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.