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)
