#### Previous topic

numpy.polynomial.polynomial.Polynomial.deriv

#### Next topic

numpy.polynomial.polynomial.Polynomial.fromroots

# numpy.polynomial.polynomial.Polynomial.fit¶

classmethod `Polynomial.``fit`(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None)[source]

Least squares fit to data.

Return a series instance that is the least squares fit to the data y sampled at x. The domain of the returned instance can be specified and this will often result in a superior fit with less chance of ill conditioning.

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 or 1-D array_like Degree(s) of the fitting polynomials. If deg is a single integer all terms up to and including the deg’th term are included in the fit. For NumPy versions >= 1.11.0 a list of integers specifying the degrees of the terms to include may be used instead. domain : {None, [beg, end], []}, optional Domain to use for the returned series. If `None`, then a minimal domain that covers the points x is chosen. If `[]` the class domain is used. The default value was the class domain in NumPy 1.4 and `None` in later versions. The `[]` option 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. New in version 1.5.0. window : {[beg, end]}, optional Window to use for the returned series. The default value is the default class domain New in version 1.6.0. new_series : series A series that represents the least squares fit to the data and has the domain specified in the call. [resid, rank, sv, rcond] : list These values are only returned if full = True resid – sum of squared residuals of the least squares fit rank – the numerical rank of the scaled Vandermonde matrix sv – singular values of the scaled Vandermonde matrix rcond – value of rcond. For more details, see linalg.lstsq.