Covariance

from pymc.gp import *
from pymc.gp.cov_funs import matern
from numpy import *

C = Covariance(eval_fun = matern.euclidean, diff_degree = 1.4, amp = .4, scale = 1.)
# C = Covariance(eval_fun = matern.euclidean, diff_degree = 1.4, amp = .4, scale = 1., rank_limit=100)
# C = FullRankCovariance(eval_fun = matern.euclidean, diff_degree = 1.4, amp = .4, scale = 1.)
# C = NearlyFullRankCovariance(eval_fun = matern.euclidean, diff_degree = 1.4, amp = .4, scale = 1.)

#### - Plot - ####
if __name__ == '__main__':
    from pylab import *

    x=arange(-1.,1.,.01)
    clf()

    # Plot the covariance function
    subplot(1,2,1)

    contourf(x,x,C(x,x).view(ndarray),origin='lower',extent=(-1.,1.,-1.,1.),cmap=cm.bone)

    xlabel('x')
    ylabel('y')
    title('C(x,y)')
    axis('tight')
    colorbar()

    # Plot a slice of the covariance function
    subplot(1,2,2)

    plot(x,C(x,0).view(ndarray).ravel(),'k-')

    xlabel('x')
    ylabel('C(x,0)')
    title('A slice of C')

    # show()