#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)
Metro.showplot(2)
# Hyper parameters to test
length_scales = np.arange(1, 50, 5)[1:]
sigma_vals = [0, 0.01, .1, .3] # Noise values to test
# Progressively take more samples from data set to train the model
for step in range(0, sampling_steps):
std_dev = []
predict = []
# Using self written library
# do 5 folds cross validation to find optimal hyper parameters l and sigma
l_opt, sigma_opt = GP.cross_validate(training_data,
l_val=length_scales,
sigma_val=sigma_vals)
# training model with optimal hyper parameters
gp = GP.GaussianProcess(train_data=training_data,
return_std=True)
predict, std_dev = gp.predict(test_data=test_data[:, :-1],
l=l_opt,
sigma=sigma_opt)
if step == sampling_steps - 1:
continue
#get more training samples
ps = progressive_sampling(training_data, test_data, std_dev,
ti)
# new training and test data
training_data, test_data, std_dev = ps.train, ps.test, ps.std
ps = None
gp = None
Ptest = test_data[:, -1:].reshape(-1, 1)