Can we take the gradients of our emulators? I investigate.



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from pearce.emulator import NashvilleHot
from GPy.kern import *
import numpy as np
from os import path

import matplotlib #matplotlib.use('Agg') from matplotlib import pyplot as plt %matplotlib inline import seaborn as sns sns.set()



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'/scratch/users/swmclau2/xi_gg_zheng07/'









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'/scratch/users/swmclau2/xi_gg_zheng07/'



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training_file = '/scratch/users/swmclau2/xi_gg_zheng07/PearceXiggCosmo.hdf5'



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em_method = 'gp'



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fixed_params = {'z':0.0}

hyperparams = {'kernel': (Linear(input_dim=7, ARD=True) + RBF(input_dim=7, ARD=True)+Bias(input_dim=7), RBF(input_dim=4, ARD=True)+Bias(input_dim=4) ), \ 'optimize': True}



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emu = NashvilleHot(training_file, hyperparams={},fixed_params = fixed_params, downsample_factor = 0.2)









    



 /home/users/swmclau2/.local/lib/python2.7/site-packages/paramz/transformations.py:111: RuntimeWarning:overflow encountered in expm1



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model = emu._emulators[0]



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bounds = [emu.get_param_bounds(name) for name in emu.get_param_names()]
x_new = np.array([np.random.uniform(b[0], b[1]) for b in bounds])



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x_new = x_new.reshape((1,-1))



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kern1 = model.kern1

mean_jac = np.empty((x_new.shape[0], x_new.shape[1], 1)) mean_jac[:, :, 0] = kern.gradients_X( self.posterior.woodbury_vector[:, i:i+1].T, Xnew, self._predictive_variable) # Gradients wrt the diagonal part k_{xx} dv_dX = kern.gradients_X_diag(np.ones(Xnew.shape[0]), Xnew)

Answer: No



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