Notebook

In [1]:

%use deeplearning4j
%use krangl
%use lets-plot

In [2]:

val iris_data = "sepal-length,sepal-width,petal-length,petal-width,species\n5.1,3.5,1.4,0.2,Iris-setosa\n4.9,3.0,1.4,0.2,Iris-setosa\n4.7,3.2,1.3,0.2,Iris-setosa\n4.6,3.1,1.5,0.2,Iris-setosa\n5.0,3.6,1.4,0.2,Iris-setosa\n5.4,3.9,1.7,0.4,Iris-setosa\n4.6,3.4,1.4,0.3,Iris-setosa\n5.0,3.4,1.5,0.2,Iris-setosa\n4.4,2.9,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.4,3.7,1.5,0.2,Iris-setosa\n4.8,3.4,1.6,0.2,Iris-setosa\n4.8,3.0,1.4,0.1,Iris-setosa\n4.3,3.0,1.1,0.1,Iris-setosa\n5.8,4.0,1.2,0.2,Iris-setosa\n5.7,4.4,1.5,0.4,Iris-setosa\n5.4,3.9,1.3,0.4,Iris-setosa\n5.1,3.5,1.4,0.3,Iris-setosa\n5.7,3.8,1.7,0.3,Iris-setosa\n5.1,3.8,1.5,0.3,Iris-setosa\n5.4,3.4,1.7,0.2,Iris-setosa\n5.1,3.7,1.5,0.4,Iris-setosa\n4.6,3.6,1.0,0.2,Iris-setosa\n5.1,3.3,1.7,0.5,Iris-setosa\n4.8,3.4,1.9,0.2,Iris-setosa\n5.0,3.0,1.6,0.2,Iris-setosa\n5.0,3.4,1.6,0.4,Iris-setosa\n5.2,3.5,1.5,0.2,Iris-setosa\n5.2,3.4,1.4,0.2,Iris-setosa\n4.7,3.2,1.6,0.2,Iris-setosa\n4.8,3.1,1.6,0.2,Iris-setosa\n5.4,3.4,1.5,0.4,Iris-setosa\n5.2,4.1,1.5,0.1,Iris-setosa\n5.5,4.2,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.0,3.2,1.2,0.2,Iris-setosa\n5.5,3.5,1.3,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n4.4,3.0,1.3,0.2,Iris-setosa\n5.1,3.4,1.5,0.2,Iris-setosa\n5.0,3.5,1.3,0.3,Iris-setosa\n4.5,2.3,1.3,0.3,Iris-setosa\n4.4,3.2,1.3,0.2,Iris-setosa\n5.0,3.5,1.6,0.6,Iris-setosa\n5.1,3.8,1.9,0.4,Iris-setosa\n4.8,3.0,1.4,0.3,Iris-setosa\n5.1,3.8,1.6,0.2,Iris-setosa\n4.6,3.2,1.4,0.2,Iris-setosa\n5.3,3.7,1.5,0.2,Iris-setosa\n5.0,3.3,1.4,0.2,Iris-setosa\n7.0,3.2,4.7,1.4,Iris-versicolor\n6.4,3.2,4.5,1.5,Iris-versicolor\n6.9,3.1,4.9,1.5,Iris-versicolor\n5.5,2.3,4.0,1.3,Iris-versicolor\n6.5,2.8,4.6,1.5,Iris-versicolor\n5.7,2.8,4.5,1.3,Iris-versicolor\n6.3,3.3,4.7,1.6,Iris-versicolor\n4.9,2.4,3.3,1.0,Iris-versicolor\n6.6,2.9,4.6,1.3,Iris-versicolor\n5.2,2.7,3.9,1.4,Iris-versicolor\n5.0,2.0,3.5,1.0,Iris-versicolor\n5.9,3.0,4.2,1.5,Iris-versicolor\n6.0,2.2,4.0,1.0,Iris-versicolor\n6.1,2.9,4.7,1.4,Iris-versicolor\n5.6,2.9,3.6,1.3,Iris-versicolor\n6.7,3.1,4.4,1.4,Iris-versicolor\n5.6,3.0,4.5,1.5,Iris-versicolor\n5.8,2.7,4.1,1.0,Iris-versicolor\n6.2,2.2,4.5,1.5,Iris-versicolor\n5.6,2.5,3.9,1.1,Iris-versicolor\n5.9,3.2,4.8,1.8,Iris-versicolor\n6.1,2.8,4.0,1.3,Iris-versicolor\n6.3,2.5,4.9,1.5,Iris-versicolor\n6.1,2.8,4.7,1.2,Iris-versicolor\n6.4,2.9,4.3,1.3,Iris-versicolor\n6.6,3.0,4.4,1.4,Iris-versicolor\n6.8,2.8,4.8,1.4,Iris-versicolor\n6.7,3.0,5.0,1.7,Iris-versicolor\n6.0,2.9,4.5,1.5,Iris-versicolor\n5.7,2.6,3.5,1.0,Iris-versicolor\n5.5,2.4,3.8,1.1,Iris-versicolor\n5.5,2.4,3.7,1.0,Iris-versicolor\n5.8,2.7,3.9,1.2,Iris-versicolor\n6.0,2.7,5.1,1.6,Iris-versicolor\n5.4,3.0,4.5,1.5,Iris-versicolor\n6.0,3.4,4.5,1.6,Iris-versicolor\n6.7,3.1,4.7,1.5,Iris-versicolor\n6.3,2.3,4.4,1.3,Iris-versicolor\n5.6,3.0,4.1,1.3,Iris-versicolor\n5.5,2.5,4.0,1.3,Iris-versicolor\n5.5,2.6,4.4,1.2,Iris-versicolor\n6.1,3.0,4.6,1.4,Iris-versicolor\n5.8,2.6,4.0,1.2,Iris-versicolor\n5.0,2.3,3.3,1.0,Iris-versicolor\n5.6,2.7,4.2,1.3,Iris-versicolor\n5.7,3.0,4.2,1.2,Iris-versicolor\n5.7,2.9,4.2,1.3,Iris-versicolor\n6.2,2.9,4.3,1.3,Iris-versicolor\n5.1,2.5,3.0,1.1,Iris-versicolor\n5.7,2.8,4.1,1.3,Iris-versicolor\n6.3,3.3,6.0,2.5,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n7.1,3.0,5.9,2.1,Iris-virginica\n6.3,2.9,5.6,1.8,Iris-virginica\n6.5,3.0,5.8,2.2,Iris-virginica\n7.6,3.0,6.6,2.1,Iris-virginica\n4.9,2.5,4.5,1.7,Iris-virginica\n7.3,2.9,6.3,1.8,Iris-virginica\n6.7,2.5,5.8,1.8,Iris-virginica\n7.2,3.6,6.1,2.5,Iris-virginica\n6.5,3.2,5.1,2.0,Iris-virginica\n6.4,2.7,5.3,1.9,Iris-virginica\n6.8,3.0,5.5,2.1,Iris-virginica\n5.7,2.5,5.0,2.0,Iris-virginica\n5.8,2.8,5.1,2.4,Iris-virginica\n6.4,3.2,5.3,2.3,Iris-virginica\n6.5,3.0,5.5,1.8,Iris-virginica\n7.7,3.8,6.7,2.2,Iris-virginica\n7.7,2.6,6.9,2.3,Iris-virginica\n6.0,2.2,5.0,1.5,Iris-virginica\n6.9,3.2,5.7,2.3,Iris-virginica\n5.6,2.8,4.9,2.0,Iris-virginica\n7.7,2.8,6.7,2.0,Iris-virginica\n6.3,2.7,4.9,1.8,Iris-virginica\n6.7,3.3,5.7,2.1,Iris-virginica\n7.2,3.2,6.0,1.8,Iris-virginica\n6.2,2.8,4.8,1.8,Iris-virginica\n6.1,3.0,4.9,1.8,Iris-virginica\n6.4,2.8,5.6,2.1,Iris-virginica\n7.2,3.0,5.8,1.6,Iris-virginica\n7.4,2.8,6.1,1.9,Iris-virginica\n7.9,3.8,6.4,2.0,Iris-virginica\n6.4,2.8,5.6,2.2,Iris-virginica\n6.3,2.8,5.1,1.5,Iris-virginica\n6.1,2.6,5.6,1.4,Iris-virginica\n7.7,3.0,6.1,2.3,Iris-virginica\n6.3,3.4,5.6,2.4,Iris-virginica\n6.4,3.1,5.5,1.8,Iris-virginica\n6.0,3.0,4.8,1.8,Iris-virginica\n6.9,3.1,5.4,2.1,Iris-virginica\n6.7,3.1,5.6,2.4,Iris-virginica\n6.9,3.1,5.1,2.3,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n6.8,3.2,5.9,2.3,Iris-virginica\n6.7,3.3,5.7,2.5,Iris-virginica\n6.7,3.0,5.2,2.3,Iris-virginica\n6.3,2.5,5.0,1.9,Iris-virginica\n6.5,3.0,5.2,2.0,Iris-virginica\n6.2,3.4,5.4,2.3,Iris-virginica\n5.9,3.0,5.1,1.8,Iris-virginica"

In [3]:

import java.util.*
import java.io.StringReader

val iris = DataFrame.readDelim(StringReader(iris_data)).shuffle()
iris.head()

Out[3]:

sepal-length	sepal-width	petal-length	petal-width	species
5.1	3.3	1.7	0.5	Iris-setosa
5.8	2.7	5.1	1.9	Iris-virginica
5.6	2.8	4.9	2.0	Iris-virginica
4.8	3.0	1.4	0.3	Iris-setosa
7.7	2.6	6.9	2.3	Iris-virginica

In [4]:

val points = geomPoint(
    data = mapOf(
        "x" to iris["sepal-length"].values().toList(),
        "y" to iris["sepal-width"].values().toList(),
        "color" to iris["species"].values().toList()
    ), alpha=1.0)
{
    x = "x" 
    y = "y"
    color = "color"
}

ggplot() + points

Out[4]:

In [5]:

val irisWithoutLabel = iris.remove("species")
irisWithoutLabel.head()

Out[5]:

sepal-length	sepal-width	petal-length	petal-width
5.1	3.3	1.7	0.5
5.8	2.7	5.1	1.9
5.6	2.8	4.9	2.0
4.8	3.0	1.4	0.3
7.7	2.6	6.9	2.3

In [6]:

//Convert the iris data into 150x4 matrix
val row = 150
val col = 4

val irisMatrix = Array(row) { DoubleArray(col) }
var i = 0
for (r in 0 until row) {
    for (c in 0 until col) {
        irisMatrix[r][c] = irisWithoutLabel[c][r] as Double
    }
}
println(Arrays.deepToString(irisMatrix).replace("], ", "]\n"))

[[5.1, 3.3, 1.7, 0.5]
[5.8, 2.7, 5.1, 1.9]
[5.6, 2.8, 4.9, 2.0]
[4.8, 3.0, 1.4, 0.3]
[7.7, 2.6, 6.9, 2.3]
[5.6, 2.9, 3.6, 1.3]
[6.9, 3.1, 5.4, 2.1]
[5.9, 3.0, 4.2, 1.5]
[4.9, 3.1, 1.5, 0.1]
[6.8, 2.8, 4.8, 1.4]
[6.0, 2.2, 5.0, 1.5]
[6.0, 3.4, 4.5, 1.6]
[5.4, 3.9, 1.3, 0.4]
[5.7, 3.0, 4.2, 1.2]
[7.2, 3.0, 5.8, 1.6]
[6.0, 2.7, 5.1, 1.6]
[6.4, 3.2, 5.3, 2.3]
[5.7, 2.8, 4.1, 1.3]
[5.7, 2.5, 5.0, 2.0]
[6.2, 2.8, 4.8, 1.8]
[5.0, 3.5, 1.3, 0.3]
[5.7, 4.4, 1.5, 0.4]
[6.3, 2.5, 5.0, 1.9]
[7.7, 3.0, 6.1, 2.3]
[4.8, 3.0, 1.4, 0.1]
[5.8, 2.7, 3.9, 1.2]
[5.1, 2.5, 3.0, 1.1]
[6.4, 2.8, 5.6, 2.1]
[5.3, 3.7, 1.5, 0.2]
[4.6, 3.4, 1.4, 0.3]
[7.6, 3.0, 6.6, 2.1]
[4.5, 2.3, 1.3, 0.3]
[5.6, 2.7, 4.2, 1.3]
[5.7, 2.6, 3.5, 1.0]
[6.7, 3.0, 5.0, 1.7]
[6.5, 3.0, 5.8, 2.2]
[5.0, 2.3, 3.3, 1.0]
[6.1, 3.0, 4.9, 1.8]
[6.5, 3.0, 5.2, 2.0]
[6.2, 3.4, 5.4, 2.3]
[4.4, 2.9, 1.4, 0.2]
[5.2, 3.5, 1.5, 0.2]
[7.2, 3.6, 6.1, 2.5]
[5.5, 4.2, 1.4, 0.2]
[6.4, 2.9, 4.3, 1.3]
[4.9, 3.0, 1.4, 0.2]
[6.3, 2.5, 4.9, 1.5]
[5.5, 2.4, 3.7, 1.0]
[4.7, 3.2, 1.6, 0.2]
[6.3, 2.7, 4.9, 1.8]
[6.3, 2.3, 4.4, 1.3]
[7.1, 3.0, 5.9, 2.1]
[5.0, 3.5, 1.6, 0.6]
[6.8, 3.0, 5.5, 2.1]
[4.8, 3.4, 1.9, 0.2]
[6.7, 3.1, 5.6, 2.4]
[5.8, 2.6, 4.0, 1.2]
[5.0, 3.2, 1.2, 0.2]
[6.7, 3.3, 5.7, 2.5]
[5.1, 3.5, 1.4, 0.2]
[6.4, 2.7, 5.3, 1.9]
[7.0, 3.2, 4.7, 1.4]
[6.1, 2.8, 4.7, 1.2]
[5.4, 3.4, 1.7, 0.2]
[4.9, 2.4, 3.3, 1.0]
[5.2, 3.4, 1.4, 0.2]
[6.5, 2.8, 4.6, 1.5]
[5.4, 3.0, 4.5, 1.5]
[7.3, 2.9, 6.3, 1.8]
[5.2, 2.7, 3.9, 1.4]
[5.4, 3.9, 1.7, 0.4]
[6.2, 2.2, 4.5, 1.5]
[5.1, 3.5, 1.4, 0.3]
[4.8, 3.4, 1.6, 0.2]
[7.7, 3.8, 6.7, 2.2]
[5.6, 3.0, 4.5, 1.5]
[6.3, 3.4, 5.6, 2.4]
[5.8, 2.8, 5.1, 2.4]
[5.5, 2.3, 4.0, 1.3]
[4.9, 2.5, 4.5, 1.7]
[6.0, 2.2, 4.0, 1.0]
[5.0, 2.0, 3.5, 1.0]
[5.9, 3.2, 4.8, 1.8]
[5.4, 3.4, 1.5, 0.4]
[6.9, 3.1, 4.9, 1.5]
[4.9, 3.1, 1.5, 0.1]
[5.2, 4.1, 1.5, 0.1]
[5.1, 3.8, 1.5, 0.3]
[5.1, 3.8, 1.6, 0.2]
[6.7, 3.1, 4.7, 1.5]
[5.9, 3.0, 5.1, 1.8]
[5.8, 4.0, 1.2, 0.2]
[4.3, 3.0, 1.1, 0.1]
[6.7, 2.5, 5.8, 1.8]
[6.3, 3.3, 6.0, 2.5]
[5.6, 2.5, 3.9, 1.1]
[4.4, 3.2, 1.3, 0.2]
[4.6, 3.1, 1.5, 0.2]
[5.5, 2.6, 4.4, 1.2]
[6.9, 3.1, 5.1, 2.3]
[6.0, 2.9, 4.5, 1.5]
[7.2, 3.2, 6.0, 1.8]
[6.1, 2.8, 4.0, 1.3]
[5.7, 2.9, 4.2, 1.3]
[5.8, 2.7, 4.1, 1.0]
[4.8, 3.1, 1.6, 0.2]
[6.9, 3.2, 5.7, 2.3]
[5.5, 2.4, 3.8, 1.1]
[5.0, 3.4, 1.5, 0.2]
[4.6, 3.2, 1.4, 0.2]
[4.9, 3.1, 1.5, 0.1]
[6.0, 3.0, 4.8, 1.8]
[6.3, 2.9, 5.6, 1.8]
[6.6, 3.0, 4.4, 1.4]
[7.9, 3.8, 6.4, 2.0]
[5.6, 3.0, 4.1, 1.3]
[5.7, 3.8, 1.7, 0.3]
[5.0, 3.4, 1.6, 0.4]
[5.7, 2.8, 4.5, 1.3]
[6.7, 3.3, 5.7, 2.1]
[6.7, 3.1, 4.4, 1.4]
[6.7, 3.0, 5.2, 2.3]
[5.5, 2.5, 4.0, 1.3]
[5.0, 3.3, 1.4, 0.2]
[4.4, 3.0, 1.3, 0.2]
[6.6, 2.9, 4.6, 1.3]
[7.4, 2.8, 6.1, 1.9]
[6.5, 3.0, 5.5, 1.8]
[6.3, 2.8, 5.1, 1.5]
[6.4, 3.2, 4.5, 1.5]
[6.1, 2.9, 4.7, 1.4]
[4.6, 3.6, 1.0, 0.2]
[5.4, 3.7, 1.5, 0.2]
[5.5, 3.5, 1.3, 0.2]
[6.1, 3.0, 4.6, 1.4]
[5.8, 2.7, 5.1, 1.9]
[6.8, 3.2, 5.9, 2.3]
[6.4, 3.1, 5.5, 1.8]
[7.7, 2.8, 6.7, 2.0]
[5.0, 3.0, 1.6, 0.2]
[6.2, 2.9, 4.3, 1.3]
[5.1, 3.4, 1.5, 0.2]
[6.5, 3.2, 5.1, 2.0]
[5.1, 3.7, 1.5, 0.4]
[6.4, 2.8, 5.6, 2.2]
[5.1, 3.8, 1.9, 0.4]
[5.0, 3.6, 1.4, 0.2]
[4.7, 3.2, 1.3, 0.2]
[6.3, 3.3, 4.7, 1.6]
[6.1, 2.6, 5.6, 1.4]]

In [7]:

//Now do the same for the label data
val irisLabel = iris.select("species")[0]

val rowLabel = 150
val colLabel = 3

val twodimLabel = Array(rowLabel) { DoubleArray(colLabel) }
for (r in 0 until rowLabel) {
    when (irisLabel[r]) {
        "Iris-setosa" -> twodimLabel[r][0] = 1.0
        "Iris-versicolor" -> twodimLabel[r][1] = 1.0
        "Iris-virginica" -> twodimLabel[r][2] = 1.0
    }
}
println(Arrays.deepToString(twodimLabel).replace("], ", "]\n"))

[[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 1.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[0.0, 0.0, 1.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[1.0, 0.0, 0.0]
[0.0, 1.0, 0.0]
[0.0, 0.0, 1.0]]

In [8]:

//Convert the data matrices into training INDArrays
val dataIn = Nd4j.create(irisMatrix)
val dataOut = Nd4j.create(twodimLabel)

In [9]:

import org.nd4j.linalg.lossfunctions.LossFunctions

val seed: Long = 6

val conf = NeuralNetConfiguration.Builder()
        .seed(seed) //include a random seed for reproducibility
        // use stochastic gradient descent as an optimization algorithm
        .updater(Nadam()) //specify the rate of change of the learning rate.
        .l2(1e-4)
        .list()
        .layer(DenseLayer.Builder()
                .nIn(4)
                .nOut(3)
                .activation(Activation.TANH)
                .weightInit(WeightInit.XAVIER)
                .build())
        .layer(org.deeplearning4j.nn.conf.layers.DenseLayer.Builder()
                .nIn(3)
                .nOut(3)
                .build())
        .layer(OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                .nIn(3)
                .nOut(3)
                .activation(Activation.SOFTMAX)
                .weightInit(WeightInit.XAVIER)
                .build())
        .build()

val model = MultiLayerNetwork(conf)

In [10]:

import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize

//Create a data set from the INDArrays and shuffle it 
val fullDataSet = DataSet(dataIn, dataOut)
fullDataSet.shuffle(seed)

val splitedSet = fullDataSet.splitTestAndTrain(0.90)
val trainingData = splitedSet.train;
val testData = splitedSet.test;

//We need to normalize our data. We'll use NormalizeStandardize (which gives us mean 0, unit variance):
val normalizer: DataNormalization = NormalizerStandardize()
normalizer.fit(trainingData) //Collect the statistics (mean/stdev) from the training data. This does not modify the input data
normalizer.transform(trainingData) //Apply normalization to the training data
normalizer.transform(testData) //Apply normalization to the test data. This is using statistics calculated from the *training* set

// train the network
model.setListeners(ScoreIterationListener(100))
for (l in 0..2000) {
    model.fit(trainingData)
}

// evaluate the network
val eval = Evaluation()
val output: INDArray = model.output(testData.features)
eval.eval(testData.labels, output)
println("Score " + eval.stats())

Score 

========================Evaluation Metrics========================
 # of classes:    3
 Accuracy:        1,0000
 Precision:       1,0000
 Recall:          1,0000
 F1 Score:        1,0000
Precision, recall & F1: macro-averaged (equally weighted avg. of 3 classes)


=========================Confusion Matrix=========================
 0 1 2
-------
 5 0 0 | 0 = 0
 0 3 0 | 1 = 1
 0 0 7 | 2 = 2

Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
==================================================================

In [ ]:

sepal-length	sepal-width	petal-length	petal-width
5.1	3.3	1.7	0.5
5.8	2.7	5.1	1.9
5.6	2.8	4.9	2.0
4.8	3.0	1.4	0.3
7.7	2.6	6.9	2.3

sepal-length	sepal-width	petal-length	petal-width
5.1	3.3	1.7	0.5
5.8	2.7	5.1	1.9
5.6	2.8	4.9	2.0
4.8	3.0	1.4	0.3
7.7	2.6	6.9	2.3

sepal-length	sepal-width	petal-length	petal-width
5.1	3.3	1.7	0.5
5.8	2.7	5.1	1.9
5.6	2.8	4.9	2.0
4.8	3.0	1.4	0.3
7.7	2.6	6.9	2.3