# 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.
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# 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('_neural_networks', [dirname(__file__)])
        except ImportError:
            import _neural_networks
            return _neural_networks
        if fp is not None:
            try:
                _mod = imp.load_module('_neural_networks', fp, pathname, description)
            finally:
                fp.close()
            return _mod
    _neural_networks = swig_import_helper()
    del swig_import_helper
else:
    import _neural_networks
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
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
class LearnableParametersIface(daal.data_management.SerializationIface):
    __swig_setmethods__ = {}
    for _s in [daal.data_management.SerializationIface]:
        __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {}))
    __setattr__ = lambda self, name, value: _swig_setattr(self, LearnableParametersIface, name, value)
    __swig_getmethods__ = {}
    for _s in [daal.data_management.SerializationIface]:
        __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {}))
    __getattr__ = lambda self, name: _swig_getattr(self, LearnableParametersIface, name)

    def __init__(self, *args, **kwargs):
        raise AttributeError("No constructor defined - class is abstract")
    __repr__ = _swig_repr
    __swig_destroy__ = _neural_networks.delete_LearnableParametersIface
    __del__ = lambda self: None

    def copyToTable(self, *args):
        return _neural_networks.LearnableParametersIface_copyToTable(self, *args)

    def copyFromTable(self, *args):
        return _neural_networks.LearnableParametersIface_copyFromTable(self, *args)
LearnableParametersIface_swigregister = _neural_networks.LearnableParametersIface_swigregister
LearnableParametersIface_swigregister(LearnableParametersIface)

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

    def __init__(self, *args, **kwargs):
        raise AttributeError("No constructor defined")
    __repr__ = _swig_repr
    __swig_destroy__ = _neural_networks.delete_ModelImpl
    __del__ = lambda self: None

    def getNextLayers(self):
        return _neural_networks.ModelImpl_getNextLayers(self)

    def setWeightsAndBiases(self, weightsAndBiases):
        return _neural_networks.ModelImpl_setWeightsAndBiases(self, weightsAndBiases)

    def getWeightsAndBiases(self):
        return _neural_networks.ModelImpl_getWeightsAndBiases(self)
ModelImpl_swigregister = _neural_networks.ModelImpl_swigregister
ModelImpl_swigregister(ModelImpl)

class ForwardLayers(_object):
    r"""
 <a name='DAAL-CLASS-ALGORITHMS__NEURAL_NETWORKS__FORWARDLAYERS'></a>
 \brief Represents a collection of forward stages of neural network layers"""
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, ForwardLayers, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, ForwardLayers, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _neural_networks.new_ForwardLayers(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _neural_networks.delete_ForwardLayers
    __del__ = lambda self: None

    def size(self):
        return _neural_networks.ForwardLayers_size(self)

    def capacity(self):
        return _neural_networks.ForwardLayers_capacity(self)

    def get(self, *args):
        return _neural_networks.ForwardLayers_get(self, *args)

    def push_back(self, x):
        return _neural_networks.ForwardLayers_push_back(self, x)

    def __lshift__(self, x):
        return _neural_networks.ForwardLayers___lshift__(self, x)

    def resize(self, newCapacity):
        return _neural_networks.ForwardLayers_resize(self, newCapacity)

    def clear(self):
        return _neural_networks.ForwardLayers_clear(self)

    def insert(self, *args):
        return _neural_networks.ForwardLayers_insert(self, *args)

    def erase(self, pos):
        return _neural_networks.ForwardLayers_erase(self, pos)

    def __getitem__(self, i):
        return _neural_networks.ForwardLayers___getitem__(self, i)

    def __setitem__(self, i, v):
        return _neural_networks.ForwardLayers___setitem__(self, i, v)
ForwardLayers_swigregister = _neural_networks.ForwardLayers_swigregister
ForwardLayers_swigregister(ForwardLayers)

class BackwardLayers(_object):
    r"""
 <a name='DAAL-CLASS-ALGORITHMS__NEURAL_NETWORKS__BACKWARDLAYERS'></a>
 \brief Represents a collection of backward stages of neural network layers"""
    __swig_setmethods__ = {}
    __setattr__ = lambda self, name, value: _swig_setattr(self, BackwardLayers, name, value)
    __swig_getmethods__ = {}
    __getattr__ = lambda self, name: _swig_getattr(self, BackwardLayers, name)
    __repr__ = _swig_repr

    def __init__(self, *args):
        this = _neural_networks.new_BackwardLayers(*args)
        try:
            self.this.append(this)
        except:
            self.this = this
    __swig_destroy__ = _neural_networks.delete_BackwardLayers
    __del__ = lambda self: None

    def size(self):
        return _neural_networks.BackwardLayers_size(self)

    def capacity(self):
        return _neural_networks.BackwardLayers_capacity(self)

    def get(self, *args):
        return _neural_networks.BackwardLayers_get(self, *args)

    def push_back(self, x):
        return _neural_networks.BackwardLayers_push_back(self, x)

    def __lshift__(self, x):
        return _neural_networks.BackwardLayers___lshift__(self, x)

    def resize(self, newCapacity):
        return _neural_networks.BackwardLayers_resize(self, newCapacity)

    def clear(self):
        return _neural_networks.BackwardLayers_clear(self)

    def insert(self, *args):
        return _neural_networks.BackwardLayers_insert(self, *args)

    def erase(self, pos):
        return _neural_networks.BackwardLayers_erase(self, pos)

    def __getitem__(self, i):
        return _neural_networks.BackwardLayers___getitem__(self, i)

    def __setitem__(self, i, v):
        return _neural_networks.BackwardLayers___setitem__(self, i, v)
BackwardLayers_swigregister = _neural_networks.BackwardLayers_swigregister
BackwardLayers_swigregister(BackwardLayers)

from numpy import float64, float32, intc


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


