本文整理汇总了Python中pybrain.datasets.ClassificationDataSet.nClasses方法的典型用法代码示例。如果您正苦于以下问题：Python ClassificationDataSet.nClasses方法的具体用法？Python ClassificationDataSet.nClasses怎么用？Python ClassificationDataSet.nClasses使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pybrain.datasets.ClassificationDataSet的用法示例。
在下文中一共展示了ClassificationDataSet.nClasses方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: range
# 需要导入模块: from pybrain.datasets import ClassificationDataSet [as 别名]
# 或者: from pybrain.datasets.ClassificationDataSet import nClasses [as 别名]
return ret
vecSize = 100
subjects = [2, 5, 6, 7, 8, 12, 16, 35 ,39]
ds = None
for s in subjects:
for cycleNum in range(1, 13):
fileName = '../inputs/Vicon from CMU/subjects/'+str(s)+'/'+str(cycleNum)+'.amc'
try:
data = getData(fileName, vecSize)
except IOError:
continue
if ds is None:#initialization
ds = ClassificationDataSet( len(data), 1 )
ds.appendLinked(data , subjects.index(s))
ds.nClasses = len(subjects)
decay= 0.99995
myWeightdecay = 0.8
initialLearningrate= 0.005
hidden_size = 1000
epochs=1000
splitProportion = 0.5
print 'dataset size', len(ds)
print 'input layer size', len(ds.getSample(0)[0])
tstdata, trndata = ds.splitWithProportion( splitProportion )
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )
print "Number of training patterns: ", len(trndata)示例2: ClassificationDataSet
# 需要导入模块: from pybrain.datasets import ClassificationDataSet [as 别名]
# 或者: from pybrain.datasets.ClassificationDataSet import nClasses [as 别名]
try:
data = ge.getFeatureVec(fileName)
except IOError:
continue
if ds is None:#initialization
ds = ClassificationDataSet( len(data), 1 )
excpectedLens[m]+=1
ds.appendLinked(data , moods.index(mood))
splitProportion = 0.2
decay= 0.99993
myWeightdecay = 0.5
initialLearningrate= 0.01
hidden_size = 200
epochs=1000
momentum=0.15
ds.nClasses = len(moods)
tstdata, trndata = ds.splitWithProportion( splitProportion )
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )
inLayer = LinearLayer(len(trndata.getSample(0)[0]))
hiddenLayer = SigmoidLayer(hidden_size)
outLayer = LinearLayer(len(trndata.getSample(0)[1]))
n = FeedForwardNetwork()
n.addInputModule(inLayer)
n.addModule(hiddenLayer)
b = BiasUnit()
n.addModule(b)
n.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
b_to_hidden = FullConnection(b, hiddenLayer)示例3: str
# 需要导入模块: from pybrain.datasets import ClassificationDataSet [as 别名]
# 或者: from pybrain.datasets.ClassificationDataSet import nClasses [as 别名]
str(typeNum)+'_'+str(take)+'.skl'
try:
data = ge.getFeatureVec(fileName)
except IOError:
continue
if ds is None:#initialization
ds = ClassificationDataSet( len(data), 1 )
ds.appendLinked(data , couple.index(mood))
splitProportion = 0.2
decay= 0.9999
myWeightdecay = 1#0.75
initialLearningrate= 0.002
hidden_size = 75
epochs=1000
momentum=0.25
ds.nClasses = len(couple)
tstdata, trndata = ds.splitWithProportion( splitProportion )
trndata._convertToOneOfMany( )
tstdata._convertToOneOfMany( )
inLayer = LinearLayer(len(trndata.getSample(0)[0]))
hiddenLayer = SigmoidLayer(hidden_size)
outLayer = LinearLayer(len(trndata.getSample(0)[1]))
n = FeedForwardNetwork()
n.addInputModule(inLayer)
n.addModule(hiddenLayer)
b = BiasUnit()
n.addModule(b)
n.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
b_to_hidden = FullConnection(b, hiddenLayer)