Python Utility.splitData2示例

示例#1

文件： testClassicESN.py 项目： nivedita/echo-state-networks

from sklearn import preprocessing as pp
from reservoir import Utility as util
from performance import ErrorMetrics as rmse

# Read data from the file
data = np.loadtxt('MackeyGlass_t17.txt')

# Normalize the raw data
minMax = pp.MinMaxScaler((-1, 1))
data = minMax.fit_transform(data)

#Get only 6000 points
data = data[:5000].reshape((5000, 1))

# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.4)
nTesting = testingData.shape[0]

# Form feature vectors
inputTrainingData, outputTrainingData = util.formFeatureVectors(trainingData)

# Tune the network
size = 256
initialTransient = 50

# Input-to-reservoir fully connected
inputWeight = topology.ClassicInputTopology(
    inputSize=inputTrainingData.shape[1],
    reservoirSize=size).generateWeightMatrix()

# Reservoir-to-reservoir fully connected

示例#2

文件： testRNNMG.py 项目： nivedita/echo-state-networks

# Forecasting parameters
depth = 30

# Read data from the file
data = np.loadtxt('MackeyGlass_t17.txt')

# Normalize the raw data
#minMax = pp.MinMaxScaler((-1,1))
minMax = pp.StandardScaler()
data = minMax.fit_transform(data)

# Get only 6000 points
data = data[:5000].reshape((5000, 1))

# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.85)
availableData = trainingData
nTesting = testingData.shape[0]

# Divide the training data into training and validation
validationRatio = 0.4
trainingData, validationData = util.splitData2(trainingData,
                                               1.0 - validationRatio)

# Form feature vectors
trainingFeatureVectors, trainingTargetVectors = formFeatureTargetVectors(
    trainingData, depth)
validationFeatureVectors, validationTargetVectors = formFeatureTargetVectors(
    validationData, depth)
testingFeatureVectors, testingTargetVectors = formFeatureTargetVectors(
    testingData, depth)

示例#3

文件： testClassicESN.py 项目： rajcscw/echo-state-networks

from sklearn import preprocessing as pp
from reservoir import Utility as util
from performance import ErrorMetrics as rmse

# Read data from the file
data = np.loadtxt('MackeyGlass_t17.txt')

# Normalize the raw data
minMax = pp.MinMaxScaler((-1,1))
data = minMax.fit_transform(data)

#Get only 6000 points
data = data[:5000].reshape((5000, 1))

# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.4)
nTesting = testingData.shape[0]

# Form feature vectors
inputTrainingData, outputTrainingData = util.formFeatureVectors(trainingData)

# Tune the network
size = 256
initialTransient = 50

# Input-to-reservoir fully connected
inputWeight = topology.ClassicInputTopology(inputSize=inputTrainingData.shape[1], reservoirSize=size).generateWeightMatrix()

# Reservoir-to-reservoir fully connected
reservoirWeight = topology.ClassicReservoirTopology(size=size).generateWeightMatrix()

示例#4

文件： testRNNMG.py 项目： rajcscw/echo-state-networks

# Forecasting parameters
depth = 30

# Read data from the file
data = np.loadtxt("MackeyGlass_t17.txt")

# Normalize the raw data
# minMax = pp.MinMaxScaler((-1,1))
minMax = pp.StandardScaler()
data = minMax.fit_transform(data)

# Get only 6000 points
data = data[:5000].reshape((5000, 1))

# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.85)
availableData = trainingData
nTesting = testingData.shape[0]

# Divide the training data into training and validation
validationRatio = 0.4
trainingData, validationData = util.splitData2(trainingData, 1.0 - validationRatio)

# Form feature vectors
trainingFeatureVectors, trainingTargetVectors = formFeatureTargetVectors(trainingData, depth)
validationFeatureVectors, validationTargetVectors = formFeatureTargetVectors(validationData, depth)
testingFeatureVectors, testingTargetVectors = formFeatureTargetVectors(testingData, depth)

# Network parameters
in_out_neurons = 1
hidden_neurons = 200