iris test and train pre-constructed

data/README.md

@@ -130,4 +130,20 @@ Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: Uni
 
 Creator: R.A. Fisher (1936)
 
+#### iris_test40.csv and iris_train60.csv
 
+
+
+The datasets `iris_test40.csv` and `iris_train60.csv` are randomly sampled from the original `iris.csv` dataset where `iris_test40.csv` contains 40% of the original samples and the remaining 60% are contained in `iris_train60.csv`. The class labels in the 5th column where replaced by integers 1-4, where 
+
+- 1 = Iris-setosa
+- 2 = Iris-versicolor
+- 3 = Iris-virginica
+
+
+| class | # of samples in test set | # of samples in training set | total
+|-------|------|----------|
+| Iris-setosa     | 21 | 29 | 50 |
+| Iris-versicolor | 22 | 28 | 50 |
+| Iris-virginica  | 17 | 33 | 50 |
+| total           | 60 | 90 | 150|

data/iris_test40.csv

@@ -0,0 +1,60 @@
+5.6,2.5,3.9,1.1,2
+4.4,3.2,1.3,0.2,1
+6.3,3.3,4.7,1.6,2
+5,3.2,1.2,0.2,1
+5.4,3.9,1.3,0.4,1
+4.8,3.4,1.9,0.2,1
+6.1,2.8,4.7,1.2,2
+5.4,3.7,1.5,0.2,1
+5.6,2.7,4.2,1.3,2
+6.1,2.6,5.6,1.4,3
+5.1,3.5,1.4,0.3,1
+6.2,3.4,5.4,2.3,3
+6,3.4,4.5,1.6,2
+5.9,3.2,4.8,1.8,2
+6.5,3.2,5.1,2,3
+6.5,3,5.8,2.2,3
+5.2,3.4,1.4,0.2,1
+6.4,2.8,5.6,2.1,3
+5.8,2.6,4,1.2,2
+6.7,3.1,4.4,1.4,2
+5.4,3,4.5,1.5,2
+5.5,2.6,4.4,1.2,2
+5.5,4.2,1.4,0.2,1
+6.1,2.8,4,1.3,2
+4.6,3.2,1.4,0.2,1
+6.3,3.3,6,2.5,3
+5,3.6,1.4,0.2,1
+5.7,2.8,4.5,1.3,2
+4.7,3.2,1.3,0.2,1
+6.3,2.7,4.9,1.8,3
+5.1,3.8,1.5,0.3,1
+5.3,3.7,1.5,0.2,1
+6.3,2.5,5,1.9,3
+5.6,2.8,4.9,2,3
+5,2,3.5,1,2
+7.7,3.8,6.7,2.2,3
+6.4,3.1,5.5,1.8,3
+5.8,2.7,4.1,1,2
+5.1,3.4,1.5,0.2,1
+6.4,3.2,4.5,1.5,2
+4.3,3,1.1,0.1,1
+5.7,2.6,3.5,1,2
+5,3,1.6,0.2,1
+6.1,3,4.9,1.8,3
+5.7,2.8,4.1,1.3,2
+6,2.9,4.5,1.5,2
+4.9,3.1,1.5,0.1,1
+6.2,2.2,4.5,1.5,2
+6.5,2.8,4.6,1.5,2
+6.9,3.1,4.9,1.5,2
+7.7,3,6.1,2.3,3
+4.4,2.9,1.4,0.2,1
+5.5,2.5,4,1.3,2
+4.8,3,1.4,0.3,1
+6.9,3.1,5.4,2.1,3
+6.3,3.4,5.6,2.4,3
+5.4,3.4,1.7,0.2,1
+6.9,3.1,5.1,2.3,3
+5,3.5,1.3,0.3,1
+6.5,3,5.2,2,3

data/iris_train60.csv

@@ -0,0 +1,90 @@
+7.2,3.6,6.1,2.5,3
+6.8,3.2,5.9,2.3,3
+6.7,3.3,5.7,2.1,3
+5,2.3,3.3,1,2
+7.2,3.2,6,1.8,3
+5.7,3,4.2,1.2,2
+4.9,3.1,1.5,0.1,1
+5,3.3,1.4,0.2,1
+5.6,3,4.5,1.5,2
+4.6,3.4,1.4,0.3,1
+7.7,2.8,6.7,2,3
+6.9,3.2,5.7,2.3,3
+5.2,4.1,1.5,0.1,1
+4.8,3.1,1.6,0.2,1
+7.9,3.8,6.4,2,3
+5,3.4,1.6,0.4,1
+7.1,3,5.9,2.1,3
+6.7,3.1,4.7,1.5,2
+5.1,3.8,1.9,0.4,1
+5.1,3.5,1.4,0.2,1
+6.1,2.9,4.7,1.4,2
+6.4,2.8,5.6,2.2,3
+5.1,3.7,1.5,0.4,1
+7.4,2.8,6.1,1.9,3
+6.6,2.9,4.6,1.3,2
+5.1,3.8,1.6,0.2,1
+4.9,2.4,3.3,1,2
+5.8,2.7,3.9,1.2,2
+7,3.2,4.7,1.4,2
+5.7,3.8,1.7,0.3,1
+4.5,2.3,1.3,0.3,1
+6,2.2,4,1,2
+6.3,2.9,5.6,1.8,3
+4.6,3.6,1,0.2,1
+6.8,2.8,4.8,1.4,2
+6.4,2.9,4.3,1.3,2
+4.9,2.5,4.5,1.7,3
+5.7,2.9,4.2,1.3,2
+5.6,3,4.1,1.3,2
+4.4,3,1.3,0.2,1
+5.2,3.5,1.5,0.2,1
+5.5,2.4,3.8,1.1,2
+6.5,3,5.5,1.8,3
+4.8,3,1.4,0.1,1
+6,2.7,5.1,1.6,2
+6.7,3.1,5.6,2.4,3
+6.7,2.5,5.8,1.8,3
+5.1,2.5,3,1.1,2
+6.8,3,5.5,2.1,3
+6.3,2.5,4.9,1.5,2
+5.5,2.3,4,1.3,2
+6.7,3,5.2,2.3,3
+6,2.2,5,1.5,3
+7.3,2.9,6.3,1.8,3
+6.3,2.8,5.1,1.5,3
+6.4,3.2,5.3,2.3,3
+5.8,4,1.2,0.2,1
+6.6,3,4.4,1.4,2
+5.7,4.4,1.5,0.4,1
+5.4,3.9,1.7,0.4,1
+4.6,3.1,1.5,0.2,1
+6.4,2.7,5.3,1.9,3
+5.6,2.9,3.6,1.3,2
+5.5,3.5,1.3,0.2,1
+6.3,2.3,4.4,1.3,2
+6,3,4.8,1.8,3
+6.2,2.9,4.3,1.3,2
+5.9,3,4.2,1.5,2
+5.4,3.4,1.5,0.4,1
+6.7,3.3,5.7,2.5,3
+5.1,3.3,1.7,0.5,1
+5,3.5,1.6,0.6,1
+5,3.4,1.5,0.2,1
+5.5,2.4,3.7,1,2
+6.7,3,5,1.7,2
+4.8,3.4,1.6,0.2,1
+5.8,2.8,5.1,2.4,3
+5.8,2.7,5.1,1.9,3
+6.1,3,4.6,1.4,2
+7.6,3,6.6,2.1,3
+5.2,2.7,3.9,1.4,2
+5.7,2.5,5,2,3
+7.7,2.6,6.9,2.3,3
+4.9,3,1.4,0.2,1
+5.9,3,5.1,1.8,3
+4.9,3.1,1.5,0.1,1
+5.8,2.7,5.1,1.9,3
+6.2,2.8,4.8,1.8,3
+7.2,3,5.8,1.6,3
+4.7,3.2,1.6,0.2,1