removed tutorial/tools

doc/tutorial/tools.txt

-
-=============================
-Basic Tutorial Mini-Reference
-=============================
-
-      .. miniref_mode:
-
-Mode
-====
-
-================= =============================================================== ===============================================================================
-short name        Full constructor                                                What does it do?
-================= =============================================================== ===============================================================================
-(default)         ``compile.mode.Mode(linker='py', optimizer=None)``              Python implementations with zero graph modifications.
-FAST_COMPILE      ``compile.mode.Mode(linker='c|py', optimizer='fast_compile')``  C implementations where available, quick and cheap graph transformations
-FAST_RUN          ``compile.mode.Mode(linker='c|py', optimizer='fast_run')``      C implementations where available, all available graph transformations.
-DEBUG_MODE        ``compile.debugmode.DebugMode()``                               Both implementations where available, all available graph transformations.
-================= =============================================================== ===============================================================================
-
-      .. _tensortypes:
-
-Types
-=====
-
-      .. _predefinedtypes:
-
-Predefined types
-----------------
-
-Predefined types are
-located in the :ref:`theano.tensor <libdoc_tensor>` package. The name of the types follow
-a recipe:
-
-``<dtype><dimensionality>``
-
-Where ``<dtype>`` is one of:
-
-==== ======== ============== ====
-code type     domain         bits
-==== ======== ============== ====
-b    byte     signed integer 8
-w    word     signed integer 16
-i    integer  signed integer 32
-l    long     signed integer 64
-f    float    floating point 32
-d    double   floating point 64
-==== ======== ============== ====
-
-Dimensionality is one of:
-
-
-So, if you want a row of 32-bit floats, it is available
-as :ref:`theano.tensor.frow <libdoc_tensor_type>`.
-If you want a matrix of unsigned 32-bit integers it is available as
-:ref:`theano.tensor.imatrix <libdoc_tensor_type>`.
-
-Each of the types described above can be constructed by two methods:
-a singular version (e.g., :ref:`dmatrix <libdoc_tensor_creation>`)
-and a plural version (:ref:`dmatrices <libdoc_tensor_creation>`).
-When called, the singular version takes a single
-argument which is the name of the *Variable* we want to make and it
-makes a single Variable of that type. The plural version can either take
-an integer or several strings. If an integer is provided, the method
-will return that many Variables and if strings are provided, it will
-create one Variable for each string, using the string as the Variable's
-name. For example:
-
-.. code-block:: python
-
-   from theano.tensor import *
-
-   x = dmatrix() # creates one Variable with no name
-   x = dmatrix('x') # creates one Variable with name 'x'
-   xyz = dmatrix('xyz') # creates one Variable with name 'xyz'
-
-   x, y, z = dmatrices(3) # creates three Variables with no names
-   x, y, z = dmatrices('x', 'y', 'z') # creates three Variables named 'x', 'y' and 'z'
-
-
-Custom tensor types
--------------------
-
-If you wish to use a type of tensor which is not already available here
-(for example, a 3D tensor) you can build an appropriate type using
-:ref:`theano.tensor.TensorType <libdoc_tensor_type>`.
-The first argument you pass is the `dtype` and the second is the
-`broadcastable pattern`.
-
-Where `dtype` is one of:
-
-=========== ================ =================
-dtype       domain           bits
-=========== ================ =================
-int8        signed integer   8
-int16       signed integer   16
-int32       signed integer   32
-int64       signed integer   64
-uint8       unsigned integer 8
-uint16      unsigned integer 16
-uint32      unsigned integer 32
-uint64      unsigned integer 64
-float32     floating point   32
-float64     floating point   64
-complex64   complex          64 (two float32)
-complex128  complex          128 (two float64)
-=========== ================ =================
-
-.. note::
-
-   Even though :ref:`theano.tensor <libdoc_tensor>` does not define any type
-   using ``complex`` dtypes (``complex64`` or ``complex128``),
-   you can define them explicitly with
-   :ref:`TensorType <libdoc_tensor_type>` (see example
-   below). However, few operations are fully supported for complex
-   types: as of version 0.1, only elementary operations (``+-*/``)
-   have C implementations. Additionally, complex types have received
-   little testing.
-
-
-The broadcastable pattern indicates both the number of dimensions and
-whether a particular dimension must have length 1.
-Here is a table mapping the :ref:`broadcastable <libdoc_tensor_broadcastable>` pattern to what kind of tensor it encodes:
-
-===================== =================================
-pattern               interpretation
-===================== =================================
-[]                    scalar
-[True]                1D scalar (vector of length 1)
-[True, True]          2D scalar (1x1 matrix)
-[False]               vector
-[False, False]        matrix
-[False] * n           nD tensor
-[True, False]         row (1xN matrix)
-[False, True]         column (Mx1 matrix)
-[False, True, False]  A Mx1xP tensor (a)
-[True, False, False]  A 1xNxP tensor (b)
-[False, False, False] A MxNxP tensor (pattern of a + b)
-===================== =================================
-
-For dimensions in which broadcasting is False, the length of this
-dimension can be 1 or more.  For dimensions in which broadcasting is True,
-the length of this dimension must be 1.
-
-When two tensors have a different number of dimensions, the broadcastable
-pattern is *expanded to the left*, by padding with ``True``. For example,
-a vector's pattern, ``[False]``, could be expanded to ``[True, False]``, and
-would behave like a row (1xN matrix). In the same way, a matrix (``[False,
-False]``) would behave like a 1xNxP tensor (``[True, False, False]``).
-
-If we wanted to create a type representing a 3D array of unsigned
-bytes, we would do:
-
-.. code-block:: python
-
-   # 3D tensor of signed bytes
-   mytype = theano.tensor.TensorType('uint8', [False]*3)
-
-   # complex types (based on complex64)
-   my_cscalar = theano.tensor.TensorType('complex64', [])
-   my_cmatrix = theano.tensor.TensorType('complex64', [False, False])
-
-