Thus either tSNE coordinates or the first two PCs or HOGH features.Ĭluster ( str, ( default: 'agglomerative' )) – Type of clustering.Įvaluate ( str, ( default: 'silhouette' )) – Cluster evaluation method. ’low’ : Input are the xycoordinates that are determined by “embedding”. This can either be on high or low feature space. Parameters :Ĭluster_space ( str, ( default: 'high' )) – Selection of the features that are used for clustering. This function is build on clusteval, which is a python package that provides various evalution methods for unsupervised cluster validation. cluster ( cluster = 'agglomerative', evaluate = 'silhouette', metric = 'euclidean', linkage = 'ward', min_clust = 3, max_clust = 25, cluster_space = 'high' ) ĭetect the optimal number of clusters given the input set of features. scatter () > clean_files ( clean_tempdir = False ) Ĭlean or removing previous results and models to ensure correct working. find ( X, k = None, alpha = 0.05 ) > cl. dendrogram () > # Find images > results_find = cl. plot ( labels = 8 ) > # Plot dendrogram > cl. scatter ( zoom = 8, plt_all = True, figsize = ( 150, 100 )) > # Plot clustered images > cl. scatter ( img_mean = False, zoom = 3 ) > cl. fit_transform ( X ) > # Cluster evaluation > cl.

import_example ( data = 'mnist' ) > # Cluster digits > results = cl. > from clustimage import Clustimage > # Init with default settings > cl = Clustimage ( method = 'pca' ) > # load example with faces > X = cl. Model ( dict) – dict containing keys with results. Verbose ( int, ( default: 20 )) – Print progress to screen.

Clustimage ( method = 'pca', embedding = 'tsne', grayscale = False, dim = (128, 128), dim_face = (64, 64), dirpath = None, store_to_disk = True, ext =, params_pca = ) – Parameters to extract hog features. Python package clustimage is for unsupervised clustering of images.

Set image filenames using pandas dataframes.

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