DjW^gdZddlZddlZddlmcmZddlmcmZ ddl m cm Z ddlmZmZddlmZmZmZmZmZmZmZmZddlmZddlmZddlmZddl m!Z!Gd d eZ"Gd d e"Z#Gd de"Z$Gdde"Z%GddeZ&Gdde&Z'Gdde&Z(Gdde&Z)Gdde&Z*GddeZ+GddeZ,Gdd e,Z-Gd!d"e,Z.Gd#d$ee,Z/Gd%d&e j`Z1e jde1e,Gd'd(e j`Z3e jde3e/Gd)d*eZ4Gd+d,ee4Z5Gd-d.e j`Z6e jde6e4Gd/d0e j`Z7e jde7e5y)1)TruncatedLFPoissonTruncatedLFNegativeBinomialPHurdleCountModelN)truncatedpoissontruncatednegbin) DiscreteModel CountModel CountResultsL1CountResultsPoissonNegativeBinomialPGeneralizedPoisson_discrete_results_docs) approx_hess)cache_readonly)ConvergenceWarning)deepcopyceZdZdejdej zdzZ d fd ZdZdZ dZ dZ dfd Z e jj e _ dfd Ze jj e_d Z dd ZxZS)TruncatedLFGenerica Generic Truncated model for count data .. versionadded:: 0.14.0 %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. truncation : int, optional Truncation parameter specify truncation point out of the support of the distribution. pmf(k) = 0 for k <= truncation offset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. params extra_paramsc Xt |||f|||d||j|kD}|j||_|j||_||j||_||j ||_||_||_|jjdgg|_ y)Noffsetexposuremissing truncation) super__init__endogexogrrtruncr _init_keysextend_null_drop_keys) selfr"r#rrrrkwargsmask __class__s hC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels/discrete/truncated_model.pyr!zTruncatedLFGeneric.__init__9s         zzJ&IIdO ZZ%  ++d+DK   MM$/DM $  ~.!cJtj|j|S)a` Loglikelihood of Generic Truncated model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- npsum loglikeobsr(rs r,loglikezTruncatedLFGeneric.loglikeQ&vvdoof-..r-c|jj|}|jdz}|j|dt j |j d}t j|tj }|dkD}tj||<|dk}t jd||z ||<||z }|S)a Loglikelihood for observations of Generic Truncated model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at `params`. See Notes Notes -----  prob-basewhichy_values) model_mainr2r$predictr0aranger1 full_likeinfnanlog)r(rllf_mainytpmf log_1_m_pmflocllfs r,r2zTruncatedLFGeneric.loglikeobsfs&??--f5 ZZ!^ll + " ??Bs2w ll30 Ag66 CAg66!c#h,/ C$ r-c |jj|}tj|jtj }tj|tj }t |jdzD]}|jjtj|j|z|jt|ddt|dd}tj|j|}||j|j|zjz }||z }||jd|z z jz}|S) Generic Truncated model score (gradient) vector of the log-likelihood Parameters ---------- params : array-like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` )dtyper7rNr)rr)r= score_obsr0 zeros_liker"float64ranger$r+ ones_liker#getattrexpr2T) r(r score_mainrF score_truncimodelpmf_idparamss r,rMzTruncatedLFGeneric.score_obss__..v6 mmDJJbjj9mmJbjjA tzzA~& AOO-- TZZ(1, tXt4 z48 .E FF5++F34E EOOF355=@@ @K 5LC  S 9<<<r-cB|j|jdS)rKrrMr1r3s r,scorezTruncatedLFGeneric.score~~f%))!,,r-c |t|ddt|ddz} tj| dk(r| dk(rd} |jj |j |j | } tj5tjdt| jdj}ddd|jdz|jz} |j jd| z |_t#|,d |||||d d | }|j%||j&}|j)|}|i}|j*d |d | d ||S#1swYxYw)Nrrrr7rignorecategorydispc|SNxs r,z(TruncatedLFGeneric.fit..qr- start_paramsmethodmaxiterre full_outputcallbackTcov_typeuse_selfuse_trh)rRr0sizer=r+r"r#warningscatch_warnings simplefilterrfitrdf_modelk_extrashapedf_residr result_class_resultsresult_class_wrapper_get_robustcov_results)r(rnrorprqrerrrtcov_kwdsrvr)rrXk_paramsmlefitzipfitresultr+s r,r{zTruncatedLFGeneric.fitst  T8Q/'$ A2NNFwwv!#! OO--djj$))5;.=E((* 8%%h9KL$yyay077  8 ==1$t||3 ((+h6 %# ""49**62  H%%% Nx/35 NDL N 5 8 8s 8E!!E*c  ztj|dk(r6|dk7r1|jjd} |tj| z}|}|t |ddt |ddz}tj|dk(r|dk(rd}|j j|j|j|}|jd ||||d|||| | | d | j}tt|&d ||||||||| | | d | }|dvr|j||}ntd|z|j|S Nr7rrrr`) rnrorprqrerralpha trim_mode auto_trim_tol size_trim_tolqc_tol)l1 l1_cvxopt_cpz+argument method == %s, which is not handledrhr0rwr#r~onesrRr=r+r"fit_regularizedrr r result_class_reg TypeErrorresult_class_reg_wrapperr(rnrorprqrerrrrrrrr)ralpha_prrXcntfit discretefitr+s r,rz"TruncatedLFGeneric.fit_regularized} 775>Q 5A:yyq)HBGGH--E  T8Q/'$ A2NNFwwv!#! OO--djj$))5;.=E0500F)&''a(++F F ?E F GMf  z48F)&''dXy +F F?E F + +//f=KAFJL L,,[99r-c.t||jS)a Generic Truncated model Hessian matrix of the loglikelihood Parameters ---------- params : array-like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- rr4r3s r,hessianzTruncatedLFGeneric.hessian$64<<00r-c |j|||\}}}tj||d|jd}||z|z}|dk(r,tj|} |j dk(r!|j j| |} | | z S|j dk(r| S|j dkDrtjtjd|j dz} |j j||tj||d| } | jd} tj|j dz| zjd}| |z d| z z }|Std |d k(r|S|d k(rtj|S|dk(r|tj|} nItjtjdtj|jdz} tj|dddf} |jdk(r)|j j#| | |j$} | S|jdk(rD|j j&}|j j#| | |d||j$} | Std |d k(r|tj(|} n6tjdtj|jdz} |j j||tj||d| } | S|dk(r=tj|} tjtjd|j dz} |j j||tj||d| } | jd} tj|j dz| zjd}| |z d| z z }tj|j dzdz| zjd}|j j+| |}| dz|z|z d| z z }||dzz }|Std|z)a Predict response variable or other statistic given exogenous variables. Parameters ---------- params : array_like The parameters of the model. exog : ndarray, optional Explanatory variables for the main count model. If ``exog`` is None, then the data from the model will be used. offset : ndarray, optional Offset is added to the linear predictor of the mean function with coefficient equal to 1. Default is zero if exog is not None, and the model offset if exog is None. exposure : ndarray, optional Log(exposure) is added to the linear predictor with coefficient equal to 1. If exposure is specified, then it will be logged by the method. The user does not need to log it first. Default is one if exog is is not None, and it is the model exposure if exog is None. which : str (optional) Statitistic to predict. Default is 'mean'. - 'mean' : the conditional expectation of endog E(y | x) - 'mean-main' : mean parameter of truncated count model. Note, this is not the mean of the truncated distribution. - 'linear' : the linear predictor of the truncated count model. - 'var' : returns the estimated variance of endog implied by the model. - 'prob-trunc' : probability of truncation. This is the probability of observing a zero count implied by the truncation model. - 'prob' : probabilities of each count from 0 to max(endog), or for y_values if those are provided. This is a multivariate return (2-dim when predicting for several observations). The probabilities in the truncated region are zero. - 'prob-base' : probabilities for untruncated base distribution. The probabilities are for each count from 0 to max(endog), or for y_values if those are provided. This is a multivariate return (2-dim when predicting for several observations). y_values : array_like Values of the random variable endog at which pmf is evaluated. Only used if ``which="prob"`` Returns ------- predicted values Notes ----- If exposure is specified, then it will be logged by the method. The user does not need to log it first. r#rrNr7meanrr<prob)r#rrr:r;zunsupported self.truncationlinear mean-mainzk_extra is not 0 or 1r8varz argument which == %s not handled)_get_predict_arraysr0dotr~rSrr= _prob_nonzero atleast_2dr?r>r1 ValueErrormaxr"r} model_distrFr$parameterizationasarray_var)r(rr#rrr:r;fittedlinpredmu prob_maincountsprobs prob_tregion mean_tregionrp mnc2_tregionvmmnc2vs r,r>zTruncatedLFGeneric.predict*st"&!9!9":"fh f^djjm458#f, F?B!# OO99"fE I~%B& 1$ryyDOOa4G'HI//x0@!&0B %yy| " $//A*= > FKKAN \)a,.>?  !>?? h N k !66'? " f_#x0ryyBFF4::4Fq4H'IJD)B||q ++FB CL "OO44++FBr ,-tzz;L!!899 k !#H-1bffTZZ&8&:;OO++TBFF8,<Vf,>EL e^B]]299Q!0C#DEFOO++TBFF8,<Vf,>E!99Q __classcell__r+s@r,rrs"++   ! !  " ###G6:>(."0/*,\!F-"=?,07;#J ##++CK-1IMIM #:J,;;CCO1(@D'+G!$wr-)NNrr rrrrrrrr!rrrs@r,rrsG"++   ! !  " ###G6;?'-K,r-rcheZdZdejdej zdzZ dfd ZdZxZ S)ra Truncated Generalized Negative Binomial model for count data .. versionadded:: 0.14.0 %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. truncation : int, optional Truncation parameter specify truncation point out of the support of the distribution. pmf(k) = 0 for k <= truncation rrc t |||f||||d|t|j|jt |ddt |dd||_|j j|_|jj|j j|j dt|_ t|_ t|_t |_t$|_yNrrr)rrr)r r!r r"r#rRr=r} exog_namesr&rr TruncatedNegativeBinomialResultsrrrrrrr r(r"r#rrrrrr)r+s r,r!z%TruncatedLFNegativeBinomialP.__init__s    !   , JJ IIT:t4440 ..  t994<<-.IJ)<$D! ;(J%r-c|d}|jj}d|||dz zzzd|z z}d|z }||z }|d|||dz zzzz}|dz|z|z } | |dzz } || fS)rr<r7r)r=r) r(rrrr prob_zerorrrrrs r,rz0TruncatedLFNegativeBinomialP._predict_mom_trunc04s&r  OO , ,ac**cEk: M F 1urAaCy(( )A aad{$wr-NNrrrrrs@r,rrsG"++   ! !  " ###G6;?,2K6r-rcbeZdZdejdej zdzZ dfd ZxZS)TruncatedLFGeneralizedPoissona Truncated Generalized Poisson model for count data .. versionadded:: 0.14.0 %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. truncation : int, optional Truncation parameter specify truncation point out of the support of the distribution. pmf(k) = 0 for k <= truncation rrc t |||f||||d|t|j|jt |ddt |dd||_|j j|_|jj|j j|j dd|_ t|_ t|_t|_t"|_yr)r r!rr"r#rRr=r}rr&rrrrrrrrrrs r,r!z&TruncatedLFGeneralizedPoisson.__init__os    !   - JJ IIT:t4440 ..  t994<<-.IJ<$D! ;(J%r-r rrrrrrrr!rrs@r,rrSsH"++   ! !  " ###G6;?,2KKr-rceZdZdejdej zdzZ d fd ZdZdZ dZ dZ d fd Z e jj e _ dfd Ze jj e_d ZxZS)_RCensoredGenerica Generic right Censored model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. rrc tj|dk(d|_tj|d|_t |||f|||d|y)Nrr)r0nonzerozero_idx nonzero_idxr r!r(r"r#rrrr)r+s r,r!z_RCensoredGeneric.__init__s_ 5A:.q1 ::e,Q/         r-cJtj|j|S)a_ Loglikelihood of Generic Censored model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : float The log-likelihood function of the model evaluated at `params`. See notes. Notes ----- r/r3s r,r4z_RCensoredGeneric.logliker5r-c |jj|}tj||jtj dtj ||jz f}|S)a Loglikelihood for observations of Generic Censored model Parameters ---------- params : array-like The parameters of the model. Returns ------- loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at `params`. See Notes Notes ----- r7)r=r2r0 concatenaterrCrSr)r(rrDrIs r,r2z_RCensoredGeneric.loglikeobssc&??--f5nn dmm $ VVAx(8(89:: ; =  r-c |jj|}|jj|}tj||j ||j jtj||j  zdtj||j z z jf}|S) Generic Censored model score (gradient) vector of the log-likelihood Parameters ---------- params : array-like The parameters of the model Returns ------- score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at `params` r7) r=rMr2r0rrrrTrS)r(rrUrDr]s r,rMz_RCensoredGeneric.score_obss__..v6 ??--f5 t}} % (( ) + +ffXd../0 01"&&$"2"2344678q   r-cB|j|jdS)rrr\r3s r,r]z_RCensoredGeneric.scorer^r-c `|t|ddt|ddz} tj| dk(r| dk(rd} |jj |j |j | } tj5tjdt| jdj}dddt|,d |||||d d | } |j|| j}|j!|}|i}|j"d |d | d ||S#1swYixYw)Nrrrr7r`rarbrdc|Srgrhris r,rkz'_RCensoredGeneric.fit..rlr-rmTrsrh)rRr0rwr=r+r"r#rxryrzrr{rr rrrr)r(rnrorprqrerrrtrrvr)rrXrrrr+s r,r{z_RCensoredGeneric.fit sB  T8Q/'$ A2NNFwwv!#! OO--djj$))5;.=E((* 8%%h9KL$yyay077  8%# ""49**62  H%%% Nx/35 NDL N + 8 8s 8D$$D-c  ztj|dk(r6|dk7r1|jjd} |tj| z}|}|t |ddt |ddz}tj|dk(r|dk(rd}|j j|j|j|}|jd ||||d|||| | | d | j}tt|&d ||||||||| | | d | }|dvr|j||}ntd|z|j|Srrrs r,rz!_RCensoredGeneric.fit_regularized.rr-c.t||jS)a Generic Censored model Hessian matrix of the loglikelihood Parameters ---------- params : array-like The parameters of the model Returns ------- hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at `params` Notes ----- rr3s r,rz_RCensoredGeneric.hessianUrr-NNrrr)rrrrrrrr!r4r2rMr]r{rrrrrs@r,rrs ++   ! !  " ##G,;? /*86-"=?,07;@ ##++CK-1IMIM #:J,;;CCO1r-rcbeZdZdejdej zdzZ dfd ZxZS)_RCensoredPoissonz Censored Poisson model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. rrc t|||f|||d|ttj|j |j |_d|_t|_ t|_ t|_t|_yNr)r r!r r0rNr"r#r=rTruncatedLFGenericResultsrrrrrrrrs r,r!z_RCensoredPoisson.__init__sx        ""-- ";TYYG5$D! ;(J%r-rrrs@r,rrjsH ++   ! !  " ##G,,0(.KKr-rcbeZdZdejdej zdzZ dfd ZxZS)_RCensoredGeneralizedPoissona Censored Generalized Poisson model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. rrc t|||f|||d|ttj|j |j |_d|_t|_ t|_ t|_t|_yr)r r!rr0rNr"r#r=rrrrrrrrr r(r"r#rrrrr)r+s r,r!z%_RCensoredGeneralizedPoisson.__init__su  4 ' & '% '- MM$** %tyy25$D! ;(J%r-NrNrrrs@r,rrsH ++   ! !  " ##G,45(. K Kr-rcbeZdZdejdej zdzZ dfd ZxZS)_RCensoredNegativeBinomialPa Censored Negative Binomial model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. rrc t|||f|||d|ttj|j |j ||_d|_t|_ t|_ t|_t|_y)Nrr)r r!r r0rNr"r#r=rrrrrrrrrrs r,r!z$_RCensoredNegativeBinomialP.__init__s         ,BMM$**,E,0II./.5$D! ;(J%r-rrrs@r,r r sH ++   ! !  " ##G,45(.KKr-r cneZdZdejdej zdzZeedddffd Z dZ xZ S) _RCensoredz Censored model for count data %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. rrNrc t |||f|||d||tj|j|j |_||_|j jx|_} | dkDr3|jj|j j| dt|_ t|_t|_t"|_y)Nrr)r r!r0rNr"r#r=rr}rr&rrrrrrrr) r(r"r#rX distributionrrrr)r}r+s r,r!z_RCensored.__init__s           djj 9499E&!%!8!88 w Q; OO " "4??#=#=whi#H I5$D! ;(J%r-c>|jj||}|S)zrProbability that count is not zero internal use in Censored model, will be refactored or removed )r=r)r(rrprob_nzs r,rz_RCensored._prob_nonzeros ////F;r-) rrrrrrrr rr!rrrs@r,rrsN ++   ! !  " ##G,+2.tK.r-rceZdZdejdej zdzZ d fd ZdZdZ d dZ e jj e _ d dZ xZ S) ra Hurdle model for count data .. versionadded:: 0.14.0 %(params)s %(extra_params)s Attributes ---------- endog : array A reference to the endogenous response variable exog : array A reference to the exogenous design. dist : string Log-likelihood type of count model family. 'poisson' or 'negbin' zerodist : string Log-likelihood type of zero hurdle model family. 'poisson', 'negbin' p : scalar Define parameterization for count model. Used when dist='negbin'. pzero : scalar Define parameterization parameter zero hurdle model family. Used when zerodist='negbin'. aJoffset : array_like Offset is added to the linear prediction with coefficient equal to 1. exposure : array_like Log(exposure) is added to the linear prediction with coefficient equal to 1. Notes ----- The parameters in the NegativeBinomial zero model are not identified if the predicted mean is constant. If there is no or only little variation in the predicted mean, then convergence might fail, hessian might not be invertible or parameter estimates will have large standard errors. References ---------- not yet rc  || d} t| t |||f||| d| d|_d|_|j ||||t |_t|_ t|_ t|_ y)Nz+Offset and exposure are not yet implementedrr)NotImplementedErrorr r!k_extra1k_extra2 _initializeHurdleCountResultsrHurdleCountResultsWrapperrL1HurdleCountResultsrL1HurdleCountResultsWrapperr) r(r"r#rdistzerodistrpzerorrr)msgr+s r,r!zHurdleCountModel.__init__Js  H$8?C%c* *            xE2.$=! 4(C%r-c|dvs|dvr td|dk(r,t|j|jt|_nE|dk(r@t|j|jt |_|xjdz c_|dk(r&t|j|j|_ y|dk(r=t|j|j||_ |xjdz c_ yy)N)poissonnegbinz,dist and zerodist must be "poisson","negbin"r")rXr#r7r ) rrr"r#r model1r rrmodel2rr)r(rrrrs r,rzHurdleCountModel._initializecs - - 55%'12 2 y $TZZ'JDK  !$TZZ+<>DK MMQ M 9 ,TZZCDK X 6tzz4999:|dk7r td|jjd |||||dd| } |jjd |||||dd| } t | } || j _| jd| jdg| jd<tj| j j| j j| j _ | j xj| j jz c_ |xjt| j ddz c_ |xjt| j ddz c_|j|jzd z|_|jj Dcgc]}d |z }}||jj z|j dddd lm}d| j _ | j j)}| j j)}|||| j _|j-|| j | | }|j/|} | Scc}w#t$r}d t+|vrYd}~Rd}~wwxYw)Nrz'robust cov_type currently not supportedc|Srgrhris r,rkz&HurdleCountModel.fit..r-rmc|Srgrhris r,rkz&HurdleCountModel.fit..r,r- convergedr}rr7zm_) block_diagzneed covariancerh)rr$r{r%rrrX mle_retvalsr0appendrr|rrRrr}r scipy.linalgr0normalized_cov_params cov_paramsstrrr)r(rnrorprqrerrrtrrvr)results1results2rnamexnames1r0cov1cov2emodelfits r,r{zHurdleCountModel.fitsZ { "FG G"4;;??%7#k #4;;??%7#k (# $+3+?+? +L+3+?+? +L+N;'!#8+<+<+C+C+3+<+<+C+C"E   H$5$5$>$>>  !2!2IqAA  !2!2IqAA   59 ,0KK,B,BCD54<CC$t{{'='== ,04- $$//1D$$//1D4>tT4JFOO 1 $$T6??HhO**84 'D  A./ s- I69A I;; JJJc|j}|dnd}|j|||\}}}d}||r |j}n|}tt ||j z |j z dz |j z} |d| } || d} tj|| d|jjd|z|z} |jj| |} |jjj| | }d|z }tj| }|jjj|| }|dk(r|tj| z|z S|d k(rtj| S|d k(r| S|d k(rtj| |z S|d k(r|S|d k(r|S|dk(rd|z S|dk(rJtj| }|jj!| |\}}||z|d|z z|dzzz}|S|dk(rK|jj| |tj||d|}||dddfz}||dddf<|St#d|z)a Predict response variable or other statistic given exogenous variables. Parameters ---------- params : array_like The parameters of the model. exog : ndarray, optional Explanatory variables for the main count model. If ``exog`` is None, then the data from the model will be used. exog_infl : ndarray, optional Explanatory variables for the zero-inflation model. ``exog_infl`` has to be provided if ``exog`` was provided unless ``exog_infl`` in the model is only a constant. offset : ndarray, optional Offset is added to the linear predictor of the mean function with coefficient equal to 1. Default is zero if exog is not None, and the model offset if exog is None. exposure : ndarray, optional Log(exposure) is added to the linear predictor with coefficient equal to 1. If exposure is specified, then it will be logged by the method. The user does not need to log it first. Default is one if exog is is not None, and it is the model exposure if exog is None. which : str (optional) Statitistic to predict. Default is 'mean'. - 'mean' : the conditional expectation of endog E(y | x) - 'mean-main' : mean parameter of truncated count model. Note, this is not the mean of the truncated distribution. - 'linear' : the linear predictor of the truncated count model. - 'var' : returns the estimated variance of endog implied by the model. - 'prob-main' : probability of selecting the main model which is the probability of observing a nonzero count P(y > 0 | x). - 'prob-zero' : probability of observing a zero count. P(y=0 | x). This is equal to is ``1 - prob-main`` - 'prob-trunc' : probability of truncation of the truncated count model. This is the probability of observing a zero count implied by the truncation model. - 'mean-nonzero' : expected value conditional on having observation larger than zero, E(y | X, y>0) - 'prob' : probabilities of each count from 0 to max(endog), or for y_values if those are provided. This is a multivariate return (2-dim when predicting for several observations). y_values : array_like Values of the random variable endog at which pmf is evaluated. Only used if ``which="prob"`` Returns ------- predicted values Notes ----- 'prob-zero' / 'prob-trunc' is the ratio of probabilities of observing a zero count between hurdle model and the truncated count model. If this ratio is larger than one, then the hurdle model has an inflated number of zeros compared to the count model. If it is smaller than one, then the number of zeros is deflated. NTFrrr7)r#rrrz mean-nonzeroz prob-zeroz prob-mainz prob-truncrrr9rzwhich = %s is not available)lowerrr#r'r(rrr0rr~r$r>r=rrSr%rr)r(rr#rrr:r;no_exog exog_zerok_zeros params_zero params_mainlin_predmu1rrmu2 prob_ntruncrmtvtr probs_mains r,r>zHurdleCountModel.predictsB ,$E!%!9!9":"fh    II  s6{T]]2T]]BaG--(Xg& WX& FF4-@diiooa.@!AB%&kk!!+D!9KK**88kJ ] ffXkk,,::3 L F?rvvh//+= = k !66(# # h O n $66(#k1 1 k !  k !  l "{? 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