carto@cartoPC:~$ python

Python 2.7.12 (default, Dec  4 2017, 14:50:18)

[GCC 5.4.0 20160609] on linux2

Type "help", "copyright", "credits" or "license" for more information.

>>> import numpy as np

>>> from sklearn import datasets

>>> from sklearn.cross_validation import train_test_split

/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.

  "This module will be removed in 0.20.", DeprecationWarning)

>>> from sklearn.neighbors import KNeighborsClassifier

>>> iris=datasets.load_iris()

>>> iris_X=iris.data

>>> iris_y=iris.target

>>> print(iris_X[:2,:])

[[ 5.1  3.5  1.4  0.2]

 [ 4.9  3.   1.4  0.2]]

>>> print(iris_y)

[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]

>>> X_train,X_test,y_train,y_test=train_test_split(iris_X,iris_y,test_size=0.3)

>>> print(y_train)

[2 1 0 0 0 2 0 0 1 1 2 2 1 1 2 2 2 0 1 0 2 2 1 1 1 1 1 0 1 1 0 2 1 0 0 2 2

 0 0 2 1 0 0 2 1 2 1 2 1 1 1 2 1 2 0 2 0 1 1 2 1 0 1 2 2 0 2 2 1 0 1 1 2 2

 1 0 1 1 2 0 0 1 0 1 0 2 0 1 1 0 2 1 2 0 2 0 2 0 2 1 0 2 0 2 2]

>>> knn=KNeighborsClassifier()

>>> knn.fit()

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

TypeError: fit() takes exactly 3 arguments (1 given)

>>> knn.fit(X_train,y_train)

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',

           metric_params=None, n_jobs=1, n_neighbors=5, p=2,

           weights='uniform')

>>> print(knn.predict(X_test))

[1 1 2 0 1 1 1 1 2 0 0 2 0 1 0 0 0 1 2 2 2 2 0 1 2 0 1 2 2 0 1 2 0 0 1 0 0

 0 0 1 0 1 1 2 0]

>>> print(y_test)

[1 1 2 0 1 1 1 1 2 0 0 2 0 1 0 0 0 1 2 2 2 2 0 1 2 0 1 2 2 0 2 2 0 0 2 0 0

 0 0 1 0 1 1 2 0]

>>>

import pandas as pd

import numpy as np

from sklearn import datasets

from sklearn import svm

from sklearn.model_selection import train_test_split

def load_data():

	iris=datasets.load_iris()

	X_train,X_test,y_train,y_test=train_test_split(

	iris.data,iris.target,test_size=0.10,random_state=0)

	return X_train,X_test,y_train,y_test

def test_LinearSVC(X_train,X_test,y_train,y_test):

	cls=svm.LinearSVC()

	cls.fit(X_train,y_train)

	print('Coefficients:%s, intercept %s'%(cls.coef_,cls.intercept_))

	print('Score: %.2f' %cls.score(X_test,y_test))

if __name__=="__main__":

	X_train,X_test,y_train,y_test=load_data()

	test_LinearSVC(X_train,X_test,y_train,y_test)

carto@cartoPC:~/python_ws$ python svmtest2.py

Coefficients:[[ 0.18424504  0.45123335 -0.80794237 -0.45071267]

 [-0.13381099 -0.75235247  0.57223898 -1.11494325]

 [-0.7943601  -0.95801711  1.31465593  1.8169808 ]], intercept [ 0.10956304  1.86593164 -1.72576407]

Score: 1.00