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This repository was archived by the owner on Dec 13, 2017. It is now read-only.
Second, I think there is a bug when the Wiener_process is conditional (e.g. has a startPosition and an endPosition). When _sample_path is called, the startPosition is ultimately ignored in the conditional branch of the code. I believe that branch of the code should read:
else:
"""
Alternatively, this can be accomplished by sampling directly from a multivariate normal given a linear
projection. Ie
N | N dot 1 = endPosition ~ Nor( 0, Sigma ), where Sigma is a diagonal matrix with elements proportional to
the delta. This only problem with this is Sigma is too large for very large len(times).
"""
T = self.endTime - self.startTime
x = self.startTime
for i, delta in enumerate( deltas ):
x = x*(1-delta/T) + (self.endPosition - self.startPosition)*delta/T + self.sigma*np.sqrt(delta/T*(T-delta))*self.Nor.rvs(N)
T = T - delta
path[:,i] = x
if abs(T -0)<1e-10:
path[:,-1] = (self.endPosition - self.startPosition)
path = path + self.startPosition