예제 #1
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def performNN(all_extracted_features, all_targets):
    from pyneurgen.neuralnet import NeuralNet
    #from pyneurgen.nodes import BiasNode, Connection
    net = NeuralNet()
    net.init_layers(len(all_extracted_features[0]), [2], 1)
    
    net.randomize_network()
    net.set_halt_on_extremes(True)
    
    #   Set to constrain beginning weights to -5 to 5
    #       Just to show we can
    #net.set_random_constraint(.5)
    net.set_learnrate(.001)
    
    net.set_all_inputs(all_extracted_features)
    net.set_all_targets(all_targets)
    
    length = len(all_extracted_features)
    learn_end_point = int(length * .8)
    
    net.set_learn_range(0, learn_end_point)
    net.set_test_range(learn_end_point + 1, length - 1)
    
    net.layers[1].set_activation_type('tanh')
    net.learn(epochs=150, show_epoch_results=True, random_testing=True)
    mse = net.test()
    print mse
예제 #2
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def buildIrisNetwork(all_inputs, all_targets):
	net = NeuralNet()
	net.init_layers(4, [6], 3)

	net.randomize_network()
	net.set_halt_on_extremes(True)

	#   Set to constrain beginning weights to -.5 to .5
	#       Just to show we can
	#net.set_random_constraint(.5)
	net.set_learnrate(.1)

	net.set_all_inputs(all_inputs)
	net.set_all_targets(all_targets)

	length = len(all_inputs)
	learn_end_point = int(length * .5)

	net.set_learn_range(0, learn_end_point)
	net.set_test_range(learn_end_point + 1, length-1)

	net.layers[0].set_activation_type('tanh')
	net.layers[1].set_activation_type('tanh')
	net.layers[2].set_activation_type('threshold')
	return net
예제 #3
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def buildIrisNetwork(all_inputs, all_targets):
    net = NeuralNet()
    net.init_layers(4, [6], 3)

    net.randomize_network()
    net.set_halt_on_extremes(True)

    #   Set to constrain beginning weights to -.5 to .5
    #       Just to show we can
    #net.set_random_constraint(.5)
    net.set_learnrate(.1)

    net.set_all_inputs(all_inputs)
    net.set_all_targets(all_targets)

    length = len(all_inputs)
    learn_end_point = int(length * .5)

    net.set_learn_range(0, learn_end_point)
    net.set_test_range(learn_end_point + 1, length - 1)

    net.layers[0].set_activation_type('tanh')
    net.layers[1].set_activation_type('tanh')
    net.layers[2].set_activation_type('threshold')
    return net
예제 #4
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from pylab import array, ylim, where, average
from pylab import plot, legend, subplot, grid, xlabel, ylabel, show, title
from pyneurgen.neuralnet import NeuralNet
from pyneurgen.nodes import BiasNode, Connection
from pybrain.utilities import percentError

from iris import neurgenData
from src.utilities import percentError

#   Build the inputs
all_inputs, all_targets = neurgenData()

net = NeuralNet()
net.init_layers(4, [6], 3)

net.randomize_network()
net.set_halt_on_extremes(True)

#   Set to constrain beginning weights to -.5 to .5
#       Just to show we can
#net.set_random_constraint(.5)
net.set_learnrate(.1)

net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)

length = len(all_inputs)
learn_end_point = int(length * .5)

net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
예제 #5
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 y_test = np.array(y_test).reshape((len(y_test), 1))
 #transformando os dados para estar no intervalo de 0 a 1
 scaler_x = MinMaxScaler()
 x_train = scaler_x.fit_transform(x_train)
 x_test = scaler_x.transform(x_test)
 scaler_y = MinMaxScaler()
 y_train = scaler_y.fit_transform(y_train)
 y_test = scaler_y.transform(y_test)
 x_input = np.concatenate(
     (x_train, x_test, np.zeros((1, np.shape(x_train)[1]))))
 y_input = np.concatenate((y_train, y_test, np.zeros((1, 1))))
 #elaboracao do modelo de rede neural com os parametros definidos
 fit1 = NeuralNet()
 fit1.init_layers(input_nodes, [hidden_nodes], output_nodes,
                  ElmanSimpleRecurrent())
 fit1.randomize_network()
 fit1.layers[1].set_activation_type('sigmoid')
 fit1.set_learnrate(0.05)
 fit1.set_all_inputs(x_input)
 fit1.set_all_targets(y_input)
 fit1.set_learn_range(0, i)
 fit1.set_test_range(i, i + 1)
 fit1.learn(epochs=100, show_epoch_results=True, random_testing=False)
 mse = fit1.test()
 all_mse.append(mse)
 print("test set MSE = ", np.round(mse, 6))
 target = [item[0][0] for item in fit1.test_targets_activations]
 target = scaler_y.inverse_transform(
     np.array(target).reshape((len(target), 1)))
 pred = [item[1][0] for item in fit1.test_targets_activations]
 pred = scaler_y.inverse_transform(np.array(pred).reshape((len(pred), 1)))
예제 #6
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    pop_sort = [item for item in population]
    random.shuffle(pop_sort)

    for item in pop_sort:
        yield item

#   Build the inputs
for position, target in population_gen(population):
    pos = float(position)
    all_inputs.append([random.random(), pos * factor])
    all_targets.append([target])

net = NeuralNet()
net.init_layers(2, [10], 1)

net.randomize_network()
net.set_halt_on_extremes(True)

#   Set to constrain beginning weights to -.5 to .5
#       Just to show we can
net.set_random_constraint(.5)
net.set_learnrate(.1)

net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)

length = len(all_inputs)
learn_end_point = int(length * .8)

net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
예제 #7
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def serNeural(sDay,nAhead,x0,hWeek):
    nLin = sDay.shape[0] + nAhead
    nFit = sDay.shape[0] if int(x0['obs_time']) <= 14 else int(x0['obs_time'])
    predS = getHistory(sDay,nAhead,x0,hWeek)
    weekS = [x.isocalendar()[1] for x in sDay.index]
    population = [[float(i),sDay['y'][i],float(i%7),weekS[i]] for i in range(sDay.shape[0])]
    all_inputs = []
    all_targets = []
    factorY = sDay['y'].mean()
    factorT = 1.0 / float(len(population))*factorY
    factorD = 1./7.*factorY
    factorW = 1./52.*factorY
    factorS = 4.*sDay['y'].std()
    factorH = factorY/sDay['hist'].mean()

    def population_gen(population):
        pop_sort = [item for item in population]
#        random.shuffle(pop_sort)
        for item in pop_sort:
            yield item
            
    for t,y,y1,y2 in population_gen(population):
        #all_inputs.append([t*factorT,(.5-random.random())*factorS+factorY,y1*factorD,y2*factorW])
        all_inputs.append([y1*factorD,(.5-random.random())*factorS+factorY,y2*factorW])
        all_targets.append([y])

    if False:
        plt.plot([x[0] for x in all_inputs],'-',label='targets0')
        plt.plot([x[1] for x in all_inputs],'-',label='targets1')
        plt.plot([x[2] for x in all_inputs],'-',label='targets2')
        # plt.plot([x[3] for x in all_inputs],'-',label='targets3')
        plt.plot([x[0] for x in all_targets],'-',label='actuals')
        plt.legend(loc='lower left', numpoints=1)
        plt.show()

    net = NeuralNet()
    net.init_layers(3,[10],1,NARXRecurrent(3,.6,2,.4))
    net.randomize_network()
    net.set_random_constraint(.5)
    net.set_learnrate(.1)
    net.set_all_inputs(all_inputs)
    net.set_all_targets(all_targets)
    #predS['pred'] = [item[0][0] for item in net.test_targets_activations]
    length = len(all_inputs)
    learn_end_point = int(length * .8)
    # random.sample(all_inputs,10)
    net.set_learn_range(0, learn_end_point)
    net.set_test_range(learn_end_point + 1, length - 1)
    net.layers[1].set_activation_type('tanh')

    net.learn(epochs=125,show_epoch_results=True,random_testing=False)
    mse = net.test()
    #net.save(os.environ['LAV_DIR'] + "/out/train/net.txt")

    test_positions = [item[0][0] for item in net.get_test_data()]
    all_targets1 = [item[0][0] for item in net.test_targets_activations]
    all_actuals = [item[1][0] for item in net.test_targets_activations]
    #   This is quick and dirty, but it will show the results
    plt.subplot(3, 1, 1)
    plt.plot([i for i in sDay['y']],'-')
    plt.title("Population")
    plt.grid(True)
    
    plt.subplot(3, 1, 2)
    plt.plot(test_positions, all_targets1, 'b-', label='targets')
    plt.plot(test_positions, all_actuals, 'r-', label='actuals')
    plt.grid(True)
    plt.legend(loc='lower left', numpoints=1)
    plt.title("Test Target Points vs Actual Points")

    plt.subplot(3, 1, 3)
    plt.plot(range(1, len(net.accum_mse) + 1, 1), net.accum_mse)
    plt.xlabel('epochs')
    plt.ylabel('mean squared error')
    plt.grid(True)
    plt.title("Mean Squared Error by Epoch")
    plt.show()