Added some stuff to a few of the examples regarding saving things to

disk with serialize().

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402622

examples/krls_ex.cpp

@@ -71,6 +71,27 @@ int main()
     // The first column is the true value of the sinc function and the second
     // column is the output from the krls estimate.  
 
+    
+
+
+
+    // Another thing that is worth knowing is that just about everything in dlib is serializable.
+    // So for example, you can save the test object to disk and recall it later like so:
+    ofstream fout("saved_krls_object.dat",ios::binary);
+    serialize(test,fout);
+    fout.close();
+
+    // now lets open that file back up and load the krls object it contains
+    ifstream fin("saved_krls_object.dat",ios::binary);
+    deserialize(test, fin);
+
+    // If you don't want to save the whole krls object (it might be a bit large) 
+    // you can save just the decision function it has learned so far.  You can get 
+    // the decision function out of it by calling test.get_decision_function() and
+    // then you can serialize that object instead.  E.g.
+    decision_function<kernel_type> funct = test.get_decision_function();
+    fout.open("saved_krls_function.dat",ios::binary);
+    serialize(funct, fout);
 }
 
 

examples/rvm_ex.cpp

@@ -192,5 +192,17 @@ int main()
     cout << "This -1 example should have low probability.  It's probability is: " << learned_pfunct(sample) << endl;
 
 
+
+    // Another thing that is worth knowing is that just about everything in dlib is serializable.
+    // So for example, you can save the learned_pfunct object to disk and recall it later like so:
+    ofstream fout("saved_function.dat",ios::binary);
+    serialize(learned_pfunct,fout);
+    fout.close();
+
+    // now lets open that file back up and load the function object it contains
+    ifstream fin("saved_function.dat",ios::binary);
+    deserialize(learned_pfunct, fin);
+
+
 }
 

examples/rvm_regression_ex.cpp

@@ -70,6 +70,19 @@ int main()
     // The first column is the true value of the sinc function and the second
     // column is the output from the rvm estimate.  
 
+
+
+    // Another thing that is worth knowing is that just about everything in dlib is serializable.
+    // So for example, you can save the test object to disk and recall it later like so:
+    ofstream fout("saved_function.dat",ios::binary);
+    serialize(test,fout);
+    fout.close();
+
+    // now lets open that file back up and load the function object it contains
+    ifstream fin("saved_function.dat",ios::binary);
+    deserialize(test, fin);
+
+
 }
 
 

examples/svm_ex.cpp

@@ -199,6 +199,24 @@ int main()
 
 
 
+    // Another thing that is worth knowing is that just about everything in dlib is serializable.
+    // So for example, you can save the learned_pfunct object to disk and recall it later like so:
+    ofstream fout("saved_function.dat",ios::binary);
+    serialize(learned_pfunct,fout);
+    fout.close();
+
+    // now lets open that file back up and load the function object it contains
+    ifstream fin("saved_function.dat",ios::binary);
+    deserialize(learned_pfunct, fin);
+
+    // Note that there is also an example program that comes with dlib called the file_to_code_ex.cpp
+    // example.  It is a simple program that takes a file and outputs a piece of C++ code 
+    // that is able to fully reproduce the file's contents in the form of a std::string object.  
+    // So you can use that along with the std::istringstream to save learned decision functions
+    // inside your actual C++ code files if you want.  
+
+
+
 
     // Lastly, note that the decision functions we trained above involved well over 100 
     // support vectors.  Support vector machines in general tend to find decision functions