xdem.fit

Functions to perform normal, weighted and robust fitting.

Functions

huber_loss(z)	Huber loss cost (reduces the weight of outliers) :type z: ndarray[Any, dtype[floating[Any]]] :param z: Residuals between predicted and true values :rtype: float :return: Huber cost
polynomial_1d(xx, *params)	N-order 1D polynomial.
polynomial_2d(xx, *params)	N-order 2D polynomial.
rmse(z)	Return root mean square error :type z: ndarray[Any, dtype[floating[Any]]] :param z: Residuals between predicted and true value :rtype: float :return: Root Mean Square Error
robust_nfreq_sumsin_fit(xdata, ydata[, ...])	Given 1D vectors x and y, compute a robust sum of sinusoid fit to the data.
robust_norder_polynomial_fit(xdata, ydata[, ...])	Given 1D vectors x and y, compute a robust polynomial fit to the data.
soft_loss(z[, scale])	Soft loss cost (reduces the weight of outliers) :type z: ndarray[Any, dtype[floating[Any]]] :param z: Residuals between predicted and true values :type scale: float :param scale: Scale factor :rtype: float :return: Soft loss cost
sumsin_1d(xx, *params)	Sum of N sinusoids in 1D.