updated docs + minor refactoring

Aspheria · Nov 1, 2015 · 6b1e9c9 · 6b1e9c9
1 parent ee30cd8
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Showing 3 changed files with 12 additions and 10 deletions.
diff --git a/src/include/fann_train.h b/src/include/fann_train.h
@@ -587,6 +587,8 @@ FANN_EXTERNAL void FANN_API fann_descale_output( struct fann *ann, fann_type *ou
    It is not recommended to use this on subsets of data as the complete input range might not be
    available in that subset.
 
+   For more powerful scaling, please consider <fann_scale_train>
+
    See also:
    	<fann_scale_output_train_data>, <fann_scale_train_data>, <fann_scala_input>
 
@@ -606,6 +608,8 @@ FANN_EXTERNAL void FANN_API fann_scale_input_train_data(struct fann_train_data *
    It is not recommended to use this on subsets of data as the complete input range might not be
    available in that subset.
 
+   For more powerful scaling, please consider <fann_scale_train>
+
    See also:
    	<fann_scale_input_train_data>, <fann_scale_train_data>
 
@@ -625,6 +629,8 @@ FANN_EXTERNAL void FANN_API fann_scale_output_train_data(struct fann_train_data
    It is not recommended to use this on subsets of data as the complete input range might not be
    available in that subset.
 
+   For more powerful scaling, please consider <fann_scale_train>
+
    See also:
    	<fann_scale_output_train_data>, <fann_scale_input_train_data>
 

diff --git a/src/include/fann_training_data_cpp.h b/src/include/fann_training_data_cpp.h
@@ -459,6 +459,8 @@ namespace FANN {
            It is not recommended to use this on subsets of data as the complete input range might not be
            available in that subset.
 
+           For more powerful scaling, please consider <neural_net::scale_train>
+
            See also:
    	        <scale_output_train_data>, <scale_train_data>, <fann_scale_input_train_data>
 
@@ -480,6 +482,8 @@ namespace FANN {
            It is not recommended to use this on subsets of data as the complete input range might not be
            available in that subset.
 
+           For more powerful scaling, please consider <neural_net::scale_train>
+
            See also:
    	        <scale_input_train_data>, <scale_train_data>, <fann_scale_output_train_data>
 
@@ -501,6 +505,8 @@ namespace FANN {
            It is not recommended to use this on subsets of data as the complete input range might not be
            available in that subset.
 
+           For more powerful scaling, please consider <neural_net::scale_train>
+
            See also:
    	        <scale_output_train_data>, <scale_input_train_data>, <fann_scale_train_data>
 

diff --git a/tests/train_data.cpp b/tests/train_data.cpp
@@ -97,11 +97,6 @@ TEST_F(FannTest, ScaleOutputData) {
     fann_type output[] = {0.0, 1.0};
     data.set_train_data(2, 3, input, 1, output);
 
-    EXPECT_DOUBLE_EQ(0.0, data.get_min_input());
-    EXPECT_DOUBLE_EQ(1.0, data.get_max_input());
-    EXPECT_DOUBLE_EQ(0.0, data.get_min_output());
-    EXPECT_DOUBLE_EQ(1.0, data.get_max_output());
-
     data.scale_output_train_data(-1.0, 2.0);
 
     EXPECT_DOUBLE_EQ(0.0, data.get_min_input());
@@ -115,11 +110,6 @@ TEST_F(FannTest, ScaleInputData) {
     fann_type output[] = {0.0, 1.0};
     data.set_train_data(2, 3, input, 1, output);
 
-    EXPECT_DOUBLE_EQ(0.0, data.get_min_input());
-    EXPECT_DOUBLE_EQ(1.0, data.get_max_input());
-    EXPECT_DOUBLE_EQ(0.0, data.get_min_output());
-    EXPECT_DOUBLE_EQ(1.0, data.get_max_output());
-
     data.scale_input_train_data(-1.0, 2.0);
     EXPECT_DOUBLE_EQ(-1.0, data.get_min_input());
     EXPECT_DOUBLE_EQ(2.0, data.get_max_input());