import GP as gp #plt.ion() #plt.style.use('ggplot') population=50 #model1=gc2.heteregeneousModel(population,[0.4,10,0.3],True,True,"gradient","uniform",False) model1=gc2.heteregeneousModel(population,[1000,20,1.5,0.3],True,True,"powerlaw","uniform",True) model1.Animate() #estimate=lk2.Estimation(model1.record,model1.geo,method="powerlaw") estimate=lk2.Estimation(model1.record,model1.geo,method="gradient") #Metro=mp3.multiMetropolis(1000,[estimate.GammaPosteriorBeta0,estimate.GammaPosteriorGamma,estimate.GammaPosteriorPhi],[0.1,0.1,5],[0.5,0.5,0.4]) #Metro=mp3.multiMetropolis(1000,[partial(estimate.GammaPriorGeneralPosterior,i=0),partial(estimate.GammaPriorGeneralPosterior,i=1),partial(estimate.GammaPriorGeneralPosterior,i=2)],[0.1,0.1,5],[0.5,0.5,0.4]) #Metro=mp3.multiMetropolis(1000,[estimate.GammaPosteriorBeta0,estimate.GammaPosteriorGamma],[0.1,0.1],[0.4,0.4]) #Metro=mp3.multiMetropolis(1000,[partial(estimate.GammaPriorGeneralPosterior,i=0),partial(estimate.GammaPriorGeneralPosterior,i=1),partial(estimate.GammaPriorGeneralPosterior,i=2),partial(estimate.GammaPriorGeneralPosterior,i=3)],[3,0.1,0.9,1],[0.5,0.5,0.4,0.4]) #InitialGP=np.zeros(population*(population-1)/2) InitialGP=gp.InitialGP(estimate.DistanceMatrix,np.array((1,1))) GPDoc=gp.GaussianProcess(estimate.DistanceMatrix,np.array((1,np.mean(estimate.DistanceMatrix)))) ################ InitialGP=GPDoc.SampleForGP(np.zeros(population*(population-1)/2)) BetaMatrix=model1.BetaMatrix BetaMatrix3=cr.BetaMatrix(model1.DistanceMatrix,[1,1]) gp.BetaMatrixPlot(model1.DistanceMatrix,[BetaMatrix,np.exp(np.log(BetaMatrix)+gp.LowerTriangularVectorToSymmetricMatrix(InitialGP,BetaMatrix.shape[0])),BetaMatrix3],3) test=estimate.GaussianPriorGP([0.1,0.9,1],GPDoc,InitialGP) Metro=mp3.multiMetropolis(1000,[partial(estimate.GammaPriorGeneralPosterior,i=0),partial(estimate.GammaPriorGeneralPosterior,i=1),partial(estimate.GammaPriorGeneralPosterior,i=2)],[0.1,0.9,1],[0.7,0.5,0.7],InitialGP,GPDoc,estimate.GaussianPriorGP,"Change") np.savetxt("GP.csv", Metro.recordGP, delimiter=",") np.savetct("ParameterRecord.csv",Metro.record,delimiter=",") Metro.showplot(0) Metro.printall(0) Metro.showplot(1) Metro.printall(1)
import GP as gp #plt.ion() #plt.style.use('ggplot') population = 100 model1 = gc2.heteregeneousModel(population, [0.4, 10, 0.3], False, False, "gradient", "uniform", False) #model1=gc2.heteregeneousModel(50,[5,0.2,1,0.3],True,True,"powerlaw","uniform",False) #model1.Animate() #estimate=lk2.Estimation(model1.record,model1.geo,method="powerlaw") estimate = lk2.Estimation(model1.record, model1.geo, method="gradient") #Metro=mp3.multiMetropolis(1000,[estimate.GammaPosteriorBeta0,estimate.GammaPosteriorGamma,estimate.GammaPosteriorPhi],[0.1,0.1,5],[0.5,0.5,0.4]) #Metro=mp3.multiMetropolis(1000,[partial(estimate.GammaPriorGeneralPosterior,i=0),partial(estimate.GammaPriorGeneralPosterior,i=1),partial(estimate.GammaPriorGeneralPosterior,i=2)],[0.1,0.1,5],[0.5,0.5,0.4]) #Metro=mp3.multiMetropolis(1000,[estimate.GammaPosteriorBeta0,estimate.GammaPosteriorGamma],[0.1,0.1],[0.4,0.4]) #Metro=mp3.multiMetropolis(1000,[partial(estimate.GammaPriorGeneralPosterior,i=0),partial(estimate.GammaPriorGeneralPosterior,i=1),partial(estimate.GammaPriorGeneralPosterior,i=2),partial(estimate.GammaPriorGeneralPosterior,i=3)],[3,0.1,0.9,1],[0.5,0.5,0.4,0.4]) #InitialGP=np.zeros(population*(population-1)/2) InitialGP = gp.InitialGP(estimate.DistanceMatrix) BetaMatrix = model1.BetaMatrix BetaMatrix3 = cr.BetaMatrix(model1.DistanceMatrix, [0.2, 7]) gp.BetaMatrixPlot(model1.DistanceMatrix, [ BetaMatrix, np.exp( np.log(BetaMatrix) + gp.LowerTriangularVectorToSymmetricMatrix( InitialGP, BetaMatrix.shape[0])), BetaMatrix3 ], 3) Metro = mp3.multiMetropolis(1000, [ partial(estimate.GammaPriorGeneralPosterior, i=0), partial(estimate.GammaPriorGeneralPosterior, i=1), partial(estimate.GammaPriorGeneralPosterior, i=2) ], [0.1, 0.9, 1], [0.7, 0.5, 0.7], InitialGP) Metro.showplot(0) Metro.printall(0)