Fixing grammar in comments.

examples/bayes_net_ex.cpp

@@ -161,7 +161,7 @@ int main()
 
 
 
-        // We have now finished setting up our bayesian network.  So lets compute some 
+        // We have now finished setting up our bayesian network.  So let's compute some 
         // probability values.  The first thing we will do is compute the prior probability
         // of each node in the network.  To do this we will use the join tree algorithm which
         // is an algorithm for performing exact inference in a bayesian network.   
@@ -198,7 +198,7 @@ int main()
         cout << "\n\n\n";
 
 
-        // Now to make things more interesting lets say that we have discovered that the C 
+        // Now to make things more interesting let's say that we have discovered that the C 
         // node really has a value of 1.  That is to say, we now have evidence that 
         // C is 1.  We can represent this in the network using the following two function
         // calls.

examples/bayes_net_from_disk_ex.cpp

@@ -44,7 +44,7 @@ int main(int argc, char** argv)
 
         cout << "Number of nodes in the network: " << bn.number_of_nodes() << endl;
 
-        // Lets compute some probability values using the loaded network using the join tree (aka. Junction 
+        // Let's compute some probability values using the loaded network using the join tree (aka. Junction 
         // Tree) algorithm.
 
         // First we need to create an undirected graph which contains set objects at each node and

examples/bayes_net_gui_ex.cpp

@@ -413,7 +413,7 @@ initialize_node_cpt_if_necessary (
 {
     node_type& node = graph_drawer.graph_node(index);
 
-    // if the cpt for this node isn't properly filled out then lets clear it out
+    // if the cpt for this node isn't properly filled out then let's clear it out
     // and populate it with some reasonable default values
     if (node_cpt_filled_out(graph_drawer.graph(), index) == false)
     {

examples/bridge_ex.cpp

@@ -103,7 +103,7 @@ void run_example_1(
 
 
 
-    // Now lets put some things into the out pipe
+    // Now let's put some things into the out pipe
     int value = 1;
     out.enqueue(value);
 
@@ -308,7 +308,7 @@ void run_example_4(
     bridge_status bs;
 
     // Once a connection is established it will generate a status message from each bridge. 
-    // Lets get those and print them.  
+    // Let's get those and print them.  
     b1_status.dequeue(bs);
     cout << "bridge 1 status: is_connected: " << boolalpha << bs.is_connected << endl;
     cout << "bridge 1 status: foreign_ip:   " << bs.foreign_ip << endl;

examples/config_reader_ex.cpp

@@ -75,7 +75,7 @@ int main()
         // Use our recursive function to print everything in the config file.
         print_config_reader_contents(cr);
 
-        // Now lets access some of the fields of the config file directly.  You 
+        // Now let's access some of the fields of the config file directly.  You 
         // use [] for accessing key values and .block() for accessing sub-blocks.
 
         // Print out the string value assigned to key1 in the config file

examples/custom_trainer_ex.cpp

@@ -174,7 +174,7 @@ int main()
     trainer.set_trainer(rbf_trainer, "upper_left", "lower_right");
 
 
-    // Now lets do 5-fold cross-validation using the one_vs_one_trainer we just setup.
+    // 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);
@@ -201,7 +201,7 @@ int main()
     */
 
 
-    // Finally, lets save our multiclass decision rule to disk.  Remember that we have
+    // 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 

examples/empirical_kernel_map_ex.cpp

@@ -76,7 +76,7 @@ using namespace dlib;
 
 // ----------------------------------------------------------------------------------------
 
-// First lets make a typedef for the kind of samples we will be using. 
+// First let's make a typedef for the kind of samples we will be using. 
 typedef matrix<double, 0, 1> sample_type;
 
 // We will be using the radial_basis_kernel in this example program.
@@ -213,7 +213,7 @@ void test_empirical_kernel_map (
 
 
 
-    // Now lets do something more interesting.  The following loop finds the centroids
+    // Now let's do something more interesting.  The following loop finds the centroids
     // of the two classes of data.
     sample_type class1_center; 
     sample_type class2_center; 
@@ -254,7 +254,7 @@ void test_empirical_kernel_map (
 
     // Next, note that classifying a point based on its distance between two other 
     // points is the same thing as using the plane that lies between those two points 
-    // as a decision boundary.  So lets compute that decision plane and use it to classify 
+    // as a decision boundary.  So let's compute that decision plane and use it to classify 
     // all the points.
     
     sample_type plane_normal_vector = class1_center - class2_center;
@@ -291,7 +291,7 @@ void test_empirical_kernel_map (
     {
         double side = dec_funct(samples[i]);
 
-        // And lets just check that the dec_funct really does compute the same thing as the previous equation.
+        // And let's just check that the dec_funct really does compute the same thing as the previous equation.
         double side_alternate_equation = dot(plane_normal_vector, projected_samples[i]) - bias;
         if (abs(side-side_alternate_equation) > 1e-14)
             cout << "dec_funct error: " << abs(side-side_alternate_equation) << endl;

examples/fhog_ex.cpp

@@ -55,7 +55,7 @@ int main(int argc, char** argv)
 
         cout << "hog image has " << hog.nr() << " rows and " << hog.nc() << " columns." << endl;
 
-        // Lets see what the image and FHOG features look like.
+        // Let's see what the image and FHOG features look like.
         image_window win(img);
         image_window winhog(draw_fhog(hog));
 

examples/fhog_object_detector_ex.cpp

@@ -161,7 +161,7 @@ int main(int argc, char** argv)
         // a face.
         image_window hogwin(draw_fhog(detector), "Learned fHOG detector");
 
-        // Now for the really fun part.  Lets display the testing images on the screen and
+        // Now for the really fun part.  Let's display the testing images on the screen and
         // show the output of the face detector overlaid on each image.  You will see that
         // it finds all the faces without false alarming on any non-faces.
         image_window win; 
@@ -191,7 +191,7 @@ int main(int argc, char** argv)
 
 
 
-        // Now lets talk about some optional features of this training tool as well as some
+        // Now let's talk about some optional features of this training tool as well as some
         // important points you should understand.
         //
         // The first thing that should be pointed out is that, since this is a sliding

examples/graph_labeling_ex.cpp

@@ -194,13 +194,13 @@ int main()
         // indicate that all nodes were correctly classified.  
         cout << "3-fold cross-validation: " << cross_validate_graph_labeling_trainer(trainer, samples, labels, 3) << endl;
 
-        // Since the trainer is working well.  Lets have it make a graph_labeler 
+        // Since the trainer is working well.  Let's have it make a graph_labeler 
         // based on the training data.
         graph_labeler<vector_type> labeler = trainer.train(samples, labels);
 
 
         /*
-            Lets try the graph_labeler on a new test graph.  In particular, lets
+            Let's try the graph_labeler on a new test graph.  In particular, let's
             use one with 5 nodes as shown below:
 
             (0 F)-----(1 T)

examples/gui_api_ex.cpp

@@ -114,7 +114,7 @@ public:
         b.set_pos(10,60);
         b.set_name("button");
 
-        // lets put the label 5 pixels below the button
+        // let's put the label 5 pixels below the button
         c.set_pos(b.left(),b.bottom()+5);
 
 
@@ -137,7 +137,7 @@ public:
         // functions or lambda functions.
 
         
-        // Lets also make a simple menu bar.  
+        // Let's also make a simple menu bar.  
         // First we say how many menus we want in our menu bar.  In this example we only want 1.
         mbar.set_number_of_menus(1);
         // Now we set the name of our menu.  The 'M' means that the M in Menu will be underlined
@@ -147,12 +147,12 @@ public:
         // Now we add some items to the menu.  Note that items in a menu are listed in the
         // order in which they were added.
 
-        // First lets make a menu item that does the same thing as our button does when it is clicked.
+        // First let's make a menu item that does the same thing as our button does when it is clicked.
         // Again, the 'C' means the C in Click is underlined in the menu. 
         mbar.menu(0).add_menu_item(menu_item_text("Click Button!",*this,&win::on_button_clicked,'C'));
-        // lets add a separator (i.e. a horizontal separating line) to the menu
+        // let's add a separator (i.e. a horizontal separating line) to the menu
         mbar.menu(0).add_menu_item(menu_item_separator());
-        // Now lets make a menu item that calls show_about when the user selects it.  
+        // Now let's make a menu item that calls show_about when the user selects it.  
         mbar.menu(0).add_menu_item(menu_item_text("About",*this,&win::show_about,'A'));
 
 

examples/image_ex.cpp

@@ -46,7 +46,7 @@ int main(int argc, char** argv)
         load_image(img, argv[1]);
 
 
-        // Now lets use some image functions.  First lets blur the image a little.
+        // Now let's use some image functions.  First let's blur the image a little.
         array2d<unsigned char> blurred_img;
         gaussian_blur(img, blurred_img); 
 
@@ -58,7 +58,7 @@ int main(int argc, char** argv)
         // now we do the non-maximum edge suppression step so that our edges are nice and thin
         suppress_non_maximum_edges(horz_gradient, vert_gradient, edge_image); 
 
-        // Now we would like to see what our images look like.  So lets use a 
+        // Now we would like to see what our images look like.  So let's use a 
         // window to display them on the screen.  (Note that you can zoom into 
         // the window by holding CTRL and scrolling the mouse wheel)
         image_window my_window(edge_image, "Normal Edge Image");

examples/iosockstream_ex.cpp

@@ -28,7 +28,7 @@ int main()
         iosockstream stream("www.google.com:80");
 
         // At this point, we can use stream the same way we would use any other
-        // C++ iostream object.  So to test it out, lets make a HTTP GET request
+        // C++ iostream object.  So to test it out, let's make a HTTP GET request
         // for the main Google page.
         stream << "GET / HTTP/1.0\r\n\r\n";
 

examples/kcentroid_ex.cpp

@@ -66,7 +66,7 @@ int main()
 
     running_stats<double> rs;
 
-    // Now lets output the distance from the centroid to some points that are from the sinc function.
+    // Now let's output the distance from the centroid to some points that are from the sinc function.
     // These numbers should all be similar.  We will also calculate the statistics of these numbers
     // by accumulating them into the running_stats object called rs.  This will let us easily
     // find the mean and standard deviation of the distances for use below.
@@ -80,7 +80,7 @@ int main()
     m(0) = -0.5; m(1) = sinc(m(0)); cout << "   " << test(m) << endl;  rs.add(test(m));
 
     cout << endl;
-    // Lets output the distance from the centroid to some points that are NOT from the sinc function.
+    // Let's output the distance from the centroid to some points that are NOT from the sinc function.
     // These numbers should all be significantly bigger than previous set of numbers.  We will also
     // use the rs.scale() function to find out how many standard deviations they are away from the 
     // mean of the test points from the sinc function.  So in this case our criterion for "significantly bigger"

examples/krls_ex.cpp

@@ -82,7 +82,7 @@ int main()
     serialize(test,fout);
     fout.close();
 
-    // now lets open that file back up and load the krls object it contains
+    // Now let's open that file back up and load the krls object it contains.
     ifstream fin("saved_krls_object.dat",ios::binary);
     deserialize(test, fin);
 

examples/krls_filter_ex.cpp

@@ -63,7 +63,7 @@ int main()
 
     dlib::rand rnd;
 
-    // Now lets loop over a big range of values from the sinc() function.  Each time
+    // Now let's loop over a big range of values from the sinc() function.  Each time
     // adding some random noise to the data we send to the krls object for training.
     sample_type m;
     double mse_noise = 0;

examples/krr_classification_ex.cpp

@@ -43,7 +43,7 @@ int main()
     std::vector<sample_type> samples;
     std::vector<double> labels;
 
-    // Now lets put some data into our samples and labels objects.  We do this
+    // Now let's put some data into our samples and labels objects.  We do this
     // by looping over a bunch of points and labeling them according to their
     // distance from the origin.
     for (double r = -20; r <= 20; r += 0.4)
@@ -129,7 +129,7 @@ int main()
     cout << "\nnumber of basis vectors in our learned_function is " 
          << learned_function.function.basis_vectors.size() << endl;
 
-    // Now lets try this decision_function on some samples we haven't seen before.
+    // Now let's try this decision_function on some samples we haven't seen before.
     // The decision function will return values >= 0 for samples it predicts
     // are in the +1 class and numbers < 0 for samples it predicts to be in the -1 class.
     sample_type sample;
@@ -200,7 +200,7 @@ int main()
     serialize(learned_pfunct,fout);
     fout.close();
 
-    // now lets open that file back up and load the function object it contains
+    // Now let's open that file back up and load the function object it contains.
     ifstream fin("saved_function.dat",ios::binary);
     deserialize(learned_pfunct, fin);
 

examples/krr_regression_ex.cpp

@@ -98,7 +98,7 @@ int main()
     serialize(test,fout);
     fout.close();
 
-    // now lets open that file back up and load the function object it contains
+    // Now let's open that file back up and load the function object it contains.
     ifstream fin("saved_function.dat",ios::binary);
     deserialize(test, fin);
 

examples/least_squares_ex.cpp

@@ -95,7 +95,7 @@ int main()
         cout << "params: " << trans(params) << endl;
 
 
-        // Now lets generate a bunch of input/output pairs according to our model.
+        // Now let's generate a bunch of input/output pairs according to our model.
         std::vector<std::pair<input_vector, double> > data_samples;
         input_vector input;
         for (int i = 0; i < 1000; ++i)
@@ -107,7 +107,7 @@ int main()
             data_samples.push_back(make_pair(input, output));
         }
 
-        // Before we do anything, lets make sure that our derivative function defined above matches
+        // Before we do anything, let's make sure that our derivative function defined above matches
         // the approximate derivative computed using central differences (via derivative()).  
         // If this value is big then it means we probably typed the derivative function incorrectly.
         cout << "derivative error: " << length(residual_derivative(data_samples[0], params) - 
@@ -117,7 +117,7 @@ int main()
 
 
 
-        // Now lets use the solve_least_squares_lm() routine to figure out what the
+        // Now let's use the solve_least_squares_lm() routine to figure out what the
         // parameters are based on just the data_samples.
         parameter_vector x;
         x = 1;

examples/linear_manifold_regularizer_ex.cpp

@@ -98,7 +98,7 @@ using namespace dlib;
 
 // ----------------------------------------------------------------------------------------
 
-// First lets make a typedef for the kind of samples we will be using. 
+// First let's make a typedef for the kind of samples we will be using. 
 typedef matrix<double, 0, 1> sample_type;
 
 // We will be using the radial_basis_kernel in this example program.

examples/matrix_ex.cpp

@@ -16,7 +16,7 @@ using namespace std;
 
 int main()
 {
-    // Lets begin this example by using the library to solve a simple 
+    // Let's begin this example by using the library to solve a simple 
     // linear system.
     // 
     // We will find the value of x such that y = M*x where
@@ -32,7 +32,7 @@ int main()
     //      5.9    0.05  1
 
 
-    // First lets declare these 3 matrices.
+    // First let's declare these 3 matrices.
     // This declares a matrix that contains doubles and has 3 rows and 1 column.
     // Moreover, it's size is a compile time constant since we put it inside the <>.
     matrix<double,3,1> y;

examples/matrix_expressions_ex.cpp

@@ -354,7 +354,7 @@ void custom_matrix_expressions_example(
 
     cout << x << endl;
 
-    // Finally, lets use the matrix expressions we defined above.
+    // Finally, let's use the matrix expressions we defined above.
 
     // prints the transpose of x
     cout << example_trans(x) << endl;
@@ -382,7 +382,7 @@ void custom_matrix_expressions_example(
     vect.push_back(3);
     vect.push_back(5);
 
-    // Now lets treat our std::vector like a matrix and print some things.
+    // Now let's treat our std::vector like a matrix and print some things.
     cout << example_vector_to_matrix(vect) << endl;
     cout << add_scalar(example_vector_to_matrix(vect), 10) << endl;
 

examples/mlp_ex.cpp

@@ -44,7 +44,7 @@ int main()
     // their default values.  
     mlp::kernel_1a_c net(2,5);
 
-    // Now lets put some data into our sample and train on it.  We do this
+    // 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)
@@ -65,7 +65,7 @@ int main()
         }
     }
 
-    // Now we have trained our mlp.  Lets see how well it did.  
+    // 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.
 

examples/model_selection_ex.cpp

@@ -101,7 +101,7 @@ int main()
         std::vector<sample_type> samples;
         std::vector<double> labels;
 
-        // Now lets put some data into our samples and labels objects.  We do this
+        // Now let's put some data into our samples and labels objects.  We do this
         // by looping over a bunch of points and labeling them according to their
         // distance from the origin.
         for (double r = -20; r <= 20; r += 0.8)

examples/multiclass_classification_ex.cpp

@@ -92,7 +92,7 @@ int main()
         // still be solved with the rbf_trainer.
         trainer.set_trainer(poly_trainer, 1, 2);
 
-        // Now lets do 5-fold cross-validation using the one_vs_one_trainer we just setup.
+        // 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);

examples/object_detector_advanced_ex.cpp

@@ -203,7 +203,7 @@ int main()
 
         typedef scan_image_pyramid<pyramid_down<5>, very_simple_feature_extractor> image_scanner_type;
         image_scanner_type scanner;
-        // Instead of using setup_grid_detection_templates() like in object_detector_ex.cpp, lets manually
+        // Instead of using setup_grid_detection_templates() like in object_detector_ex.cpp, let's manually
         // setup the sliding window box.  We use a window with the same shape as the white boxes we
         // are trying to detect.
         const rectangle object_box = compute_box_dimensions(1,    // width/height ratio
@@ -272,7 +272,7 @@ int main()
         */
 
 
-        // Lets display the output of the detector along with our training images.
+        // Let's display the output of the detector along with our training images.
         image_window win;
         for (unsigned long i = 0; i < images.size(); ++i)
         {

examples/object_detector_ex.cpp

@@ -226,7 +226,7 @@ int main()
 
 
 
-        // Lets display the output of the detector along with our training images.
+        // Let's display the output of the detector along with our training images.
         image_window win;
         for (unsigned long i = 0; i < images.size(); ++i)
         {

examples/one_class_classifiers_ex.cpp

@@ -66,7 +66,7 @@ int main()
     // anomalous (i.e. not on the sinc() curve in our case).
     decision_function<kernel_type> df = trainer.train(samples);
 
-    // So for example, lets look at the output from some points on the sinc() curve.  
+    // So for example, let's look at the output from some points on the sinc() curve.  
     cout << "Points that are on the sinc function:\n";
     m(0) = -1.5; m(1) = sinc(m(0)); cout << "   " << df(m) << endl;  
     m(0) = -1.5; m(1) = sinc(m(0)); cout << "   " << df(m) << endl;  

examples/optimization_ex.cpp

@@ -201,7 +201,7 @@ int main()
         cout << "rosen solution:\n" << starting_point << endl;
 
 
-        // Now lets try doing it again with a different starting point and the version
+        // Now let's try doing it again with a different starting point and the version
         // of find_min() that doesn't require you to supply a derivative function.  
         // This version will compute a numerical approximation of the derivative since 
         // we didn't supply one to it.
@@ -285,7 +285,7 @@ int main()
 
 
 
-        // Now lets look at using the test_function object with the optimization 
+        // Now let's look at using the test_function object with the optimization 
         // functions.  
         cout << "\nFind the minimum of the test_function" << endl;
 
@@ -306,7 +306,7 @@ int main()
         // At this point the correct value of (3,5,1,7) should be found and stored in starting_point
         cout << "test_function solution:\n" << starting_point << endl;
 
-        // Now lets try it again with the conjugate gradient algorithm.
+        // Now let's try it again with the conjugate gradient algorithm.
         starting_point = -4,5,99,3;
         find_min_using_approximate_derivatives(cg_search_strategy(),
                                                objective_delta_stop_strategy(1e-7),
@@ -315,7 +315,7 @@ int main()
 
 
 
-        // Finally, lets try the BOBYQA algorithm.  This is a technique specially
+        // Finally, let's try the BOBYQA algorithm.  This is a technique specially
         // designed to minimize a function in the absence of derivative information.  
         // Generally speaking, it is the method of choice if derivatives are not available.
         starting_point = -4,5,99,3;

examples/quantum_computing_ex.cpp

@@ -296,8 +296,8 @@ int main()
 
 
 
-    // Now lets test out the Shor 9 bit encoding
-    cout << "\n\n\n\nNow lets try playing around with Shor's 9bit error correcting code" << endl;
+    // Now let's test out the Shor 9 bit encoding
+    cout << "\n\n\n\nNow let's try playing around with Shor's 9bit error correcting code" << endl;
 
     // Reset the quantum register to contain a single bit
     reg.set_num_bits(1);

examples/rank_features_ex.cpp

@@ -36,7 +36,7 @@ int main()
 
 
 
-    // Now lets make some vector objects that can hold our samples 
+    // Now let's make some vector objects that can hold our samples 
     std::vector<sample_type> samples;
     std::vector<double> labels;
 

examples/rvm_ex.cpp

@@ -47,7 +47,7 @@ int main()
     std::vector<sample_type> samples;
     std::vector<double> labels;
 
-    // Now lets put some data into our samples and labels objects.  We do this
+    // Now let's put some data into our samples and labels objects.  We do this
     // by looping over a bunch of points and labeling them according to their
     // distance from the origin.
     for (int r = -20; r <= 20; ++r)
@@ -141,11 +141,11 @@ int main()
     learned_function.normalizer = normalizer;  // save normalization information
     learned_function.function = trainer.train(samples, labels); // perform the actual RVM training and save the results
 
-    // print out the number of relevance vectors in the resulting decision function
+    // Print out the number of relevance vectors in the resulting decision function.
     cout << "\nnumber of relevance vectors in our learned_function is " 
          << learned_function.function.basis_vectors.size() << endl;
 
-    // now lets try this decision_function on some samples we haven't seen before 
+    // Now let's try this decision_function on some samples we haven't seen before 
     sample_type sample;
 
     sample(0) = 3.123;
@@ -209,7 +209,7 @@ int main()
     serialize(learned_pfunct,fout);
     fout.close();
 
-    // now lets open that file back up and load the function object it contains
+    // Now let's 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

@@ -95,7 +95,7 @@ int main()
     serialize(test,fout);
     fout.close();
 
-    // now lets open that file back up and load the function object it contains
+    // Now let's open that file back up and load the function object it contains.
     ifstream fin("saved_function.dat",ios::binary);
     deserialize(test, fin);
 

examples/sequence_segmenter_ex.cpp

@@ -192,7 +192,7 @@ int main()
     sequence_segmenter<feature_extractor> segmenter = trainer.train(samples, segments);
 
 
-    // Lets print out all the segments our segmenter detects.
+    // Let's print out all the segments our segmenter detects.
     for (unsigned long i = 0; i < samples.size(); ++i)
     {
         // get all the detected segments in samples[i]
@@ -205,7 +205,7 @@ int main()
     }
 
 
-    // Now lets test it on a new sentence and see what it detects.  
+    // Now let's test it on a new sentence and see what it detects.  
     std::vector<std::string> sentence(split("There once was a man from Nantucket whose name rhymed with Bob Bucket"));
     std::vector<std::pair<unsigned long,unsigned long> > seg = segmenter(sentence);
     for (unsigned long j = 0; j < seg.size(); ++j)

examples/svm_ex.cpp

@@ -47,7 +47,7 @@ int main()
     std::vector<sample_type> samples;
     std::vector<double> labels;
 
-    // Now lets put some data into our samples and labels objects.  We do this by looping
+    // Now let's put some data into our samples and labels objects.  We do this by looping
     // over a bunch of points and labeling them according to their distance from the
     // origin.
     for (int r = -20; r <= 20; ++r)
@@ -149,7 +149,7 @@ int main()
     cout << "\nnumber of support vectors in our learned_function is " 
          << learned_function.function.basis_vectors.size() << endl;
 
-    // now lets try this decision_function on some samples we haven't seen before 
+    // Now let's try this decision_function on some samples we haven't seen before.
     sample_type sample;
 
     sample(0) = 3.123;
@@ -214,7 +214,7 @@ int main()
     serialize(learned_pfunct,fout);
     fout.close();
 
-    // now lets open that file back up and load the function object it contains
+    // Now let's open that file back up and load the function object it contains.
     ifstream fin("saved_function.dat",ios::binary);
     deserialize(learned_pfunct, fin);
 
@@ -242,7 +242,7 @@ int main()
     cout << "\ncross validation accuracy with only 10 support vectors: " 
          << cross_validate_trainer(reduced2(trainer,10), samples, labels, 3);
 
-    // Lets print out the original cross validation score too for comparison.
+    // Let's print out the original cross validation score too for comparison.
     cout << "cross validation accuracy with all the original support vectors: " 
          << cross_validate_trainer(trainer, samples, labels, 3);
 

examples/svm_pegasos_ex.cpp

@@ -67,7 +67,7 @@ int main()
 
     center = 20, 20;
 
-    // Now lets go into a loop and randomly generate 1000 samples.
+    // Now let's go into a loop and randomly generate 1000 samples.
     srand(time(0));
     for (int i = 0; i < 10000; ++i)
     {
@@ -96,7 +96,7 @@ int main()
         }
     }
 
-    // Now we have trained our SVM.  Lets see how well it did.  
+    // Now we have trained our SVM.  Let's see how well it did.  
     // Each of these statements prints out the output of the SVM given a particular sample.  
     // The SVM outputs a number > 0 if a sample is predicted to be in the +1 class and < 0 
     // if a sample is predicted to be in the -1 class.
@@ -123,7 +123,7 @@ int main()
     // function.  To support this the dlib library provides functions for converting an online
     // training object like svm_pegasos into a batch training object.  
 
-    // First lets clear out anything in the trainer object.
+    // First let's clear out anything in the trainer object.
     trainer.clear();
 
     // Now to begin with, you might want to compute the cross validation score of a trainer object

examples/svm_rank_ex.cpp

@@ -38,7 +38,7 @@ int main()
         typedef matrix<double,2,1> sample_type;
 
 
-        // Now lets make some testing data.  To make it really simple, lets
+        // Now let's make some testing data.  To make it really simple, let's
         // suppose that vectors with positive values in the first dimension
         // should rank higher than other vectors.  So what we do is make
         // examples of relevant (i.e. high ranking) and non-relevant (i.e. low

examples/svm_sparse_ex.cpp

@@ -45,7 +45,7 @@ int main()
     // description of what this parameter does. 
     trainer.set_lambda(0.00001);
 
-    // Lets also use the svm trainer specially optimized for the linear_kernel and
+    // Let's also use the svm trainer specially optimized for the linear_kernel and
     // sparse_linear_kernel.
     svm_c_linear_trainer<kernel_type> linear_trainer;
     // This trainer solves the "C" formulation of the SVM.  See the documentation for
@@ -59,7 +59,7 @@ int main()
     sample_type sample;
 
 
-    // Now lets go into a loop and randomly generate 10000 samples.
+    // Now let's go into a loop and randomly generate 10000 samples.
     srand(time(0));
     double label = +1;
     for (int i = 0; i < 10000; ++i)
@@ -87,11 +87,11 @@ int main()
         labels.push_back(label);
     }
 
-    // In addition to the rule we learned with the pegasos trainer lets also use our linear_trainer
-    // to learn a decision rule.
+    // In addition to the rule we learned with the pegasos trainer, let's also use our
+    // linear_trainer to learn a decision rule.
     decision_function<kernel_type> df = linear_trainer.train(samples, labels);
 
-    // Now we have trained our SVMs.  Lets test them out a bit.  
+    // Now we have trained our SVMs.  Let's test them out a bit.  
     // Each of these statements prints the output of the SVMs given a particular sample.  
     // Each SVM outputs a number > 0 if a sample is predicted to be in the +1 class and < 0 
     // if a sample is predicted to be in the -1 class.

examples/svm_struct_ex.cpp

@@ -245,7 +245,7 @@ public:
         // are the four virtual functions defined below.
 
 
-        // So lets make an empty 9-dimensional PSI vector
+        // So let's make an empty 9-dimensional PSI vector
         feature_vector_type psi(get_num_dimensions());
         psi = 0; // zero initialize it
 

examples/train_object_detector.cpp

@@ -23,7 +23,7 @@
         cmake --build . --config Release
     Note that you may need to install CMake (www.cmake.org) for this to work.  
 
-    Next, lets assume you have a folder of images called /tmp/images.  These images 
+    Next, let's assume you have a folder of images called /tmp/images.  These images 
     should contain examples of the objects you want to learn to detect.  You will 
     use the imglab tool to label these objects.  Do this by typing the following
         ./imglab -c mydataset.xml /tmp/images

examples/using_custom_kernels_ex.cpp

@@ -139,7 +139,7 @@ int main()
     typedef ukf_kernel<sample_type> kernel_type;
 
 
-    // Now lets generate some training data
+    // Now let's generate some training data
     std::vector<sample_type> samples;
     std::vector<double> labels;
     for (double r = -20; r <= 20; r += 0.9)
@@ -177,7 +177,7 @@ int main()
     trainer.use_classification_loss_for_loo_cv();
 
 
-    // Finally, lets test how good our new kernel is by doing some leave-one-out cross-validation.
+    // Finally, let's test how good our new kernel is by doing some leave-one-out cross-validation.
     cout << "\ndoing leave-one-out cross-validation" << endl;
     for (double sigma = 0.01; sigma <= 100; sigma *= 3)
     {