# cupy.polyfit¶

cupy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)[source]

Returns the least squares fit of polynomial of degree deg to the data y sampled at x.

Parameters
• x (cupy.ndarray) – x-coordinates of the sample points of shape (M,).

• y (cupy.ndarray) – y-coordinates of the sample points of shape (M,) or (M, K).

• deg (int) – degree of the fitting polynomial.

• rcond (float, optional) – relative condition number of the fit. The default value is `len(x) * eps`.

• full (bool, optional) – indicator of the return value nature. When False (default), only the coefficients are returned. When True, diagnostic information is also returned.

• w (cupy.ndarray, optional) – weights applied to the y-coordinates of the sample points of shape (M,).

• cov (bool or str, optional) – if given, returns the coefficients along with the covariance matrix.

Returns

p (cupy.ndarray of shape (deg + 1,) or (deg + 1, K)):

Polynomial coefficients from highest to lowest degree

residuals, rank, singular_values, rcond (cupy.ndarray, int, cupy.ndarray, float):

Present only if `full=True`. Sum of squared residuals of the least-squares fit, rank of the scaled Vandermonde coefficient matrix, its singular values, and the specified value of `rcond`.

V (cupy.ndarray of shape (M, M) or (M, M, K)):

Present only if `full=False` and `cov=True`. The covariance matrix of the polynomial coefficient estimates.

Return type

Warning

numpy.RankWarning: The rank of the coefficient matrix in the least-squares fit is deficient. It is raised if `full=False`.