import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import Metropolis3 as mp3
import generator_temp_transprob as gc2
import likelihodPhi as lk2
import coordinate as cr
from scipy.stats import beta
from functools import partial
import GP as gp
population = 50
model1 = gc2.heteregeneousModel(population, [0.4, 0.1, 0.3], True, True)
model1.Animate()
estimate = lk2.Estimation(model1.record, model1.geo)
InitialGP = np.zeros(population * (population - 1) /
2) #zero GP and no transform
GPDoc = gp.GaussianProcess(estimate.DistanceMatrix,
np.array((1, np.mean(estimate.DistanceMatrix))))
InitialGP = GPDoc.SampleForGP(np.zeros(population * (population - 1) / 2))
GPDoc = gp.GaussianProcess(estimate.DistanceMatrix,
np.array((0.01, np.mean(estimate.DistanceMatrix))))
Metro = mp3.multiMetropolis(3000, None, [0.4, 0.1, 0.3], None, InitialGP,
GPDoc, estimate.GaussianStandardPriorGP, "Change",
"c", 1000)
gp.kernelFunctonPlot(model1.DistanceMatrix, Metro.recordGP, Metro.record,
"gradient")
gp.kernelFunctonPlotRebuild(model1.DistanceMatrix, Metro.recordGP,
Metro.record[0, :], "gradient", Metro.record[0, :],
InitialGP)
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
#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")
def negativeLikelihood(parameter,GP):
return -Likelihood.Likelihood(parameter,GP)
Z=np.zeros((X.size,Y.size))
for j in range(X.size):
for i in range(Y.size):
Z[i,j]=np.exp(-negativeLikelihood(np.array((X[i],Y[j],0.3)),NONGP))
#Z[i,j]=np.exp(Likelihood.Likelihood([X[i],Y[j],0.3],NONGP))
X, Y = np.meshgrid(X, Y)
#surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,linewidth=0, antialiased=False)
#ax.plot_surface(X,Y,Z, rstride=1, cstride=1, color='b')
#ax.set_zlim(0.1, 0.501)
#ax.zaxis.set_major_locator(LinearLocator(10))
#ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
#fig.colorbar(surf, shrink=0.5, aspect=5)
plt.contour(X,Y,Z)
plt.show()
if __name__ == "__main__":
population=50
distanceMethod="gradient"
if distanceMethod=="gradient" :
parameter=[0.4,0.1,0.3]
elif distanceMethod=="powerlaw":
parameter=[200,0.1,1.5,0.3]
model1=gc.heteregeneousModel(population,parameter,True,True,distanceMethod)
#model1.Animate()
Likelihood=lk.Estimation(model1.record,model1.geo)
mleGamma(Likelihood)
MLE(Likelihood)
countourLikelihood(Likelihood)
