def all_tests(gaussian1,gaussian2,n=200):
oracle_samples1 = gaussian1.sample(n=n).samples
oracle_samples2 = gaussian2.sample(n=n).samples
distribution1 = GaussianFullConditionals(gaussian1, list(gaussian1.mu))
distribution2 = GaussianFullConditionals(gaussian2, list(gaussian2.mu))
mcmc_sampler1 = Gibbs(distribution1)
mcmc_sampler2 = Gibbs(distribution2)
start = zeros(2)
mcmc_params = MCMCParams(start=start, num_iterations=2000+n, burnin=2000)
chain1 = MCMCChain(mcmc_sampler1, mcmc_params)
chain1.run()
gibbs_samples1 = chain1.get_samples_after_burnin()
chain2 = MCMCChain(mcmc_sampler2, mcmc_params)
chain2.run()
gibbs_samples2 = chain2.get_samples_after_burnin()
sigma = GaussianKernel.get_sigma_median_heuristic(concatenate((oracle_samples1,oracle_samples2),axis=0))
kernel = GaussianKernel(sigma=sigma)
vanillap=empty((2,2))
blockp=empty((2,2))
wildp=empty((2,2))
vanillap[0,0]=kernel.TwoSampleTest(oracle_samples1,oracle_samples2,method='vanilla')
vanillap[0,1]=kernel.TwoSampleTest(oracle_samples1,gibbs_samples2,method='vanilla')
vanillap[1,0]=kernel.TwoSampleTest(gibbs_samples1,oracle_samples2,method='vanilla')
vanillap[1,1]=kernel.TwoSampleTest(gibbs_samples1,gibbs_samples2,method='vanilla')
blockp[0,0]=kernel.TwoSampleTest(oracle_samples1,oracle_samples2,method='block')
blockp[0,1]=kernel.TwoSampleTest(oracle_samples1,gibbs_samples2,method='block')
blockp[1,0]=kernel.TwoSampleTest(gibbs_samples1,oracle_samples2,method='block')
blockp[1,1]=kernel.TwoSampleTest(gibbs_samples1,gibbs_samples2,method='block')
wildp[0,0]=kernel.TwoSampleTest(oracle_samples1,oracle_samples2,method='wild')
wildp[0,1]=kernel.TwoSampleTest(oracle_samples1,gibbs_samples2,method='wild')
wildp[1,0]=kernel.TwoSampleTest(gibbs_samples1,oracle_samples2,method='wild')
wildp[1,1]=kernel.TwoSampleTest(gibbs_samples1,gibbs_samples2,method='wild')
return vanillap,blockp,wildp
