function Y = rbfpredict(NetParams,X)
%  Inputs:  NetParams from rbfOLS
%           X is dim x numpts
%  Output:  Y is out dim x numpts

    C=NetParams.C;  %Centers for the RBF
    W=NetParams.W;  %Weights
    b=NetParams.b;  % bias
    spr=NetParams.spr;  %Spread (actually, the reciprocal of spread)
    A=radbas1(dist(X',C')*spr);
    Y=A*W+b;
    Y=Y';
end

%======================================================
function a = radbas1(n)
    a = exp(-(n.*n));
end

%======================================================
function z=dist(w,p)
% To be consistent with Matlab's dist function, the first
% matrix is transposed with what it ought to be.
% Incoming is numpts x dim and dim x numpts
p=p'; %This should convert "dist" to "edm" input

% EDM starts here; w and p should both be numpts x dim at this point
[S,R] = size(w);
[Q,R2] = size(p);
p=p';
if (R ~= R2), error('Inner matrix dimensions do not match.\n'), end
z = zeros(S,Q);
if (Q<S)
p = p';
copies = zeros(1,S);
for q=1:Q
z(:,q) = sum((w-p(q+copies,:)).^2,2);
end
else
w = w';
copies = zeros(1,Q);
for i=1:S
z(i,:) = sum((w(:,i+copies)-p).^2,1);
end
end
z = z.^0.5;
end