Fix for out of bounds error in RBM with odd # of rows

Fixes out of bounds error in RBM with odd numbered matrix rows due to
gen_even_slices generating even slices that overshoot bounds of matrix.

/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/rbm.pyc
in fit(self, X, y)
    310
    311             for batch_slice in batch_slices:
--> 312                 pl_batch = self._fit(X[batch_slice], rng)
    313
    314                 if verbose:

/usr/lib/python2.7/dist-packages/scipy/sparse/csr.pyc in
__getitem__(self, key)
    279
    280         elif isintlike(key) or isinstance(key,slice):
--> 281             return self[key,:]
#[i] or [1:2]
    282         else:
    283             return self[asindices(key),:]
#[[1,2]]

/usr/lib/python2.7/dist-packages/scipy/sparse/csr.pyc in
__getitem__(self, key)
    239                 #[1:2,??]
    240                 if isintlike(col) or isinstance(col, slice):
--> 241                     return self._get_submatrix(row, col)
#[1:2,j]
    242                 else:
    243                     P = extractor(col,self.shape[1]).T
#[1:2,[1,2]]

/usr/lib/python2.7/dist-packages/scipy/sparse/csr.pyc in
_get_submatrix(self, row_slice, col_slice)
    380         i0, i1 = process_slice( row_slice, M )
    381         j0, j1 = process_slice( col_slice, N )
--> 382         check_bounds( i0, i1, M )
    383         check_bounds( j0, j1, N )
    384

/usr/lib/python2.7/dist-packages/scipy/sparse/csr.pyc in
check_bounds(i0, i1, num)
    376                 raise IndexError( \
    377                       "index out of bounds: 0<=%d<%d, 0<=%d<%d,
%d<%d" %\
--> 378                       (i0, num, i1, num, i0, i1) )
    379
    380         i0, i1 = process_slice( row_slice, M )

IndexError: index out of bounds: 0<=113000<113966, 0<=114000<113966,
113000<114000

Author: jfelectron

sklearn/utils/__init__.py

@@ -350,9 +350,21 @@ def gen_batches(n, batch_size):
         yield slice(start, n)
 
 
-def gen_even_slices(n, n_packs):
+def gen_even_slices(n, n_packs,n_max=None):
     """Generator to create n_packs slices going up to n.
 
+    Args:
+
+        n : int
+            correct end_point if dimensions are even_slices
+        max_n: 
+            int
+            maximum dimension, correct is dimension are odd
+        n_pack:
+            number of packs to slice 
+
+
+
     Examples
     --------
     >>> from sklearn.utils import gen_even_slices
@@ -366,12 +378,13 @@ def gen_even_slices(n, n_packs):
     [slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)]
     """
     start = 0
+    n_max= n_max if n_max else n
     for pack_num in range(n_packs):
         this_n = n // n_packs
         if pack_num < n % n_packs:
             this_n += 1
         if this_n > 0:
-            end = start + this_n
+            end = start + this_n if end<n_max else n_max
             yield slice(start, end, None)
             start = end