Comments (1)
Hi @PARODBE,
thanks for checking out the library.
The nodes differences plot can be printed with the differences=net.diff_graph()
, which is a function of your map object net
. It gives as output (differences
in this example) a list with the difference values calculated for each node. I know that there are different ways of calculating it, so if you want the freedom to define your own difference function you can just use node.get_distance()
, which is an attribute of each node and calculates its Euclidean distance to any other node.
Regarding your last question, yes, reduced=net.project(input_data)
actually can project any kind of data (assuming it's in the right format) and can be used for prediction. Once you have trained your map net
you can just give your new samples in input to this function and it will project them onto the map. It gives in output (reduced
in the example ) the position of your data points in the reduced map space. You can then run any kind of clustering/classification function you wish on the projected data. If you want to try and run one of the internal clustering methods just use net.cluster(input_data, type='qthresh')
. As clustering type it takes 'qthresh'
for quality threshold, 'dpeak'
for density peak, 'MeanShift'
, 'DBSCAN'
, and 'KMeans'
as directly taken from scikit-learn.
Once you have the coordinates on the data you can calculate centroids and their distances.
Hope this answers your questions, let me know if you have any doubt.
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Related Issues (20)
- how to load weight.npy file to predict? HOT 1
- Labels HOT 9
- Predicting winning cell for data? HOT 2
- Cannot locate raw_data or any detailed API. HOT 4
- Parallelization of the code HOT 2
- Node's coordinates in the SOM HOT 3
- pip version doesn't have colnames parameter in nodes_graph HOT 1
- Module Not Found error during import HOT 2
- net.project() function is slow HOT 5
- PyPi not using latest changes HOT 2
- Bug in learning rate? HOT 2
- Cloning of repository fails due to too long filenames. HOT 2
- MemoryError HOT 2
- problem due cyclical error? HOT 3
- I think that when updating weights, should not target all nodes. HOT 2
- Unbound Local Error While training HOT 4
- How to reference this package HOT 1
- a little error happened on densitypeak.py HOT 3
- Time complexity HOT 5
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