
.. _numpy:


***************
NumPy refresher
***************

Here are some quick guides to NumPy:
  * `Numpy quick guide for Matlab users <http://www.scipy.org/NumPy_for_Matlab_Users>`__
  * `Numpy User Guide <http://docs.scipy.org/doc/numpy/user/index.html>`__
  * `More detailed Numpy tutorial <http://www.scipy.org/Tentative_NumPy_Tutorial>`__
  * `100 NumPy exercises <https://github.com/rougier/numpy-100>`__
  * `Numpy tutorial <http://www.labri.fr/perso/nrougier/teaching/numpy/numpy.html>`__

    .. [TODO: More doc, e.g. see _test_tensor.py]


Matrix conventions for machine learning
=======================================


Rows are horizontal and columns are vertical.
Every row is an example. Therefore, inputs[10,5] is a matrix of 10 examples 
where each example has dimension 5. If this would be the input of a
neural network then the weights from the input to the first hidden
layer would represent a matrix of size (5, #hid). 

Consider this array:

>>> numpy.asarray([[1., 2], [3, 4], [5, 6]])
array([[ 1.,  2.],
       [ 3.,  4.],
       [ 5.,  6.]])
>>> numpy.asarray([[1., 2], [3, 4], [5, 6]]).shape
(3, 2)

This is a 3x2 matrix, i.e. there are 3 rows and 2 columns.

To access the entry in the 3rd row (row #2) and the 1st column (column #0):

>>> numpy.asarray([[1., 2], [3, 4], [5, 6]])[2, 0]
5.0


To remember this, keep in mind that we read left-to-right, top-to-bottom,
so each thing that is contiguous is a row.  That is, there are 3 rows
and 2 columns.

Broadcasting
============

Numpy does *broadcasting* of arrays of different shapes during
arithmetic operations. What this means in general is that the smaller 
array (or scalar) is *broadcasted* across the larger array so that they have
compatible shapes. The example below shows an instance of
*broadcastaing*:

>>> a = numpy.asarray([1.0, 2.0, 3.0])
>>> b = 2.0
>>> a * b
array([2., 4., 6.])

The smaller array ``b`` (actually a scalar here, which works like a 0-d array) in this case is *broadcasted* to the same size
as ``a`` during the multiplication. This trick is often useful in
simplifying how expression are written. More detail about *broadcasting*
can be found in the `numpy user guide <http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`__.
