B b@s@dZddlZddlZGdddZd ddZddZd d ZdS) a:Code for doing logistic regressions. Classes: - LogisticRegression Holds information for a LogisticRegression classifier. Functions: - train Train a new classifier. - calculate Calculate the probabilities of each class, given an observation. - classify Classify an observation into a class. Nc@seZdZdZddZdS)LogisticRegressionzHolds information necessary to do logistic regression classification. Attributes: - beta - List of the weights for each dimension. cCs g|_dS)zInitialize the class.N)beta)selfr5lib/python3.7/site-packages/Bio/LogisticRegression.py__init__ szLogisticRegression.__init__N)__name__ __module__ __qualname____doc__rrrrrrsrcCst|t|krtdt|}|ddhkr4td|dkr@d}t|t|dd}}|dksj|dkrrtdt||f|}||ddddf<t|}t||} t||} d} d } d } d}d}}x || krtt | |}|d|}| t |d| t d|}t |}|dk r<|t ||dk rr||kr\| d } |} t ||| krrP|| }}|d7}t||}t || |}t t |||}tj||}t | d d kr|| 9}| |7} qWtd t}dd| D|_|S)a^Train a logistic regression classifier on a training set. Argument xs is a list of observations and ys is a list of the class assignments, which should be 0 or 1. xs and ys should contain the same number of elements. update_fn is an optional callback function that takes as parameters that iteration number and log likelihood. z$xs and ys should be the same length.rzClasses should be 0's and 1'sNdz.No observations or observation of 0 dimension.ig{Gz?g?g@gMbP?zDidn't converge.cSsg|] }t|qSr)float).0xrrr lsztrain..)len ValueErrorsetnumpyZonesZ transposeasarrayZzerosexpdotlogsumiterZfabsZidentityZlinalgZsolve RuntimeErrorrr)ZxsZysZ update_fntypecodeclassesNZndimsXZXtyrZMAX_ITERATIONSZCONVERGE_THRESHOLDZstepsizeiZold_betaZold_llikebetaXpZlogpZllikWZXtypZXtWXZdeltalrrrrtrain%s\     $      r'cCs<tdg|}tt|j|}|d|}d||gS)zCalculate the probability for each class. Arguments: - lr is a LogisticRegression object. - x is the observed data. Returns a list of the probability that it fits each class. g?r )rrrrr)r&rr#r$rrr calculateps  r(cCs"t||}|d|dkrdSdS)z%Classify an observation into a class.rr )r()r&rZprobsrrrclassifys r))NN)r rZ numpy.linalgrr'r(r)rrrrs  K