Add checks into the vector_normalizer and vector_normalizer_pca objects

to detect users giving infinite and NaN values as inputs.

dlib/statistics/statistics.h

@@ -1148,6 +1148,8 @@ namespace dlib
 
             m = mean(mat(samples));
             sd = reciprocal(sqrt(variance(mat(samples))));
+
+            DLIB_ASSERT(is_finite(m), "Some of the input vectors to vector_normalizer::train() have infinite or NaN values");
         }
 
         long in_vector_size (
@@ -1310,6 +1312,8 @@ namespace dlib
                 << "\n\tthis: " << this
                 );
             train_pca_impl(mat(samples),eps);
+
+            DLIB_ASSERT(is_finite(m), "Some of the input vectors to vector_normalizer_pca::train() have infinite or NaN values");
         }
 
         long in_vector_size (

dlib/statistics/statistics_abstract.h

@@ -770,6 +770,7 @@ namespace dlib
                 - samples == a column matrix or something convertible to a column 
                   matrix via mat().  Also, x should contain 
                   matrix_type objects that represent nonempty column vectors.
+                - samples does not contain any infinite or NaN values
             ensures
                 - #in_vector_size() == samples(0).nr()
                 - #out_vector_size() == samples(0).nr()
@@ -928,6 +929,7 @@ namespace dlib
                 - samples == a column matrix or something convertible to a column 
                   matrix via mat().  Also, x should contain 
                   matrix_type objects that represent nonempty column vectors.
+                - samples does not contain any infinite or NaN values
             ensures
                 - This object has learned how to normalize vectors that look like
                   vectors in the given set of samples.