5.8,  1.6],
        [ 7.4,  2.8,  6.1,  1.9],
        [ 7.9,  3.8,  6.4,  2. ],
        [ 6.4,  2.8,  5.6,  2.2],
        [ 6.3,  2.8,  5.1,  1.5],
        [ 6.1,  2.6,  5.6,  1.4],
        [ 7.7,  3. ,  6.1,  2.3],
        [ 6.3,  3.4,  5.6,  2.4],
        [ 6.4,  3.1,  5.5,  1.8],
        [ 6. ,  3. ,  4.8,  1.8],
        [ 6.9,  3.1,  5.4,  2.1],
        [ 6.7,  3.1,  5.6,  2.4],
        [ 6.9,  3.1,  5.1,  2.3],
        [ 5.8,  2.7,  5.1,  1.9],
        [ 6.8,  3.2,  5.9,  2.3],
        [ 6.7,  3.3,  5.7,  2.5],
        [ 6.7,  3. ,  5.2,  2.3],
        [ 6.3,  2.5,  5. ,  1.9],
        [ 6.5,  3. ,  5.2,  2. ],
        [ 6.2,  3.4,  5.4,  2.3],
        [ 5.9,  3. ,  5.1,  1.8]]),
 'feature_names': ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),
 'target_names': array(['setosa', 'versicolor', 'virginica'], 
       dtype='|S10')}

In [ ]: