In [1]:

    

x = [[698.1612903,602.2580645,535.1612903,515.9677419,507.2258065,513.8709677,608,716,732.9354839,646.483871,522.5483871,494.7419355,484.516129,483.2258065,490.5806452,515.7741935,618.0322581,770.6129032,857.3870968,853.4193548,848.3548387,843.0322581,824.516129,784.7096774], 
     [686.9032258,588.8387097,524.483871,507.483871,500.0645161,505.7096774,599.9677419,706,722.1290323,636.5483871,536,509.2258065,497.3225806,486.2903226,485.0322581,513.0322581,616.9677419,770.8387097,857.5483871,849.8064516,843.516129,834.0967742,812.3548387,774.2258065], 24*[774]]


    

In [2]:

    

y = [1, 1, 0]


    

In [13]:

    

from sklearn.preprocessing import normalize
x_norm = normalize(x)
print x_norm


    

    




[[ 0.21628697  0.18657662  0.16579037  0.1598443   0.15713609  0.15919473
   0.18835544  0.22181332  0.22705985  0.20027756  0.16188295  0.15326865
   0.15010074  0.14970101  0.1519795   0.15978434  0.19146339  0.23873213
   0.26561435  0.26438517  0.26281621  0.2611673   0.25543109  0.24309924]
 [ 0.21434438  0.18374389  0.16366231  0.15835755  0.15604239  0.15780393
   0.18721664  0.22030342  0.2253364   0.19863143  0.16725586  0.15890112
   0.15518678  0.15174422  0.15135165  0.1600889   0.1925214   0.24053599
   0.26759326  0.26517744  0.26321458  0.26027532  0.25349087  0.24159291]
 [ 0.20412415  0.20412415  0.20412415  0.20412415  0.20412415  0.20412415
   0.20412415  0.20412415  0.20412415  0.20412415  0.20412415  0.20412415
   0.20412415  0.20412415  0.20412415  0.20412415  0.20412415  0.20412415
   0.20412415  0.20412415  0.20412415  0.20412415  0.20412415  0.20412415]]

In [14]:

    

from sklearn import svm
clf = svm.SVC()
clf.fit(x_norm, y)


    

    
Out[14]:





SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

In [15]:

    

clf.predict(normalize([[
    6366.129032,
5492.590323,
4975.387097,
4851.66129,
4780.245161,
4866.445161,
5798.525806,
6693.770968,
6754.951613,
5919.303226,
5105.545161,
4919.867742,
4804.022581,
4689.051613,
4672.954839,
4964.296774,
5944.219355,
7304.648387,
7972.877419,
7882.506452,
7850.612903,
7753.345161,
7555.229032,
7177.283871

], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], 24*[700]]))


    

    
Out[15]:





array([1, 1, 1])

In [ ]:

    

import pandas
frame = pandas.read_csv('cf/' + str(row['orispl_code']) + '_' + row['unitid'] + '_' + str(year) + '.csv')