Adding upstream version 1.46.3.upstream

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

1 files changed, 0 insertions, 277 deletions

diff --git a/ml/dlib/examples/custom_trainer_ex.cpp b/ml/dlib/examples/custom_trainer_ex.cpp
deleted file mode 100644
index 39af53f39..000000000
--- a/ml/dlib/examples/custom_trainer_ex.cpp
+++ /dev/null
@@ -1,277 +0,0 @@
-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-    This example program shows you how to create your own custom binary classification
-    trainer object and use it with the multiclass classification tools in the dlib C++
-    library.  This example assumes you have already become familiar with the concepts
-    introduced in the multiclass_classification_ex.cpp example program.
-
-
-    In this example we will create a very simple trainer object that takes a binary
-    classification problem and produces a decision rule which says a test point has the
-    same class as whichever centroid it is closest to.  
-
-    The multiclass training dataset will consist of four classes.  Each class will be a blob 
-    of points in one of the quadrants of the cartesian plane.   For fun, we will use 
-    std::string labels and therefore the labels of these classes will be the following:
-        "upper_left",
-        "upper_right",
-        "lower_left",
-        "lower_right"
-*/
-
-#include <dlib/svm_threaded.h>
-
-#include <iostream>
-#include <vector>
-
-#include <dlib/rand.h>
-
-using namespace std;
-using namespace dlib;
-
-// Our data will be 2-dimensional data. So declare an appropriate type to contain these points.
-typedef matrix<double,2,1> sample_type;
-
-// ----------------------------------------------------------------------------------------
-
-struct custom_decision_function
-{
-    /*!
-        WHAT THIS OBJECT REPRESENTS
-            This object is the representation of our binary decision rule.  
-    !*/
-
-    // centers of the two classes
-    sample_type positive_center, negative_center;
-
-    double operator() (
-        const sample_type& x
-    ) const
-    {
-        // if x is closer to the positive class then return +1 
-        if (length(positive_center - x) < length(negative_center - x))
-            return +1;
-        else
-            return -1;
-    }
-};
-
-// Later on in this example we will save our decision functions to disk.  This
-// pair of routines is needed for this functionality.
-void serialize (const custom_decision_function& item, std::ostream& out)
-{
-    // write the state of item to the output stream
-    serialize(item.positive_center, out);
-    serialize(item.negative_center, out);
-}
-
-void deserialize (custom_decision_function& item, std::istream& in)
-{
-    // read the data from the input stream and store it in item
-    deserialize(item.positive_center, in);
-    deserialize(item.negative_center, in);
-}
-
-// ----------------------------------------------------------------------------------------
-
-class simple_custom_trainer
-{
-    /*!
-        WHAT THIS OBJECT REPRESENTS
-            This is our example custom binary classifier trainer object.  It simply 
-            computes the means of the +1 and -1 classes, puts them into our 
-            custom_decision_function, and returns the results.
-
-            Below we define the train() function.  I have also included the
-            requires/ensures definition for a generic binary classifier's train()
-    !*/
-public:
-
-
-    custom_decision_function train (
-        const std::vector<sample_type>& samples,
-        const std::vector<double>& labels
-    ) const
-    /*!
-        requires
-            - is_binary_classification_problem(samples, labels) == true
-              (e.g. labels consists of only +1 and -1 values, samples.size() == labels.size())
-        ensures
-            - returns a decision function F with the following properties:
-                - if (new_x is a sample predicted have +1 label) then
-                    - F(new_x) >= 0
-                - else
-                    - F(new_x) < 0
-    !*/
-    {
-        sample_type positive_center, negative_center;
-
-        // compute sums of each class 
-        positive_center = 0;
-        negative_center = 0;
-        for (unsigned long i = 0; i < samples.size(); ++i)
-        {
-            if (labels[i] == +1)
-                positive_center += samples[i];
-            else // this is a -1 sample
-                negative_center += samples[i];
-        }
-
-        // divide by number of +1 samples
-        positive_center /= sum(mat(labels) == +1);
-        // divide by number of -1 samples
-        negative_center /= sum(mat(labels) == -1);
-
-        custom_decision_function df;
-        df.positive_center = positive_center;
-        df.negative_center = negative_center;
-
-        return df;
-    }
-};
-
-// ----------------------------------------------------------------------------------------
-
-void generate_data (
-    std::vector<sample_type>& samples,
-    std::vector<string>& labels
-);
-/*!
-    ensures
-        - make some four class data as described above.  
-        - each class will have 50 samples in it
-!*/
-
-// ----------------------------------------------------------------------------------------
-
-int main()
-{
-    std::vector<sample_type> samples;
-    std::vector<string> labels;
-
-    // First, get our labeled set of training data
-    generate_data(samples, labels);
-
-    cout << "samples.size(): "<< samples.size() << endl;
-
-    // Define the trainer we will use.  The second template argument specifies the type
-    // of label used, which is string in this case.
-    typedef one_vs_one_trainer<any_trainer<sample_type>, string> ovo_trainer;
-
-
-    ovo_trainer trainer;
-
-    // Now tell the one_vs_one_trainer that, by default, it should use the simple_custom_trainer
-    // to solve the individual binary classification subproblems.
-    trainer.set_trainer(simple_custom_trainer());
-
-    // Next, to make things a little more interesting, we will setup the one_vs_one_trainer
-    // to use kernel ridge regression to solve the upper_left vs lower_right binary classification
-    // subproblem.  
-    typedef radial_basis_kernel<sample_type> rbf_kernel;
-    krr_trainer<rbf_kernel> rbf_trainer;
-    rbf_trainer.set_kernel(rbf_kernel(0.1));
-    trainer.set_trainer(rbf_trainer, "upper_left", "lower_right");
-
-
-    // Now let's do 5-fold cross-validation using the one_vs_one_trainer we just setup.
-    // As an aside, always shuffle the order of the samples before doing cross validation.  
-    // For a discussion of why this is a good idea see the svm_ex.cpp example.
-    randomize_samples(samples, labels);
-    cout << "cross validation: \n" << cross_validate_multiclass_trainer(trainer, samples, labels, 5) << endl;
-    // This dataset is very easy and everything is correctly classified.  Therefore, the output of 
-    // cross validation is the following confusion matrix.
-    /*
-        50  0  0  0 
-         0 50  0  0 
-         0  0 50  0 
-         0  0  0 50 
-    */
-
-
-    // We can also obtain the decision rule as always.
-    one_vs_one_decision_function<ovo_trainer> df = trainer.train(samples, labels);
-
-    cout << "predicted label: "<< df(samples[0])  << ", true label: "<< labels[0] << endl;
-    cout << "predicted label: "<< df(samples[90]) << ", true label: "<< labels[90] << endl;
-    // The output is:
-    /*
-        predicted label: upper_right, true label: upper_right
-        predicted label: lower_left, true label: lower_left
-    */
-
-
-    // Finally, let's save our multiclass decision rule to disk.  Remember that we have
-    // to specify the types of binary decision function used inside the one_vs_one_decision_function.
-    one_vs_one_decision_function<ovo_trainer, 
-            custom_decision_function,                             // This is the output of the simple_custom_trainer 
-            decision_function<radial_basis_kernel<sample_type> >  // This is the output of the rbf_trainer
-        > df2, df3;
-
-    df2 = df;
-    // save to a file called df.dat
-    serialize("df.dat") << df2;
-
-    // load the function back in from disk and store it in df3.  
-    deserialize("df.dat") >> df3;
-
-
-    // Test df3 to see that this worked.
-    cout << endl;
-    cout << "predicted label: "<< df3(samples[0])  << ", true label: "<< labels[0] << endl;
-    cout << "predicted label: "<< df3(samples[90]) << ", true label: "<< labels[90] << endl;
-    // Test df3 on the samples and labels and print the confusion matrix.
-    cout << "test deserialized function: \n" << test_multiclass_decision_function(df3, samples, labels) << endl;
-
-}
-
-// ----------------------------------------------------------------------------------------
-
-void generate_data (
-    std::vector<sample_type>& samples,
-    std::vector<string>& labels
-)
-{
-    const long num = 50;
-
-    sample_type m;
-
-    dlib::rand rnd;
-
-
-    // add some points in the upper right quadrant
-    m = 10, 10;
-    for (long i = 0; i < num; ++i)
-    {
-        samples.push_back(m + randm(2,1,rnd));
-        labels.push_back("upper_right");
-    }
-
-    // add some points in the upper left quadrant
-    m = -10, 10;
-    for (long i = 0; i < num; ++i)
-    {
-        samples.push_back(m + randm(2,1,rnd));
-        labels.push_back("upper_left");
-    }
-
-    // add some points in the lower right quadrant
-    m = 10, -10;
-    for (long i = 0; i < num; ++i)
-    {
-        samples.push_back(m + randm(2,1,rnd));
-        labels.push_back("lower_right");
-    }
-
-    // add some points in the lower left quadrant
-    m = -10, -10;
-    for (long i = 0; i < num; ++i)
-    {
-        samples.push_back(m + randm(2,1,rnd));
-        labels.push_back("lower_left");
-    }
-
-}
-
-// ----------------------------------------------------------------------------------------
-