Hi Roman, I will check that function too. Thanks for help.
Have a good day, Jaime On Fri, Nov 4, 2016 at 4:28 AM, Roman Yurchak <[email protected]> wrote: > Hi Jaime, > > Alternatively, in scikit learn I think, you could use > hac = AgglomerativeClustering(n_clusters, linkage="ward") > hac.fit(data) > clusters = hac.labels_ > there in an example on how to plot a dendrogram from this in > https://github.com/scikit-learn/scikit-learn/pull/3464 > > AgglomerativeClustering internally calls scikit learn's version of > cut_tree. I would be curious to know whether this is equivalent to > scipy's fcluster. > > Roman > > On 03/11/16 23:12, Jaime Lopez Carvajal wrote: > > Hi Juan, > > > > The fcluster function was that I needed. I can now proceed from here to > > classify images. > > Thank you very much, > > > > Jaime > > > > On Thu, Nov 3, 2016 at 5:00 PM, Juan Nunez-Iglesias <[email protected] > > <mailto:[email protected]>> wrote: > > > > Hi Jaime, > > > > From /Elegant SciPy/: > > > > """ > > The *fcluster* function takes a linkage matrix, as returned by > > linkage, and a threshold, and returns cluster identities. It's > > difficult to know a-priori what the threshold should be, but we can > > obtain the appropriate threshold for a fixed number of clusters by > > checking the distances in the linkage matrix. > > > > from scipy.cluster.hierarchy import fcluster > > n_clusters = 3 > > threshold_distance = (Z[-n_clusters, 2] + Z[-n_clusters+1, 2]) / 2 > > clusters = fcluster(Z, threshold_distance, 'distance') > > > > """ > > > > As an aside, I imagine this question is better placed in the SciPy > > mailing list than scikit-learn (which has its own hierarchical > > clustering API). > > > > Juan. > > > > On Fri, Nov 4, 2016 at 2:16 AM, Jaime Lopez Carvajal > > <[email protected] <mailto:[email protected]>> wrote: > > > > Hi there, > > > > I am trying to do image classification using hierarchical > > clustering. > > So, I have my data, and apply this steps: > > > > from scipy.cluster.hierarchy import dendrogram, linkage > > > > data1 = np.array(data) > > Z = linkage(data, 'ward') > > dendrogram(Z, truncate_mode='lastp', p=12, > > show_leaf_counts=False, leaf_rotation=90., > > leaf_font_size=12.,show_contracted=True) > > plt.show() > > > > So, I can see the dendrogram with 12 clusters as I want, but I > > dont know how to use this to classify the image. > > Also, I understand that funtion cluster.hierarchy.cut_tree(Z, > > n_clusters), that cut the tree at that number of clusters, but > > again I dont know how to procedd from there. I would like to > > have something like: cluster = predict(Z, instance) > > > > Any advice or direction would be really appreciate, > > > > Thanks in advance, Jaime > > > > > > -- > > /*Jaime Lopez Carvajal > > */ > > > > _______________________________________________ > > scikit-learn mailing list > > [email protected] <mailto:[email protected]> > > https://mail.python.org/mailman/listinfo/scikit-learn > > <https://mail.python.org/mailman/listinfo/scikit-learn> > > > > > > > > _______________________________________________ > > scikit-learn mailing list > > [email protected] <mailto:[email protected]> > > https://mail.python.org/mailman/listinfo/scikit-learn > > <https://mail.python.org/mailman/listinfo/scikit-learn> > > > > > > > > > > -- > > /*Jaime Lopez Carvajal > > */ > > > > > > _______________________________________________ > > scikit-learn mailing list > > [email protected] > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > -- *Jaime Lopez Carvajal*
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