本文整理汇总了Python中parameters.Parameters.getValue方法的典型用法代码示例。如果您正苦于以下问题：Python Parameters.getValue方法的具体用法？Python Parameters.getValue怎么用？Python Parameters.getValue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parameters.Parameters的用法示例。
在下文中一共展示了Parameters.getValue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: len
# 需要导入模块: from parameters import Parameters [as 别名]
# 或者: from parameters.Parameters import getValue [as 别名]
parameters.define("numCols", [16])
#parameters.define("numCols", [256,512,1024,2048])
parameters.define("synPermConn", [0.3])
#parameters.define("synPermConn", [0.9, 0.7, 0.5, 0.3, 0.1])
parameters.define("synPermDecFrac", [1.0])
#parameters.define("synPermDecFrac", [1.0, 0.5, 0.1])
parameters.define("synPermIncFrac", [1.0])
#parameters.define("synPermIncFrac", [1.0, 0.5, 0.1])
# Run the model until all combinations have been tried
combinations = [] # list for storing parameter combinations
results = [] # list for storing image recognition accuracy results
while len(results) < parameters.combinations:
dataSet = parameters.getValue("dataSet")
trainingDataset = 'DataSets/OCR/characters/' + dataSet
trainingImages, trainingTags = data.getImagesAndTags(trainingDataset)
trainingVectors = encoder.imagesToVectors(trainingImages)
testingDataset = 'DataSets/OCR/characters/' + dataSet
# Pick a random combination of parameter values
#parameters.generateRandomCombination()
numCols = parameters.getValue("numCols")
synPermConn = parameters.getValue("synPermConn")
synPermDec = synPermConn*parameters.getValue("synPermDecFrac")
synPermInc = synPermConn*parameters.getValue("synPermIncFrac")
# Run it if it hasn't been tried yet
if parameters.getAllValues() not in combinations:
print "Parameter Combination: ", parameters.getAllValues()示例2: Parameters
# 需要导入模块: from parameters import Parameters [as 别名]
# 或者: from parameters.Parameters import getValue [as 别名]
trainingVectors = encoder.imagesToVectors(trainingImages)
# Specify parameter values to search
parameters = Parameters()
parameters.define("synPermConn", [0.5])
parameters.define("synPermDecFrac", [1.0, 0.5, 0.1])
parameters.define("synPermIncFrac", [1.0, 0.5, 0.1])
# Run the model until all combinations have been tried
while parameters.getNumResults() < parameters.numCombinations:
# Pick a combination of parameter values
parameters.nextCombination()
#parameters.nextRandomCombination()
synPermConn = parameters.getValue("synPermConn")
synPermDec = synPermConn*parameters.getValue("synPermDecFrac")
synPermInc = synPermConn*parameters.getValue("synPermIncFrac")
# Instantiate our spatial pooler
sp = SpatialPooler(
inputDimensions= (32, 32), # Size of image patch
columnDimensions = (32, 32),
potentialRadius = 10000, # Ensures 100% potential pool
potentialPct = 0.8,
globalInhibition = True,
localAreaDensity = -1, # Using numActiveColumnsPerInhArea
numActiveColumnsPerInhArea = 64,
# All input activity can contribute to feature output
stimulusThreshold = 0,
synPermInactiveDec = synPermDec,示例3: SpatialPooler
# 需要导入模块: from parameters import Parameters [as 别名]
# 或者: from parameters.Parameters import getValue [as 别名]
'9.xml', '10.xml', '11.xml', '12.xml', '13.xml', '14.xml', '15.xml',
'16.xml', '17.xml', '18.xml', '19.xml', '20.xml', '21.xml', '22.xml',
'23.xml', '24.xml', '25.xml', '26.xml', '27.xml', '28.xml', '29.xml',
'30.xml', '31.xml', '32.xml', '33.xml', '34.xml', '35.xml', '36.xml',
'37.xml', '38.xml', '39.xml', '40.xml', '41.xml', '42.xml', '43.xml',
'44.xml', '45.xml', '46.xml', '47.xml', '48.xml', '49.xml', '50.xml',
'51.xml', '52.xml', '53.xml', '54.xml', '55.xml', '56.xml', '57.xml',
'58.xml', '59.xml', '60.xml', '61.xml', '62.xml'])
# Run the model until all combinations have been tried
while parameters.getNumResults() < parameters.numCombinations:
# Pick a combination of parameter values
parameters.nextCombination()
dataSet = parameters.getValue("dataSet")
trainingDataset = 'DataSets/OCR/characters/capacity_datasets/' + dataSet
trainingImages, trainingTags = data.getImagesAndTags(trainingDataset)
trainingVectors = encoder.imagesToVectors(trainingImages)
testingDataset = 'DataSets/OCR/characters/capacity_datasets/' + dataSet
# Instantiate our spatial pooler
sp = SpatialPooler(
inputDimensions= (32, 32), # Size of image patch
columnDimensions = (32, 32),
potentialRadius = 10000, # Ensures 100% potential pool
potentialPct = 0.8,
globalInhibition = True,
localAreaDensity = -1, # Using numActiveColumnsPerInhArea
numActiveColumnsPerInhArea = 64,
# All input activity can contribute to feature output