from __future__ import division

__all__ = ["ZeroInflatedPoisson", "ZeroInflatedGeneralizedPoisson",
           "ZeroInflatedNegativeBinomialP"]

import warnings
import numpy as np
import statsmodels.base.model as base
import statsmodels.base.wrapper as wrap
import statsmodels.regression.linear_model as lm
from statsmodels.discrete.discrete_model import (DiscreteModel, CountModel,
                                                 Poisson, Logit, CountResults,
                                                 L1CountResults, Probit,
                                                 _discrete_results_docs,
                                                 GeneralizedPoisson,
                                                 NegativeBinomialP)
from statsmodels.distributions import zipoisson, zigenpoisson, zinegbin
from statsmodels.tools.numdiff import (approx_fprime, approx_hess,
                                       approx_hess_cs, approx_fprime_cs)
from statsmodels.tools.decorators import (resettable_cache, cache_readonly)
from statsmodels.tools.sm_exceptions import ConvergenceWarning


_doc_zi_params = """
    exog_infl : array_like or None
        Explanatory variables for the binary inflation model, i.e. for
        mixing probability model. If None, then a constant is used.
    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.
    inflation : string, 'logit' or 'probit'
        The model for the zero inflation, either Logit (default) or Probit
    """


class GenericZeroInflated(CountModel):
    __doc__ = """
    Generiz Zero Inflated 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.
    exog_infl: array
        A reference to the zero-inflated exogenous design.
    """ % {'params' : base._model_params_doc,
           'extra_params' : _doc_zi_params + base._missing_param_doc}

    def __init__(self, endog, exog, exog_infl=None, offset=None,
                 inflation='logit', exposure=None, missing='none', **kwargs):
        super(GenericZeroInflated, self).__init__(endog, exog, offset=offset,
                                                  exposure=exposure,
                                                  missing=missing, **kwargs)

        if exog_infl is None:
            self.k_inflate = 1
            self.exog_infl = np.ones((endog.size, self.k_inflate),
                                     dtype=np.float64)
        else:
            self.exog_infl = exog_infl
            self.k_inflate = exog_infl.shape[1]

        if len(exog.shape) == 1:
            self.k_exog = 1
        else:
            self.k_exog = exog.shape[1]

        self.infl = inflation
        if inflation == 'logit':
            self.model_infl = Logit(np.zeros(self.exog_infl.shape[0]),
                                    self.exog_infl)
            self._hessian_inflate = self._hessian_logit
        elif inflation == 'probit':
            self.model_infl = Probit(np.zeros(self.exog_infl.shape[0]),
                                    self.exog_infl)
            self._hessian_inflate = self._hessian_probit

        else:
            raise TypeError("inflation == %s, which is not handled"
                % inflation)

        self.inflation = inflation
        self.k_extra = self.k_inflate

        if len(self.exog) != len(self.exog_infl):
            raise ValueError('exog and exog_infl have different number of'
                             'observation. `missing` handling is not supported')

        infl_names = ['inflate_%s' % i for i in self.model_infl.data.param_names]
        self.exog_names[:] = infl_names + list(self.exog_names)
        self.exog_infl = np.asarray(self.exog_infl, dtype=np.float64)

        self._init_keys.extend(['exog_infl', 'inflation'])
        self._null_drop_keys = ['exog_infl']

    def loglike(self, params):
        """
        Loglikelihood of Generic Zero Inflated 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
        --------
        .. math:: \\ln L=\\sum_{y_{i}=0}\\ln(w_{i}+(1-w_{i})*P_{main\\_model})+
            \\sum_{y_{i}>0}(\\ln(1-w_{i})+L_{main\\_model})
            where P - pdf of main model, L - loglike function of main model.

        """
        return np.sum(self.loglikeobs(params))

    def loglikeobs(self, params):
        """
        Loglikelihood for observations of Generic Zero Inflated model

        Parameters
        ----------
        params : array-like
            The parameters of the model.

        Returns
        -------
        loglike : ndarray
            The log likelihood for each observation of the model evaluated
            at `params`. See Notes

        Notes
        --------
        .. math:: \\ln L=\\ln(w_{i}+(1-w_{i})*P_{main\\_model})+
            \\ln(1-w_{i})+L_{main\\_model}
            where P - pdf of main model, L - loglike function of main model.

        for observations :math:`i=1,...,n`

        """
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        y = self.endog
        w = self.model_infl.predict(params_infl)

        w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps)
        llf_main = self.model_main.loglikeobs(params_main)
        zero_idx = np.nonzero(y == 0)[0]
        nonzero_idx = np.nonzero(y)[0]

        llf = np.zeros_like(y, dtype=np.float64)
        llf[zero_idx] = (np.log(w[zero_idx] +
            (1 - w[zero_idx]) * np.exp(llf_main[zero_idx])))
        llf[nonzero_idx] = np.log(1 - w[nonzero_idx]) + llf_main[nonzero_idx]

        return llf

    def fit(self, start_params=None, method='bfgs', maxiter=35,
            full_output=1, disp=1, callback=None,
            cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs):
        if start_params is None:
            offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
            if np.size(offset) == 1 and offset == 0:
                offset = None
            start_params = self._get_start_params()

        if callback is None:
            # work around perfect separation callback #3895
            callback = lambda *x: x

        mlefit = super(GenericZeroInflated, self).fit(start_params=start_params,
                       maxiter=maxiter, disp=disp, method=method,
                       full_output=full_output, callback=callback,
                       **kwargs)

        zipfit = self.result_class(self, mlefit._results)
        result = self.result_class_wrapper(zipfit)

        if cov_kwds is None:
            cov_kwds = {}

        result._get_robustcov_results(cov_type=cov_type,
                                      use_self=True, use_t=use_t, **cov_kwds)
        return result

    fit.__doc__ = DiscreteModel.fit.__doc__

    def fit_regularized(self, start_params=None, method='l1',
            maxiter='defined_by_method', full_output=1, disp=1, callback=None,
            alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
            qc_tol=0.03, **kwargs):

        if np.size(alpha) == 1 and alpha != 0:
            k_params = self.k_exog + self.k_inflate
            alpha = alpha * np.ones(k_params)

        extra = self.k_extra - self.k_inflate
        alpha_p = alpha[:-(self.k_extra - extra)] if (self.k_extra
            and np.size(alpha) > 1) else alpha
        if start_params is None:
            offset = getattr(self, "offset", 0) + getattr(self, "exposure", 0)
            if np.size(offset) == 1 and offset == 0:
                offset = None
            start_params = self.model_main.fit_regularized(
                start_params=start_params, method=method, maxiter=maxiter,
                full_output=full_output, disp=0, callback=callback,
                alpha=alpha_p, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
                size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs).params
            start_params = np.append(np.ones(self.k_inflate), start_params)
        cntfit = super(CountModel, self).fit_regularized(
                start_params=start_params, method=method, maxiter=maxiter,
                full_output=full_output, disp=disp, callback=callback,
                alpha=alpha, trim_mode=trim_mode, auto_trim_tol=auto_trim_tol,
                size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)

        if method in ['l1', 'l1_cvxopt_cp']:
            discretefit = self.result_class_reg(self, cntfit)
        else:
            raise TypeError(
                    "argument method == %s, which is not handled" % method)

        return self.result_class_reg_wrapper(discretefit)

    fit_regularized.__doc__ = DiscreteModel.fit_regularized.__doc__

    def score_obs(self, params):
        """
        Generic Zero Inflated 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`
        """
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        y = self.endog
        w = self.model_infl.predict(params_infl)
        w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps)
        score_main = self.model_main.score_obs(params_main)
        llf_main = self.model_main.loglikeobs(params_main)
        llf = self.loglikeobs(params)
        zero_idx = np.nonzero(y == 0)[0]
        nonzero_idx = np.nonzero(y)[0]

        mu = self.model_main.predict(params_main)

        dldp = np.zeros((self.exog.shape[0], self.k_exog), dtype=np.float64)
        dldw = np.zeros_like(self.exog_infl, dtype=np.float64)

        dldp[zero_idx,:] = (score_main[zero_idx].T *
                     (1 - (w[zero_idx]) / np.exp(llf[zero_idx]))).T
        dldp[nonzero_idx,:] = score_main[nonzero_idx]

        if self.inflation == 'logit':
            dldw[zero_idx,:] =  (self.exog_infl[zero_idx].T * w[zero_idx] *
                                 (1 - w[zero_idx]) *
                                 (1 - np.exp(llf_main[zero_idx])) /
                                  np.exp(llf[zero_idx])).T
            dldw[nonzero_idx,:] = -(self.exog_infl[nonzero_idx].T *
                                    w[nonzero_idx]).T
        elif self.inflation == 'probit':
            return approx_fprime(params, self.loglikeobs)

        return np.hstack((dldw, dldp))

    def score(self, params):
        return self.score_obs(params).sum(0)

    def _hessian_main(self, params):
        pass

    def _hessian_logit(self, params):
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        y = self.endog
        w = self.model_infl.predict(params_infl)
        w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps)
        score_main = self.model_main.score_obs(params_main)
        llf_main = self.model_main.loglikeobs(params_main)
        llf = self.loglikeobs(params)
        zero_idx = np.nonzero(y == 0)[0]
        nonzero_idx = np.nonzero(y)[0]

        hess_arr = np.zeros((self.k_inflate, self.k_exog + self.k_inflate))

        pmf = np.exp(llf)

        #d2l/dw2
        for i in range(self.k_inflate):
            for j in range(i, -1, -1):
                hess_arr[i, j] = ((
                    self.exog_infl[zero_idx, i] * self.exog_infl[zero_idx, j] *
                    (w[zero_idx] * (1 - w[zero_idx]) * ((1 -
                    np.exp(llf_main[zero_idx])) * (1 - 2 * w[zero_idx]) *
                    np.exp(llf[zero_idx]) - (w[zero_idx] - w[zero_idx]**2) *
                    (1 - np.exp(llf_main[zero_idx]))**2) /
                    pmf[zero_idx]**2)).sum() -
                    (self.exog_infl[nonzero_idx, i] * self.exog_infl[nonzero_idx, j] *
                    w[nonzero_idx] * (1 - w[nonzero_idx])).sum())

        #d2l/dpdw
        for i in range(self.k_inflate):
            for j in range(self.k_exog):
                hess_arr[i, j + self.k_inflate] = -(score_main[zero_idx, j] *
                    w[zero_idx] * (1 - w[zero_idx]) *
                    self.exog_infl[zero_idx, i] / pmf[zero_idx]).sum()

        return hess_arr

    def _hessian_probit(self, params):
        pass

    def hessian(self, params):
        """
        Generic Zero Inflated 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
        -----
        """
        hess_arr_main = self._hessian_main(params)
        hess_arr_infl = self._hessian_inflate(params)

        if hess_arr_main is None or hess_arr_infl is None:
            return approx_hess(params, self.loglike)

        dim = self.k_exog + self.k_inflate

        hess_arr = np.zeros((dim, dim))

        hess_arr[:self.k_inflate,:] = hess_arr_infl
        hess_arr[self.k_inflate:,self.k_inflate:] = hess_arr_main

        tri_idx = np.triu_indices(self.k_exog + self.k_inflate, k=1)
        hess_arr[tri_idx] = hess_arr.T[tri_idx]

        return hess_arr

    def predict(self, params, exog=None, exog_infl=None, exposure=None,
                offset=None, which='mean'):
        """
        Predict response variable of a count model given exogenous variables.

        Parameters
        ----------
        params : array-like
            The parameters of the model
        exog : array, optional
            A reference to the exogenous design.
            If not assigned, will be used exog from fitting.
        exog_infl : array, optional
            A reference to the zero-inflated exogenous design.
            If not assigned, will be used exog from fitting.
        offset : array, optional
            Offset is added to the linear prediction with coefficient equal to 1.
        exposure : array, optional
            Log(exposure) is added to the linear prediction 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.
        which : string, optional
            Define values that will be predicted.
            'mean', 'mean-main', 'linear', 'mean-nonzero', 'prob-zero, 'prob', 'prob-main'
            Default is 'mean'.

        Notes
        -----
        """
        if exog is None:
            exog = self.exog

        if exog_infl is None:
            exog_infl = self.exog_infl

        if exposure is None:
            exposure = getattr(self, 'exposure', 0)
        else:
            exposure = np.log(exposure)

        if offset is None:
            offset = 0

        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        prob_main = 1 - self.model_infl.predict(params_infl, exog_infl)

        lin_pred = np.dot(exog, params_main[:self.exog.shape[1]]) + exposure + offset

        # Refactor: This is pretty hacky,
        # there should be an appropriate predict method in model_main
        # this is just prob(y=0 | model_main)
        tmp_exog = self.model_main.exog
        tmp_endog = self.model_main.endog
        tmp_offset = getattr(self.model_main, 'offset', ['no'])
        tmp_exposure = getattr(self.model_main, 'exposure', ['no'])
        self.model_main.exog = exog
        self.model_main.endog = np.zeros((exog.shape[0]))
        self.model_main.offset = offset
        self.model_main.exposure = exposure
        llf = self.model_main.loglikeobs(params_main)
        self.model_main.exog = tmp_exog
        self.model_main.endog = tmp_endog
        # tmp_offset might be an array with elementwise equality testing
        if len(tmp_offset) == 1 and tmp_offset[0] == 'no':
            del self.model_main.offset
        else:
            self.model_main.offset = tmp_offset
        if len(tmp_exposure) == 1 and tmp_exposure[0] == 'no':
            del self.model_main.exposure
        else:
            self.model_main.exposure = tmp_exposure
        # end hack

        prob_zero = (1 - prob_main) + prob_main * np.exp(llf)

        if which == 'mean':
            return prob_main * np.exp(lin_pred)
        elif which == 'mean-main':
            return np.exp(lin_pred)
        elif which == 'linear':
            return lin_pred
        elif which == 'mean-nonzero':
            return prob_main * np.exp(lin_pred) / (1 - prob_zero)
        elif which == 'prob-zero':
            return prob_zero
        elif which == 'prob-main':
            return prob_main
        elif which == 'prob':
            return self._predict_prob(params, exog, exog_infl, exposure, offset)
        else:
            raise ValueError('which = %s is not available' % which)


class ZeroInflatedPoisson(GenericZeroInflated):
    __doc__ = """
    Poisson Zero Inflated 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.
    exog_infl: array
        A reference to the zero-inflated exogenous design.
    """ % {'params' : base._model_params_doc,
           'extra_params' : _doc_zi_params + base._missing_param_doc}

    def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None,
                 inflation='logit', missing='none', **kwargs):
        super(ZeroInflatedPoisson, self).__init__(endog, exog, offset=offset,
                                                  inflation=inflation,
                                                  exog_infl=exog_infl,
                                                  exposure=exposure,
                                                  missing=missing, **kwargs)
        self.model_main = Poisson(self.endog, self.exog, offset=offset,
                                  exposure=exposure)
        self.distribution = zipoisson
        self.result_class = ZeroInflatedPoissonResults
        self.result_class_wrapper = ZeroInflatedPoissonResultsWrapper
        self.result_class_reg = L1ZeroInflatedPoissonResults
        self.result_class_reg_wrapper = L1ZeroInflatedPoissonResultsWrapper

    def _hessian_main(self, params):
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        y = self.endog
        w = self.model_infl.predict(params_infl)
        w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps)
        score = self.score(params)
        zero_idx = np.nonzero(y == 0)[0]
        nonzero_idx = np.nonzero(y)[0]

        mu = self.model_main.predict(params_main)

        hess_arr = np.zeros((self.k_exog, self.k_exog))

        coeff = (1 + w[zero_idx] * (np.exp(mu[zero_idx]) - 1))

        #d2l/dp2
        for i in range(self.k_exog):
            for j in range(i, -1, -1):
                hess_arr[i, j] = ((
                    self.exog[zero_idx, i] * self.exog[zero_idx, j] *
                    mu[zero_idx] * (w[zero_idx] - 1) * (1 / coeff -
                    w[zero_idx] * mu[zero_idx] * np.exp(mu[zero_idx]) /
                    coeff**2)).sum() - (mu[nonzero_idx] * self.exog[nonzero_idx, i] *
                    self.exog[nonzero_idx, j]).sum())

        return hess_arr

    def _predict_prob(self, params, exog, exog_infl, exposure, offset):
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1))

        if len(exog_infl.shape) < 2:
            transform = True
            w = np.atleast_2d(
                self.model_infl.predict(params_infl, exog_infl))[:, None]
        else:
            transform = False
            w = self.model_infl.predict(params_infl, exog_infl)[:, None]

        w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps)
        mu = self.model_main.predict(params_main, exog,
            offset=offset)[:, None]
        result = self.distribution.pmf(counts, mu, w)
        return result[0] if transform else result

    def _get_start_params(self):
        start_params = self.model_main.fit(disp=0, method="nm").params
        start_params = np.append(np.ones(self.k_inflate) * 0.1, start_params)
        return start_params


class ZeroInflatedGeneralizedPoisson(GenericZeroInflated):
    __doc__ = """
    Zero Inflated 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.
    exog_infl: array
        A reference to the zero-inflated exogenous design.
    p: scalar
        P denotes parametrizations for ZIGP regression.
    """ % {'params' : base._model_params_doc,
           'extra_params' : _doc_zi_params +
           """p : float
        dispersion power parameter for the GeneralizedPoisson model.  p=1 for
        ZIGP-1 and p=2 for ZIGP-2. Default is p=2
    """ + base._missing_param_doc}

    def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None,
                 inflation='logit', p=2, missing='none', **kwargs):
        super(ZeroInflatedGeneralizedPoisson, self).__init__(endog, exog,
                                                  offset=offset,
                                                  inflation=inflation,
                                                  exog_infl=exog_infl,
                                                  exposure=exposure,
                                                  missing=missing, **kwargs)
        self.model_main = GeneralizedPoisson(self.endog, self.exog,
            offset=offset, exposure=exposure, p=p)
        self.distribution = zigenpoisson
        self.k_exog += 1
        self.k_extra += 1
        self.exog_names.append("alpha")
        self.result_class = ZeroInflatedGeneralizedPoissonResults
        self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper
        self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults
        self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper

    def _get_init_kwds(self):
        kwds = super(ZeroInflatedGeneralizedPoisson, self)._get_init_kwds()
        kwds['p'] = self.model_main.parameterization + 1
        return kwds

    def _predict_prob(self, params, exog, exog_infl, exposure, offset):
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        p = self.model_main.parameterization
        counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1))

        if len(exog_infl.shape) < 2:
            transform = True
            w = np.atleast_2d(
                self.model_infl.predict(params_infl, exog_infl))[:, None]
        else:
            transform = False
            w = self.model_infl.predict(params_infl, exog_infl)[:, None]

        w[w == 1.] = np.nextafter(1, 0)
        mu = self.model_main.predict(params_main, exog,
            exposure=exposure, offset=offset)[:, None]
        result = self.distribution.pmf(counts, mu, params_main[-1], p, w)
        return result[0] if transform else result

    def _get_start_params(self):
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", category=ConvergenceWarning)
            start_params = ZeroInflatedPoisson(self.endog, self.exog,
                exog_infl=self.exog_infl).fit(disp=0).params
        start_params = np.append(start_params, 0.1)
        return start_params


class ZeroInflatedNegativeBinomialP(GenericZeroInflated):
    __doc__ = """
    Zero Inflated Generalized 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.
    exog_infl: array
        A reference to the zero-inflated exogenous design.
    p: scalar
        P denotes parametrizations for ZINB regression. p=1 for ZINB-1 and
    p=2 for ZINB-2. Default is p=2
    """ % {'params' : base._model_params_doc,
           'extra_params' : _doc_zi_params +
           """p : float
        dispersion power parameter for the NegativeBinomialP model.  p=1 for
        ZINB-1 and p=2 for ZINM-2. Default is p=2
    """ + base._missing_param_doc}

    def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None,
                 inflation='logit', p=2, missing='none', **kwargs):
        super(ZeroInflatedNegativeBinomialP, self).__init__(endog, exog,
                                                  offset=offset,
                                                  inflation=inflation,
                                                  exog_infl=exog_infl,
                                                  exposure=exposure,
                                                  missing=missing, **kwargs)
        self.model_main = NegativeBinomialP(self.endog, self.exog,
            offset=offset, exposure=exposure, p=p)
        self.distribution = zinegbin
        self.k_exog += 1
        self.k_extra += 1
        self.exog_names.append("alpha")
        self.result_class = ZeroInflatedNegativeBinomialResults
        self.result_class_wrapper = ZeroInflatedNegativeBinomialResultsWrapper
        self.result_class_reg = L1ZeroInflatedNegativeBinomialResults
        self.result_class_reg_wrapper = L1ZeroInflatedNegativeBinomialResultsWrapper

    def _get_init_kwds(self):
        kwds = super(ZeroInflatedNegativeBinomialP, self)._get_init_kwds()
        kwds['p'] = self.model_main.parameterization
        return kwds

    def _predict_prob(self, params, exog, exog_infl, exposure, offset):
        params_infl = params[:self.k_inflate]
        params_main = params[self.k_inflate:]

        p = self.model_main.parameterization
        counts = np.arange(0, np.max(self.endog)+1)

        if len(exog_infl.shape) < 2:
            transform = True
            w = np.atleast_2d(
                self.model_infl.predict(params_infl, exog_infl))[:, None]
        else:
            transform = False
            w = self.model_infl.predict(params_infl, exog_infl)[:, None]

        w = np.clip(w, np.finfo(float).eps, 1 - np.finfo(float).eps)
        mu = self.model_main.predict(params_main, exog,
            exposure=exposure, offset=offset)[:, None]
        result = self.distribution.pmf(counts, mu, params_main[-1], p, w)
        return result[0] if transform else result

    def _get_start_params(self):
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", category=ConvergenceWarning)
            start_params = self.model_main.fit(disp=0, method='nm').params
        start_params = np.append(np.zeros(self.k_inflate), start_params)
        return start_params


class ZeroInflatedPoissonResults(CountResults):
    __doc__ = _discrete_results_docs % {
        "one_line_description" : "A results class for Zero Inflated Poisson",
                    "extra_attr" : ""}

    @cache_readonly
    def _dispersion_factor(self):
        mu = self.predict(which='linear')
        w = 1 - self.predict() / np.exp(self.predict(which='linear'))
        return (1 + w * np.exp(mu))

    def get_margeff(self, at='overall', method='dydx', atexog=None,
            dummy=False, count=False):
        """Get marginal effects of the fitted model.

        Not yet implemented for Zero Inflated Models
        """
        raise NotImplementedError("not yet implemented for zero inflation")


class L1ZeroInflatedPoissonResults(L1CountResults, ZeroInflatedPoissonResults):
    pass


class ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper):
    pass
wrap.populate_wrapper(ZeroInflatedPoissonResultsWrapper,
                      ZeroInflatedPoissonResults)


class L1ZeroInflatedPoissonResultsWrapper(lm.RegressionResultsWrapper):
    pass
wrap.populate_wrapper(L1ZeroInflatedPoissonResultsWrapper,
                      L1ZeroInflatedPoissonResults)


class ZeroInflatedGeneralizedPoissonResults(CountResults):
    __doc__ = _discrete_results_docs % {
        "one_line_description" : "A results class for Zero Inflated Generalized Poisson",
                    "extra_attr" : ""}

    @cache_readonly
    def _dispersion_factor(self):
        p = self.model.model_main.parameterization
        alpha = self.params[self.model.k_inflate:][-1]
        mu = np.exp(self.predict(which='linear'))
        w = 1 - self.predict() / mu
        return ((1 + alpha * mu**p)**2 + w * mu)

    def get_margeff(self, at='overall', method='dydx', atexog=None,
            dummy=False, count=False):
        """Get marginal effects of the fitted model.

        Not yet implemented for Zero Inflated Models
        """
        raise NotImplementedError("not yet implemented for zero inflation")


class L1ZeroInflatedGeneralizedPoissonResults(L1CountResults,
        ZeroInflatedGeneralizedPoissonResults):
    pass


class ZeroInflatedGeneralizedPoissonResultsWrapper(
        lm.RegressionResultsWrapper):
    pass
wrap.populate_wrapper(ZeroInflatedGeneralizedPoissonResultsWrapper,
                      ZeroInflatedGeneralizedPoissonResults)


class L1ZeroInflatedGeneralizedPoissonResultsWrapper(
        lm.RegressionResultsWrapper):
    pass
wrap.populate_wrapper(L1ZeroInflatedGeneralizedPoissonResultsWrapper,
                      L1ZeroInflatedGeneralizedPoissonResults)


class ZeroInflatedNegativeBinomialResults(CountResults):
    __doc__ = _discrete_results_docs % {
        "one_line_description" : "A results class for Zero Inflated Genaralized Negative Binomial",
                    "extra_attr" : ""}

    @cache_readonly
    def _dispersion_factor(self):
        p = self.model.model_main.parameterization
        alpha = self.params[self.model.k_inflate:][-1]
        mu = np.exp(self.predict(which='linear'))
        w = 1 - self.predict() / mu
        return (1 + alpha * mu**(p-1) + w * mu)

    def get_margeff(self, at='overall', method='dydx', atexog=None,
            dummy=False, count=False):
        """Get marginal effects of the fitted model.

        Not yet implemented for Zero Inflated Models
        """
        raise NotImplementedError("not yet implemented for zero inflation")


class L1ZeroInflatedNegativeBinomialResults(L1CountResults,
        ZeroInflatedNegativeBinomialResults):
    pass


class ZeroInflatedNegativeBinomialResultsWrapper(
        lm.RegressionResultsWrapper):
    pass
wrap.populate_wrapper(ZeroInflatedNegativeBinomialResultsWrapper,
                      ZeroInflatedNegativeBinomialResults)


class L1ZeroInflatedNegativeBinomialResultsWrapper(
        lm.RegressionResultsWrapper):
    pass
wrap.populate_wrapper(L1ZeroInflatedNegativeBinomialResultsWrapper,
                      L1ZeroInflatedNegativeBinomialResults)
