from utils.Data import Data
from factories.NetworkFactory import NetworkFactory
from utils.Tags import NetworkTags
from utils.Functions import ActivaionFunction
from utils.NetworkHelper import NetworkHelper
data = Data("resources/IrisDataTrain.xls", 125, shufle_data=True)
data.label_iris_dat()
normalized_data = data.normalize_data()
layers = [data.def_input_neurons(), 3, data.def_input_neurons()]
encoder = NetworkFactory.create(NetworkTags.AutoEncoder, layers)
encoder.train(normalized_data,
normalized_data,
repetitions=500,
activation_function=ActivaionFunction.HYPERBOLIC_TANGENT)
encoder.remove_unneeded_layers()
layers = [data.def_input_neurons(), 15, 7, data.def_output_neurons()]
mlp = NetworkFactory.create(NetworkTags.MLPWithEachLayerConnection, layers)
merged = NetworkHelper.merge_autoencoder_to_mlp(encoder, mlp)
finnal = NetworkHelper.add_neurons_to_first_hidden_layer(merged, 6)
finnal.update_id()
finnal.train(data.normalize_data(),
data.label_iris_dat(),
repetitions=500,
activation_function=ActivaionFunction.HYPERBOLIC_TANGENT)
predict_data = Data("resources/IrisData.xls", 150, shufle_data=False)
returned_value = finnal.predict(
predict_data.normalize_data(),
from utils.Data import Data
from factories.NetworkFactory import NetworkFactory
from utils.Tags import NetworkTags
from utils.WinnerHolder import WinnerHolder
data = Data("resources/IrisDataTrain.xls", 125, shufle_data=True)
data.label_iris_dat()
normalized_data = data.normalize_data()
layers = [data.def_input_neurons(), 4, 4, 4, 4]
som = NetworkFactory.create(NetworkTags.SOM, layers)
# som.print()
som.train(data.get_raw_data(), data.label_iris_dat(), 2000)
# som.print()
# print("=============================================")
# print(WinnerHolder.get_winner())
# print("distance")
# print(som.layers_list[1].neuron_vector[0])