# Path: csc/divisi/tensor.py
# class View(Tensor):
#     """
#     A *view* is a wrapper around a tensor (always ``self.tensor``)
#     that performs some operations (usually ``__getitem__`` and/or
#     ``__setitem__``) differently.
# 
#     For almost all purposes, it acts like a ``Tensor`` itself. Unknown
#     methods are passed through to the underlying ``Tensor``.
# 
#     This is just the abstract base class. Some useful ``View``s
#     include :class:`csc.divisi.labeled_view.LabeledView` and
#     :class:`csc.divisi.labeled_view.NormalizedView`.
#     """
# 
#     def __init__(self, tensor):
#         """
#         Create a new View wrapping a Tensor.
#         """
#         self.tensor = tensor
#         self.ndim = tensor.ndim
# 
#     def __repr__(self):
#         return '<View of %s>' % repr(self.tensor)
# 
#     def __getattr__(self, name):
#         '''Fall back to the tensor operation.'''
#         if name in forward_to_tensor:
#             attr = getattr(self.tensor, name)
#             #setattr(self, name, attr)
#             return attr
#         else:
#             raise AttributeError(name)
# 
#     def __iter__(self):
#         return self.tensor.__iter__()
# 
#     def __len__(self):
#         return len(self.tensor)
# 
#     def add_layer(self, cls, *a, **kw):
#         """
#         Given a class of View, determine where it should go in the stack
#         and add it as a layer there.
#         """
#         try:
#             return cls(self, *a, **kw)
#         except InvalidLayeringException:
#             # Layer it below here.
#             return self.layer_on(self.tensor.add_layer(cls, *a, **kw))
# 
#     def layer_on(self, tensor):
#         # Just make a copy of the same class viewing the new tensor.
#         # Subclasses should override this.
#         return self.__class__(tensor)
# 
#     def unwrap(self):
#         """
#         Get the object being wrapped by this View. This may be a
#         :class:`Tensor`,
#         or another :class:`View`.
# 
#         Essentially, this removes the outermost View layer, so that you can
#         get at what's inside.
#         """
#         return self.tensor
# 
# Path: csc/divisi/exceptions.py
# class InvalidLayeringException(Error):
#     pass


from csc.divisi.tensor import View
from csc.divisi.exceptions import InvalidLayeringException
from numpy import zeros
from math import sqrt, log
import numpy as np
        import inspect
EPSILON = 0.000000001   # for floating point comparisons


class BaseNormalizedView(View):
    '''
    The base class for normalized views -- views that scale the values in a
    tensor in some way.
    '''
    # Specify that this view maps numeric labels to (the same) numeric labels.
    input_is_numeric = True
    output_is_numeric = True

    # Default to normalizing on mode 0.
    default_modes = 0

    def __init__(self, tensor, mode=None):
        '''
        * tensor: tensor to view.
        * mode: which mode to normalize over (0 = rows, 1 = columns, etc.)
        '''
        if len(tensor) == 0:
            raise ValueError('Tensor ' + repr(tensor) + ' is empty.')

        # If this isn't a MeanSubtractedView, the underlying tensor can't be a normalized view.
        if not isinstance(self, MeanSubtractedView) and tensor.stack_contains(BaseNormalizedView):
            raise TypeError('Already normalized; .unnormalized() first.')

        # The underlying tensor must be numeric.
        for part in tensor.example_key():
            if not isinstance(part, (int, long)):

# Path: csc/divisi/tensor.py
# class View(Tensor):
#     """
#     A *view* is a wrapper around a tensor (always ``self.tensor``)
#     that performs some operations (usually ``__getitem__`` and/or
#     ``__setitem__``) differently.
# 
#     For almost all purposes, it acts like a ``Tensor`` itself. Unknown
#     methods are passed through to the underlying ``Tensor``.
# 
#     This is just the abstract base class. Some useful ``View``s
#     include :class:`csc.divisi.labeled_view.LabeledView` and
#     :class:`csc.divisi.labeled_view.NormalizedView`.
#     """
# 
#     def __init__(self, tensor):
#         """
#         Create a new View wrapping a Tensor.
#         """
#         self.tensor = tensor
#         self.ndim = tensor.ndim
# 
#     def __repr__(self):
#         return '<View of %s>' % repr(self.tensor)
# 
#     def __getattr__(self, name):
#         '''Fall back to the tensor operation.'''
#         if name in forward_to_tensor:
#             attr = getattr(self.tensor, name)
#             #setattr(self, name, attr)
#             return attr
#         else:
#             raise AttributeError(name)
# 
#     def __iter__(self):
#         return self.tensor.__iter__()
# 
#     def __len__(self):
#         return len(self.tensor)
# 
#     def add_layer(self, cls, *a, **kw):
#         """
#         Given a class of View, determine where it should go in the stack
#         and add it as a layer there.
#         """
#         try:
#             return cls(self, *a, **kw)
#         except InvalidLayeringException:
#             # Layer it below here.
#             return self.layer_on(self.tensor.add_layer(cls, *a, **kw))
# 
#     def layer_on(self, tensor):
#         # Just make a copy of the same class viewing the new tensor.
#         # Subclasses should override this.
#         return self.__class__(tensor)
# 
#     def unwrap(self):
#         """
#         Get the object being wrapped by this View. This may be a
#         :class:`Tensor`,
#         or another :class:`View`.
# 
#         Essentially, this removes the outermost View layer, so that you can
#         get at what's inside.
#         """
#         return self.tensor
# 
# Path: csc/divisi/exceptions.py
# class InvalidLayeringException(Error):
#     pass


# Path: csc/divisi/normalized_view.py
from csc.divisi.tensor import View
from csc.divisi.exceptions import InvalidLayeringException
from numpy import zeros
from math import sqrt, log
import numpy as np
        import inspect

EPSILON = 0.000000001   # for floating point comparisons


class BaseNormalizedView(View):
    '''
    The base class for normalized views -- views that scale the values in a
    tensor in some way.
    '''
    # Specify that this view maps numeric labels to (the same) numeric labels.
    input_is_numeric = True
    output_is_numeric = True

    # Default to normalizing on mode 0.
    default_modes = 0

    def __init__(self, tensor, mode=None):
        '''
        * tensor: tensor to view.
        * mode: which mode to normalize over (0 = rows, 1 = columns, etc.)
        '''
        if len(tensor) == 0:
            raise ValueError('Tensor ' + repr(tensor) + ' is empty.')

        # If this isn't a MeanSubtractedView, the underlying tensor can't be a normalized view.
        if not isinstance(self, MeanSubtractedView) and tensor.stack_contains(BaseNormalizedView):
            raise TypeError('Already normalized; .unnormalized() first.')

        # The underlying tensor must be numeric.
        for part in tensor.example_key():
            if not isinstance(part, (int, long)):

                raise InvalidLayeringException