1 files changed, 0 insertions, 58 deletions
diff --git a/ml/dlib/dlib/general_hash/random_hashing_abstract.h b/ml/dlib/dlib/general_hash/random_hashing_abstract.h
deleted file mode 100644
index 3d196d8c0..000000000
--- a/ml/dlib/dlib/general_hash/random_hashing_abstract.h
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (C) 2012  Davis E. King (davis@dlib.net)
-// License: Boost Software License   See LICENSE.txt for the full license.
-#undef DLIB_RANDOM_HAsHING_ABSTRACT_Hh_ 
-#ifdef DLIB_RANDOM_HAsHING_ABSTRACT_Hh_ 
-
-#include "random_hashing_abstract.h"
-#include "murmur_hash3.h"
-
-namespace dlib
-{
-
-// ----------------------------------------------------------------------------------------
-
-    double uniform_random_hash (
-        const uint64& k1,
-        const uint64& k2,
-        const uint64& k3
-    );
-    /*!
-        ensures
-            - This function uses hashing to generate uniform random values in the range [0,1).
-            - To define this function precisely, assume we have an arbitrary sequence of
-              input triplets.  Then calling uniform_random_hash() on each of them should
-              result in a sequence of double values that look like numbers sampled
-              independently and uniformly at random from the interval [0,1).  This is true
-              even if there is some simple pattern in the inputs.  For example, (0,0,0),
-              (1,0,0), (2,0,0), (3,0,0), etc.
-            - This function is deterministic.  That is, the same output is always returned
-              when given the same input.
-    !*/
-
-// ----------------------------------------------------------------------------------------
-
-    double gaussian_random_hash (
-        const uint64& k1,
-        const uint64& k2,
-        const uint64& k3
-    );
-    /*!
-        ensures
-            - This function uses hashing to generate Gaussian distributed random values
-              with mean 0 and variance 1.  
-            - To define this function precisely, assume we have an arbitrary sequence of
-              input triplets.  Then calling gaussian_random_hash() on each of them should
-              result in a sequence of double values that look like numbers sampled
-              independently from a standard normal distribution.  This is true even if
-              there is some simple pattern in the inputs.  For example, (0,0,0), (1,0,0),
-              (2,0,0), (3,0,0), etc.
-            - This function is deterministic.  That is, the same output is always returned
-              when given the same input.
-    !*/
-
-// ----------------------------------------------------------------------------------------
-
-}
-
-#endif // DLIB_RANDOM_HAsHING_ABSTRACT_Hh_
-