B ‰°bm\ã@s*dZddlZy ejZWn4ek rJddlZe dej¡dd„ZYnXdd„Zej  ¡dZ e  e ¡Z Gd d „d ƒZ d d „Zd d„Zdd„Zd4dd„ZdZd5dd„Zdd„Zdd„Zdd„Zd6dd„Zdd„Zd7d d!„Zd"d#„Zd$d%„Zd&d'„Zd(d)„Zd*d+„Zd,d-„Zd.d/„Z d0d1„Z!d2d3„Z"dS)8a¸A state-emitting MarkovModel. Note terminology similar to Manning and Schutze is used. Functions: train_bw Train a markov model using the Baum-Welch algorithm. train_visible Train a visible markov model using MLE. find_states Find the a state sequence that explains some observations. load Load a MarkovModel. save Save a MarkovModel. Classes: MarkovModel Holds the description of a markov model éNzVFor optimal speed, please update to Numpy version 1.3 or later (current version is %s)cCsP||dkr|S||dkr |St||ƒ}|t t ||¡t ||¡¡S)z>Implement logaddexp method if Numpy version is older than 1.3.éd)ÚminÚnumpyÚlogÚexp)ZlogxZlogyZminxy©rú.lib/python3.7/site-packages/Bio/MarkovModel.pyÚ logaddexp*s    r cCsDi}t|ddd…ƒ}t|ƒd}x|D]\}}||||<q(W|S)zAReturn a dictionary of values with their sequence offset as keys.Néÿÿÿÿé)Ú enumerateÚlen)ÚvaluesÚdÚentriesÚnÚindexÚkeyrrrÚ itemindex4s  rgYóøÂn¥c@s"eZdZdZddd„Zdd„ZdS)Ú MarkovModelz+Create a state-emitting MarkovModel object.NcCs"||_||_||_||_||_dS)zInitialize the class.N)ÚstatesÚalphabetÚ p_initialÚ p_transitionÚ p_emission)ÚselfrrrrrrrrÚ__init__Gs zMarkovModel.__init__cCs.ddlm}|ƒ}t||ƒ| d¡| ¡S)z9Create a string representation of the MarkovModel object.r)ÚStringIO)ÚiorÚsaveÚseekÚread)rrÚhandlerrrÚ__str__Qs    zMarkovModel.__str__)NNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rr#rrrrrDs rcCs&| ¡}| |¡s"td||fƒ‚|S)zNRead the first line and evaluate that begisn with the correct start (PRIVATE).zI expected %r but got %r)ÚreadlineÚ startswithÚ ValueError)r"ÚstartÚlinerrrÚ_readline_and_check_start[s r-cCs~t|dƒ}| ¡dd…}t|dƒ}| ¡dd…}t||ƒ}t|ƒt|ƒ}}t |¡|_t|dƒ}x}t|d||ƒ}d d „| ¡dd…Dƒ|j |dd…f<qÌWt ||f¡|_ t|d ƒ}xPtt|ƒƒD]@}t|d||ƒ}d d „| ¡dd…Dƒ|j |dd…f<q6W|S) z.Parse a file handle into a MarkovModel object.zSTATES:r Nz ALPHABET:zINITIAL:z %s:r z TRANSITION:cSsg|] }t|ƒ‘qSr)Úfloat)Ú.0Úvrrrú |szload..z EMISSION:cSsg|] }t|ƒ‘qSr)r.)r/r0rrrr1ƒs) r-Úsplitrr rÚzerosrÚranger.rr)r"r,rrÚmmÚNÚMÚirrrÚloadcs,      , .r9c Cs|j}|dd |j¡ƒ|dd |j¡ƒ|dƒx2tt|jƒƒD] }|d|j||j|fƒqFW|dƒxBtt|jƒƒD]0}|d|j|d dd „|j|Dƒ¡fƒq‚W|d ƒxBtt|jƒƒD]0}|d|j|d d d „|j|Dƒ¡fƒqÎWd S) z$Save MarkovModel object into handle.z STATES: %s ú z ALPHABET: %s z INITIAL: z %s: %g z TRANSITION: z %s: %s css|]}t|ƒVqdS)N)Ústr)r/Úxrrrú “szsave..z EMISSION: css|]}t|ƒVqdS)N)r;)r/r<rrrr=–sN) ÚwriteÚjoinrrr4r rrr)r5r"Úwr8rrrrˆs 0rc st|ƒt|ƒ}}|stdƒ‚|dk rDt |¡}|j|fkrDtdƒ‚|dk rlt |¡}|j||fkrltdƒ‚|dk r”t |¡}|j||fkr”tdƒ‚g} t|ƒ‰x$|D]} |  ‡fdd„| Dƒ¡q¦Wdd„| Dƒ} t| ƒd krètd ƒ‚t||| ||||d } | \} }}t ||| ||ƒS) a Train a MarkovModel using the Baum-Welch algorithm. Train a MarkovModel using the Baum-Welch algorithm. states is a list of strings that describe the names of each state. alphabet is a list of objects that indicate the allowed outputs. training_data is a list of observations. Each observation is a list of objects from the alphabet. pseudo_initial, pseudo_transition, and pseudo_emission are optional parameters that you can use to assign pseudo-counts to different matrices. They should be matrices of the appropriate size that contain numbers to add to each parameter matrix, before normalization. update_fn is an optional callback that takes parameters (iteration, log_likelihood). It is called once per iteration. zNo training data given.Nz$pseudo_initial not shape len(states)z5pseudo_transition not shape len(states) X len(states)z5pseudo_emission not shape len(states) X len(alphabet)csg|] }ˆ|‘qSrr)r/r<)Úindexesrrr1Êsztrain_bw..cSsg|] }t|ƒ‘qSr)r )r/r<rrrr1Ísrz,I got training data with outputs of length 0)Úpseudo_initialÚpseudo_transitionÚpseudo_emissionÚ update_fn) r r*rÚasarrayÚshaperÚappendrÚ _baum_welchr)rrÚ training_datarBrCrDrEr6r7Útraining_outputsÚoutputsZlengthsr<rrrr)rArÚtrain_bwšs@       rMièc  Csh|dkrt|ƒ}n t||fƒ}|dkr4t||fƒ}nt|||fƒ}|dkrXt||fƒ}nt|||fƒ}t |¡} t |¡} t |¡} |dk r˜t |¡} nd} |dk r°t |¡}nd}|dk rÈt |¡}nd}d}x‚ttƒD]j}t}x(|D] }|t|||| | | | ||ƒ 7}qèW| dk r | ||ƒ|dk r@t ||¡dkr@P|}qÚWt dtƒ‚dd„| | | fDƒS)zfImplement the Baum-Welch algorithm to evaluate unknown parameters in the MarkovModel object (PRIVATE).Ngš™™™™™¹?z%HMM did not converge in %d iterationscSsg|]}t |¡‘qSr)rr)r/Ú_rrrr1)sz_baum_welch..) Ú _random_normÚ_copy_and_checkrrr4ÚMAX_ITERATIONSÚLOG0Ú_baum_welch_oneÚfabsÚ RuntimeError)r6r7rKrrrrBrCrDrEÚ lp_initialÚ lp_transitionÚ lp_emissionÚlpseudo_initialÚlpseudo_transitionÚlpseudo_emissionZ prev_llikr8ZllikrLrrrrIâsT              rIc  Csàt|ƒ} t|| ||||ƒ} t|| |||ƒ} t ||| f¡} x¨t| ƒD]œ} || }t ||f¡}xbt|ƒD]V}xPt|ƒD]D}| || ||||||| || d}||||<qvWqhW|t|ƒ| dd…dd…| f<qDWt || f¡}xBt| ƒD]6} x0t|ƒD]$}t| |dd…| fƒ||| <q WqüWt |¡}x*t|ƒD]}t||dd…fƒ||<qJW|dd…df}|dk rœt||ƒ}|t|ƒ}x€t|ƒD]t}x8t|ƒD],}t| ||dd…fƒ|||||<q´W|dk r¦t|||ƒ||<||t||ƒ||<q¦Wx¬t|ƒD] }t |¡t}xJt| ƒD]>} || }x.t|ƒD]"}t ||| ||| fƒ||<qZWqDW|t|ƒ}|dk r¶t|||ƒ}|t|ƒ}|||dd…f<q(Wt| dd…| fƒS)zÊExecute one step for Baum-Welch algorithm (PRIVATE). Do one iteration of Baum-Welch based on a sequence of output. Changes the value for lp_initial, lp_transition and lp_emission in place. r Nr) r Ú_forwardÚ _backwardrr3r4Ú_logsumÚ _logvecaddrRr )r6r7rLrVrWrXrYrZr[ÚTZfmatZbmatZlp_arcÚtÚkZ lp_traverser8ÚjÚlpZ lp_arcout_tZ lp_arcoutZksumrrrrS,sR 4"(    , (    rSc Cs°t ||df¡}||dd…df<xˆtd|dƒD]v}||d}xdt|ƒD]X} t} xBt|ƒD]6} || |d|| | || |} t| | ƒ} q^W| || |<qLWq2W|S)zŽImplement forward algorithm (PRIVATE). Calculate a Nx(T+1) matrix, where the last column is the total probability of the output. r Nr)rr3r4rRr ) r6r`rVrWrXrLÚmatrixrarbrcÚlprobr8rdrrrr\Šs (r\c Csžt ||df¡}x†t|dddƒD]r}||}xdt|ƒD]X}t} xBt|ƒD]6} || |d||| |||} t| | ƒ} qLW| |||<q:Wq$W|S)z'Implement backward algorithm (PRIVATE).r r )rr3r4rRr ) r6r`rWrXrLrerarbr8rfrcrdrrrr]¡s(r]cs*t|ƒt|ƒ}}|dk r8t |¡}|j|fkr8tdƒ‚|dk r`t |¡}|j||fkr`tdƒ‚|dk rˆt |¡}|j||fkrˆtdƒ‚gg}} t|ƒ‰t|ƒ‰xX|D]P\} } t| ƒt| ƒkrÈtdƒ‚| ‡fdd„| Dƒ¡|  ‡fdd„| Dƒ¡q¨Wt||| ||||ƒ} | \} }}t||| ||ƒS) a Train a visible MarkovModel using maximum likelihoood estimates for each of the parameters. Train a visible MarkovModel using maximum likelihoood estimates for each of the parameters. states is a list of strings that describe the names of each state. alphabet is a list of objects that indicate the allowed outputs. training_data is a list of (outputs, observed states) where outputs is a list of the emission from the alphabet, and observed states is a list of states from states. pseudo_initial, pseudo_transition, and pseudo_emission are optional parameters that you can use to assign pseudo-counts to different matrices. They should be matrices of the appropriate size that contain numbers to add to each parameter matrix. Nz$pseudo_initial not shape len(states)z5pseudo_transition not shape len(states) X len(states)z5pseudo_emission not shape len(states) X len(alphabet)zstates and outputs not alignedcsg|] }ˆ|‘qSrr)r/r<)Ústates_indexesrrr1ßsz!train_visible..csg|] }ˆ|‘qSrr)r/r<)Úoutputs_indexesrrr1às) r rrFrGr*rrHÚ_mler)rrrJrBrCrDr6r7Útraining_statesrKZtoutputsZtstatesr<rrrr)rhrgrÚ train_visible±s>      rkcCs¶t |¡}|r||}x |D]}||dd7<qWt|ƒ}t ||f¡} |rZ| |} xP|D]H}xBtt|ƒdƒD].} || || d} } | | | fd7<qvWq`WxDtt| ƒƒD]4} | | dd…ft| | dd…fƒ| | dd…f<qºWt ||f¡} |r| |} t ||f¡} xFt||ƒD]8\}}x,t||ƒD]\}}| ||fd7<q.csg|]}ˆj|‘qSr)r)r/r<)r5rrr19s) r rrrrÚVERY_SMALL_NUMBERrrrrÚ_viterbir4r) Z markov_modelÚoutputr6rVrWrXÚresultsr8rÚscorer)rAr5rÚ find_states#s   $rxcCs¬t|ƒ}g}x t|ƒD]}| dg|¡qWt ||f¡}||dd…|df|dd…df<x„td|ƒD]v} || } xht|ƒD]\} |dd…| df|dd…| f|| | f} t| ƒ} | | d|| | f<| || | <q‚WqlWg}g}t|dd…|dfƒ} x0| D](}| |d|g|||df¡q Wxn|r¦| ¡\} }}| dkrh| ||f¡n:||d| } x(| D] }| | d|g||f¡q~Wq:W|S)zSImplement Viterbi algorithm to find most likely states for a given input (PRIVATE).Nrr )r r4rHrr3rrÚpop)r6rVrWrXrur`Z backtracer8ZscoresrarbrcZi_scoresrAZ in_processrvrrwrrrrt=s4$0 (  &rtcCs‚t|jƒdkr |tt|ƒƒ}n^t|jƒdkrvxNtt|ƒƒD]4}||dd…ft||dd…fƒ||dd…f<q 2 dimensions)rZarrayrGr*r rTrmr4)reZ desired_shaper8rrrrPs  rPcCsJt|jƒdkr&t |t |j¡f¡}n|}t}x|D]}t||ƒ}q4W|S)z/Implement logsum for a matrix object (PRIVATE).r )r rGrZreshapeÚproductrRr )reZvecrmZnumrrrr^“s r^cCsVt|ƒt|ƒkstdƒ‚t t|ƒ¡}x*tt|ƒƒD]}t||||ƒ||<q4W|S)z5Implement a log sum for two vector objects (PRIVATE).zvectors aren't the same length)r ÚAssertionErrorrr3r4r )Zlogvec1Zlogvec2Zsumvecr8rrrr_Ÿs r_cCst|ƒ}t |¡S)z-Return the exponential of a logsum (PRIVATE).)r^rr)ZnumbersrmrrrÚ _exp_logsum¨sr€)NNNN)NNNNNNN)NNN)N)#r'rr ÚAttributeErrorÚwarningsÚwarnÚ __version__rr|ZseedrsrrRrr-r9rrMrQrIrSr\r]rkrirrrxrtrlr{rOrPr^r_r€rrrrÚs\     % > @^ 9. )