scikit-learn學習筆記 Python中的支持向量機SVM的使用（有實例） 只需十四步：從零開始掌握 Python 機器學習（附資源）

參考資料：

python機器學習庫scikit-learn簡明教程之：隨機森林 

http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb

Python中的支持向量機SVM的使用（有實例） 

基於SIFT特徵和SVM的圖像分類

scikit-learn sklearn 0.18 官方文檔中文版  

只需十四步：從零開始掌握 Python 機器學習（附資源） 

https://github.com/jakevdp/sklearn_pycon2015 

官網：http://scikit-learn.org/stable/

Scikit-learn (sklearn) 優雅地學會機器學習 (莫煩 Python 教程) 

python機器學習庫scikit-learn簡明教程之：AdaBoost算法 

http://www.docin.com/p-1775095945.html

https://www.bilibili.com/video/av22530538/?p=6 

三維點雲目標提取總結 

https://github.com/Fdevmsy/Image_Classification_with_5_methods

https://github.com/huangchuchuan/SVM-HOG-images-classifier

https://blog.csdn.net/always2015/article/details/47100713

DBScan http://www.javashuo.com/article/p-zcsnpguy-ce.html

1.KNN的使用

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]
>>> 

 2.SVC的使用

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)

 調用

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