MIML - MeteoInfo机器学习工具箱

MeteoInfo

MIML - MeteoInfo机器学习工具箱。目前是0.2版本，在MeteoInfo 2.0.1版本中包含了该工具箱，包括神经网络, KNN, SVM, Random Forest, K-Means等等常用的机器学习算法。

用法：参考MeteoInfo网站的示例脚本：http://www.meteothink.org/examples/miml/index.html

源代码：https://github.com/meteoinfo/miml

欢迎使用！

一个DBSCAN聚类算法的例子：

1. from miml import datasets
   
2. from miml.cluster import DBSCAN
   
3. 
   
4. fn = os.path.join(datasets.get_data_home(), 'clustering', 'chameleon',
   
5.     't4.8k.txt')
   
6. df = DataFrame.read_table(fn, header=None, names=['x1','x2'],
   
7.     format='%2f')
   
8. x = df.values
   
9. 
   
10. clusters = DBSCAN(x, min_pts=20, radius=10)
    
11. y = clusters.get_cluster_label()
    
12. 
    
13. k = 6
    
14. levs = range(k)
    
15. levs.append(int(y.max()))
    
16. cols = makecolors(k)
    
17. cols.append('gray')
    
18. scatter(x[:,0], x[:,1], c=y, edgecolor=None, s=3, levels=levs, colors=cols)
    
19. title('Density-Based Spatial Clustering of Applications with Noise example')

from miml import datasets<br /> from miml.cluster import DBSCAN<br /> <br /> fn = os.path.join(datasets.get_data_home(), 'clustering', 'chameleon', <br />     't4.8k.txt')<br /> df = DataFrame.read_table(fn, header=None, names=['x1','x2'], <br />     format='%2f')<br /> x = df.values<br /> <br /> clusters = DBSCAN(x, min_pts=20, radius=10)<br /> y = clusters.get_cluster_label()<br /> <br /> k = 6<br /> levs = range(k)<br /> levs.append(int(y.max()))<br /> cols = makecolors(k)<br /> cols.append('gray')<br /> scatter(x[:,0], x[:,1], c=y, edgecolor=None, s=3, levels=levs, colors=cols)<br /> title('Density-Based Spatial Clustering of Applications with Noise example')

随缘

{:eb502:}{:eb502:}哇！强大！

oydl1985

火钳刘明！！

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老师好厉害！