# Plotting of the prediction output and error
outputFolderName = "Outputs/Erdos_Renyi_Outputs" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
os.mkdir(outputFolderName)
outplot = outputPlot.OutputPlot(
outputFolderName + "/Prediction.html",
"Mackey-Glass Time Series - GA Optimization)",
"Prediction on Testing data",
"Time",
"Output",
)
outplot.setXSeries(np.arange(1, nValidation + nTesting + 1))
outplot.setYSeries("Actual Output", actualOutputData)
outplot.setYSeries("Predicted Output", predictedOutputData)
outplot.createOutput()
# Plotting of the best population details
# utilityGA.plotNetworkPerformance(bestPopulation, topology=utilityGA.Topology.ErdosRenyi, fileName=outputFolderName+"/NetworkPerformance.html", networkSize=networkSize)
# Store the best population in a file (for later analysis)
popFileName = outputFolderName + "/population.pkl"
utilityGA.storeBestPopulationAndStats(bestPopulation, popFileName, utilityGA.Topology.ErdosRenyi, networkSize)
# Load the best population
print(utilityGA.loadBestPopulation(popFileName))
endTime = time()
run_time = endTime - startTime
print("The run time:" + str(run_time))
print("Done!")
from reservoir import GAUtility as utilityGA
from plotting import ScatterPlot as plot
import numpy as np
import os
from datetime import datetime
# File name for
folderName = "Outputs/GAResults_Small_World_Graphs_" + datetime.now().strftime(
"%Y_%m_%d_%H_%M_%S")
os.mkdir(folderName)
# Load the best population from the file
bestPopulation = utilityGA.loadBestPopulation("population.pkl")
# Iterate over all the elements and get the network properties
networkSize = 500
meanDegreeList = []
betaList = []
errorList = []
averageDegreeList = []
averagePathLengthList = []
averageDiameterList = []
averageClusteringCoefficientList = []
for item in bestPopulation:
# Fitness
meanDegree = item[0][0, 0]
beta = item[0][0, 1]
error = item[1]
# Network properties
averageDegree = item[2]['averageDegree']
averagePathLength = item[2]['averagePathLength']
#Plotting of the prediction output and error
outputFolderName = "Outputs/Random_Graph_Outputs" + datetime.now().strftime(
"%Y_%m_%d_%H_%M_%S")
os.mkdir(outputFolderName)
outplot = outputPlot.OutputPlot(outputFolderName + "/Prediction.html",
"Mackey-Glass Time Series - GA Optimization)",
"Prediction on Testing data", "Time", "Output")
outplot.setXSeries(np.arange(1, nValidation + nTesting + 1))
outplot.setYSeries('Actual Output', actualOutputData)
outplot.setYSeries('Predicted Output', predictedOutputData)
outplot.createOutput()
# Plotting of the best population details
utilityGA.plotNetworkPerformance(bestPopulation,
topology=utilityGA.Topology.Random,
fileName=outputFolderName +
"/NetworkPerformance.html",
networkSize=networkSize)
# Store the best population in a file (for later analysis)
popFileName = outputFolderName + "/population.pkl"
utilityGA.storeBestPopulationAndStats(bestPopulation, popFileName,
utilityGA.Topology.Random, networkSize)
# Load the best population
print(utilityGA.loadBestPopulation(popFileName))
endTime = time()
run_time = endTime - startTime
print("The run time:" + str(run_time))
print("Done!")
from reservoir import GAUtility as utilityGA
from plotting import ScatterPlot as plot
import numpy as np
import os
from datetime import datetime
# File name for
folderName = "Outputs/GAResults_Combined_" + datetime.now().strftime(
"%Y_%m_%d_%H_%M_%S")
os.mkdir(folderName)
# Load the best population from the file
bestPopulationErdos = utilityGA.loadBestPopulation("erdos.pkl")
bestPopulationScaleFree = utilityGA.loadBestPopulation("scalefree.pkl")
bestPopulationSmallWorld = utilityGA.loadBestPopulation("smallworld.pkl")
bestPopulationList = [
bestPopulationErdos, bestPopulationScaleFree, bestPopulationSmallWorld
]
# Iterate over all the elements and get the network properties
networkSize = 500
errorList = []
averageDegreeList = []
averagePathLengthList = []
averageDiameterList = []
averageClusteringCoefficientList = []
for bestPopulation in bestPopulationList:
for item in bestPopulation:
# Fitness
from reservoir import GAUtility as utilityGA
from plotting import ScatterPlot as plot
import numpy as np
import os
from datetime import datetime
# File name for
folderName = "Outputs/GAResults_Small_World_Graphs_" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
os.mkdir(folderName)
# Load the best population from the file
bestPopulation = utilityGA.loadBestPopulation("population.pkl")
# Iterate over all the elements and get the network properties
networkSize = 500
meanDegreeList = []
betaList = []
errorList = []
averageDegreeList = []
averagePathLengthList = []
averageDiameterList = []
averageClusteringCoefficientList = []
for item in bestPopulation:
# Fitness
meanDegree = item[0][0,0]
beta = item[0][0,1]
error = item[1]
# Network properties
averageDegree = item[2]['averageDegree']
from reservoir import GAUtility as utilityGA
from plotting import ScatterPlot as plot
import numpy as np
import os
from datetime import datetime
# File name for
folderName = "Outputs/GAResults_Combined_" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
os.mkdir(folderName)
# Load the best population from the file
bestPopulationErdos = utilityGA.loadBestPopulation("erdos.pkl")
bestPopulationScaleFree = utilityGA.loadBestPopulation("scalefree.pkl")
bestPopulationSmallWorld = utilityGA.loadBestPopulation("smallworld.pkl")
bestPopulationList = [bestPopulationErdos, bestPopulationScaleFree, bestPopulationSmallWorld]
# Iterate over all the elements and get the network properties
networkSize = 500
errorList = []
averageDegreeList = []
averagePathLengthList = []
averageDiameterList = []
averageClusteringCoefficientList = []
for bestPopulation in bestPopulationList:
for item in bestPopulation:
# Fitness
attachment = item[0][0,0]/networkSize
