numpy.polynomial.polynomial.polyfit()
numpy.polynomial.polynomial.polyfit
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numpy.polynomial.polynomial.polyfit(x, y, deg, rcond=None, full=False, w=None)
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Least-squares fit of a polynomial to data.
Return the coefficients of a polynomial of degree
deg
that is the least squares fit to the data valuesy
given at pointsx
. Ify
is 1-D the returned coefficients will also be 1-D. Ify
is 2-D multiple fits are done, one for each column ofy
, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are in the formwhere
n
isdeg
.Parameters: x : array_like, shape (
M
,)x-coordinates of the
M
sample (data) points(x[i], y[i])
.y : array_like, shape (
M
,) or (M
,K
)y-coordinates of the sample points. Several sets of sample points sharing the same x-coordinates can be (independently) fit with one call to
polyfit
by passing in fory
a 2-D array that contains one data set 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 thedeg
‘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.rcond : float, optional
Relative condition number of the fit. Singular values smaller than
rcond
, relative to the largest singular value, will be ignored. The default value islen(x)*eps
, whereeps
is the relative precision of the platform’s float type, about 2e-16 in most cases.full : bool, optional
Switch determining the nature of the return value. When
False
(the default) just the coefficients are returned; whenTrue
, diagnostic information from the singular value decomposition (used to solve the fit’s matrix equation) is also returned.w : array_like, shape (
M
,), optionalWeights. If not None, the contribution of each point
(x[i],y[i])
to the fit is weighted byw[i]
. Ideally the weights are chosen so that the errors of the productsw[i]*y[i]
all have the same variance. The default value is None.New in version 1.5.0.
Returns: coef : ndarray, shape (
deg
+ 1,) or (deg
+ 1,K
)Polynomial coefficients ordered from low to high. If
y
was 2-D, the coefficients in columnk
ofcoef
represent the polynomial fit to the data iny
‘sk
-th column.[residuals, rank, singular_values, rcond] : list
These values are only returned if
full
= Trueresid – 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
.Raises: RankWarning
Raised if the matrix in the least-squares fit is rank deficient. The warning is only raised if
full
== False. The warnings can be turned off by:>>> import warnings >>> warnings.simplefilter('ignore', RankWarning)
See also
chebfit
,legfit
,lagfit
,hermfit
,hermefit
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polyval
- Evaluates a polynomial.
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polyvander
- Vandermonde matrix for powers.
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linalg.lstsq
- Computes a least-squares fit from the matrix.
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scipy.interpolate.UnivariateSpline
- Computes spline fits.
Notes
The solution is the coefficients of the polynomial
p
that minimizes the sum of the weighted squared errorswhere the
are the weights. This problem is solved by setting up the (typically) over-determined matrix equation:
where
V
is the weighted pseudo Vandermonde matrix ofx
,c
are the coefficients to be solved for,w
are the weights, andy
are the observed values. This equation is then solved using the singular value decomposition ofV
.If some of the singular values of
V
are so small that they are neglected (andfull
==False
), aRankWarning
will be raised. This means that the coefficient values may be poorly determined. Fitting to a lower order polynomial will usually get rid of the warning (but may not be what you want, of course; if you have independent reason(s) for choosing the degree which isn’t working, you may have to: a) reconsider those reasons, and/or b) reconsider the quality of your data). Thercond
parameter can also be set to a value smaller than its default, but the resulting fit may be spurious and have large contributions from roundoff error.Polynomial fits using double precision tend to “fail” at about (polynomial) degree 20. Fits using Chebyshev or Legendre series are generally better conditioned, but much can still depend on the distribution of the sample points and the smoothness of the data. If the quality of the fit is inadequate, splines may be a good alternative.
Examples
>>> from numpy.polynomial import polynomial as P >>> x = np.linspace(-1,1,51) # x "data": [-1, -0.96, ..., 0.96, 1] >>> y = x**3 - x + np.random.randn(len(x)) # x^3 - x + N(0,1) "noise" >>> c, stats = P.polyfit(x,y,3,full=True) >>> c # c[0], c[2] should be approx. 0, c[1] approx. -1, c[3] approx. 1 array([ 0.01909725, -1.30598256, -0.00577963, 1.02644286]) >>> stats # note the large SSR, explaining the rather poor results [array([ 38.06116253]), 4, array([ 1.38446749, 1.32119158, 0.50443316, 0.28853036]), 1.1324274851176597e-014]
Same thing without the added noise
>>> y = x**3 - x >>> c, stats = P.polyfit(x,y,3,full=True) >>> c # c[0], c[2] should be "very close to 0", c[1] ~= -1, c[3] ~= 1 array([ -1.73362882e-17, -1.00000000e+00, -2.67471909e-16, 1.00000000e+00]) >>> stats # note the minuscule SSR [array([ 7.46346754e-31]), 4, array([ 1.38446749, 1.32119158, 0.50443316, 0.28853036]), 1.1324274851176597e-014]
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.polynomial.polynomial.polyfit.html