def test():
DS = loadPybrainData()
train, test = DS.splitWithProportion(0.1)
fnn = joblib.load(PKL)
# 预测test情况
output = fnn.activateOnDataset(test)
# ann.activate(onedata)可以只对一个数据进行预测
outputs = []
target = []
count = 0
for out in output:
outs = out.argmax()
outputs.append(outs)
for tar in test['target']:
ta = tar.argmax()
target.append(ta)
for i in range(0, len(target)):
if outputs[i] == target[i]:
count += 1
right = count / len(target) #单个字符正确率
rate = (right**4)
print("分类正确率是：%.4f%%" % (rate * 100))
v = Validator()
print(u'均方和差为：', v.MSE(output,
test['target'])) #计算test的原始值和预测值的均方差和,两者格式必须相等
def dataset_eval(dataset):
"""Return dataset hit rate and MSE"""
# Transform output values to bit vectors, similar to the targets
predicted = bit_array_transform(ff_network.activate(x)
for x in dataset['input'])
target = dataset['target']
# Lists of positions holding predicted and target classes to compare
predicted_pos = [list(x).index(1) for x in predicted]
target_pos = [list(x).index(1) for x in target]
hits = Validator.classificationPerformance(predicted_pos, target_pos)
mse = Validator.MSE(predicted, target)
return hits, mse
def __init__(self, evolino_network, dataset, **kwargs):
""" @param evolino_network: an instance of NetworkWrapper()
@param dataset: The evaluation dataset
@param evalfunc: Compares output to target values and returns a scalar, denoting the fitness.
Defaults to -mse(output, target).
@param wtRatio: Float array of two values denoting the ratio between washout and training length.
Defaults to [1,2]
@param verbosity: Verbosity level. Defaults to 0
"""
Filter.__init__(self)
ap = KWArgsProcessor(self, kwargs)
ap.add( 'verbosity', default=0 )
ap.add( 'evalfunc', default=lambda output, target: -Validator.MSE(output, target) )
ap.add( 'wtRatio', default=array([1,2], float) )
self.network = evolino_network
self.dataset = dataset
self.max_fitness = -Infinity
plotname = os.path.join(plotdir, ('jpq2layers_plot' + str(iter)))
pylab.savefig(plotname)
# set-up the neural network
nneuron = 5
mom = 0.98
netname = "LSL-" + str(nneuron) + "-" + str(mom)
mv = ModuleValidator()
v = Validator()
#create the test DataSet
x = numpy.arange(0.0, 1.0 + 0.01, 0.01)
s = 0.5 + 0.4 * numpy.sin(2 * numpy.pi * x)
tsts = SupervisedDataSet(1, 1)
tsts.setField('input', x.reshape(len(x), 1))
tsts.setField('target', s.reshape(len(s), 1))
#read the train DataSet from file
trndata = SupervisedDataSet.loadFromFile(os.path.join(os.getcwd(), 'trndata'))
myneuralnet = os.path.join(os.getcwd(), 'myneuralnet.xml')
if os.path.isfile(myneuralnet):
n = NetworkReader.readFrom(myneuralnet, name=netname)
#calculate the test DataSet based on the trained Neural Network
ctsts = mv.calculateModuleOutput(n, tsts)
tserr = v.MSE(ctsts, tsts['target'])
print 'MSE error on TSTS:', tserr
myplot(trndata, tsts=tsts, ctsts=ctsts)
pylab.show()
tsts.setField('target', s.reshape(len(s), 1))
#read the train DataSet from file
trndata = SupervisedDataSet.loadFromFile(os.path.join(os.getcwd(), 'trndata'))
#create the trainer
t = BackpropTrainer(n, learningrate=0.01, momentum=mom)
#train the neural network from the train DataSet
cterrori = 1.0
print("trainer momentum:" + str(mom))
for iter in range(25):
t.trainOnDataset(trndata, 1000)
ctrndata = mv.calculateModuleOutput(n, trndata)
cterr = v.MSE(ctrndata, trndata['target'])
relerr = abs(cterr - cterrori)
cterrori = cterr
print('iteration:', iter + 1, 'MSE error:', cterr)
myplot(trndata, ctrndata, iter=iter + 1)
if cterr < 1.e-5 or relerr < 1.e-7:
break
#write the network using xml file
myneuralnet = os.path.join(os.getcwd(), 'myneuralnet.xml')
if os.path.isfile(myneuralnet):
NetworkWriter.appendToFile(n, myneuralnet)
else:
NetworkWriter.writeToFile(n, myneuralnet)
#calculate the test DataSet based on the trained Neural Network
ctsts = mv.calculateModuleOutput(n, tsts)
