import matplotlib.pyplot as plt
import numpy as np
from dataSetTool import DataSetTool
from neuralNetwork.feedForwardNeuralNetwork import NeuralNetwork
from neuralNetwork.structure.layer import Layer
from numberGenerator.bounds import Bounds
np.set_printoptions(suppress=True)
dataSetTool = DataSetTool()
training, generalization, testing = dataSetTool.getIrisDataSets(
'../../dataSet/iris/iris.data')
plt.grid(1)
plt.xlabel('Iterations')
plt.ylabel('Error')
plt.ylim([0, 1])
plt.ion()
l_rate = 0.5
bounds = Bounds(-2, 2)
inputLayer = Layer(bounds,
size=len(training[0][0]),
prev=None,
l_rate=l_rate,
bias=True,
label="Input layer")
hiddenLayer = Layer(bounds,
import matplotlib.pyplot as plt
import numpy as np
from dataSetTool import DataSetTool
from neuralNetwork.feedForwardNeuralNetwork import NeuralNetwork
from neuralNetwork.structure.layer import Layer
from numberGenerator.bounds import Bounds
np.set_printoptions(suppress=True)
dataSetTool = DataSetTool()
training, testing, generalization = dataSetTool.getGlassDataSets(
'../../dataSet/glass/glass.data')
l_rate = 0.01
bounds = Bounds(-2, 2)
inputLayer = Layer(bounds,
size=len(training[0][0]),
prev=None,
l_rate=l_rate,
bias=True,
label="Input layer")
hiddenLayer = Layer(bounds,
size=12,
prev=inputLayer,
l_rate=l_rate,
bias=True,
label="Hidden layer")
outputLayer = Layer(bounds,
import matplotlib.pyplot as plt
import numpy as np
from dataSetTool import DataSetTool
from neuralNetwork.feedForwardNeuralNetwork import NeuralNetwork
from neuralNetwork.structure.layer import Layer
from numberGenerator.bounds import Bounds
np.set_printoptions(suppress=True)
dataSetTool = DataSetTool()
training, generalization, testing = dataSetTool.getHeartDataSets(
'../../dataSet/heart/processed.cleveland.data')
plt.xlabel('Iterations')
plt.ylabel('Error')
plt.ion()
l_rate = 0.1
bounds = Bounds(-2, 2)
inputLayer = Layer(bounds,
size=len(training[0][0]),
prev=None,
l_rate=l_rate,
bias=True,
label="Input layer")
hiddenLayer = Layer(bounds,
size=6,
prev=inputLayer,
input = np.array([[0, 0],
[1, 0],
[0, 1],
[1, 1]])
target = np.array([[0],
[1],
[1],
[0]])
training = []
for x, y in zip(input, target):
training.append((x, y))
# Get data set
dataSetTool = DataSetTool()
psonn.training, psonn.testing = training, training
psonn.bounds = Bounds(-5, 5)
# Create neural network
l_rate = None
inputLayer = Layer(psonn.bounds, size = 2, prev = None, l_rate = l_rate, bias = True, label = "Input layer")
hiddenLayer = Layer(psonn.bounds, size = 4, prev = inputLayer, l_rate = l_rate, bias = True, label = "Hidden layer")
outputLayer = Layer(psonn.bounds, size = 1, prev = hiddenLayer, l_rate = l_rate, bias = False, label = "Output layer")
psonn.nn = NeuralNetwork()
psonn.nn.appendLayer(inputLayer)
psonn.nn.appendLayer(hiddenLayer)
psonn.nn.appendLayer(outputLayer)
# Create the pso with the nn weights
import matplotlib.pyplot as plt
import numpy as np
from dataSetTool import DataSetTool
from neuralNetwork.feedForwardNeuralNetwork import NeuralNetwork
from neuralNetwork.structure.layer import Layer
from numberGenerator.bounds import Bounds
np.set_printoptions(suppress=True)
dataSetTool = DataSetTool()
fileName = '../../dataSet/pima-indians-diabetes/pima-indians-diabetes.data'
training, generalization, testing = dataSetTool.getPrimaIndiansDiabetesSets(fileName)
plt.xlabel('Iterations')
plt.ylabel('Error')
plt.ion()
l_rate = 0.1
bounds = Bounds(-1, 1)
inputLayer = Layer(bounds, size = len(training[0][0]), prev = None, l_rate = l_rate, bias = True, label = "Input layer")
hiddenLayer = Layer(bounds, size = 20, prev = inputLayer, l_rate = l_rate, bias = True, label = "Hidden layer")
outputLayer = Layer(bounds, size = len(training[0][1]), prev = hiddenLayer, l_rate = l_rate, bias = False, label = "Output layer")
fnn = NeuralNetwork()
fnn.appendLayer(inputLayer)
fnn.appendLayer(hiddenLayer)
fnn.appendLayer(outputLayer)