U o¤žeÿã@s”dZddlZz ddlZWn*ek rBddlmZedƒd‚YnXddlmZe de¡Gdd„dƒZ d d „Z dd d „Z dd d„Z ddd„Z dS)aCode for doing k-nearest-neighbors classification (DEPRECATED). k Nearest Neighbors is a supervised learning algorithm that classifies a new observation based the classes in its surrounding neighborhood. Glossary: - distance The distance between two points in the feature space. - weight The importance given to each point for classification. Classes: - kNN Holds information for a nearest neighbors classifier. Functions: - train Train a new kNN classifier. - calculate Calculate the probabilities of each class, given an observation. - classify Classify an observation into a class. Weighting Functions: - equal_weight Every example is given a weight of 1. This module has been deprecated, please consider an alternative like scikit-learn insead. éN)ÚMissingPythonDependencyErrorzJPlease install NumPy if you want to use Bio.kNN. See http://www.numpy.org/)ÚBiopythonDeprecationWarningz}The 'Bio.kNN' module is deprecated and will be removed in a future release of Biopython. Consider using scikit-learn instead.c@seZdZdZdd„ZdS)ÚkNNaHolds information necessary to do nearest neighbors classification. Attributes: - classes Set of the possible classes. - xs List of the neighbors. - ys List of the classes that the neighbors belong to. - k Number of neighbors to look at. cCstƒ|_g|_g|_d|_dS)zInitialize the class.N)ÚsetÚclassesÚxsÚysÚk)Úself©r ú&lib/python3.8/site-packages/Bio/kNN.pyÚ__init__?sz kNN.__init__N)Ú__name__Ú __module__Ú __qualname__Ú__doc__r r r r r r5s rcCsdS)z8Return integer one (dummy method for equally weighting).ér )ÚxÚyr r r Ú equal_weightGsrcCs.tƒ}t|ƒ|_t ||¡|_||_||_|S)a(Train a k nearest neighbors classifier on a training set. xs is a list of observations and ys is a list of the class assignments. Thus, xs and ys should contain the same number of elements. k is the number of neighbors that should be examined when doing the classification. )rrrÚnpÚasarrayrrr )rrr ÚtypecodeÚknnr r r ÚtrainMs  rc Cs |dkr t}t |¡}g}|rRtt|jƒƒD]"}|||j|ƒ}| ||f¡q,nXt t|ƒ¡}tt|jƒƒD]:}||j||dd…<t t  ||¡¡}| ||f¡qn|  ¡i}|j D] } d|| <q¼|d|j …D].\}}|j |} || |||j|ƒ|| <qØ|S)aÎCalculate the probability for each class. Arguments: - x is the observed data. - weight_fn is an optional function that takes x and a training example, and returns a weight. - distance_fn is an optional function that takes two points and returns the distance between them. If distance_fn is None (the default), the Euclidean distance is used. Returns a dictionary of the class to the weight given to the class. Ng)rrrÚrangeÚlenrÚappendZzerosZsqrtÚdotÚsortrr r) rrÚ weight_fnÚ distance_fnÚorderÚiZdistZtempÚweightsr Úklassr r r Ú calculate]s*     r&c CsR|dkr t}t||||d}d}d}| ¡D] \}}|dksD||kr,|}|}q,|S)a%Classify an observation into a class. If not specified, weight_fn will give all neighbors equal weight. distance_fn is an optional function that takes two points and returns the distance between them. If distance_fn is None (the default), the Euclidean distance is used. N)r r!)rr&Úitems) rrr r!r$Z most_classZ most_weightr%Zweightr r r ÚclassifyŠsr()N)NN)NN)rÚwarningsZnumpyrÚ ImportErrorZBiorrÚwarnrrrr&r(r r r r Ús(  ÿþ ý  -