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)
net.layers[1].set_activation_type('tanh')
net.learn(epochs = 125, show_epoch_results = True, random_testing = False)
mse = net.test()
import matplotlib
from pylab import plot, legend, subplot, grid
from pylab import xlabel, ylabel, show, title
test_positions = [item[0][1] * 1000.0 for item in net.get_test_data()]
all_targets1 = [item[0][0] for item in net.test_actuals_targets]
allactuals = [item[1][0] for item in net.test_actuals_targets]
# This is quick and dirty, but it will show the results
subplot(3, 1, 1)
plot([i[1] for i in population])
title("Population")
grid(True)
subplot(3, 1, 2)
plot(test_positions, all_targets1, 'bo', label = 'targets')
plot(test_positions, allactuals, 'ro', label = 'actuals')
grid(True)
legend(loc = 'lower left', numpoints = 1)
net.layers[0].set_activation_type('tanh')
net.layers[1].set_activation_type('tanh')
net.layers[2].set_activation_type('tanh')
net.layers[3].set_activation_type('tanh')
###training network
net.learn(epochs=1200, show_epoch_results=True,
random_testing=True)
mse = net.test()
#extract predicted velues
all_learn = [item[1][0] for item in net.get_learn_data()]
learn_positions = [item[0][3] for item in net.get_learn_data()]
test_positions = [item[0][3] for item in net.get_test_data()]
all_targets1 = [item[0][0] for item in net.test_targets_activations]
allactuals = [item[1][0] for item in net.test_targets_activations]
# This is quick and dirty, but it will show the results
subplot(3, 1, 1)
plot(learn_positions, all_learn,'bo')
title("all_targets")
grid(True)
subplot(3, 1, 2)
plot(test_positions, all_targets1, 'bo', label='targets')
plot(test_positions, allactuals, 'ro', label='actuals')
grid(True)
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)
net.layers[1].set_activation_type('tanh')
net.learn(epochs=125, show_epoch_results=True,
random_testing=False)
mse = net.test()
test_positions = [item[0][1] * 1000.0 for item in net.get_test_data()]
all_targets1 = [item[0][0] for item in net.test_targets_activations]
allactuals = [item[1][0] for item in net.test_targets_activations]
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.plot([i[1] for i in population])
ax1.set_title("Population")
ax1.grid(True)
ax2 = fig.add_subplot(312)
ax2.plot(test_positions, all_targets1, 'bo', label='targets')
ax2.plot(test_positions, allactuals, 'ro', label='actuals')
ax2.grid(True)
ax2.legend(loc='lower left', numpoints=1)
# set inputs and outputs
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
# set lengths
length = len(all_inputs)
learn_end_point = int(length * .8)
# set ranges
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
# add activation to layer 1
net.layers[1].set_activation_type('tanh')
# fit data to model
net.learn(epochs=150, show_epoch_results=True, random_testing=False)
# define mean squared error
mse = net.test()
print("Testing mse = ", mse)
# define data
x = [item[0][0] * 200.0 for item in net.get_test_data()]
y = [item[0][0] for item in net.test_targets_activations]
y_true = [item[1][0] for item in net.test_targets_activations]
# plot results
plot_results(x, y, y_true)
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()
net.load(saved_model)
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
test_start_point = int(pop_len * .8) + 1
net.set_test_range(test_start_point, pop_len - 1)
mse = net.test()
print("The selected model has the following characteristics")
print("Activation Type:", net.layers[1].nodes[1].get_activation_type())
print("Hidden Nodes:", len(net.layers[1].nodes), ' + 1 bias node')
print("Learn Rate:", net.get_learnrate())
print("Epochs:", net.get_epochs())
test_positions = [item[0][0] * pop_len for item in net.get_test_data()]
all_targets1 = [item[0][0] for item in net.test_targets_activations]
allactuals = [item[1][0] for item in net.test_targets_activations]
# This is quick and dirty, but it will show the results
fig = figure()
ax1 = subplot(211)
ax1.plot([i[1] for i in population])
ax1.set_title("Population")
grid(True)
a = [i for i in ax1.get_yticklabels()]
for i in a:
i.set_fontsize(9)
a = [i for i in ax1.get_xticklabels()]
for i in a:
net.load(saved_model)
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
test_start_point = int(pop_len * .8) + 1
net.set_test_range(test_start_point, pop_len - 1)
mse = net.test()
print "The selected model has the following characteristics"
print "Activation Type:", net.layers[1].nodes[1].get_activation_type()
print "Hidden Nodes:", len(net.layers[1].nodes), ' + 1 bias node'
print "Learn Rate:", net.get_learnrate()
print "Epochs:", net.get_epochs()
test_positions = [item[0][0] * pop_len for item in net.get_test_data()]
all_targets1 = [item[0][0] for item in net.test_targets_activations]
allactuals = [item[1][0] for item in net.test_targets_activations]
# This is quick and dirty, but it will show the results
fig = figure()
ax1 = subplot(211)
ax1.plot([i[1] for i in population])
ax1.set_title("Population")
grid(True)
a = [i for i in ax1.get_yticklabels()]
for i in a: i.set_fontsize(9)
a = [i for i in ax1.get_xticklabels()]
for i in a: i.set_fontsize(9)
