# Path: glimix_core/_util/check.py
# def check_covariates(X):
#     if not X.ndim == 2:
#         raise ValueError("Covariates must be a bidimensional array.")
# 
#     if not npall(isfinite(X)):
#         raise ValueError("Covariates must have finite values only.")
# 
#     return X
# 
# def check_economic_qs(QS):
#     if not isinstance(QS, tuple):
#         raise ValueError("QS must be a tuple.")
# 
#     if not isinstance(QS[0], tuple):
#         raise ValueError("QS[0] must be a tuple.")
# 
#     fmsg = "QS has non-finite values."
# 
#     if not all(npall(isfinite(Q)) for Q in QS[0]):
#         raise ValueError(fmsg)
# 
#     if not npall(isfinite(QS[1])):
#         raise ValueError(fmsg)
# 
#     return QS
# 
# def check_outcome(y, lik):
#     if not isinstance(lik, (list, tuple)):
#         lik = (lik,)
# 
#     str_err = "The first item of ``lik`` has to be a string."
#     if not isinstance(lik[0], str):
#         raise ValueError(str_err)
# 
#     lik_name = lik[0].lower()
# 
#     y = ascontiguousarray(y, float)
#     lik = lik[:1] + tuple(ascontiguousarray(i, float) for i in lik[1:])
# 
#     if not npall(isfinite(y)):
#         raise ValueError("Outcome must be finite.")
# 
#     if lik_name == "poisson":
#         return _check_poisson_outcome(y)
# 
#     if lik_name in ("binomial", "normal"):
#         if len(lik) != 2:
#             msg = "``lik`` must be a tuple of two elements for"
#             msg += " {} likelihood.".format(lik_name[0].upper() + lik_name[1:])
#             raise ValueError(msg)
# 
#     return y
# 
# Path: glimix_core/_util/eigen.py
# def economic_qs_zeros(n: int):
#     """
#     Eigen decomposition of a zero matrix.
#     """
# 
#     Q0 = empty((n, 0))
#     Q1 = eye(n)
#     S0 = empty(0)
# 
#     return ((Q0, Q1), S0)


from copy import copy
from numpy import asarray, ascontiguousarray, clip, dot, exp, log, zeros
from optimix import Function, Scalar, Vector
from .._util import (
    check_covariates,
    check_economic_qs,
    check_outcome,
    economic_qs_zeros,
)
        from numpy_sugar.linalg import ddot, sum2diag
        from numpy_sugar import epsilon
        Outcome variable.
    lik : tuple
        Likelihood definition. The first item is one of the following likelihood names:
        ``"Bernoulli"``, ``"Binomial"``, ``"Normal"``, and ``"Poisson"``. For
        `Binomial`, the second item is an array of outcomes.
    X : array_like
        Covariates.
    QS : tuple
        Economic eigen decomposition.
    """

    def __init__(self, y, lik, X, QS=None):
        y = ascontiguousarray(y, float)
        X = asarray(X, float)

        Function.__init__(
            self,
            "GLMM",
            beta=Vector(zeros(X.shape[1])),
            logscale=Scalar(0.0),
            logitdelta=Scalar(0.0),
        )

        if not isinstance(lik, (tuple, list)):
            lik = (lik,)

        self._lik = (lik[0].lower(),) + tuple(ascontiguousarray(i) for i in lik[1:])
        self._y = check_outcome(y, self._lik)
        self._X = check_covariates(X)
        if QS is None:

# Path: glimix_core/_util/check.py
# def check_covariates(X):
#     if not X.ndim == 2:
#         raise ValueError("Covariates must be a bidimensional array.")
# 
#     if not npall(isfinite(X)):
#         raise ValueError("Covariates must have finite values only.")
# 
#     return X
# 
# def check_economic_qs(QS):
#     if not isinstance(QS, tuple):
#         raise ValueError("QS must be a tuple.")
# 
#     if not isinstance(QS[0], tuple):
#         raise ValueError("QS[0] must be a tuple.")
# 
#     fmsg = "QS has non-finite values."
# 
#     if not all(npall(isfinite(Q)) for Q in QS[0]):
#         raise ValueError(fmsg)
# 
#     if not npall(isfinite(QS[1])):
#         raise ValueError(fmsg)
# 
#     return QS
# 
# def check_outcome(y, lik):
#     if not isinstance(lik, (list, tuple)):
#         lik = (lik,)
# 
#     str_err = "The first item of ``lik`` has to be a string."
#     if not isinstance(lik[0], str):
#         raise ValueError(str_err)
# 
#     lik_name = lik[0].lower()
# 
#     y = ascontiguousarray(y, float)
#     lik = lik[:1] + tuple(ascontiguousarray(i, float) for i in lik[1:])
# 
#     if not npall(isfinite(y)):
#         raise ValueError("Outcome must be finite.")
# 
#     if lik_name == "poisson":
#         return _check_poisson_outcome(y)
# 
#     if lik_name in ("binomial", "normal"):
#         if len(lik) != 2:
#             msg = "``lik`` must be a tuple of two elements for"
#             msg += " {} likelihood.".format(lik_name[0].upper() + lik_name[1:])
#             raise ValueError(msg)
# 
#     return y
# 
# Path: glimix_core/_util/eigen.py
# def economic_qs_zeros(n: int):
#     """
#     Eigen decomposition of a zero matrix.
#     """
# 
#     Q0 = empty((n, 0))
#     Q1 = eye(n)
#     S0 = empty(0)
# 
#     return ((Q0, Q1), S0)


# Path: glimix_core/glmm/_glmm.py
from copy import copy
from numpy import asarray, ascontiguousarray, clip, dot, exp, log, zeros
from optimix import Function, Scalar, Vector
from .._util import (
    check_covariates,
    check_economic_qs,
    check_outcome,
    economic_qs_zeros,
)
        from numpy_sugar.linalg import ddot, sum2diag
        from numpy_sugar import epsilon

        Outcome variable.
    lik : tuple
        Likelihood definition. The first item is one of the following likelihood names:
        ``"Bernoulli"``, ``"Binomial"``, ``"Normal"``, and ``"Poisson"``. For
        `Binomial`, the second item is an array of outcomes.
    X : array_like
        Covariates.
    QS : tuple
        Economic eigen decomposition.
    """

    def __init__(self, y, lik, X, QS=None):
        y = ascontiguousarray(y, float)
        X = asarray(X, float)

        Function.__init__(
            self,
            "GLMM",
            beta=Vector(zeros(X.shape[1])),
            logscale=Scalar(0.0),
            logitdelta=Scalar(0.0),
        )

        if not isinstance(lik, (tuple, list)):
            lik = (lik,)

        self._lik = (lik[0].lower(),) + tuple(ascontiguousarray(i) for i in lik[1:])
        self._y = check_outcome(y, self._lik)
        self._X = check_covariates(X)
        if QS is None:

            self._QS = economic_qs_zeros(self._y.shape[0])