Fixing grammar in comments.

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