Home  Trees  Indices  Help 



KMeans clustering This node has been automatically generated by wrapping the ``sklearn.cluster.k_means_.KMeans`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Read more in the :ref:`User Guide <k_means>`. **Parameters** n_clusters : int, optional, default: 8 The number of clusters to form as well as the number of centroids to generate. max_iter : int, default: 300 Maximum number of iterations of the kmeans algorithm for a single run. n_init : int, default: 10 Number of time the kmeans algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. init : {'kmeans++', 'random' or an ndarray} Method for initialization, defaults to 'kmeans++': 'kmeans++' : selects initial cluster centers for kmean clustering in a smart way to speed up convergence. See section Notes in k_init for more details. 'random': choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. precompute_distances : {'auto', True, False} Precompute distances (faster but takes more memory). 'auto' : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision. True : always precompute distances False : never precompute distances tol : float, default: 1e4 Relative tolerance with regards to inertia to declare convergence n_jobs : int The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel. If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used. random_state : integer or numpy.RandomState, optional The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator. verbose : int, default 0 Verbosity mode. copy_x : boolean, default True When precomputing distances it is more numerically accurate to center the data first. If copy_x is True, then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean. **Attributes** ``cluster_centers_`` : array, [n_clusters, n_features] Coordinates of cluster centers ``labels_`` :  Labels of each point ``inertia_`` : float Sum of distances of samples to their closest cluster center. **Notes** The kmeans problem is solved using Lloyd's algorithm. The average complexity is given by O(k n T), were n is the number of samples and T is the number of iteration. The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, 'How slow is the kmeans method?' SoCG2006) In practice, the kmeans algorithm is very fast (one of the fastest clustering algorithms available), but it falls in local minima. That's why it can be useful to restart it several times. See also MiniBatchKMeans:  Alternative online implementation that does incremental updates  of the centers positions using minibatches.  For large scale learning (say n_samples > 10k) MiniBatchKMeans is  probably much faster to than the default batch implementation.














Inherited from Inherited from 

Inherited from Cumulator  





Inherited from Node  


































































Inherited from 

Inherited from Node  

_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

KMeans clustering This node has been automatically generated by wrapping the ``sklearn.cluster.k_means_.KMeans`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Read more in the :ref:`User Guide <k_means>`. **Parameters** n_clusters : int, optional, default: 8 The number of clusters to form as well as the number of centroids to generate. max_iter : int, default: 300 Maximum number of iterations of the kmeans algorithm for a single run. n_init : int, default: 10 Number of time the kmeans algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. init : {'kmeans++', 'random' or an ndarray} Method for initialization, defaults to 'kmeans++': 'kmeans++' : selects initial cluster centers for kmean clustering in a smart way to speed up convergence. See section Notes in k_init for more details. 'random': choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers. precompute_distances : {'auto', True, False} Precompute distances (faster but takes more memory). 'auto' : do not precompute distances if n_samples * n_clusters > 12 million. This corresponds to about 100MB overhead per job using double precision. True : always precompute distances False : never precompute distances tol : float, default: 1e4 Relative tolerance with regards to inertia to declare convergence n_jobs : int The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel. If 1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below 1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = 2, all CPUs but one are used. random_state : integer or numpy.RandomState, optional The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator. verbose : int, default 0 Verbosity mode. copy_x : boolean, default True When precomputing distances it is more numerically accurate to center the data first. If copy_x is True, then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean. **Attributes** ``cluster_centers_`` : array, [n_clusters, n_features] Coordinates of cluster centers ``labels_`` :  Labels of each point ``inertia_`` : float Sum of distances of samples to their closest cluster center. **Notes** The kmeans problem is solved using Lloyd's algorithm. The average complexity is given by O(k n T), were n is the number of samples and T is the number of iteration. The worst case complexity is given by O(n^(k+2/p)) with n = n_samples, p = n_features. (D. Arthur and S. Vassilvitskii, 'How slow is the kmeans method?' SoCG2006) In practice, the kmeans algorithm is very fast (one of the fastest clustering algorithms available), but it falls in local minima. That's why it can be useful to restart it several times. See also MiniBatchKMeans:  Alternative online implementation that does incremental updates  of the centers positions using minibatches.  For large scale learning (say n_samples > 10k) MiniBatchKMeans is  probably much faster to than the default batch implementation.




Transform X to a clusterdistance space. This node has been automatically generated by wrapping the sklearn.cluster.k_means_.KMeans class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
Parameters
Returns



Compute kmeans clustering. This node has been automatically generated by wrapping the sklearn.cluster.k_means_.KMeans class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters X : arraylike or sparse matrix, shape=(n_samples, n_features)

Home  Trees  Indices  Help 


Generated by Epydoc 3.0.1 on Tue Mar 8 12:39:48 2016  http://epydoc.sourceforge.net 