Note: This is the guide for v 2.x.
For the v3, please follow this url: https://blog.csdn.net/sam_shan/article/details/80585240 Thanks @liy-cn for contributing.
For the v6, please follow the comment below: https://gist.github.com/trandaison/40b1d83618ae8e3d2da59df8c395093a?permalink_comment_id=5079514#gistcomment-5079514
Download: StarUML.io
| n02119789 1 kit_fox | |
| n02100735 2 English_setter | |
| n02110185 3 Siberian_husky | |
| n02096294 4 Australian_terrier | |
| n02102040 5 English_springer | |
| n02066245 6 grey_whale | |
| n02509815 7 lesser_panda | |
| n02124075 8 Egyptian_cat | |
| n02417914 9 ibex | |
| n02123394 10 Persian_cat |
| #!/public/spark-0.9.1/bin/pyspark | |
| import os | |
| import sys | |
| # Set the path for spark installation | |
| # this is the path where you have built spark using sbt/sbt assembly | |
| os.environ['SPARK_HOME'] = "/public/spark-0.9.1" | |
| # os.environ['SPARK_HOME'] = "/home/jie/d2/spark-0.9.1" | |
| # Append to PYTHONPATH so that pyspark could be found |
A dendrogram is a common way to represent hierarchical data. For Python users, Scipy has a hierarchical clustering module that performs hierarchical clustering and outputs the results as dendrogram plots via matplotlib. When it's time to make a prettier, more customized, or web-version of the dendogram, however, it can be tricky to use Scipy's dendrogram to create a suitable visualization. My preferred method of visualizing data -- especially on the web -- is D3. This example includes a script to convert a Scipy dendrogram into JSON format used by D3's cluster method.
In the example, I cluster six genes by their expression values from two experiments. You can easily replace that data with your own, larger data set, to harness the power of both Scipy and D3 for analyzing hierarchical data. The D3 code I used to generate this example is straigh
| license: gpl-3.0 |
