The all-important kernels and their hyperparameters. Created on Dec 11, 2014 @author: ntraft ''' from __future__ import division import numpy as np from com.ntraft.gp import ParametricGPModel import com.ntraft.covariance as cov # Hyperparameters from seq_eth #175 model1 = ParametricGPModel( cov.summed_kernel(cov.matern_kernel(np.exp(3.5128), np.exp(2 * 5.3844)), cov.linear_kernel(np.exp(-2 * -2.8770)), cov.noise_kernel(np.exp(2 * -0.3170))), cov.summed_kernel(cov.matern_kernel(np.exp(2.2839), np.exp(2 * 2.5229)), cov.linear_kernel(np.exp(-2 * -4.8792)), cov.noise_kernel(np.exp(2 * -0.2407)))) # Hyperparameters from seq_eth #48 - HUGE variance and smooth model2 = ParametricGPModel( cov.summed_kernel(cov.matern_kernel(np.exp(2.0194), np.exp(2 * 2.7259)), cov.linear_kernel(np.exp(-2 * -3.2502)), cov.noise_kernel(np.exp(2 * -1.1128))), cov.summed_kernel(cov.matern_kernel(np.exp(3.5181), np.exp(2 * 5.4197)), cov.linear_kernel(np.exp(-2 * -0.8087)), cov.noise_kernel(np.exp(2 * -0.5089)))) # Hyperparameters from seq_eth #201 - pretty squirrely
# ykernel = cov.summed_kernel(cov.sq_exp_kernel(2.5, 1), cov.noise_kernel(0.01)) # Cafeteria Hyperparams (pre-evaluated) # xkernel = cov.summed_kernel( # cov.matern_kernel(33.542, 47517), # cov.linear_kernel(315.46), # cov.noise_kernel(0.53043) # ) # ykernel = cov.summed_kernel( # cov.matern_kernel(9.8147, 155.36), # cov.linear_kernel(17299), # cov.noise_kernel(0.61790) # ) # Cafeteria Hyperparams xkernel = cov.summed_kernel( cov.matern_kernel(np.exp(3.5128), np.exp(2*5.3844)), cov.linear_kernel(np.exp(-2*-2.8770)), cov.noise_kernel(np.exp(2*-0.3170)) ) ykernel = cov.summed_kernel( cov.matern_kernel(np.exp(2.2839), np.exp(2*2.5229)), cov.linear_kernel(np.exp(-2*-4.8792)), cov.noise_kernel(np.exp(2*-0.2407)) ) xgp = GaussianProcess(T, x, Ttest, xkernel) ygp = GaussianProcess(T, y, Ttest, ykernel) # PLOTS: # draw samples from the prior at our test points. xs = xgp.sample_prior(10) ys = ygp.sample_prior(10)
# ykernel = cov.summed_kernel(cov.sq_exp_kernel(2.5, 1), cov.noise_kernel(0.01)) # Cafeteria Hyperparams (pre-evaluated) # xkernel = cov.summed_kernel( # cov.matern_kernel(33.542, 47517), # cov.linear_kernel(315.46), # cov.noise_kernel(0.53043) # ) # ykernel = cov.summed_kernel( # cov.matern_kernel(9.8147, 155.36), # cov.linear_kernel(17299), # cov.noise_kernel(0.61790) # ) # Cafeteria Hyperparams xkernel = cov.summed_kernel( cov.matern_kernel(np.exp(3.5128), np.exp(2 * 5.3844)), cov.linear_kernel(np.exp(-2 * -2.8770)), cov.noise_kernel(np.exp(2 * -0.3170))) ykernel = cov.summed_kernel( cov.matern_kernel(np.exp(2.2839), np.exp(2 * 2.5229)), cov.linear_kernel(np.exp(-2 * -4.8792)), cov.noise_kernel(np.exp(2 * -0.2407))) xgp = GaussianProcess(T, x, Ttest, xkernel) ygp = GaussianProcess(T, y, Ttest, ykernel) # PLOTS: # draw samples from the prior at our test points. xs = xgp.sample_prior(10) ys = ygp.sample_prior(10) pl.figure(1) pl.plot(xs, ys)