1 files changed, 0 insertions, 58 deletions
diff --git a/ml/dlib/examples/running_stats_ex.cpp b/ml/dlib/examples/running_stats_ex.cpp
deleted file mode 100644
index d94faf35b..000000000
--- a/ml/dlib/examples/running_stats_ex.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-    This is an example illustrating the use of the running_stats object from the dlib C++
-    Library.  It is a simple tool for computing basic statistics on a stream of numbers.
-    In this example, we sample 100 points from the sinc function and then then compute the
-    unbiased sample mean, variance, skewness, and excess kurtosis.
-
-*/    
-#include <iostream>
-#include <vector>
-#include <dlib/statistics.h>
-
-using namespace std;
-using namespace dlib;
-
-// Here we define the sinc function so that we may generate sample data. We compute the mean,
-// variance, skewness, and excess kurtosis of this sample data.
-
-double sinc(double x)
-{
-    if (x == 0)
-        return 1;
-    return sin(x)/x;
-}
-
-int main()
-{
-    running_stats<double> rs;
-
-    double tp1 = 0;
-    double tp2 = 0;
-
-    // We first generate the data and add it sequentially to our running_stats object.  We
-    // then print every fifth data point.
-    for (int x = 1; x <= 100; x++)
-    {
-        tp1 = x/100.0;
-        tp2 = sinc(pi*x/100.0);
-        rs.add(tp2);
-
-        if(x % 5 == 0)
-        {
-            cout << " x = " << tp1 << " sinc(x) = " << tp2 << endl;
-        }
-    }
-
-    // Finally, we compute and print the mean, variance, skewness, and excess kurtosis of
-    // our data.
-
-    cout << endl;
-    cout << "Mean:           " << rs.mean() << endl;
-    cout << "Variance:       " << rs.variance() << endl;
-    cout << "Skewness:       " << rs.skewness() << endl;
-    cout << "Excess Kurtosis " << rs.ex_kurtosis() << endl;
-
-    return 0;
-}
-