From 415e3256c0767a938ecab3e855a3d2d2c85d2adf Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Mon, 16 Dec 2013 11:35:47 +0000
Subject: [PATCH] subarray indexing

---
 GPy/util/subarray_and_sorting.py | 56 ++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 GPy/util/subarray_and_sorting.py

diff --git a/GPy/util/subarray_and_sorting.py b/GPy/util/subarray_and_sorting.py
new file mode 100644
index 00000000..49385771
--- /dev/null
+++ b/GPy/util/subarray_and_sorting.py
@@ -0,0 +1,56 @@
+'''
+.. module:: GPy.util.subarray_and_sorting
+
+.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
+
+'''
+__updated__ = '2013-12-02'
+
+import numpy as np
+
+def common_subarrays(X, axis=0):
+    """
+    Find common subarrays of 2 dimensional X, where axis is the axis to apply the search over.
+    Common subarrays are returned as a dictionary of <subarray, [index]> pairs, where
+    the subarray is a tuple representing the subarray and the index is the index
+    for the subarray in X, where index is the index to the remaining axis.
+    
+    :param :class:`np.ndarray` X: 2d array to check for common subarrays in
+    :param int axis: axis to apply subarray detection over. 
+        When the index is 0, compare rows, columns, otherwise.   
+    
+    Examples:
+    =========
+
+    In a 2d array:    
+    >>> import numpy as np
+    >>> X = np.zeros((3,6), dtype=bool)
+    >>> X[[1,1,1],[0,4,5]] = 1; X[1:,[2,3]] = 1
+    >>> X
+    array([[False, False, False, False, False, False],
+           [ True, False,  True,  True,  True,  True],
+           [False, False,  True,  True, False, False]], dtype=bool)
+    >>> d = common_subarrays(X,axis=1)
+    >>> len(d)
+    3
+    >>> X[:, d[tuple(X[:,0])]]
+    array([[False, False, False],
+           [ True,  True,  True],
+           [False, False, False]], dtype=bool)
+    >>> d[tuple(X[:,4])] == d[tuple(X[:,0])] == [0, 4, 5]
+    True
+    >>> d[tuple(X[:,1])]
+    [1]
+    """
+    from collections import defaultdict
+    from itertools import count
+    from operator import iadd
+    assert X.ndim == 2 and axis in (0,1), "Only implemented for 2D arrays"
+    subarrays = defaultdict(list)
+    cnt = count()
+    np.apply_along_axis(lambda x: iadd(subarrays[tuple(x)], [cnt.next()]), 1-axis, X)
+    return subarrays
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()
\ No newline at end of file