Hi everyone,
I was using SymPy to derive an analytic expression for a Jacobian matrix in
a least squares system. It worked very well, and I simplified the system
down to an equation which is very easy to evaluate, which however involves
a vector of residuals:
The final SymPy expression is this:
Sum((-10.0*exp(-0.1*t) + 10.0)*d, (t, 1, 50))
Where t is a variable, and d is an indexed base. How can I numerically replace
d with a vector containing 50 residuals, and actually compute the sum? It
would be trivial to code this up in numpy using a loop, but since I was
preparing an iPython notebook, I thought it would be nice to do as much as
possible within SymPy.
The equivalent numpy code would be this:
tot = 0.0
for t in range(50):
tot += (-10*np.exp(-0.1*t) + 10)*d[t]
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