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18-Nov-2020 03:45

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Our kriging function takes the data set def krige( P, model, hs, bw, u, N ): ''' Input (P) ndarray, data (model) modeling function - spherical - exponential - gaussian (hs) kriging distances (bw) kriging bandwidth (u) unsampled point (N) number of neighboring points to consider ''' # covariance function covfct = cvmodel( P, model, hs, bw ) # mean of the variable mu = np.mean( P[:,2] ) # distance between u and each data point in P d = np.sqrt( ( P[:,0]-u[0] )**2.0 ( P[:,1]-u[1] )**2.0 ) # add these distances to P P = np.vstack(( P. T # sort P by these distances # take the first N of them P = P[d.argsort()[: N]] # apply the covariance model to the distances k = covfct( P[:,3] ) # cast as a matrix k = np.matrix( k ).… continue reading »

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Curry and Rice and All Things Nice Asian cam girls are always eager to please and will go to great lengths to satisfy their partners, especially when you take these ladies 1-on-1.… continue reading »

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