Submit
Path:
~
/
/
proc
/
thread-self
/
root
/
opt
/
alt
/
python35
/
lib64
/
python3.5
/
site-packages
/
sklearn
/
cluster
/
__pycache__
/
File Content:
affinity_propagation_.cpython-35.pyc
��(X�) � @ s� d Z d d l Z d d l m Z m Z d d l m Z m Z d d l m Z d d l m Z d d l m Z d d d d d d d d d � Z Gd d � d e e � Z d S)z*Affinity Propagation clustering algorithm.� N� )� BaseEstimator� ClusterMixin)�as_float_array� check_array)�check_is_fitted)�euclidean_distances)�pairwise_distances_argmin� �� g �?TFc C s} t | d | �} | j d } | j d | j d k rR t d t | j � � � | d k rm t j | � } | d k s� | d k r� t d � � t j j d � } | | j d d | d � <t j | | f � } t j | | f � } t j | | f � } | t j t j � j | t j t j � j d | j | | � 7} t j | | f � } t j | � } xtt | � D]V} t j | | | � t j | d d �} | | | f } t j | | | f <t j | d d �} t j | | d d � d f | � | | | f | | | | f <| d | 9} | | 9} | | 7} t j | d | � | j d d | d � | j d d | d � <| t j | d d �8} t j | � j � } | j d t j | � | | j d d | d � <| d | 9} | | 9} | | 8} t j | � t j | � d k } | | d d � | | f <t j | d d �} | | k rqt j | d d �} t j | | k | d k � | k } | r�| d k s�| | k rq| r�t d | � PqqW| r�t d � t j t j | | � d k � d } | j } | d k r-t j | d d � | f d d �} t j | � | | <xv t | � D]h } t j | | k � d } t j t j | | d d � t j f | f d d �� } | | | | <q[Wt j | d d � | f d d �} t j | � | | <| | } t j | � } t j | | � } n+ t j! | d f � } d } | j" t j# � | ro| | | d f S| | f Sd S) a� Perform Affinity Propagation Clustering of data Read more in the :ref:`User Guide <affinity_propagation>`. Parameters ---------- S : array-like, shape (n_samples, n_samples) Matrix of similarities between points preference : array-like, shape (n_samples,) or float, optional Preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, i.e. of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities (resulting in a moderate number of clusters). For a smaller amount of clusters, this can be set to the minimum value of the similarities. convergence_iter : int, optional, default: 15 Number of iterations with no change in the number of estimated clusters that stops the convergence. max_iter : int, optional, default: 200 Maximum number of iterations damping : float, optional, default: 0.5 Damping factor between 0.5 and 1. copy : boolean, optional, default: True If copy is False, the affinity matrix is modified inplace by the algorithm, for memory efficiency verbose : boolean, optional, default: False The verbosity level return_n_iter : bool, default False Whether or not to return the number of iterations. Returns ------- cluster_centers_indices : array, shape (n_clusters,) index of clusters centers labels : array, shape (n_samples,) cluster labels for each point n_iter : int number of iterations run. Returned only if `return_n_iter` is set to True. Notes ----- See examples/cluster/plot_affinity_propagation.py for an example. References ---------- Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Points", Science Feb. 2007 �copyr � z#S must be a square array (shape=%s)Ng �?z damping must be >= 0.5 and < 1�d Zaxisz Converged after %d iterations.zDid not converge)$r �shape� ValueError�repr�npZmedian�randomZ RandomStateZflatZzerosZfinfoZdoubleZepsZtinyZrandnZarange�range�addZargmax�inf�max�subtractZmaximum�sumZdiagr Zclip�printZwhere�sizeZnewaxis�uniqueZ searchsorted�emptyZfill�nan) �S� preference�convergence_iter�max_iter�dampingr �verbose� return_n_iterZ n_samplesZ random_state�A�R�tmp�eZind�it�I�YZY2ZdA�E�KZseZ unconverged�c�kZii�jZlabelsZcluster_centers_indices� r2 �/affinity_propagation_.py�affinity_propagation s� @ 0# . " & %;% r4 c @ sd e Z d Z d Z d d d d d d d d d � Z e d d � � Z d d d � Z d d � Z d S)�AffinityPropagationaP Perform Affinity Propagation Clustering of data. Read more in the :ref:`User Guide <affinity_propagation>`. Parameters ---------- damping : float, optional, default: 0.5 Damping factor between 0.5 and 1. convergence_iter : int, optional, default: 15 Number of iterations with no change in the number of estimated clusters that stops the convergence. max_iter : int, optional, default: 200 Maximum number of iterations. copy : boolean, optional, default: True Make a copy of input data. preference : array-like, shape (n_samples,) or float, optional Preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, ie of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities. affinity : string, optional, default=``euclidean`` Which affinity to use. At the moment ``precomputed`` and ``euclidean`` are supported. ``euclidean`` uses the negative squared euclidean distance between points. verbose : boolean, optional, default: False Whether to be verbose. Attributes ---------- cluster_centers_indices_ : array, shape (n_clusters,) Indices of cluster centers cluster_centers_ : array, shape (n_clusters, n_features) Cluster centers (if affinity != ``precomputed``). labels_ : array, shape (n_samples,) Labels of each point affinity_matrix_ : array, shape (n_samples, n_samples) Stores the affinity matrix used in ``fit``. n_iter_ : int Number of iterations taken to converge. Notes ----- See examples/cluster/plot_affinity_propagation.py for an example. The algorithmic complexity of affinity propagation is quadratic in the number of points. References ---------- Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Points", Science Feb. 2007 g �?r r TN� euclideanFc C sC | | _ | | _ | | _ | | _ | | _ | | _ | | _ d S)N)r# r" r! r r$ r �affinity)�selfr# r" r! r r r7 r$ r2 r2 r3 �__init__ s z AffinityPropagation.__init__c C s | j d k S)N� precomputed)r7 )r8 r2 r2 r3 � _pairwise s z AffinityPropagation._pairwisec C s� t | d d �} | j d k r- | | _ nA | j d k rU t | d d � | _ n t d t | j � � � t | j | j d | j d | j d | j d | j d | j d d �\ | _ | _ | _ | j d k r� | | j j � | _ | S)a[ Create affinity matrix from negative euclidean distances, then apply affinity propagation clustering. Parameters ---------- X: array-like, shape (n_samples, n_features) or (n_samples, n_samples) Data matrix or, if affinity is ``precomputed``, matrix of similarities / affinities. Z accept_sparseZcsrr: r6 ZsquaredTz=Affinity must be 'precomputed' or 'euclidean'. Got %s insteadr" r! r# r r$ r% )r r7 Zaffinity_matrix_r r �strr4 r r" r! r# r r$ �cluster_centers_indices_Zlabels_Zn_iter_�cluster_centers_)r8 �X�yr2 r2 r3 �fit s -zAffinityPropagation.fitc C s8 t | d � t | d � s( t d � � t | | j � S)aU Predict the closest cluster each sample in X belongs to. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) New data to predict. Returns ------- labels : array, shape (n_samples,) Index of the cluster each sample belongs to. r= r> z<Predict method is not supported when affinity='precomputed'.)r �hasattrr r r> )r8 r? r2 r2 r3 �predict2 s zAffinityPropagation.predict) �__name__� __module__� __qualname__�__doc__r9 �propertyr; rA rC r2 r2 r2 r3 r5 � s A r5 )rG Znumpyr �baser r Zutilsr r Zutils.validationr Zmetricsr r r4 r5 r2 r2 r2 r3 �<module> s �
Edit
Rename
Chmod
Delete
FILE
FOLDER
Name
Size
Permission
Action
__init__.cpython-35.opt-1.pyc
1160 bytes
0644
__init__.cpython-35.pyc
1160 bytes
0644
_feature_agglomeration.cpython-35.opt-1.pyc
2478 bytes
0644
_feature_agglomeration.cpython-35.pyc
2478 bytes
0644
affinity_propagation_.cpython-35.opt-1.pyc
9332 bytes
0644
affinity_propagation_.cpython-35.pyc
9332 bytes
0644
bicluster.cpython-35.opt-1.pyc
17924 bytes
0644
bicluster.cpython-35.pyc
17924 bytes
0644
birch.cpython-35.opt-1.pyc
17712 bytes
0644
birch.cpython-35.pyc
17712 bytes
0644
dbscan_.cpython-35.opt-1.pyc
11471 bytes
0644
dbscan_.cpython-35.pyc
11471 bytes
0644
hierarchical.cpython-35.opt-1.pyc
26136 bytes
0644
hierarchical.cpython-35.pyc
26172 bytes
0644
k_means_.cpython-35.opt-1.pyc
43828 bytes
0644
k_means_.cpython-35.pyc
43903 bytes
0644
mean_shift_.cpython-35.opt-1.pyc
14364 bytes
0644
mean_shift_.cpython-35.pyc
14364 bytes
0644
setup.cpython-35.opt-1.pyc
1312 bytes
0644
setup.cpython-35.pyc
1312 bytes
0644
spectral.cpython-35.opt-1.pyc
15802 bytes
0644
spectral.cpython-35.pyc
15802 bytes
0644
N4ST4R_ID | Naxtarrr
