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Fix robust_fit for large timestamps by subtracting offset #73

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Dec 30, 2020
Merged

Fix robust_fit for large timestamps by subtracting offset #73

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22edf50
Fix robust_fit for large timestamps by subtracting offset
cboulay Dec 29, 2020
710b877
robust_fit: copy mutable input A.
cboulay Dec 29, 2020
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12 changes: 8 additions & 4 deletions pyxdf/pyxdf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -611,11 +611,12 @@ def _clock_sync(
if range_i[0] != range_i[1]:
start, stop = range_i[0], range_i[1] + 1
e = np.ones((stop - start,))
X = np.column_stack([e, clock_times[start:stop]])
X /= winsor_threshold
y = clock_values[start:stop] / winsor_threshold
X = np.column_stack([e, np.array(clock_times[start:stop]) / winsor_threshold])
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y = np.array(clock_values[start:stop]) / winsor_threshold
# noinspection PyTypeChecker
coef.append(_robust_fit(X, y))
_coefs = _robust_fit(X, y)
_coefs[0] *= winsor_threshold
coef.append(_coefs)
else:
coef.append((clock_values[range_i[0]], 0))

Expand Down Expand Up @@ -702,6 +703,8 @@ def _robust_fit(A, y, rho=1, iters=1000):
http://www.stanford.edu/~boyd/papers/distr_opt_stat_learning_admm.html

"""
offset = np.min(A[:, 1])
A[:, 1] = A[:, 1] - offset # Don't use -= operator here!
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Aty = np.dot(A.T, y)
L = np.linalg.cholesky(np.dot(A.T, A))
U = L.T
Expand All @@ -715,6 +718,7 @@ def _robust_fit(A, y, rho=1, iters=1000):
tmp = np.maximum(0, (1 - (1 + 1 / rho) * np.abs(d_inv)))
z = rho / (1 + rho) * d + 1 / (1 + rho) * tmp * d
u = d - z
x[0] -= x[1] * offset
return x


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