ENH: Add support for Classifiers.

Classifiers are computations that represent grouping keys. They can be
used in conjuction with normalization functions like ``zscore`` or
``demean`` to perform normalizations over subsets of a dataset.

Notable changes:

- Added ``demean()`` and ``zscore()`` methods to ``Factor``.

- Added a classifier versions of ``Latest`` and ``CustomTermMixin``.
  The .latest attribute of int64 dataset columns no produces a
  classifier by default.

- Added ``Everything``, a classifier that maps all data to the same
  value.

- Added ``zipline.lib.normalize``, which implements a naive, pure-Python
  grouped normalize function.  This will likely be moved to Cython in a
  subsequent PR.

tests/pipeline/base.py

@@ -147,6 +147,24 @@ class BasePipelineTestCase(TestCase):
         """
         return arange(prod(shape), dtype=dtype).reshape(shape)
 
+    @with_default_shape
+    def randn_data(self, seed, shape):
+        """
+        Build a block of testing data from a seeded RandomState.
+        """
+        return np.random.RandomState(seed).randn(*shape)
+
+    @with_default_shape
+    def eye_mask(self, shape):
+        """
+        Build a mask using np.eye.
+        """
+        return ~np.eye(*shape, dtype=bool)
+
+    @with_default_shape
+    def ones_mask(self, shape):
+        return np.ones(shape, dtype=bool)
+
 
 class EventLoaderCommonMixin(object):
     @abc.abstractproperty