fixed more grammar

python_examples/max_cost_assignment.py

@@ -17,14 +17,14 @@
 
 import dlib
 
-# Lets imagine you need to assign N people to N jobs.  Additionally, each person will make
+# Let's imagine you need to assign N people to N jobs.  Additionally, each person will make
 # your company a certain amount of money at each job, but each person has different skills
 # so they are better at some jobs and worse at others.  You would like to find the best way
 # to assign people to these jobs.  In particular, you would like to maximize the amount of
 # money the group makes as a whole.  This is an example of an assignment problem and is
 # what is solved by the dlib.max_cost_assignment() routine.
 
-# So in this example, lets imagine we have 3 people and 3 jobs.  We represent the amount of
+# So in this example, let's imagine we have 3 people and 3 jobs.  We represent the amount of
 # money each person will produce at each job with a cost matrix.  Each row corresponds to a
 # person and each column corresponds to a job.  So for example, below we are saying that
 # person 0 will make $1 at job 0, $2 at job 1, and $6 at job 2.  

python_examples/sequence_segmenter.py

@@ -78,7 +78,7 @@ def print_segment(sentence, names):
 
 
 
-# Now lets make some training data.  Each example is a sentence as well as a set of ranges
+# Now let's make some training data.  Each example is a sentence as well as a set of ranges
 # which indicate the locations of any names.   
 names = dlib.ranges()     # make an array of dlib.range objects.
 segments = dlib.rangess() # make an array of arrays of dlib.range objects.
@@ -159,13 +159,13 @@ params.C = 10
 model = dlib.train_sequence_segmenter(training_sequences, segments, params)
 
 
-# Lets print out the things the model thinks are names.  The output is a set of ranges
+# Let's print out the things the model thinks are names.  The output is a set of ranges
 # which are predicted to contain names.  If you run this example program you will see that
 # it gets them all correct. 
 for i in range(len(sentences)):
     print_segment(sentences[i], model(training_sequences[i]))
 
-# Lets also try segmenting a new sentence.  This will print out "Bob Bucket".  Note that we
+# Let's also try segmenting a new sentence.  This will print out "Bob Bucket".  Note that we
 # need to remember to use the same vector representation as we used during training.
 test_sentence = "There once was a man from Nantucket whose name rhymed with Bob Bucket"
 if use_sparse_vects:

python_examples/svm_rank.py

@@ -25,7 +25,7 @@
 import dlib
 
 
-# Now lets make some testing data.  To make it really simple, lets suppose that
+# Now let's make some testing data.  To make it really simple, let's suppose that
 # we are ranking 2D vectors and 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 ranking)
@@ -47,7 +47,7 @@ trainer = dlib.svm_rank_trainer()
 # selecting a "simpler" solution which might generalize better. 
 trainer.c = 10
 
-# So lets do the training.
+# So let's do the training.
 rank = trainer.train(data)
 
 # Now if you call rank on a vector it will output a ranking score.  In