numpy.polynomial.hermite.hermfit   polynomial.hermite.hermfit(x, y, deg, rcond=None, full=False, w=None)[source]
 
Least squares fit of Hermite series to data. Return the coefficients of a Hermite series of degree deg that is the least squares fit to the data values y given at points x. If y is 1-D the returned coefficients will also be 1-D. If y is 2-D multiple fits are done, one for each column of y, and the resulting coefficients are stored in the corresponding columns of a 2-D return. The fitted polynomial(s) are in the form  \[p(x) = c_0 + c_1 * H_1(x) + ... + c_n * H_n(x),\] where n is deg.  Parameters 
 
xarray_like, shape (M,)


x-coordinates of the M sample points (x[i], y[i]).  
yarray_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.  
degint 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.  
rcondfloat, 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.  
fullbool, 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.  
warray_like, shape (M,), optional


Weights. If not None, the weight w[i] applies to the unsquared residual y[i] - y_hat[i] at x[i]. Ideally the weights are chosen so that the errors of the products w[i]*y[i] all have the same variance. When using inverse-variance weighting, use w[i] = 1/sigma(y[i]). The default value is None.    Returns 
 
coefndarray, shape (M,) or (M, K)


Hermite coefficients ordered from low to high. If y was 2-D, the coefficients for the data in column k of y are in column k.  
[residuals, rank, singular_values, rcond]list


These values are only returned if full == True  residuals – sum of squared residuals of the least squares fit rank – the numerical rank of the scaled Vandermonde matrix singular_values – singular values of the scaled Vandermonde matrix rcond – value of rcond.  For more details, see numpy.linalg.lstsq.    Warns 
 RankWarning

The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if full == False. The warnings can be turned off by >>> import warnings
>>> warnings.simplefilter('ignore', np.RankWarning)
      See also  numpy.polynomial.chebyshev.chebfit
numpy.polynomial.legendre.legfit
numpy.polynomial.laguerre.lagfit
numpy.polynomial.polynomial.polyfit
numpy.polynomial.hermite_e.hermefit
hermval

Evaluates a Hermite series.  hermvander

Vandermonde matrix of Hermite series.  hermweight

Hermite weight function  numpy.linalg.lstsq

Computes a least-squares fit from the matrix.  scipy.interpolate.UnivariateSpline

Computes spline fits.    Notes The solution is the coefficients of the Hermite series p that minimizes the sum of the weighted squared errors  \[E = \sum_j w_j^2 * |y_j - p(x_j)|^2,\] where the \(w_j\) are the weights. This problem is solved by setting up the (typically) overdetermined matrix equation  \[V(x) * c = w * y,\] where V is the weighted pseudo Vandermonde matrix of x, c are the coefficients to be solved for, w are the weights, y are the observed values. This equation is then solved using the singular value decomposition of V. If some of the singular values of V are so small that they are neglected, then a RankWarning will be issued. This means that the coefficient values may be poorly determined. Using a lower order fit will usually get rid of the warning. The rcond 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. Fits using Hermite series are probably most useful when the data can be approximated by sqrt(w(x)) * p(x), where w(x) is the Hermite weight. In that case the weight sqrt(w(x[i])) should be used together with data values y[i]/sqrt(w(x[i])). The weight function is available as hermweight. References  1 
Wikipedia, “Curve fitting”, https://en.wikipedia.org/wiki/Curve_fitting   Examples >>> from numpy.polynomial.hermite import hermfit, hermval
>>> x = np.linspace(-10, 10)
>>> err = np.random.randn(len(x))/10
>>> y = hermval(x, [1, 2, 3]) + err
>>> hermfit(x, y, 2)
array([1.0218, 1.9986, 2.9999]) # may vary