This example will train or evolve(NEAT or HyperNEAT) a Neural Network to classify the Iris Dataset.
usage: IrisClassification -df <dataInput> <options> [-m <filename>]
examples:
IrisClassification -df iris.data.csv -t // to train from zero
IrisClassification -df iris.data.csv -t -m irisModel.json // to keep training irisModel.json
V evolve a population till loss is under 0.07 if hn HyperNeat will be used.
IrisClassification -df iris.data.csv -e 0.07 [hn]
-df <dataFilename> The file with the Iris Dataset.
dataInput is a csv file with this format <sepal length>,<sepal width>,<petal length>,<petal width>,Iris-setosa || Iris-versicolor || Iris-virginica
-m, --model <filename> Will load a IrisClassifier Model
filename should be a json file of an IrisClassifier if empty it will create a random nn and save it as irisModel.json to train or test.
-n, --norm Will normalize features of the data
-t, --train Will train the network specified.
-c, --classify Will test the network with the test data.
-e, --evolve <lossThreshold> [hn] Will evolve a population and select the best.
[hn] will use hyperneat instead of neat
-h, --help this menu.