import numpy as np from scipy.stats import norm import matplotlib.pyplot as plt #import Metropolis as mp #import Metropolis2 as mp2 import Metropolis3 as mp3 #import generator_temp as gc import generator_temp_transprob as gc2 #import likelihood as lk import likelihodPhi as lk2 import coordinate as cr from scipy.stats import beta from functools import partial import GP as gp ''' geo=cr.geodata(50) geo=cr.geodata(50,"uniform",xbound=100.0,ybound=100.0,history=False) Distance=cr.DistanceMatrix(geo) BetaMatrix=cr.BetaMatrix(Distance,[0.3,5]) gp.BetaMatrixPlot(Distance,BetaMatrix) ''' model1 = gc2.heteregeneousModel(50, [0.3, 5, 0.3]) gp.BetaMatrixPlot(model1.DistanceMatrix, model1.BetaMatrix, 1) #gp.BetaMatrixPlot(model1.DistanceMatrix,model1.BetaMatrix)
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)
Metro.showplot(2)
Metro.printall(2)
Metro.plotcountour(0,1)
Metro.plotcountour(1,2)
Metro.plotcountour(0,2)
gp.GPPlot(model1.DistanceMatrix,Metro.recordGP)