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/* { dg-do preprocess } */ /* { dg-options -pedantic-errors } */ #if 'a' != 'a' || '\001' != 1 || '\x12' != 0x12 #error a,1,0x12 /* { dg-bogus "#error" "basic charconst recognition" } */ #endif #if

Use of KNNR for Iris data: Size of design set (odd-indexed data)= 75 Size of test set (even-indexed data) = 75 After k-means clustering, size of design set = 9 Recognition rate = 97.3333%

Construct 15 KNN models, each with up to 4 inputs selected from 4 candidates... modelIndex 1/15: sepal length --> Recognition rate = 58.666667% modelIndex 2/15: sepal width --> Recognition rate =

Construct 15 KNN models, each with up to 4 inputs selected from 4 candidates... modelIndex 1/15: 1 --> Recognition rate = 58.666667% modelIndex 2/15: 2 --> Recognition rate = 48.000000% modelInde

Construct 15 KNN models, each with up to 4 inputs selected from 4 candidates... modelIndex 1/15: sepal length --> Recognition rate = 58.666667% modelIndex 2/15: sepal width --> Recognition rate =

Construct 10 KNN models, each with up to 4 inputs selected from 4 candidates... Selecting input 1: Model 1/10:1 --> Recognition rate = 58.7% Model 2/10:2 --> Recognition rate = 48.0% Model 3/1

Construct 2379 KNN models, each with up to 5 inputs selected from 13 candidates... modelIndex 1/2379: 1 --> Recognition rate = 59.550562% modelIndex 2/2379: 2 --> Recognition rate = 55.056180%

Construct 2379 KNN models, each with up to 5 inputs selected from 13 candidates... modelIndex 1/2379: 1 --> Recognition rate = 59.550562% modelIndex 2/2379: 2 --> Recognition rate = 55.056180%

Construct 63 KNN models, each with up to 6 inputs selected from 6 candidates... modelIndex 1/63: 1 --> Recognition rate = 49.500000% modelIndex 2/63: 2 --> Recognition rate = 49.000000% modelInde

Construct 63 KNN models, each with up to 6 inputs selected from 6 candidates... modelIndex 1/63: 1 --> Recognition rate = 42.500000% modelIndex 2/63: 2 --> Recognition rate = 46.000000% modelInde