parameters: (cls, x, y, deg, domain=None, rcond=None, full=False, w=None,
          window=None)
docstring: 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.
  
              .. versionadded:: 1.5.0
          window : {[beg, end]}, optional
              Window to use for the returned series. The default
              value is the default class domain
  
              .. versionadded:: 1.6.0
  
          Returns
          -------
          new_series : series
              A series that represents the least squares fit to the data and
              has the domain and window specified in the call. If the
              coefficients for the unscaled and unshifted basis polynomials are
              of interest, do ``new_series.convert().coef``.
  
          [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`.
docstring_summary: Least squares fit to data.
docstring_tokens: Least squares fit to data .
function: def fit(cls, x, y, deg, domain=None, rcond=None, full=False, w=None,
          window=None):
          """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.
  
              .. versionadded:: 1.5.0
          window : {[beg, end]}, optional
              Window to use for the returned series. The default
              value is the default class domain
  
              .. versionadded:: 1.6.0
  
          Returns
          -------
          new_series : series
              A series that represents the least squares fit to the data and
              has the domain and window specified in the call. If the
              coefficients for the unscaled and unshifted basis polynomials are
              of interest, do ``new_series.convert().coef``.
  
          [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`.
  
          """
          if domain is None:
              domain = pu.getdomain(x)
          elif type(domain) is list and len(domain) == 0:
              domain = cls.domain
  
          if window is None:
              window = cls.window
  
          xnew = pu.mapdomain(x, domain, window)
          res = cls._fit(xnew, y, deg, w=w, rcond=rcond, full=full)
          if full:
              [coef, status] = res
              return cls(coef, domain=domain, window=window), status
          else:
              coef = res
              return cls(coef, domain=domain, window=window)
function_tokens: def fit ( cls , x , y , deg , domain = None , rcond = None , full = False , w = None , window = None ) : if domain is None : domain = pu . getdomain ( x ) elif type ( domain ) is list and len ( domain ) == 0 : domain = cls . domain if window is None : window = cls . window xnew = pu . mapdomain ( x , domain , window ) res = cls . _fit ( xnew , y , deg , w = w , rcond = rcond , full = full ) if full : [ coef , status ] = res return cls ( coef , domain = domain , window = window ) , status else : coef = res return cls ( coef , domain = domain , window = window )