Adding upstream version 1.44.3.upstream/1.44.3

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 58 insertions, 0 deletions

diff --git a/ml/dlib/examples/running_stats_ex.cpp b/ml/dlib/examples/running_stats_ex.cpp
new file mode 100644
index 000000000..d94faf35b
--- /dev/null
+++ b/ml/dlib/examples/running_stats_ex.cpp
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+// 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;
+}
+