# Copyright 2014-2019 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('_prediction15_', [dirname(__file__)])
        except ImportError:
            import _prediction15_
            return _prediction15_
        if fp is not None:
            try:
                _mod = imp.load_module('_prediction15_', fp, pathname, description)
            finally:
                fp.close()
            return _mod
    _prediction15_ = swig_import_helper()
    del swig_import_helper
else:
    import _prediction15_
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.multi_class_classifier
import daal.algorithms.classifier
import daal.algorithms
import daal.data_management
import daal
import daal.services
import daal.data_management.features
import daal.data_management.modifiers.csv
import daal.data_management.modifiers
import daal.algorithms.classifier.training
import daal.algorithms.classifier.prediction

_prediction15_.defaultDense_swigconstant(_prediction15_)
defaultDense = _prediction15_.defaultDense

_prediction15_.multiClassClassifierWu_swigconstant(_prediction15_)
multiClassClassifierWu = _prediction15_.multiClassClassifierWu

_prediction15_.voteBased_swigconstant(_prediction15_)
voteBased = _prediction15_.voteBased
class Input(daal.algorithms.classifier.prediction.Input):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Input]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Input, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Input]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Input, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _prediction15_.new_Input(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _prediction15_.delete_Input
    __del__ = lambda self: None

    def getTable(self, id):
        return _prediction15_.Input_getTable(self, id)

    def getModel(self, id):
        return _prediction15_.Input_getModel(self, id)

    def setTable(self, id, ptr):
        return _prediction15_.Input_setTable(self, id, ptr)

    def set(self, id, ptr):
        return _prediction15_.Input_set(self, id, ptr)

    def check(self, parameter, method):
        return _prediction15_.Input_check(self, parameter, method)
Input_swigregister = _prediction15_.Input_swigregister
Input_swigregister(Input)

class Batch_Float64VoteBasedOneAgainstOne(daal.algorithms.classifier.prediction.Batch):
    r"""
    This class is an alias of Batch()
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Batch_Float64VoteBasedOneAgainstOne, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Batch_Float64VoteBasedOneAgainstOne, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _prediction15_.Batch_Float64VoteBasedOneAgainstOne_input_set
    __swig_getmethods__["input"] = _prediction15_.Batch_Float64VoteBasedOneAgainstOne_input_get
    if _newclass:
        input = _swig_property(_prediction15_.Batch_Float64VoteBasedOneAgainstOne_input_get, _prediction15_.Batch_Float64VoteBasedOneAgainstOne_input_set)
    __swig_setmethods__["parameter"] = _prediction15_.Batch_Float64VoteBasedOneAgainstOne_parameter_set
    __swig_getmethods__["parameter"] = _prediction15_.Batch_Float64VoteBasedOneAgainstOne_parameter_get
    if _newclass:
        parameter = _swig_property(_prediction15_.Batch_Float64VoteBasedOneAgainstOne_parameter_get, _prediction15_.Batch_Float64VoteBasedOneAgainstOne_parameter_set)

    def __init__(self, *args):
        this = _prediction15_.new_Batch_Float64VoteBasedOneAgainstOne(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _prediction15_.delete_Batch_Float64VoteBasedOneAgainstOne
    __del__ = lambda self: None

    def getInput(self):
        return _prediction15_.Batch_Float64VoteBasedOneAgainstOne_getInput(self)

    def getMethod(self):
        return _prediction15_.Batch_Float64VoteBasedOneAgainstOne_getMethod(self)

    def clone(self):
        return _prediction15_.Batch_Float64VoteBasedOneAgainstOne_clone(self)

    def compute(self):
        return _prediction15_.Batch_Float64VoteBasedOneAgainstOne_compute(self)
Batch_Float64VoteBasedOneAgainstOne_swigregister = _prediction15_.Batch_Float64VoteBasedOneAgainstOne_swigregister
Batch_Float64VoteBasedOneAgainstOne_swigregister(Batch_Float64VoteBasedOneAgainstOne)

class Batch_Float64MultiClassClassifierWuOneAgainstOne(daal.algorithms.classifier.prediction.Batch):
    r"""
    This class is an alias of Batch()
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Batch_Float64MultiClassClassifierWuOneAgainstOne, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Batch_Float64MultiClassClassifierWuOneAgainstOne, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_input_set
    __swig_getmethods__["input"] = _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_input_get
    if _newclass:
        input = _swig_property(_prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_input_get, _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_input_set)
    __swig_setmethods__["parameter"] = _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_parameter_set
    __swig_getmethods__["parameter"] = _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_parameter_get
    if _newclass:
        parameter = _swig_property(_prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_parameter_get, _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_parameter_set)

    def __init__(self, *args):
        this = _prediction15_.new_Batch_Float64MultiClassClassifierWuOneAgainstOne(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _prediction15_.delete_Batch_Float64MultiClassClassifierWuOneAgainstOne
    __del__ = lambda self: None

    def getInput(self):
        return _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_getInput(self)

    def getMethod(self):
        return _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_getMethod(self)

    def clone(self):
        return _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_clone(self)

    def compute(self):
        return _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_compute(self)
Batch_Float64MultiClassClassifierWuOneAgainstOne_swigregister = _prediction15_.Batch_Float64MultiClassClassifierWuOneAgainstOne_swigregister
Batch_Float64MultiClassClassifierWuOneAgainstOne_swigregister(Batch_Float64MultiClassClassifierWuOneAgainstOne)

class Batch_Float32VoteBasedOneAgainstOne(daal.algorithms.classifier.prediction.Batch):
    r"""
    This class is an alias of Batch()
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Batch_Float32VoteBasedOneAgainstOne, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Batch_Float32VoteBasedOneAgainstOne, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _prediction15_.Batch_Float32VoteBasedOneAgainstOne_input_set
    __swig_getmethods__["input"] = _prediction15_.Batch_Float32VoteBasedOneAgainstOne_input_get
    if _newclass:
        input = _swig_property(_prediction15_.Batch_Float32VoteBasedOneAgainstOne_input_get, _prediction15_.Batch_Float32VoteBasedOneAgainstOne_input_set)
    __swig_setmethods__["parameter"] = _prediction15_.Batch_Float32VoteBasedOneAgainstOne_parameter_set
    __swig_getmethods__["parameter"] = _prediction15_.Batch_Float32VoteBasedOneAgainstOne_parameter_get
    if _newclass:
        parameter = _swig_property(_prediction15_.Batch_Float32VoteBasedOneAgainstOne_parameter_get, _prediction15_.Batch_Float32VoteBasedOneAgainstOne_parameter_set)

    def __init__(self, *args):
        this = _prediction15_.new_Batch_Float32VoteBasedOneAgainstOne(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _prediction15_.delete_Batch_Float32VoteBasedOneAgainstOne
    __del__ = lambda self: None

    def getInput(self):
        return _prediction15_.Batch_Float32VoteBasedOneAgainstOne_getInput(self)

    def getMethod(self):
        return _prediction15_.Batch_Float32VoteBasedOneAgainstOne_getMethod(self)

    def clone(self):
        return _prediction15_.Batch_Float32VoteBasedOneAgainstOne_clone(self)

    def compute(self):
        return _prediction15_.Batch_Float32VoteBasedOneAgainstOne_compute(self)
Batch_Float32VoteBasedOneAgainstOne_swigregister = _prediction15_.Batch_Float32VoteBasedOneAgainstOne_swigregister
Batch_Float32VoteBasedOneAgainstOne_swigregister(Batch_Float32VoteBasedOneAgainstOne)

class Batch_Float32MultiClassClassifierWuOneAgainstOne(daal.algorithms.classifier.prediction.Batch):
    r"""
    This class is an alias of Batch()
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Batch_Float32MultiClassClassifierWuOneAgainstOne, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.classifier.prediction.Batch]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Batch_Float32MultiClassClassifierWuOneAgainstOne, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_input_set
    __swig_getmethods__["input"] = _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_input_get
    if _newclass:
        input = _swig_property(_prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_input_get, _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_input_set)
    __swig_setmethods__["parameter"] = _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_parameter_set
    __swig_getmethods__["parameter"] = _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_parameter_get
    if _newclass:
        parameter = _swig_property(_prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_parameter_get, _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_parameter_set)

    def __init__(self, *args):
        this = _prediction15_.new_Batch_Float32MultiClassClassifierWuOneAgainstOne(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _prediction15_.delete_Batch_Float32MultiClassClassifierWuOneAgainstOne
    __del__ = lambda self: None

    def getInput(self):
        return _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_getInput(self)

    def getMethod(self):
        return _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_getMethod(self)

    def clone(self):
        return _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_clone(self)

    def compute(self):
        return _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_compute(self)
Batch_Float32MultiClassClassifierWuOneAgainstOne_swigregister = _prediction15_.Batch_Float32MultiClassClassifierWuOneAgainstOne_swigregister
Batch_Float32MultiClassClassifierWuOneAgainstOne_swigregister(Batch_Float32MultiClassClassifierWuOneAgainstOne)

from daal.algorithms.multi_class_classifier.training import oneAgainstOne

from numpy import float64, float32, intc

class Batch(object):
    r"""Factory class for different types of Batch."""
    def __new__(cls,
                *args, **kwargs):
        if 'fptype' not in kwargs or kwargs['fptype'] == float64:
            if 'pmethod' in kwargs and kwargs['pmethod'] == voteBased:
                    if 'tmethod' not in kwargs or kwargs['tmethod'] == daal.algorithms.multi_class_classifier.training.oneAgainstOne:
                        return Batch_Float64VoteBasedOneAgainstOne(*args)
            if 'pmethod' not in kwargs or kwargs['pmethod'] == multiClassClassifierWu:
                    if 'tmethod' not in kwargs or kwargs['tmethod'] == daal.algorithms.multi_class_classifier.training.oneAgainstOne:
                        return Batch_Float64MultiClassClassifierWuOneAgainstOne(*args)
        if 'fptype' in kwargs and kwargs['fptype'] == float32:
            if 'pmethod' in kwargs and kwargs['pmethod'] == voteBased:
                    if 'tmethod' not in kwargs or kwargs['tmethod'] == daal.algorithms.multi_class_classifier.training.oneAgainstOne:
                        return Batch_Float32VoteBasedOneAgainstOne(*args)
            if 'pmethod' not in kwargs or kwargs['pmethod'] == multiClassClassifierWu:
                    if 'tmethod' not in kwargs or kwargs['tmethod'] == daal.algorithms.multi_class_classifier.training.oneAgainstOne:
                        return Batch_Float32MultiClassClassifierWuOneAgainstOne(*args)

        raise RuntimeError("No appropriate constructor found for Batch")


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


