# Copyright 2014-2017 Intel Corporation
# All Rights Reserved.
#
# If this  software was obtained  under the  Intel Simplified  Software License,
# the following terms apply:
#
# The source code,  information  and material  ("Material") contained  herein is
# owned by Intel Corporation or its  suppliers or licensors,  and  title to such
# Material remains with Intel  Corporation or its  suppliers or  licensors.  The
# Material  contains  proprietary  information  of  Intel or  its suppliers  and
# licensors.  The Material is protected by  worldwide copyright  laws and treaty
# provisions.  No part  of  the  Material   may  be  used,  copied,  reproduced,
# modified, published,  uploaded, posted, transmitted,  distributed or disclosed
# in any way without Intel's prior express written permission.  No license under
# any patent,  copyright or other  intellectual property rights  in the Material
# is granted to  or  conferred  upon  you,  either   expressly,  by implication,
# inducement,  estoppel  or  otherwise.  Any  license   under such  intellectual
# property rights must be express and approved by Intel in writing.
#
# Unless otherwise agreed by Intel in writing,  you may not remove or alter this
# notice or  any  other  notice   embedded  in  Materials  by  Intel  or Intel's
# suppliers or licensors in any way.
#
#
# If this  software  was obtained  under the  Apache License,  Version  2.0 (the
# "License"), the following terms apply:
#
# You may  not use this  file except  in compliance  with  the License.  You may
# obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless  required  by   applicable  law  or  agreed  to  in  writing,  software
# distributed under the License  is distributed  on an  "AS IS"  BASIS,  WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# See the   License  for the   specific  language   governing   permissions  and
# limitations under 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('_training16', [dirname(__file__)])
        except ImportError:
            import _training16
            return _training16
        if fp is not None:
            try:
                _mod = imp.load_module('_training16', fp, pathname, description)
            finally:
                fp.close()
            return _mod
    _training16 = swig_import_helper()
    del swig_import_helper
else:
    import _training16
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


import daal.algorithms.neural_networks
import daal.algorithms
import daal.data_management
import daal
import daal.services
import daal.data_management.data_feature_utils
import daal.algorithms.neural_networks.layers.backward
import daal.algorithms.neural_networks.layers
import daal.algorithms.neural_networks.initializers
import daal.algorithms.engines.mt19937
import daal.algorithms.engines
import daal.algorithms.neural_networks.layers.forward
import daal.algorithms.optimization_solver.iterative_solver
import daal.algorithms.optimization_solver
import daal.algorithms.neural_networks.prediction
import daal.algorithms.optimization_solver.sgd
import daal.algorithms.optimization_solver.sum_of_functions
import daal.algorithms.optimization_solver.objective_function

_training16.defaultDense_swigconstant(_training16)
defaultDense = _training16.defaultDense

_training16.feedforwardDense_swigconstant(_training16)
feedforwardDense = _training16.feedforwardDense

_training16.model_swigconstant(_training16)
model = _training16.model

_training16.lastResultId_swigconstant(_training16)
lastResultId = _training16.lastResultId
class Result(daal.algorithms.Result):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Result]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Result, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Result]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Result, name)
    __repr__ = _swig_repr
    __swig_getmethods__["serializationTag"] = lambda x: _training16.Result_serializationTag
    if _newclass:
        serializationTag = staticmethod(_training16.Result_serializationTag)

    def getSerializationTag(self):
        return _training16.Result_getSerializationTag(self)

    def __init__(self):
        this = _training16.new_Result()
        try:
            self.this.append(this)
        except:
            self.this = this

    def get(self, id):
        return _training16.Result_get(self, id)

    def set(self, id, value):
        return _training16.Result_set(self, id, value)

    def check(self, input, par, method):
        return _training16.Result_check(self, input, par, method)

    def allocate_Float64(self, input, parameter, method):
        r"""
This function is specialized for float64"""
        return _training16.Result_allocate_Float64(self, input, parameter, method)


    def allocate_Float32(self, input, parameter, method):
        r"""
This function is specialized for float32"""
        return _training16.Result_allocate_Float32(self, input, parameter, method)

    __swig_destroy__ = _training16.delete_Result
    __del__ = lambda self: None
Result_swigregister = _training16.Result_swigregister
Result_swigregister(Result)

def Result_serializationTag():
    return _training16.Result_serializationTag()
Result_serializationTag = _training16.Result_serializationTag


_training16.data_swigconstant(_training16)
data = _training16.data

_training16.groundTruth_swigconstant(_training16)
groundTruth = _training16.groundTruth

_training16.lastInputId_swigconstant(_training16)
lastInputId = _training16.lastInputId

_training16.groundTruthCollection_swigconstant(_training16)
groundTruthCollection = _training16.groundTruthCollection

_training16.lastInputCollectionId_swigconstant(_training16)
lastInputCollectionId = _training16.lastInputCollectionId

_training16.inputModel_swigconstant(_training16)
inputModel = _training16.inputModel

_training16.lastStep1LocalInputId_swigconstant(_training16)
lastStep1LocalInputId = _training16.lastStep1LocalInputId

_training16.partialResults_swigconstant(_training16)
partialResults = _training16.partialResults

_training16.lastStep2MasterInputId_swigconstant(_training16)
lastStep2MasterInputId = _training16.lastStep2MasterInputId
class Input(daal.algorithms.Input):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.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.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 = _training16.new_Input(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Input
    __del__ = lambda self: None

    def getInput(self, id):
        return _training16.Input_getInput(self, id)

    def getInputCollection(self, *args):
        return _training16.Input_getInputCollection(self, *args)

    def setInput(self, id, value):
        return _training16.Input_setInput(self, id, value)

    def setInputCollection(self, id, value):
        return _training16.Input_setInputCollection(self, id, value)

    def add(self, id, key, value):
        return _training16.Input_add(self, id, key, value)

    def check(self, par, method):
        return _training16.Input_check(self, par, method)
Input_swigregister = _training16.Input_swigregister
Input_swigregister(Input)

class Topology(daal.Base):
    __swig_setmethods__ = {}
    for _s in [daal.Base]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Topology, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.Base]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Topology, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _training16.new_Topology(*args)
        try:
            self.this.append(this)
        except:
            self.this = this

    def size(self):
        return _training16.Topology_size(self)

    def push_back(self, layer):
        return _training16.Topology_push_back(self, layer)

    def add(self, *args):
        return _training16.Topology_add(self, *args)

    def clear(self):
        return _training16.Topology_clear(self)

    def get(self, *args):
        return _training16.Topology_get(self, *args)

    def addNext(self, index, next):
        return _training16.Topology_addNext(self, index, next)

    def __getitem__(self, i):
        return _training16.Topology___getitem__(self, i)

    def __setitem__(self, i, v):
        return _training16.Topology___setitem__(self, i, v)
    __swig_destroy__ = _training16.delete_Topology
    __del__ = lambda self: None
Topology_swigregister = _training16.Topology_swigregister
Topology_swigregister(Topology)

class Parameter(daal.algorithms.Parameter):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.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.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 = _training16.new_Parameter(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_setmethods__["optimizationSolver"] = _training16.Parameter_optimizationSolver_set
    __swig_getmethods__["optimizationSolver"] = _training16.Parameter_optimizationSolver_get
    if _newclass:
        optimizationSolver = _swig_property(_training16.Parameter_optimizationSolver_get, _training16.Parameter_optimizationSolver_set)
    __swig_setmethods__["engine"] = _training16.Parameter_engine_set
    __swig_getmethods__["engine"] = _training16.Parameter_engine_get
    if _newclass:
        engine = _swig_property(_training16.Parameter_engine_get, _training16.Parameter_engine_set)
    __swig_destroy__ = _training16.delete_Parameter
    __del__ = lambda self: None
Parameter_swigregister = _training16.Parameter_swigregister
Parameter_swigregister(Parameter)

class Model(daal.algorithms.neural_networks.ModelImpl):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.neural_networks.ModelImpl]:
        __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.neural_networks.ModelImpl]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Model, name)
    __repr__ = _swig_repr
    __swig_getmethods__["serializationTag"] = lambda x: _training16.Model_serializationTag
    if _newclass:
        serializationTag = staticmethod(_training16.Model_serializationTag)

    def getSerializationTag(self):
        return _training16.Model_getSerializationTag(self)
    __swig_getmethods__["create"] = lambda x: _training16.Model_create
    if _newclass:
        create = staticmethod(_training16.Model_create)

    def __init__(self, *args):
        this = _training16.new_Model(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Model
    __del__ = lambda self: None

    def getForwardLayers(self):
        return _training16.Model_getForwardLayers(self)

    def getForwardLayer(self, index):
        return _training16.Model_getForwardLayer(self, index)

    def getBackwardLayers(self):
        return _training16.Model_getBackwardLayers(self)

    def getBackwardLayer(self, index):
        return _training16.Model_getBackwardLayer(self, index)

    def getWeightsAndBiasesStorageStatus(self):
        return _training16.Model_getWeightsAndBiasesStorageStatus(self)

    def setWeightsAndBiases(self, *args):
        return _training16.Model_setWeightsAndBiases(self, *args)

    def getWeightsAndBiases(self, *args):
        return _training16.Model_getWeightsAndBiases(self, *args)

    def getWeightsAndBiasesDerivatives(self, *args):
        return _training16.Model_getWeightsAndBiasesDerivatives(self, *args)

    def setErrors(self, errors):
        return _training16.Model_setErrors(self, errors)

    def getErrors(self):
        return _training16.Model_getErrors(self)

    def getSolverOptionalArgument(self, index):
        return _training16.Model_getSolverOptionalArgument(self, index)

    def setSolverOptionalArgument(self, solverOptionalArgument, index):
        return _training16.Model_setSolverOptionalArgument(self, solverOptionalArgument, index)

    def getSolverOptionalArgumentCollection(self):
        return _training16.Model_getSolverOptionalArgumentCollection(self)

    def setSolverOptionalArgumentCollection(self, solverOptionalArgumentCollection):
        return _training16.Model_setSolverOptionalArgumentCollection(self, solverOptionalArgumentCollection)

    def allocate_Float64(self, *args):
        r"""
This function is specialized for float64"""
        return _training16.Model_allocate_Float64(self, *args)


    def allocate_Float32(self, *args):
        r"""
This function is specialized for float32"""
        return _training16.Model_allocate_Float32(self, *args)


    def getPredictionModel_Float64(self):
        r"""
This function is specialized for float64"""
        return _training16.Model_getPredictionModel_Float64(self)


    def getPredictionModel_Float32(self):
        r"""
This function is specialized for float32"""
        return _training16.Model_getPredictionModel_Float32(self)


    def initialize_Float64(self, *args):
        r"""
This function is specialized for float64"""
        return _training16.Model_initialize_Float64(self, *args)


    def initialize_Float32(self, *args):
        r"""
This function is specialized for float32"""
        return _training16.Model_initialize_Float32(self, *args)

Model_swigregister = _training16.Model_swigregister
Model_swigregister(Model)

def Model_serializationTag():
    return _training16.Model_serializationTag()
Model_serializationTag = _training16.Model_serializationTag

def Model_create(stat=None):
    return _training16.Model_create(stat)
Model_create = _training16.Model_create


_training16.derivatives_swigconstant(_training16)
derivatives = _training16.derivatives

_training16.batchSize_swigconstant(_training16)
batchSize = _training16.batchSize

_training16.lastStep1LocalPartialResultId_swigconstant(_training16)
lastStep1LocalPartialResultId = _training16.lastStep1LocalPartialResultId

_training16.resultFromMaster_swigconstant(_training16)
resultFromMaster = _training16.resultFromMaster

_training16.lastStep2MasterPartialResultId_swigconstant(_training16)
lastStep2MasterPartialResultId = _training16.lastStep2MasterPartialResultId
class PartialResult(daal.algorithms.PartialResult):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.PartialResult]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, PartialResult, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.PartialResult]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, PartialResult, name)
    __repr__ = _swig_repr
    __swig_getmethods__["serializationTag"] = lambda x: _training16.PartialResult_serializationTag
    if _newclass:
        serializationTag = staticmethod(_training16.PartialResult_serializationTag)

    def getSerializationTag(self):
        return _training16.PartialResult_getSerializationTag(self)

    def __init__(self):
        this = _training16.new_PartialResult()
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_PartialResult
    __del__ = lambda self: None

    def get(self, id):
        return _training16.PartialResult_get(self, id)

    def set(self, id, value):
        return _training16.PartialResult_set(self, id, value)

    def check(self, input, par, method):
        return _training16.PartialResult_check(self, input, par, method)

    def allocate_Float64(self, input, parameter, method):
        r"""
This function is specialized for float64"""
        return _training16.PartialResult_allocate_Float64(self, input, parameter, method)


    def allocate_Float32(self, input, parameter, method):
        r"""
This function is specialized for float32"""
        return _training16.PartialResult_allocate_Float32(self, input, parameter, method)

PartialResult_swigregister = _training16.PartialResult_swigregister
PartialResult_swigregister(PartialResult)

def PartialResult_serializationTag():
    return _training16.PartialResult_serializationTag()
PartialResult_serializationTag = _training16.PartialResult_serializationTag

class DistributedPartialResult(daal.algorithms.PartialResult):
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.PartialResult]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, DistributedPartialResult, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.PartialResult]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, DistributedPartialResult, name)
    __repr__ = _swig_repr
    __swig_getmethods__["serializationTag"] = lambda x: _training16.DistributedPartialResult_serializationTag
    if _newclass:
        serializationTag = staticmethod(_training16.DistributedPartialResult_serializationTag)

    def getSerializationTag(self):
        return _training16.DistributedPartialResult_getSerializationTag(self)

    def __init__(self):
        this = _training16.new_DistributedPartialResult()
        try:
            self.this.append(this)
        except:
            self.this = this

    def get(self, id):
        return _training16.DistributedPartialResult_get(self, id)

    def set(self, id, value):
        return _training16.DistributedPartialResult_set(self, id, value)

    def check(self, input, par, method):
        return _training16.DistributedPartialResult_check(self, input, par, method)

    def allocate_Float64(self, input, parameter, method):
        r"""
This function is specialized for float64"""
        return _training16.DistributedPartialResult_allocate_Float64(self, input, parameter, method)


    def allocate_Float32(self, input, parameter, method):
        r"""
This function is specialized for float32"""
        return _training16.DistributedPartialResult_allocate_Float32(self, input, parameter, method)

    __swig_destroy__ = _training16.delete_DistributedPartialResult
    __del__ = lambda self: None
DistributedPartialResult_swigregister = _training16.DistributedPartialResult_swigregister
DistributedPartialResult_swigregister(DistributedPartialResult)

def DistributedPartialResult_serializationTag():
    return _training16.DistributedPartialResult_serializationTag()
DistributedPartialResult_serializationTag = _training16.DistributedPartialResult_serializationTag

class DistributedInput_Step1Local(Input):
    r"""
This class is an alias of DistributedInput(step=daal.step1Local)
    """
    __swig_setmethods__ = {}
    for _s in [Input]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, DistributedInput_Step1Local, name, value)
    __swig_getmethods__ = {}
    for _s in [Input]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, DistributedInput_Step1Local, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _training16.new_DistributedInput_Step1Local(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_DistributedInput_Step1Local
    __del__ = lambda self: None

    def getStep1LocalInput(self, id):
        return _training16.DistributedInput_Step1Local_getStep1LocalInput(self, id)

    def setStep1LocalInput(self, id, value):
        return _training16.DistributedInput_Step1Local_setStep1LocalInput(self, id, value)

    def check(self, par, method):
        return _training16.DistributedInput_Step1Local_check(self, par, method)
DistributedInput_Step1Local_swigregister = _training16.DistributedInput_Step1Local_swigregister
DistributedInput_Step1Local_swigregister(DistributedInput_Step1Local)

class DistributedInput_Step2Master(daal.algorithms.Input):
    r"""
This class is an alias of DistributedInput(step=daal.step2Master)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Input]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, DistributedInput_Step2Master, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Input]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, DistributedInput_Step2Master, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _training16.new_DistributedInput_Step2Master(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_DistributedInput_Step2Master
    __del__ = lambda self: None

    def getStep2MasterInput(self, id):
        return _training16.DistributedInput_Step2Master_getStep2MasterInput(self, id)

    def setStep2MasterInput(self, id, value):
        return _training16.DistributedInput_Step2Master_setStep2MasterInput(self, id, value)

    def add(self, id, key, value):
        return _training16.DistributedInput_Step2Master_add(self, id, key, value)

    def check(self, par, method):
        return _training16.DistributedInput_Step2Master_check(self, par, method)
DistributedInput_Step2Master_swigregister = _training16.DistributedInput_Step2Master_swigregister
DistributedInput_Step2Master_swigregister(DistributedInput_Step2Master)

class Batch_Float64FeedforwardDense(daal.algorithms.Training_Batch):
    r"""
This class is an alias of Batch(fptype=float64, method=daal.algorithms.neural_networks.training.feedforwardDense)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Training_Batch]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Batch_Float64FeedforwardDense, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Training_Batch]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Batch_Float64FeedforwardDense, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _training16.new_Batch_Float64FeedforwardDense(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Batch_Float64FeedforwardDense
    __del__ = lambda self: None

    def initialize(self, sampleSize, topology):
        return _training16.Batch_Float64FeedforwardDense_initialize(self, sampleSize, topology)

    def getResult(self):
        return _training16.Batch_Float64FeedforwardDense_getResult(self)

    def setResult(self, res):
        return _training16.Batch_Float64FeedforwardDense_setResult(self, res)

    def clone(self):
        return _training16.Batch_Float64FeedforwardDense_clone(self)

    def getMethod(self):
        return _training16.Batch_Float64FeedforwardDense_getMethod(self)
    __swig_setmethods__["input"] = _training16.Batch_Float64FeedforwardDense_input_set
    __swig_getmethods__["input"] = _training16.Batch_Float64FeedforwardDense_input_get
    if _newclass:
        input = _swig_property(_training16.Batch_Float64FeedforwardDense_input_get, _training16.Batch_Float64FeedforwardDense_input_set)
    __swig_setmethods__["parameter"] = _training16.Batch_Float64FeedforwardDense_parameter_set
    __swig_getmethods__["parameter"] = _training16.Batch_Float64FeedforwardDense_parameter_get
    if _newclass:
        parameter = _swig_property(_training16.Batch_Float64FeedforwardDense_parameter_get, _training16.Batch_Float64FeedforwardDense_parameter_set)

    def compute(self):
        return _training16.Batch_Float64FeedforwardDense_compute(self)
Batch_Float64FeedforwardDense_swigregister = _training16.Batch_Float64FeedforwardDense_swigregister
Batch_Float64FeedforwardDense_swigregister(Batch_Float64FeedforwardDense)

class Batch_Float32FeedforwardDense(daal.algorithms.Training_Batch):
    r"""
This class is an alias of Batch(fptype=float32, method=daal.algorithms.neural_networks.training.feedforwardDense)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Training_Batch]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Batch_Float32FeedforwardDense, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Training_Batch]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Batch_Float32FeedforwardDense, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _training16.new_Batch_Float32FeedforwardDense(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Batch_Float32FeedforwardDense
    __del__ = lambda self: None

    def initialize(self, sampleSize, topology):
        return _training16.Batch_Float32FeedforwardDense_initialize(self, sampleSize, topology)

    def getResult(self):
        return _training16.Batch_Float32FeedforwardDense_getResult(self)

    def setResult(self, res):
        return _training16.Batch_Float32FeedforwardDense_setResult(self, res)

    def clone(self):
        return _training16.Batch_Float32FeedforwardDense_clone(self)

    def getMethod(self):
        return _training16.Batch_Float32FeedforwardDense_getMethod(self)
    __swig_setmethods__["input"] = _training16.Batch_Float32FeedforwardDense_input_set
    __swig_getmethods__["input"] = _training16.Batch_Float32FeedforwardDense_input_get
    if _newclass:
        input = _swig_property(_training16.Batch_Float32FeedforwardDense_input_get, _training16.Batch_Float32FeedforwardDense_input_set)
    __swig_setmethods__["parameter"] = _training16.Batch_Float32FeedforwardDense_parameter_set
    __swig_getmethods__["parameter"] = _training16.Batch_Float32FeedforwardDense_parameter_get
    if _newclass:
        parameter = _swig_property(_training16.Batch_Float32FeedforwardDense_parameter_get, _training16.Batch_Float32FeedforwardDense_parameter_set)

    def compute(self):
        return _training16.Batch_Float32FeedforwardDense_compute(self)
Batch_Float32FeedforwardDense_swigregister = _training16.Batch_Float32FeedforwardDense_swigregister
Batch_Float32FeedforwardDense_swigregister(Batch_Float32FeedforwardDense)

class Distributed_Step1LocalFloat64FeedforwardDense(daal.algorithms.Training_Distributed):
    r"""
This class is an alias of Distributed(step=daal.step1Local, fptype=float64, method=daal.algorithms.neural_networks.training.feedforwardDense)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Distributed_Step1LocalFloat64FeedforwardDense, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Distributed_Step1LocalFloat64FeedforwardDense, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _training16.Distributed_Step1LocalFloat64FeedforwardDense_input_set
    __swig_getmethods__["input"] = _training16.Distributed_Step1LocalFloat64FeedforwardDense_input_get
    if _newclass:
        input = _swig_property(_training16.Distributed_Step1LocalFloat64FeedforwardDense_input_get, _training16.Distributed_Step1LocalFloat64FeedforwardDense_input_set)
    __swig_setmethods__["parameter"] = _training16.Distributed_Step1LocalFloat64FeedforwardDense_parameter_set
    __swig_getmethods__["parameter"] = _training16.Distributed_Step1LocalFloat64FeedforwardDense_parameter_get
    if _newclass:
        parameter = _swig_property(_training16.Distributed_Step1LocalFloat64FeedforwardDense_parameter_get, _training16.Distributed_Step1LocalFloat64FeedforwardDense_parameter_set)

    def __init__(self, *args):
        this = _training16.new_Distributed_Step1LocalFloat64FeedforwardDense(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Distributed_Step1LocalFloat64FeedforwardDense
    __del__ = lambda self: None

    def setPartialResult(self, partialResult):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_setPartialResult(self, partialResult)

    def getPartialResult(self):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_getPartialResult(self)

    def getResult(self):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_getResult(self)

    def setResult(self, res):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_setResult(self, res)

    def clone(self):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_clone(self)

    def getMethod(self):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_getMethod(self)

    def compute(self):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_compute(self)

    def finalizeCompute(self):
        return _training16.Distributed_Step1LocalFloat64FeedforwardDense_finalizeCompute(self)
Distributed_Step1LocalFloat64FeedforwardDense_swigregister = _training16.Distributed_Step1LocalFloat64FeedforwardDense_swigregister
Distributed_Step1LocalFloat64FeedforwardDense_swigregister(Distributed_Step1LocalFloat64FeedforwardDense)

class Distributed_Step1LocalFloat32FeedforwardDense(daal.algorithms.Training_Distributed):
    r"""
This class is an alias of Distributed(step=daal.step1Local, fptype=float32, method=daal.algorithms.neural_networks.training.feedforwardDense)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Distributed_Step1LocalFloat32FeedforwardDense, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Distributed_Step1LocalFloat32FeedforwardDense, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _training16.Distributed_Step1LocalFloat32FeedforwardDense_input_set
    __swig_getmethods__["input"] = _training16.Distributed_Step1LocalFloat32FeedforwardDense_input_get
    if _newclass:
        input = _swig_property(_training16.Distributed_Step1LocalFloat32FeedforwardDense_input_get, _training16.Distributed_Step1LocalFloat32FeedforwardDense_input_set)
    __swig_setmethods__["parameter"] = _training16.Distributed_Step1LocalFloat32FeedforwardDense_parameter_set
    __swig_getmethods__["parameter"] = _training16.Distributed_Step1LocalFloat32FeedforwardDense_parameter_get
    if _newclass:
        parameter = _swig_property(_training16.Distributed_Step1LocalFloat32FeedforwardDense_parameter_get, _training16.Distributed_Step1LocalFloat32FeedforwardDense_parameter_set)

    def __init__(self, *args):
        this = _training16.new_Distributed_Step1LocalFloat32FeedforwardDense(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Distributed_Step1LocalFloat32FeedforwardDense
    __del__ = lambda self: None

    def setPartialResult(self, partialResult):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_setPartialResult(self, partialResult)

    def getPartialResult(self):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_getPartialResult(self)

    def getResult(self):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_getResult(self)

    def setResult(self, res):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_setResult(self, res)

    def clone(self):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_clone(self)

    def getMethod(self):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_getMethod(self)

    def compute(self):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_compute(self)

    def finalizeCompute(self):
        return _training16.Distributed_Step1LocalFloat32FeedforwardDense_finalizeCompute(self)
Distributed_Step1LocalFloat32FeedforwardDense_swigregister = _training16.Distributed_Step1LocalFloat32FeedforwardDense_swigregister
Distributed_Step1LocalFloat32FeedforwardDense_swigregister(Distributed_Step1LocalFloat32FeedforwardDense)

class Distributed_Step2MasterFloat64FeedforwardDense(daal.algorithms.Training_Distributed):
    r"""
This class is an alias of Distributed(step=daal.step2Master, fptype=float64, method=daal.algorithms.neural_networks.training.feedforwardDense)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Distributed_Step2MasterFloat64FeedforwardDense, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Distributed_Step2MasterFloat64FeedforwardDense, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _training16.Distributed_Step2MasterFloat64FeedforwardDense_input_set
    __swig_getmethods__["input"] = _training16.Distributed_Step2MasterFloat64FeedforwardDense_input_get
    if _newclass:
        input = _swig_property(_training16.Distributed_Step2MasterFloat64FeedforwardDense_input_get, _training16.Distributed_Step2MasterFloat64FeedforwardDense_input_set)
    __swig_setmethods__["parameter"] = _training16.Distributed_Step2MasterFloat64FeedforwardDense_parameter_set
    __swig_getmethods__["parameter"] = _training16.Distributed_Step2MasterFloat64FeedforwardDense_parameter_get
    if _newclass:
        parameter = _swig_property(_training16.Distributed_Step2MasterFloat64FeedforwardDense_parameter_get, _training16.Distributed_Step2MasterFloat64FeedforwardDense_parameter_set)

    def __init__(self, *args):
        this = _training16.new_Distributed_Step2MasterFloat64FeedforwardDense(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Distributed_Step2MasterFloat64FeedforwardDense
    __del__ = lambda self: None

    def initialize(self, dataSize, topology):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_initialize(self, dataSize, topology)

    def setPartialResult(self, partialResult):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_setPartialResult(self, partialResult)

    def getPartialResult(self):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_getPartialResult(self)

    def getResult(self):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_getResult(self)

    def clone(self):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_clone(self)

    def getMethod(self):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_getMethod(self)

    def compute(self):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_compute(self)

    def finalizeCompute(self):
        return _training16.Distributed_Step2MasterFloat64FeedforwardDense_finalizeCompute(self)
Distributed_Step2MasterFloat64FeedforwardDense_swigregister = _training16.Distributed_Step2MasterFloat64FeedforwardDense_swigregister
Distributed_Step2MasterFloat64FeedforwardDense_swigregister(Distributed_Step2MasterFloat64FeedforwardDense)

class Distributed_Step2MasterFloat32FeedforwardDense(daal.algorithms.Training_Distributed):
    r"""
This class is an alias of Distributed(step=daal.step2Master, fptype=float32, method=daal.algorithms.neural_networks.training.feedforwardDense)
    """
    __swig_setmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, Distributed_Step2MasterFloat32FeedforwardDense, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.algorithms.Training_Distributed]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, Distributed_Step2MasterFloat32FeedforwardDense, name)
    __repr__ = _swig_repr
    __swig_setmethods__["input"] = _training16.Distributed_Step2MasterFloat32FeedforwardDense_input_set
    __swig_getmethods__["input"] = _training16.Distributed_Step2MasterFloat32FeedforwardDense_input_get
    if _newclass:
        input = _swig_property(_training16.Distributed_Step2MasterFloat32FeedforwardDense_input_get, _training16.Distributed_Step2MasterFloat32FeedforwardDense_input_set)
    __swig_setmethods__["parameter"] = _training16.Distributed_Step2MasterFloat32FeedforwardDense_parameter_set
    __swig_getmethods__["parameter"] = _training16.Distributed_Step2MasterFloat32FeedforwardDense_parameter_get
    if _newclass:
        parameter = _swig_property(_training16.Distributed_Step2MasterFloat32FeedforwardDense_parameter_get, _training16.Distributed_Step2MasterFloat32FeedforwardDense_parameter_set)

    def __init__(self, *args):
        this = _training16.new_Distributed_Step2MasterFloat32FeedforwardDense(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _training16.delete_Distributed_Step2MasterFloat32FeedforwardDense
    __del__ = lambda self: None

    def initialize(self, dataSize, topology):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_initialize(self, dataSize, topology)

    def setPartialResult(self, partialResult):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_setPartialResult(self, partialResult)

    def getPartialResult(self):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_getPartialResult(self)

    def getResult(self):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_getResult(self)

    def clone(self):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_clone(self)

    def getMethod(self):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_getMethod(self)

    def compute(self):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_compute(self)

    def finalizeCompute(self):
        return _training16.Distributed_Step2MasterFloat32FeedforwardDense_finalizeCompute(self)
Distributed_Step2MasterFloat32FeedforwardDense_swigregister = _training16.Distributed_Step2MasterFloat32FeedforwardDense_swigregister
Distributed_Step2MasterFloat32FeedforwardDense_swigregister(Distributed_Step2MasterFloat32FeedforwardDense)

from numpy import float64, float32, intc

class DistributedInput(object):
    r"""Factory class for different types of DistributedInput."""
    def __new__(cls,
                step,
                *args, **kwargs):
        if step == daal.step1Local:
            return DistributedInput_Step1Local(*args)
        if step == daal.step2Master:
            return DistributedInput_Step2Master(*args)

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

class Distributed(object):
    r"""Factory class for different types of Distributed."""
    def __new__(cls,
                step,
                *args, **kwargs):
        if step == daal.step1Local:
            if 'fptype' in kwargs and kwargs['fptype'] == float64:
                    if 'method' not in kwargs or kwargs['method'] == feedforwardDense:
                        return Distributed_Step1LocalFloat64FeedforwardDense(*args)
            if 'fptype' not in kwargs or kwargs['fptype'] == float32:
                    if 'method' not in kwargs or kwargs['method'] == feedforwardDense:
                        return Distributed_Step1LocalFloat32FeedforwardDense(*args)
        if step == daal.step2Master:
            if 'fptype' in kwargs and kwargs['fptype'] == float64:
                    if 'method' not in kwargs or kwargs['method'] == feedforwardDense:
                        return Distributed_Step2MasterFloat64FeedforwardDense(*args)
            if 'fptype' not in kwargs or kwargs['fptype'] == float32:
                    if 'method' not in kwargs or kwargs['method'] == feedforwardDense:
                        return Distributed_Step2MasterFloat32FeedforwardDense(*args)

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

class Batch(object):
    r"""Factory class for different types of Batch."""
    def __new__(cls,
                *args, **kwargs):
        if 'fptype' in kwargs and kwargs['fptype'] == float64:
            if 'method' not in kwargs or kwargs['method'] == feedforwardDense:
                return Batch_Float64FeedforwardDense(*args)
        if 'fptype' not in kwargs or kwargs['fptype'] == float32:
            if 'method' not in kwargs or kwargs['method'] == feedforwardDense:
                return Batch_Float32FeedforwardDense(*args)

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


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


