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,
size=8,
prev=inputLayer,
l_rate=l_rate,
bias=True,
label="Hidden layer")
outputLayer = Layer(bounds,
size=len(training[0][1]),
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)
group_training = np.array([input[0] for input in training])
group_target = np.array([output[1] for output in training])
errors = []
[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
psonn.num_particles = 20
psonn.inertia_weight = 0.729
psonn.cognitiveConstant = 1.49445
cpso_dissipative_errors = [] cpso_dissipative_error = [] cpso_dissipative_generalization_error = [] pso_errors = [] pso_error = [] pso_generalization_error = [] iterations = 5000 samples = 30 NUM_PARTICLES_Y = 5 NUM_PARTICLES_X = 5 INERTIA_WEIGHT = 0.729844 COGNITIVE_CONSTANT = 1.496180 SOCIAL_CONSTANT = 1.496180 BOUNDS = Bounds(-5, 5) # DESC = 'Glass' # DATA_SET_FUNC = dataSetTool.getGlassDataSets # DATA_SET_FILE_LOC = '../../../dataSet/glass/glass.data' # HIDDEN_LAYER_NEURONS = [12] # DESC = 'Iris' # DATA_SET_FUNC = dataSetTool.getIrisDataSets # DATA_SET_FILE_LOC = '../../../dataSet/iris/iris.data' # HIDDEN_LAYER_NEURONS = [8] # DESC = 'Wine' # DATA_SET_FUNC = dataSetTool.getWineDataSets # DATA_SET_FILE_LOC = '../../../dataSet/wine/wine.data' # HIDDEN_LAYER_NEURONS = [10]