# Copyright 2014-2018 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel's prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.



from sys import version_info
if version_info >= (2, 6, 0):
    def swig_import_helper():
        from os.path import dirname
        import imp
        fp = None
        try:
            fp, pathname, description = imp.find_module('_multinomial_naive_bayes', [dirname(__file__)])
        except ImportError:
            import _multinomial_naive_bayes
            return _multinomial_naive_bayes
        if fp is not None:
            try:
                _mod = imp.load_module('_multinomial_naive_bayes', fp, pathname, description)
            finally:
                fp.close()
            return _mod
    _multinomial_naive_bayes = swig_import_helper()
    del swig_import_helper
else:
    import _multinomial_naive_bayes
del version_info
try:
    _swig_property = property
except NameError:
    pass  # Python < 2.2 doesn't have 'property'.


def _swig_setattr_nondynamic(self, class_type, name, value, static=1):
    if (name == "thisown"):
        return self.this.own(value)
    if (name == "this"):
        if type(value).__name__ == 'SwigPyObject':
            self.__dict__[name] = value
            return
    method = class_type.__swig_setmethods__.get(name, None)
    if method:
        return method(self, value)
    if (not static):
        if _newclass:
            object.__setattr__(self, name, value)
        else:
            self.__dict__[name] = value
    else:
        raise AttributeError("You cannot add attributes to %s" % self)


def _swig_setattr(self, class_type, name, value):
    return _swig_setattr_nondynamic(self, class_type, name, value, 0)


def _swig_getattr_nondynamic(self, class_type, name, static=1):
    if (name == "thisown"):
        return self.this.own()
    method = class_type.__swig_getmethods__.get(name, None)
    if method:
        return method(self)
    if (not static):
        return object.__getattr__(self, name)
    else:
        raise AttributeError(name)

def _swig_getattr(self, class_type, name):
    return _swig_getattr_nondynamic(self, class_type, name, 0)


def _swig_repr(self):
    try:
        strthis = "proxy of " + self.this.__repr__()
    except:
        strthis = ""
    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)

try:
    _object = object
    _newclass = 1
except AttributeError:
    class _object:
        pass
    _newclass = 0


try:
    import weakref
    weakref_proxy = weakref.proxy
except:
    weakref_proxy = lambda x: x


import daal.algorithms.classifier
import daal.algorithms
import daal.data_management
import daal
import daal.services
import daal.data_management.data_feature_utils
class Parameter(daal.algorithms.classifier.Parameter):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.Parameter]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Parameter, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.Parameter]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Parameter, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _multinomial_naive_bayes.new_Parameter(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_setmethods__["priorClassEstimates"] = _multinomial_naive_bayes.Parameter_priorClassEstimates_set
    __swig_getmethods__["priorClassEstimates"] = _multinomial_naive_bayes.Parameter_priorClassEstimates_get
    if _newclass:
        priorClassEstimates = _swig_property(_multinomial_naive_bayes.Parameter_priorClassEstimates_get, _multinomial_naive_bayes.Parameter_priorClassEstimates_set)
    __swig_setmethods__["alpha"] = _multinomial_naive_bayes.Parameter_alpha_set
    __swig_getmethods__["alpha"] = _multinomial_naive_bayes.Parameter_alpha_get
    if _newclass:
        alpha = _swig_property(_multinomial_naive_bayes.Parameter_alpha_get, _multinomial_naive_bayes.Parameter_alpha_set)

    def check(self):
        return _multinomial_naive_bayes.Parameter_check(self)
    __swig_destroy__ = _multinomial_naive_bayes.delete_Parameter
    __del__ = lambda self: None
Parameter_swigregister = _multinomial_naive_bayes.Parameter_swigregister
Parameter_swigregister(Parameter)

class Model(daal.algorithms.classifier.Model):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.Model]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Model, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.Model]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Model, name)
    __repr__ = _swig_repr
    __swig_getmethods__["serializationTag"] = lambda x: _multinomial_naive_bayes.Model_serializationTag
    if _newclass:
        serializationTag = staticmethod(_multinomial_naive_bayes.Model_serializationTag)

    def getSerializationTag(self):
        return _multinomial_naive_bayes.Model_getSerializationTag(self)
    __swig_getmethods__["downCast"] = lambda x: _multinomial_naive_bayes.Model_downCast
    if _newclass:
        downCast = staticmethod(_multinomial_naive_bayes.Model_downCast)
    __swig_destroy__ = _multinomial_naive_bayes.delete_Model
    __del__ = lambda self: None

    def getLogP(self):
        return _multinomial_naive_bayes.Model_getLogP(self)

    def getLogTheta(self):
        return _multinomial_naive_bayes.Model_getLogTheta(self)

    def getAuxTable(self):
        return _multinomial_naive_bayes.Model_getAuxTable(self)

    def getNumberOfFeatures(self):
        return _multinomial_naive_bayes.Model_getNumberOfFeatures(self)

    def __init__(self, *args):
        this = _multinomial_naive_bayes.new_Model(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
Model_swigregister = _multinomial_naive_bayes.Model_swigregister
Model_swigregister(Model)

def Model_serializationTag():
    return _multinomial_naive_bayes.Model_serializationTag()
Model_serializationTag = _multinomial_naive_bayes.Model_serializationTag

def Model_downCast(r):
    return _multinomial_naive_bayes.Model_downCast(r)
Model_downCast = _multinomial_naive_bayes.Model_downCast

class PartialModel(daal.algorithms.classifier.Model):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.Model]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, PartialModel, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.Model]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, PartialModel, name)
    __repr__ = _swig_repr
    __swig_getmethods__["serializationTag"] = lambda x: _multinomial_naive_bayes.PartialModel_serializationTag
    if _newclass:
        serializationTag = staticmethod(_multinomial_naive_bayes.PartialModel_serializationTag)

    def getSerializationTag(self):
        return _multinomial_naive_bayes.PartialModel_getSerializationTag(self)
    __swig_destroy__ = _multinomial_naive_bayes.delete_PartialModel
    __del__ = lambda self: None

    def getNObservations(self):
        return _multinomial_naive_bayes.PartialModel_getNObservations(self)

    def setNObservations(self, nObservations):
        return _multinomial_naive_bayes.PartialModel_setNObservations(self, nObservations)

    def getNumberOfFeatures(self):
        return _multinomial_naive_bayes.PartialModel_getNumberOfFeatures(self)

    def getNFeatures(self):
        return _multinomial_naive_bayes.PartialModel_getNFeatures(self)

    def setNFeatures(self, nFeatures):
        return _multinomial_naive_bayes.PartialModel_setNFeatures(self, nFeatures)

    def getClassSize(self):
        return _multinomial_naive_bayes.PartialModel_getClassSize(self)

    def getClassGroupSum(self):
        return _multinomial_naive_bayes.PartialModel_getClassGroupSum(self)

    def __init__(self, *args):
        this = _multinomial_naive_bayes.new_PartialModel(*args)
        try:
            self.this.append(this)
        except:
            self.this = this

    def initialize_Float64(self):
        r"""
    This function is specialized for float64"""
        return _multinomial_naive_bayes.PartialModel_initialize_Float64(self)


    def initialize_Float32(self):
        r"""
    This function is specialized for float32"""
        return _multinomial_naive_bayes.PartialModel_initialize_Float32(self)

PartialModel_swigregister = _multinomial_naive_bayes.PartialModel_swigregister
PartialModel_swigregister(PartialModel)

def PartialModel_serializationTag():
    return _multinomial_naive_bayes.PartialModel_serializationTag()
PartialModel_serializationTag = _multinomial_naive_bayes.PartialModel_serializationTag

from numpy import float64, float32, intc


# This file is compatible with both classic and new-style classes.


