# TOYAI

`toyai` is a small collection of ai algorithms to perform some simple prediction
on structured data.


IRIS DATASET for tests 
```
Relevant Information:
   --- This is perhaps the best known database to be found in the pattern
       recognition literature.  Fisher's paper is a classic in the field
       and is referenced frequently to this day.  (See Duda & Hart, for
       example.)  The data set contains 3 classes of 50 instances each,
       where each class refers to a type of iris plant.  One class is
       linearly separable from the other 2; the latter are NOT linearly
       separable from each other.
   --- Predicted attribute: class of iris plant.
   --- This is an exceedingly simple domain.
   --- This data differs from the data presented in Fishers article
	(identified by Steve Chadwick,  spchadwick@espeedaz.net )
	The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
	where the error is in the fourth feature.
	The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
	where the errors are in the second and third features.
```
Number of Instances: 150 (50 in each of three classes)

Number of Attributes: 4 numeric, predictive attributes and the class
```
Attribute Information:
   1. sepal length in cm
   2. sepal width in cm
   3. petal length in cm
   4. petal width in cm
   5. class:
      -- Iris Setosa
      -- Iris Versicolour
      -- Iris Virginica
```
Missing Attribute Values: None
```
Summary Statistics:
           Min  Max   Mean    SD   Class Correlation
   sepal length: 4.3  7.9   5.84  0.83    0.7826
    sepal width: 2.0  4.4   3.05  0.43   -0.4194
   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)

Class Distribution: 33.3% for each of 3 classes.
```

NOTE: for the experiments Iris virginica was removed