本文整理汇总了Python中pybrain.tools.customxml.networkreader.NetworkReader.readFrom方法的典型用法代码示例。如果您正苦于以下问题:Python NetworkReader.readFrom方法的具体用法?Python NetworkReader.readFrom怎么用?Python NetworkReader.readFrom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.tools.customxml.networkreader.NetworkReader
的用法示例。
在下文中一共展示了NetworkReader.readFrom方法的15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: runThirdStageClassifier
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def runThirdStageClassifier(self):
out = []
true = []
#SingleBatIDToAdd = [1, 2, 3, 5, 6] # for single
Correct = 0
print "Loading Network.."
net = NetworkReader.readFrom("C:\Users\Anoch\PycharmProjects\BatClassification\ThirdStageClassifier.xml")
print "Loading feature data with SSC = 1 (Single call type)"
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage, target, path = self.getDistrubedTestDataRUNVERSIONTSC()
SAMPLE_SIZE = len(minFreq)
for i in range(0, SAMPLE_SIZE):
ClassifierOutput= net.activate([minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i]])
ClassifierOutputID = np.argmax(ClassifierOutput)
currentTarget = self.convertIDSingleTSC(target[i])
out.append(ClassifierOutputID)
true.append(currentTarget)
#MAPPING FROM BATID TO TSC value:
TSC_value = ClassifierOutputID
# Metadata Setup, get path and write: TSC = value
ds = self.HDFFile[path[i]]
ds.attrs["TSC"] = TSC_value
self.HDFFile.flush()
self.ConfusionMatrix = self.CorrectRatio(out, true)
return self.ConfusionMatrix
示例2: __init__
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def __init__(self, loadWeightsFromFile, filename):
#neural network as function approximator
#Initialize neural network
if loadWeightsFromFile:
self.nn = NetworkReader.readFrom(filename)
else:
self.nn = buildNetwork(NODE_INPUT, NODE_HIDDEN, NODE_OUTPUT, bias = True)
示例3: buildNet
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def buildNet(self):
print "Building a network..."
if os.path.isfile(self.path):
self.trained = True
return NetworkReader.readFrom(self.path)
else:
return buildNetwork(self.all_data.indim, self.d[self.path]['hidden_dim'], self.all_data.outdim, outclass=SoftmaxLayer)
示例4: runClassifier
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def runClassifier(self):
out = []
true = []
#BatIDToAdd = [1, 2, 3, 5, 6, 10, 11, 12, 14, 8, 9] #1-14 are bats; 8 is noise; 9 is something else
print "Loading Network.."
net = NetworkReader.readFrom("SecondStageClassifier.xml")
print "Loading feature data with FSC = 1 (Bat calls)"
minFreq, maxFreq, Durantion, fl1, fl2, fl3, fl4, fl5, fl6, fl7, fl8, fl9, fl10, pixelAverage, target, path = self.getDistrubedTestDataRUNVERSION()
SAMPLE_SIZE = len(minFreq)
for i in range(0, SAMPLE_SIZE):
ClassifierOutput = net.activate([minFreq[i], maxFreq[i], Durantion[i], fl1[i], fl2[i], fl3[i], fl4[i], fl5[i], fl6[i], fl7[i], fl8[i], fl9[i], fl10[i], pixelAverage[i]])
ClassifierOutputID = np.argmax(ClassifierOutput)
currentTarget = self.convertIDMultiSingle(target[i])
out.append(ClassifierOutputID)
true.append(currentTarget)
#MAPPING FROM BATID TO TSC value:
SSC_value = ClassifierOutputID
# Metadata Setup, get path and write: TSC = value
ds = self.HDFFile[path[i]]
ds.attrs["SSC"] = SSC_value
# Close HDF5 file to save to disk. This is also done to make sure the next stage classifier can open the file
self.HDFFile.flush()
self.HDFFile.close()
self.ConfusionMatrix = self.CorrectRatio(out, true)
return self.ConfusionMatrix
示例5: getPersistedData
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def getPersistedData(self, name):
pathToData = self.relPathFromFilename(name)
if os.path.isfile(pathToData):
with open(pathToData, "rb") as f:
data = pickle.load(f)
if name == NEURAL_NET_DUMP_NAME:
data.net = NetworkReader.readFrom(self.relPathFromFilename(name + DATA_DUMP_NN_EXT))
return data
示例6: testNets
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def testNets():
ds = SupervisedDataSet.loadFromFile('SynapsemonPie/boards')
net20 = NetworkReader.readFrom('SynapsemonPie/synapsemon_primer20.xml')
net50 = NetworkReader.readFrom('SynapsemonPie/synapsemon_primer50.xml')
net80 = NetworkReader.readFrom('SynapsemonPie/synapsemon_primer80.xml')
net110 = NetworkReader.readFrom('SynapsemonPie/synapsemon_primer110.xml')
net140 = NetworkReader.readFrom('SynapsemonPie/synapsemon_primer140.xml')
trainer20 = BackpropTrainer(net20, ds)
trainer50 = BackpropTrainer(net50, ds)
trainer80 = BackpropTrainer(net80, ds)
trainer110 = BackpropTrainer(net110, ds)
trainer140 = BackpropTrainer(net140, ds)
print trainer20.train()
print trainer50.train()
print trainer80.train()
print trainer110.train()
print trainer140.train()
示例7: main
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def main():
train_file = 'data/train.csv'
# validation_file = 'data/validation.csv'
output_model_file = 'model.xml'
# hidden_size = 4
epochs = 500
# load data
# def loadData():
train = np.loadtxt(train_file, delimiter=' ')
Input = train[0:,0:3]
Output = train[0:,3:5]
# validation = np.loadtxt(validation_file, delimiter=',')
# train = np.vstack((train, validation))
# x_train = train[:, 0:-1]
# y_train = train[:, -1]
# y_train = y_train.reshape(-1, 1)
# input_size = x_train.shape[1]
# target_size = y_train.shape[1]
# prepare dataset
# def prepare dataset(input_size, target_size):
ds = SDS(Input,Output)
# ds.addSample(input_size)
# ds.setField('input', x_train)
# ds.setField('target', y_train)
# init and train
# def initTrain(input_size, hidden_size, input, output):
# net = buildNetwork(input_size, hidden_size, target_size, bias=True)
net = buildNetwork(3, # input layer
4, # hidden0
2, # output
hiddenclass=SigmoidLayer,
outclass=SigmoidLayer,
bias=True
)
net = NetworkReader.readFrom('model.xml')
for i,o in zip(Input,Output):
ds.addSample(i,o)
print i, o
trainer = BackpropTrainer(net, ds)
print "training for {} epochs...".format(epochs)
for i in range(epochs):
mse = trainer.train()
rmse = sqrt(mse)
print "training RMSE, epoch {}: {}".format(i + 1, rmse)
if os.path.isfile("../stopfile.txt") == True:
break
NetworkWriter.writeToFile(net, output_model_file)
示例8: __init__
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def __init__(self):
print "start a new instance"
self.loaded=False
self.has_data_source=False
try:
self.net=NetworkReader.readFrom('pickled_ANN')
print "ANN has been found from an ash jar"
self.loaded=True
except IOError:
print "ash jar is empty, use train() to start a new ANN"
示例9: nfq_action_value
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def nfq_action_value(network_fname, state=[0, 0, 0, 0, 0]):
# TODO generalize away from 9 action values. Ask the network how many
# discrete action values there are.
n_actions = 9
network = NetworkReader.readFrom(network_fname)
actionvalues = np.empty(n_actions)
for i_action in range(n_actions):
network_input = r_[state, one_to_n(i_action, n_actions)]
actionvalues[i_action] = network.activate(network_input)
return actionvalues
示例10: exoplanet_search
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def exoplanet_search(self,
find=default_find):
"""
This method searches for exoplanets.
The output will have the format:
(exostar1_streak, exostar2_streak, ...)
where an exostar is a star with an exoplanet, and a streak is
a list of states in which the exostar was observed to have exoplanetary
behaviour.
At least 5 stars must be tracked.
"""
stars, deleted = self.find_objects(find=find)
print str(deleted / len(self.photos)) + "% of the data was ignored"
"""
There must be an integer multiple of 5 stars
in stars, and the stars must be grouped together in lumps
of 5.
"""
exostreaks = []
net = NetworkReader.readFrom("../../Identifier/network.xml")
for starnum in range(0, len(stars), 5):
search_stars = stars[starnum: starnum + 5]
start_time = search_stars[0].states[0].time
stop_time = search_stars[0].states[-1].time
for photonum in range(start_time, stop_time + 1, 10):
print self.photos[photonum]
photonum = min(photonum, stop_time - 10)
intensities = []
for slide in range(photonum, photonum + 10):
intensities.append([])
photo = self.photos[slide]
photo.load()
for star in search_stars:
state = star.track(slide)
brightness = photo.intensity(state.position, state.radius)
intensities[-1].append(brightness)
photo.close()
inpt = []
for starothernum in range(5):
lightcurve = []
for slides_from_zero in range(10):
lightcurve.append(intensities[slides_from_zero][starothernum])
array_version = array(lightcurve)
array_version /= average(array_version)
inpt += list(array_version)
nnet_output = net.activate(tuple(inpt))
for o in range(5):
if nnet_output[o] > 0.5:
exostreak = []
for slide in range(photonum, photonum + 10):
state = search_stars[o].track(slide)
exostreak.append(state)
exostreaks.append(exostreak)
return exostreaks
示例11: load_network_from_file
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def load_network_from_file(self, filename):
"""Load Network from File
Using a NetworkWriter written file, data from the saved network
will be reconstituted into a new PathPlanningNetwork class.
This is used to load saved networks.
Arguments:
filename: The filename of the saved xml file.
"""
self._network = NetworkReader.readFrom(filename)
return
示例12: __init__
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def __init__(self, data, machineID, eta, lmda, netPath, input_size=30, epochs=20, train_str_index=1000, train_end_index=3000):
'''
Constructor
'''
self.data = data
self.machineID = machineID
self.eta = eta
self.lmda = lmda
self.INPUT_SIZE = input_size
self.epochs = epochs
self.str_train = train_str_index
self.end_train = train_end_index
self.net = NetworkReader.readFrom(netPath)
示例13: trainNetwork
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def trainNetwork():
print "[Training] Network has Started..."
inputSize = 0
with open('file1.txt', 'r') as f: #automatically closes file at the end of the block
#first_line = f.readline()
#inputSize = len(first_line)
dataset = SupervisedDataSet(4, 1) #specify size of data and target
f.seek(0) #Move back to beginnning of file
#iterate through the file. 1 picture per line
for line in f:
mylist = json.loads(line) #list object
target = mylist[-1] #retrieve and then delete the target classification
del mylist[-2:]
#print target
dataset.addSample(tuple(mylist), (target,))
#print json.loads(line)
if os.path.isfile('annModel.xml'):
skynet = NetworkReader.readFrom('annModel.xml')#for use if individual sample files used
else:
skynet = buildNetwork(dataset.indim, 8, dataset.outdim, bias=True, hiddenclass=TanhLayer) #input,hidden,output
#SoftmaxLayer, SigmoidLayer, LinearLayer, GaussianLayer
#Note hidden neuron number is arbitrary, can try 1 or 4 or 3 or 5 if this methods doesnt work out
trainer = BackpropTrainer(skynet, dataset,learningrate = 0.3, weightdecay = 0.01,momentum = 0.9)
#trainer.trainUntilConvergence()
for i in xrange(1000):
trainer.train()
#trainer.trainEpochs(1000)
#Save the now trained neural network
NetworkWriter.writeToFile(skynet,'annModel.xml')
print "[Network] has been Written"
################## SVM Method #######################
#Change append method in write method for target persistence
dataX = []
datay = []
with open(writeFile, 'r') as f:
for line in f:
mylist = json.loads(line)
target2 = mylist[-1]
dataX.append(mylist[:-2])
datay.append(target2)
#datay = [target2] * len(dataX) #Targets, size is n_samples, for use with indiviual sample files with same target
print [target2]
print dataX
print datay
clf = svm.LinearSVC()
clf.fit(dataX,datay)
#Persist the trained model
joblib.dump(clf,'svmModel.pkl')
示例14: __init__
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def __init__(self, data, machineID, netPath, eta, lmda, input_size=30, epochs=20, train_str_index=1000, train_end_index=3000):
'''
Constructor
'''
self.cpuData = data[0]
self.memData = data[1]
self.machineID = machineID
self.eta = eta
self.lmda = lmda
self.INPUT_SIZE = input_size
self.epochs = epochs
self.str_train = train_str_index
self.end_train = train_end_index
self.net = NetworkReader.readFrom(netPath)
self.memForecasts = np.genfromtxt("d:/data/memory_fnn/"+machineID.replace("cpu", "memory"),delimiter=',').ravel()
示例15: LoadNetwork
# 需要导入模块: from pybrain.tools.customxml.networkreader import NetworkReader [as 别名]
# 或者: from pybrain.tools.customxml.networkreader.NetworkReader import readFrom [as 别名]
def LoadNetwork(self):
"""
Loading network dump from file.
"""
FCLogger.debug('Loading network from PyBrain xml-formatted file...')
net = None
if os.path.exists(self.networkFile):
net = NetworkReader.readFrom(self.networkFile)
FCLogger.info('Network loaded from dump-file: {}'.format(os.path.abspath(self.networkFile)))
else:
FCLogger.warning('{} - file with Neural Network configuration not exist!'.format(os.path.abspath(self.networkFile)))
self.network = net