&hM dZddlZddlmZejde ddlZdZ Gdd Z d d Z d Z dd Z y#e$rddlmZeddwxYw)aGeneral Naive Bayes learner (DEPRECATED). Naive Bayes is a supervised classification algorithm that uses Bayes rule to compute the fit between a new observation and some previously observed data. The observations are discrete feature vectors, with the Bayes assumption that the features are independent. Although this is hardly ever true, the classifier works well enough in practice. Glossary: - observation - A feature vector of discrete data. - class - A possible classification for an observation. Classes: - NaiveBayes - Holds information for a naive Bayes classifier. Functions: - train - Train a new naive Bayes classifier. - calculate - Calculate the probabilities of each class, given an observation. - classify - Classify an observation into a class. N)BiopythonDeprecationWarningzThe 'Bio.NaiveBayes' module is deprecated and will be removed in a future release of Biopython. Consider using scikit-learn instead.)MissingPythonDependencyErrorzQPlease install NumPy if you want to use Bio.NaiveBayes. See http://www.numpy.org/cddt|z }i}|D]}|j|d|z||<|S)zdReturn a dictionary where the key is the item and the value is the probability associated (PRIVATE).g?r)lenget)itemstermcountsitems ]/mounts/lovelace/software/anaconda3/envs/py312/lib/python3.12/site-packages/Bio/NaiveBayes.py _contentsr 5sB U D F2zz$*T1t 2 MceZdZdZdZy) NaiveBayesaUHold information for a NaiveBayes classifier. Attributes: - classes - List of the possible classes of data. - p_conditional - CLASS x DIM array of dicts of value -> ``P(value|class,dim)`` - p_prior - List of the prior probabilities for every class. - dimensionality - Dimensionality of the data. c<g|_d|_g|_d|_y)zInitialize the class.N)classes p_conditionalp_priordimensionality)selfs r __init__zNaiveBayes.__init__Is  ! "rN)__name__ __module__ __qualname____doc__rrr rr>s #rrcdt||jk7r$tdt|d|jt|j}t j |}t |D]}dgt|z}t t|D]*}|j||j||d||<,t jt j|dd}t|||<t j|j} d} |rKt jt j| |zdd } t jt| } i} t t|jD] }||| |z| z | |j|<"| S) aCalculate the logarithmic conditional probability for each class. Arguments: - nb - A NaiveBayes classifier that has been trained. - observation - A list representing the observed data. - scale - Boolean to indicate whether the probability should be scaled by ``P(observation)``. By default, no scaling is done. A dictionary is returned where the key is the class and the value is the log probability of the class. zobservation in z dimension, but classifier in NrgYngu<7~giDi)rr ValueErrorrnpzerosrangerrlogclipsumrexp) nb observationscalenlp_observation_classiprobsjlprobslp_priorlp_observationobslp_class_observations r calculater3Qs" ;2,,,c+./0""$"3"3!4 6  BJJA88A; 1X.[))s;'( EA''*1-11+a.!DE!H Ex9:"%f+Q .vvbjj!HN ffRWWX(<,s J J")F)NN)rwarningsBiorwarnnumpyr ImportErrorrr rr3r8rRrrr rXs}.+  A##&3 lOM0 & $  s >A