*ë ŦŠ;;csĄdZ y!dkTWn$"ej oZ#ed‚nX%dklZlZ'dfd„ƒYZ7dd„Zkd„Z {e e d „Z d S( s This provides code for a general Naive Bayes learner. 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. (s*sThis module requires NumPy(smathfnsslistfnss NaiveBayescs'dZ01d„ZRS(s?Holds information for a NaiveBayes classifier. Members: 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. cs712g|_3t|_4g|_5t|_dS(N(sselfsclassessNones p_conditionalsp_priorsdimensionality(sself((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pys__init__1s   (s__doc__s__init__(((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pys NaiveBayes's ic s7AGt|ƒ|ijo#Htdt|ƒ|if‚nLg} Mxđtt|iƒƒdMr}Ot gt|ƒ} PxGtt|ƒƒdPr.}Q|i ||i ||dƒ| | probability dict Calculate log P(class|observation) for each class. nb is a NaiveBayes classifier that has been trained. observation is a list representing the observed data. scale is whether the probability should be scaled by P(observation). By default, no scaling is done. The return value is a dictionary where the keys is the class and the value is the log probability of the class. s1observation in %d dimension, but classifier in %dicsRti|d ƒS(Ni'(smathfnsssafe_logsx(sx((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pysRsf0.0sunderf1e-300N(slens observationsnbsdimensionalitys ValueErrorslp_observation_classsrangesclassessisNonesprobssjs p_conditionalsgetsmapslprobsssumslprobsappendsmathslogsp_priorslp_priorslp_observationsscaleszerossFloat32sobssmathfnsssafe_expslp_class_observation( snbs observationsscalesobsslp_class_observationslp_observationsjsislp_priorsprobsslprobsslprobslp_observation_class((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pys calculate7s6 #   ,     (cs’kprt||ddƒ}st}tt}uxQ|idurA}v|tjp|||jow||}x|}nq?Wy|SdS(sSclassify(nb, observation) -> class Classify an observation into a class. sscaleiN( s calculatesnbs observationsprobssNonesmax_probs max_classsclassessklass(snbs observations max_classsprobssklasssmax_prob((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pysclassifyks   ! csL{…†t|ƒ o‡td‚nˆt|ƒt|ƒjo‰td‚n‹t}Œx\|dŒrO}|tjoŽt|ƒ}n'|t|ƒjotd‚nqhW’tƒ} “|| _ –t i |ƒ| _ —| i i ƒš|tj o ›|}nt i|ƒ}žtt| i ƒtƒ| _Ÿx=tt| i ƒƒdŸr!}  || i | | i| NaiveBayes Train a naive bayes classifier on a training set. training_set is a list of observations. results is a list of the class assignments for each observation. Thus, training_set and results must be the same length. priors is an optional dictionary specifying the prior probabilities for each type of result. If not specified, the priors will be estimated from the training results. sNo data in the training set.s2training_set and results should be parallel lists.is*observations have different dimensionalityiN(%slens training_sets ValueErrorsresultssNonesdimsobss NaiveBayessnbsdimensionalityslistfnssitemssclassesssortspriorsspercsscontentsszerossFloat16sp_priorsrangesisindexs observationss itemindexsc2iscounts class_countssklasssappendstypecodesinstancesindssyss p_conditionalsjsvalues(s training_setsresultsspriorsstypecodesobssc2isdims class_countssklasssinstancesnbsisjs observationssindexspercssindssyssvalues((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pystrain{s^          &      %N( s__doc__sNumerics ImportErrorsxs Bio.Toolssmathfnsslistfnss NaiveBayess calculatesclassifysNonestrain(s NaiveBayessclassifysxslistfnssmathfnss calculatestrain((s</mit/seven/lib/python/Bio/Tools/Classification/NaiveBayes.pys?s 4