polyFit

polyFit(X, Y, n, mode)

Return a vector indicating the least-squares fit polynomial coefficients in ascending powers for a polynomial p(X) of degree n that is a best fit (in a least-squares sense) for the data in Y.

Difference from Python's numpy.polyfit: Both functions perform a least-squares polynomial fit. NumPy's polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False) returns coefficients in descending powers and supports parameters such as rcond, full, w, and cov. Its y can also be a 2-D array for fitting multiple data sets at once. DolphinDB's polyFit(X, Y, n, mode) requires X and Y to be numeric vectors of the same length without null values, and returns coefficients in ascending powers. DolphinDB uses mode to control whether to return a vector or a dictionary containing prediction-function information.

X is a numeric vector specifying the query points. The points in X correspond to the fitted function values contained in Y.

Y is a numeric vector of the same length as X, which specifies the fitted values at query points. It must not contain null values.

n is a non-negative scalar indicating the degree of polynomial fit.

mode is a Boolean scalar indicating whether to return a dictionary of a vector. Defaults to 0, meaning to return a vector.

A DOUBLE vector indicating the coefficients in ascending order.

x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
y = [0.0, 0.8, 0.9, 0.1, -0.8, -1.0]
z = polyFit(x, y, 3)
z
// output: [-0.0397,1.6931,-0.8135,0.087]

z = polyFit(x, y, 3, 1)
/*output: 
modelName->polyFit
z->[-0.039682539682536,1.693121693121692,-0.813492063492063,0.087037037037037]
predict->polyPredict
*/

Related Function: polyPredict