# numpy.polynomial.polynomial.Polynomial.fit¶

static Polynomial.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=[-1, 1])

Least squares fit to data.

Return a Polynomial instance that is the least squares fit to the data y sampled at x. Unlike polyfit, the domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning. Support for NA was added in version 1.7.0. See polyfit for full documentation of the implementation.

Parameters : x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column. deg : int Degree of the fitting polynomial. domain : {None, [beg, end], []}, optional Domain to use for the returned Polynomial instance. If None, then a minimal domain that covers the points x is chosen. If [] the default domain [-1,1] is used. The default value is [-1,1] in numpy 1.4.x and None in later versions. The '[] value was added in numpy 1.5.0. rcond : float, optional Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases. full : bool, optional Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (M,), optional Weights. If not None the contribution of each point (x[i],y[i]) to the fit is weighted by w[i]. Ideally the weights are chosen so that the errors of the products w[i]*y[i] all have the same variance. The default value is None. .. versionadded:: 1.5.0 window : {[beg, end]}, optional Window to use for the returned Polynomial instance. The default value is [-1,1] .. versionadded:: 1.6.0 least_squares_fit : instance of Polynomial The Polynomial instance is the least squares fit to the data and has the domain specified in the call. [residuals, rank, singular_values, rcond] : only if full = True Residuals of the least squares fit, the effective rank of the scaled Vandermonde matrix and its singular values, and the specified value of rcond. For more details, see linalg.lstsq.