# Gaussian Process Regression-- reparameterizing to us pm.gp class

I am trying to speed up a Gaussian Process Regression model. I notice there is a pm.gp.marginalsparse method. However, I have not been using the pm.gp class in my model definition. I assume the first step to speeding up my GP model is to re parameterize using the pm.gp class. I am confused on how to do this in my case. Any help or suggestions would be appreciated

``````with pm.Model() as GP:
""" This model uses a precalculated distance matrix to model dependencies between individual models that are geographically close, defined in the distance matrix. """

# ====== covariance matrix ========
etasq = pm.HalfCauchy('etasq', 1) # sets maximum covariance ij
rhosq = pm.HalfCauchy('rhosq', 1e-5) # determines rate of covariance decline between farms
# will have very small posterior because distances are so large in Dmatsq
Kij = etasq*(np.exp(-rhosq*Dmatsq)+np.diag([.01]*Num_farms))

# ========== gaussian process prior =========
w = pm.MvNormal('w', mu=np.zeros(Num_farms), cov=Kij, shape=Num_farms)
a = pm.Normal('a', 0, .5, shape=Num_farms)

# ========== Linear Model ===============
u = a[zero_farm_idx]  + w[zero_farm_idx]*df.zprsum3

#============ Likelihood ==============
sigma = pm.Uniform('sigma', 0,2)
y = pm.Normal('NDVI', mu=u, sd=sigma, observed=df.zNDVIsum3)
``````

You need to first express the above as a pm.gp.kernel, and then replace the MvNormal with pm.gp.marginalsparse with the kernel as an input.