1 files changed, 86 insertions, 0 deletions
diff --git a/src/ml/dlib/examples/mlp_ex.cpp b/src/ml/dlib/examples/mlp_ex.cpp
new file mode 100644
index 000000000..372753c81
--- /dev/null
+++ b/src/ml/dlib/examples/mlp_ex.cpp
@@ -0,0 +1,86 @@
+// 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 multilayer perceptron 
+    from the dlib C++ Library.  
+
+    This example creates a simple set of data to train on and shows
+    you how to train a mlp object on that data.
+
+
+    The data used in this example will be 2 dimensional data and will
+    come from a distribution where points with a distance less than 10
+    from the origin are labeled 1 and all other points are labeled
+    as 0.
+        
+*/
+
+
+#include <iostream>
+#include <dlib/mlp.h>
+
+using namespace std;
+using namespace dlib;
+
+
+int main()
+{
+    // The mlp takes column vectors as input and gives column vectors as output.  The dlib::matrix
+    // object is used to represent the column vectors. So the first thing we do here is declare 
+    // a convenient typedef for the matrix object we will be using.
+
+    // This typedef declares a matrix with 2 rows and 1 column.  It will be the
+    // object that contains each of our 2 dimensional samples.   (Note that if you wanted 
+    // more than 2 features in this vector you can simply change the 2 to something else)
+    typedef matrix<double, 2, 1> sample_type;
+
+
+    // make an instance of a sample matrix so we can use it below
+    sample_type sample;
+
+    // Create a multi-layer perceptron network.   This network has 2 nodes on the input layer 
+    // (which means it takes column vectors of length 2 as input) and 5 nodes in the first 
+    // hidden layer.  Note that the other 4 variables in the mlp's constructor are left at
+    // their default values.  
+    mlp::kernel_1a_c net(2,5);
+
+    // Now let's put some data into our sample and train on it.  We do this
+    // by looping over 41*41 points and labeling them according to their
+    // distance from the origin.
+    for (int i = 0; i < 1000; ++i)
+    {
+        for (int r = -20; r <= 20; ++r)
+        {
+            for (int c = -20; c <= 20; ++c)
+            {
+                sample(0) = r;
+                sample(1) = c;
+
+                // if this point is less than 10 from the origin
+                if (sqrt((double)r*r + c*c) <= 10)
+                    net.train(sample,1);
+                else
+                    net.train(sample,0);
+            }
+        }
+    }
+
+    // Now we have trained our mlp.  Let's see how well it did.  
+    // Note that if you run this program multiple times you will get different results. This
+    // is because the mlp network is randomly initialized.
+
+    // each of these statements prints out the output of the network given a particular sample.
+
+    sample(0) = 3.123;
+    sample(1) = 4;
+    cout << "This sample should be close to 1 and it is classified as a " << net(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 9.3545;
+    cout << "This sample should be close to 0 and it is classified as a " << net(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 0;
+    cout << "This sample should be close to 0 and it is classified as a " << net(sample) << endl;
+}
+