class Smoothing_F extends Error
The Smoothing_F
class fits a time-dependent data vector 'y' to B-Splines.
y(t(i)) = x(t(i)) + ε(t(i)) x(t) = cΦ(t)
where 'x' is the signal, 'ε' is the noise, 'c' is a coefficient vector and 'Φ(t)' is a vector of basis functions. -----------------------------------------------------------------------------
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new
Smoothing_F(y: VectorD, t: VectorD, τ: VectorD = null, ord: Int = 4)
- y
the (raw) data points/vector
- t
the data time points/vector
- τ
the time points/vector for the knots
- ord
the order (degree+1) of B-Splines (2, 3, 4, 5 or 6)
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- def makeKnots: VectorD
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def
predict(tv: VectorD): VectorD
Predict the y-values at all time points in vector 'tv'.
Predict the y-values at all time points in vector 'tv'.
- tv
the given vector of time points
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def
predict(tt: Double): Double
Predict the y-value at time point 'tt'.
Predict the y-value at time point 'tt'.
- tt
the given time point
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def
train(): VectoD
Train the model, i.e., determine the optimal cofficients 'c' for the basis functions.
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