""" Objects for dealing with Hermite series. This module provides a number of objects (mostly functions) useful for dealing with Hermite series, including a `Hermite` class that encapsulates the usual arithmetic operations. (General information on how this module represents and works with such polynomials is in the docstring for its "parent" sub-package, `numpy.polynomial`). Constants --------- - `hermdomain` -- Hermite series default domain, [-1,1]. - `hermzero` -- Hermite series that evaluates identically to 0. - `hermone` -- Hermite series that evaluates identically to 1. - `hermx` -- Hermite series for the identity map, ``f(x) = x``. Arithmetic ---------- - `hermmulx` -- multiply a Hermite series in ``P_i(x)`` by ``x``. - `hermadd` -- add two Hermite series. - `hermsub` -- subtract one Hermite series from another. - `hermmul` -- multiply two Hermite series. - `hermdiv` -- divide one Hermite series by another. - `hermval` -- evaluate a Hermite series at given points. Calculus -------- - `hermder` -- differentiate a Hermite series. - `hermint` -- integrate a Hermite series. Misc Functions -------------- - `hermfromroots` -- create a Hermite series with specified roots. - `hermroots` -- find the roots of a Hermite series. - `hermvander` -- Vandermonde-like matrix for Hermite polynomials. - `hermfit` -- least-squares fit returning a Hermite series. - `hermtrim` -- trim leading coefficients from a Hermite series. - `hermline` -- Hermite series of given straight line. - `herm2poly` -- convert a Hermite series to a polynomial. - `poly2herm` -- convert a polynomial to a Hermite series. Classes ------- - `Hermite` -- A Hermite series class. See also -------- `numpy.polynomial` """ from __future__ import division __all__ = ['hermzero', 'hermone', 'hermx', 'hermdomain', 'hermline', 'hermadd', 'hermsub', 'hermmulx', 'hermmul', 'hermdiv', 'hermval', 'hermder', 'hermint', 'herm2poly', 'poly2herm', 'hermfromroots', 'hermvander', 'hermfit', 'hermtrim', 'hermroots', 'Hermite'] import numpy as np import numpy.linalg as la import polyutils as pu import warnings from polytemplate import polytemplate hermtrim = pu.trimcoef def poly2herm(pol) : """ poly2herm(pol) Convert a polynomial to a Hermite series. Convert an array representing the coefficients of a polynomial (relative to the "standard" basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Hermite series, ordered from lowest to highest degree. Parameters ---------- pol : array_like 1-d array containing the polynomial coefficients Returns ------- cs : ndarray 1-d array containing the coefficients of the equivalent Hermite series. See Also -------- herm2poly Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples -------- >>> from numpy.polynomial.hermite_e import poly2herme >>> poly2herm(np.arange(4)) array([ 1. , 2.75 , 0.5 , 0.375]) """ [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 for i in range(deg, -1, -1) : res = hermadd(hermmulx(res), pol[i]) return res def herm2poly(cs) : """ Convert a Hermite series to a polynomial. Convert an array representing the coefficients of a Hermite series, ordered from lowest degree to highest, to an array of the coefficients of the equivalent polynomial (relative to the "standard" basis) ordered from lowest to highest degree. Parameters ---------- cs : array_like 1-d array containing the Hermite series coefficients, ordered from lowest order term to highest. Returns ------- pol : ndarray 1-d array containing the coefficients of the equivalent polynomial (relative to the "standard" basis) ordered from lowest order term to highest. See Also -------- poly2herm Notes ----- The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples -------- >>> from numpy.polynomial.hermite import herm2poly >>> herm2poly([ 1. , 2.75 , 0.5 , 0.375]) array([ 0., 1., 2., 3.]) """ from polynomial import polyadd, polysub, polymulx [cs] = pu.as_series([cs]) n = len(cs) if n == 1: return cs if n == 2: cs[1] *= 2 return cs else: c0 = cs[-2] c1 = cs[-1] # i is the current degree of c1 for i in range(n - 1, 1, -1) : tmp = c0 c0 = polysub(cs[i - 2], c1*(2*(i - 1))) c1 = polyadd(tmp, polymulx(c1)*2) return polyadd(c0, polymulx(c1)*2) # # These are constant arrays are of integer type so as to be compatible # with the widest range of other types, such as Decimal. # # Hermite hermdomain = np.array([-1,1]) # Hermite coefficients representing zero. hermzero = np.array([0]) # Hermite coefficients representing one. hermone = np.array([1]) # Hermite coefficients representing the identity x. hermx = np.array([0, 1/2]) def hermline(off, scl) : """ Hermite series whose graph is a straight line. Parameters ---------- off, scl : scalars The specified line is given by ``off + scl*x``. Returns ------- y : ndarray This module's representation of the Hermite series for ``off + scl*x``. See Also -------- polyline, chebline Examples -------- >>> from numpy.polynomial.hermite import hermline, hermval >>> hermval(0,hermline(3, 2)) 3.0 >>> hermval(1,hermline(3, 2)) 5.0 """ if scl != 0 : return np.array([off,scl/2]) else : return np.array([off]) def hermfromroots(roots) : """ Generate a Hermite series with the given roots. Return the array of coefficients for the P-series whose roots (a.k.a. "zeros") are given by *roots*. The returned array of coefficients is ordered from lowest order "term" to highest, and zeros of multiplicity greater than one must be included in *roots* a number of times equal to their multiplicity (e.g., if `2` is a root of multiplicity three, then [2,2,2] must be in *roots*). Parameters ---------- roots : array_like Sequence containing the roots. Returns ------- out : ndarray 1-d array of the Hermite series coefficients, ordered from low to high. If all roots are real, ``out.dtype`` is a float type; otherwise, ``out.dtype`` is a complex type, even if all the coefficients in the result are real (see Examples below). See Also -------- polyfromroots, chebfromroots Notes ----- What is returned are the :math:`c_i` such that: .. math:: \\sum_{i=0}^{n} c_i*P_i(x) = \\prod_{i=0}^{n} (x - roots[i]) where ``n == len(roots)`` and :math:`P_i(x)` is the `i`-th Hermite (basis) polynomial over the domain `[-1,1]`. Note that, unlike `polyfromroots`, due to the nature of the Hermite basis set, the above identity *does not* imply :math:`c_n = 1` identically (see Examples). Examples -------- >>> from numpy.polynomial.hermite import hermfromroots, hermval >>> coef = hermfromroots((-1, 0, 1)) >>> hermval((-1, 0, 1), coef) array([ 0., 0., 0.]) >>> coef = hermfromroots((-1j, 1j)) >>> hermval((-1j, 1j), coef) array([ 0.+0.j, 0.+0.j]) """ if len(roots) == 0 : return np.ones(1) else : [roots] = pu.as_series([roots], trim=False) prd = np.array([1], dtype=roots.dtype) for r in roots: prd = hermsub(hermmulx(prd), r*prd) return prd def hermadd(c1, c2): """ Add one Hermite series to another. Returns the sum of two Hermite series `c1` + `c2`. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-d arrays of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Array representing the Hermite series of their sum. See Also -------- hermsub, hermmul, hermdiv, hermpow Notes ----- Unlike multiplication, division, etc., the sum of two Hermite series is a Hermite series (without having to "reproject" the result onto the basis set) so addition, just like that of "standard" polynomials, is simply "component-wise." Examples -------- >>> from numpy.polynomial.hermite import hermadd >>> hermadd([1, 2, 3], [1, 2, 3, 4]) array([ 2., 4., 6., 4.]) """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2) : c1[:c2.size] += c2 ret = c1 else : c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) def hermsub(c1, c2): """ Subtract one Hermite series from another. Returns the difference of two Hermite series `c1` - `c2`. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-d arrays of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Of Hermite series coefficients representing their difference. See Also -------- hermadd, hermmul, hermdiv, hermpow Notes ----- Unlike multiplication, division, etc., the difference of two Hermite series is a Hermite series (without having to "reproject" the result onto the basis set) so subtraction, just like that of "standard" polynomials, is simply "component-wise." Examples -------- >>> from numpy.polynomial.hermite import hermsub >>> hermsub([1, 2, 3, 4], [1, 2, 3]) array([ 0., 0., 0., 4.]) """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2) : c1[:c2.size] -= c2 ret = c1 else : c2 = -c2 c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) def hermmulx(cs): """Multiply a Hermite series by x. Multiply the Hermite series `cs` by x, where x is the independent variable. Parameters ---------- cs : array_like 1-d array of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Array representing the result of the multiplication. Notes ----- The multiplication uses the recursion relationship for Hermite polynomials in the form .. math:: xP_i(x) = (P_{i + 1}(x)/2 + i*P_{i - 1}(x)) Examples -------- >>> from numpy.polynomial.hermite import hermmulx >>> hermmulx([1, 2, 3]) array([ 2. , 6.5, 1. , 1.5]) """ # cs is a trimmed copy [cs] = pu.as_series([cs]) # The zero series needs special treatment if len(cs) == 1 and cs[0] == 0: return cs prd = np.empty(len(cs) + 1, dtype=cs.dtype) prd[0] = cs[0]*0 prd[1] = cs[0]/2 for i in range(1, len(cs)): prd[i + 1] = cs[i]/2 prd[i - 1] += cs[i]*i return prd def hermmul(c1, c2): """ Multiply one Hermite series by another. Returns the product of two Hermite series `c1` * `c2`. The arguments are sequences of coefficients, from lowest order "term" to highest, e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-d arrays of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Of Hermite series coefficients representing their product. See Also -------- hermadd, hermsub, hermdiv, hermpow Notes ----- In general, the (polynomial) product of two C-series results in terms that are not in the Hermite polynomial basis set. Thus, to express the product as a Hermite series, it is necessary to "re-project" the product onto said basis set, which may produce "un-intuitive" (but correct) results; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermmul >>> hermmul([1, 2, 3], [0, 1, 2]) array([ 52., 29., 52., 7., 6.]) """ # s1, s2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if len(c1) > len(c2): cs = c2 xs = c1 else: cs = c1 xs = c2 if len(cs) == 1: c0 = cs[0]*xs c1 = 0 elif len(cs) == 2: c0 = cs[0]*xs c1 = cs[1]*xs else : nd = len(cs) c0 = cs[-2]*xs c1 = cs[-1]*xs for i in range(3, len(cs) + 1) : tmp = c0 nd = nd - 1 c0 = hermsub(cs[-i]*xs, c1*(2*(nd - 1))) c1 = hermadd(tmp, hermmulx(c1)*2) return hermadd(c0, hermmulx(c1)*2) def hermdiv(c1, c2): """ Divide one Hermite series by another. Returns the quotient-with-remainder of two Hermite series `c1` / `c2`. The arguments are sequences of coefficients from lowest order "term" to highest, e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- c1, c2 : array_like 1-d arrays of Hermite series coefficients ordered from low to high. Returns ------- [quo, rem] : ndarrays Of Hermite series coefficients representing the quotient and remainder. See Also -------- hermadd, hermsub, hermmul, hermpow Notes ----- In general, the (polynomial) division of one Hermite series by another results in quotient and remainder terms that are not in the Hermite polynomial basis set. Thus, to express these results as a Hermite series, it is necessary to "re-project" the results onto the Hermite basis set, which may produce "un-intuitive" (but correct) results; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermdiv >>> hermdiv([ 52., 29., 52., 7., 6.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 0.])) >>> hermdiv([ 54., 31., 52., 7., 6.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 2., 2.])) >>> hermdiv([ 53., 30., 52., 7., 6.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 1., 1.])) """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) if c2[-1] == 0 : raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) if lc1 < lc2 : return c1[:1]*0, c1 elif lc2 == 1 : return c1/c2[-1], c1[:1]*0 else : quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): p = hermmul([0]*i + [1], c2) q = rem[-1]/p[-1] rem = rem[:-1] - q*p[:-1] quo[i] = q return quo, pu.trimseq(rem) def hermpow(cs, pow, maxpower=16) : """Raise a Hermite series to a power. Returns the Hermite series `cs` raised to the power `pow`. The arguement `cs` is a sequence of coefficients ordered from low to high. i.e., [1,2,3] is the series ``P_0 + 2*P_1 + 3*P_2.`` Parameters ---------- cs : array_like 1d array of Hermite series coefficients ordered from low to high. pow : integer Power to which the series will be raised maxpower : integer, optional Maximum power allowed. This is mainly to limit growth of the series to umanageable size. Default is 16 Returns ------- coef : ndarray Hermite series of power. See Also -------- hermadd, hermsub, hermmul, hermdiv Examples -------- >>> from numpy.polynomial.hermite import hermpow >>> hermpow([1, 2, 3], 2) array([ 81., 52., 82., 12., 9.]) """ # cs is a trimmed copy [cs] = pu.as_series([cs]) power = int(pow) if power != pow or power < 0 : raise ValueError("Power must be a non-negative integer.") elif maxpower is not None and power > maxpower : raise ValueError("Power is too large") elif power == 0 : return np.array([1], dtype=cs.dtype) elif power == 1 : return cs else : # This can be made more efficient by using powers of two # in the usual way. prd = cs for i in range(2, power + 1) : prd = hermmul(prd, cs) return prd def hermder(cs, m=1, scl=1) : """ Differentiate a Hermite series. Returns the series `cs` differentiated `m` times. At each iteration the result is multiplied by `scl` (the scaling factor is for use in a linear change of variable). The argument `cs` is the sequence of coefficients from lowest order "term" to highest, e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``. Parameters ---------- cs: array_like 1-d array of Hermite series coefficients ordered from low to high. m : int, optional Number of derivatives taken, must be non-negative. (Default: 1) scl : scalar, optional Each differentiation is multiplied by `scl`. The end result is multiplication by ``scl**m``. This is for use in a linear change of variable. (Default: 1) Returns ------- der : ndarray Hermite series of the derivative. See Also -------- hermint Notes ----- In general, the result of differentiating a Hermite series does not resemble the same operation on a power series. Thus the result of this function may be "un-intuitive," albeit correct; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermder >>> hermder([ 1. , 0.5, 0.5, 0.5]) array([ 1., 2., 3.]) >>> hermder([-0.5, 1./2., 1./8., 1./12., 1./16.], m=2) array([ 1., 2., 3.]) """ cnt = int(m) if cnt != m: raise ValueError, "The order of derivation must be integer" if cnt < 0 : raise ValueError, "The order of derivation must be non-negative" # cs is a trimmed copy [cs] = pu.as_series([cs]) if cnt == 0: return cs elif cnt >= len(cs): return cs[:1]*0 else : for i in range(cnt): n = len(cs) - 1 cs *= scl der = np.empty(n, dtype=cs.dtype) for j in range(n, 0, -1): der[j - 1] = (2*j)*cs[j] cs = der return cs def hermint(cs, m=1, k=[], lbnd=0, scl=1): """ Integrate a Hermite series. Returns a Hermite series that is the Hermite series `cs`, integrated `m` times from `lbnd` to `x`. At each iteration the resulting series is **multiplied** by `scl` and an integration constant, `k`, is added. The scaling factor is for use in a linear change of variable. ("Buyer beware": note that, depending on what one is doing, one may want `scl` to be the reciprocal of what one might expect; for more information, see the Notes section below.) The argument `cs` is a sequence of coefficients, from lowest order Hermite series "term" to highest, e.g., [1,2,3] represents the series :math:`P_0(x) + 2P_1(x) + 3P_2(x)`. Parameters ---------- cs : array_like 1-d array of Hermite series coefficients, ordered from low to high. m : int, optional Order of integration, must be positive. (Default: 1) k : {[], list, scalar}, optional Integration constant(s). The value of the first integral at ``lbnd`` is the first value in the list, the value of the second integral at ``lbnd`` is the second value, etc. If ``k == []`` (the default), all constants are set to zero. If ``m == 1``, a single scalar can be given instead of a list. lbnd : scalar, optional The lower bound of the integral. (Default: 0) scl : scalar, optional Following each integration the result is *multiplied* by `scl` before the integration constant is added. (Default: 1) Returns ------- S : ndarray Hermite series coefficients of the integral. Raises ------ ValueError If ``m < 0``, ``len(k) > m``, ``np.isscalar(lbnd) == False``, or ``np.isscalar(scl) == False``. See Also -------- hermder Notes ----- Note that the result of each integration is *multiplied* by `scl`. Why is this important to note? Say one is making a linear change of variable :math:`u = ax + b` in an integral relative to `x`. Then :math:`dx = du/a`, so one will need to set `scl` equal to :math:`1/a` - perhaps not what one would have first thought. Also note that, in general, the result of integrating a C-series needs to be "re-projected" onto the C-series basis set. Thus, typically, the result of this function is "un-intuitive," albeit correct; see Examples section below. Examples -------- >>> from numpy.polynomial.hermite import hermint >>> hermint([1,2,3]) # integrate once, value 0 at 0. array([ 1. , 0.5, 0.5, 0.5]) >>> hermint([1,2,3], m=2) # integrate twice, value & deriv 0 at 0 array([-0.5 , 0.5 , 0.125 , 0.08333333, 0.0625 ]) >>> hermint([1,2,3], k=1) # integrate once, value 1 at 0. array([ 2. , 0.5, 0.5, 0.5]) >>> hermint([1,2,3], lbnd=-1) # integrate once, value 0 at -1 array([-2. , 0.5, 0.5, 0.5]) >>> hermint([1,2,3], m=2, k=[1,2], lbnd=-1) array([ 1.66666667, -0.5 , 0.125 , 0.08333333, 0.0625 ]) """ cnt = int(m) if np.isscalar(k) : k = [k] if cnt != m: raise ValueError, "The order of integration must be integer" if cnt < 0 : raise ValueError, "The order of integration must be non-negative" if len(k) > cnt : raise ValueError, "Too many integration constants" # cs is a trimmed copy [cs] = pu.as_series([cs]) if cnt == 0: return cs k = list(k) + [0]*(cnt - len(k)) for i in range(cnt) : n = len(cs) cs *= scl if n == 1 and cs[0] == 0: cs[0] += k[i] else: tmp = np.empty(n + 1, dtype=cs.dtype) tmp[0] = cs[0]*0 tmp[1] = cs[0]/2 for j in range(1, n): tmp[j + 1] = cs[j]/(2*(j + 1)) tmp[0] += k[i] - hermval(lbnd, tmp) cs = tmp return cs def hermval(x, cs): """Evaluate a Hermite series. If `cs` is of length `n`, this function returns : ``p(x) = cs[0]*P_0(x) + cs[1]*P_1(x) + ... + cs[n-1]*P_{n-1}(x)`` If x is a sequence or array then p(x) will have the same shape as x. If r is a ring_like object that supports multiplication and addition by the values in `cs`, then an object of the same type is returned. Parameters ---------- x : array_like, ring_like Array of numbers or objects that support multiplication and addition with themselves and with the elements of `cs`. cs : array_like 1-d array of Hermite coefficients ordered from low to high. Returns ------- values : ndarray, ring_like If the return is an ndarray then it has the same shape as `x`. See Also -------- hermfit Examples -------- Notes ----- The evaluation uses Clenshaw recursion, aka synthetic division. Examples -------- >>> from numpy.polynomial.hermite import hermval >>> coef = [1,2,3] >>> hermval(1, coef) 11.0 >>> hermval([[1,2],[3,4]], coef) array([[ 11., 51.], [ 115., 203.]]) """ # cs is a trimmed copy [cs] = pu.as_series([cs]) if isinstance(x, tuple) or isinstance(x, list) : x = np.asarray(x) x2 = x*2 if len(cs) == 1 : c0 = cs[0] c1 = 0 elif len(cs) == 2 : c0 = cs[0] c1 = cs[1] else : nd = len(cs) c0 = cs[-2] c1 = cs[-1] for i in range(3, len(cs) + 1) : tmp = c0 nd = nd - 1 c0 = cs[-i] - c1*(2*(nd - 1)) c1 = tmp + c1*x2 return c0 + c1*x2 def hermvander(x, deg) : """Vandermonde matrix of given degree. Returns the Vandermonde matrix of degree `deg` and sample points `x`. This isn't a true Vandermonde matrix because `x` can be an arbitrary ndarray and the Hermite polynomials aren't powers. If ``V`` is the returned matrix and `x` is a 2d array, then the elements of ``V`` are ``V[i,j,k] = P_k(x[i,j])``, where ``P_k`` is the Hermite polynomial of degree ``k``. Parameters ---------- x : array_like Array of points. The values are converted to double or complex doubles. If x is scalar it is converted to a 1D array. deg : integer Degree of the resulting matrix. Returns ------- vander : Vandermonde matrix. The shape of the returned matrix is ``x.shape + (deg+1,)``. The last index is the degree. Examples -------- >>> from numpy.polynomial.hermite import hermvander >>> x = np.array([-1, 0, 1]) >>> hermvander(x, 3) array([[ 1., -2., 2., 4.], [ 1., 0., -2., -0.], [ 1., 2., 2., -4.]]) """ ideg = int(deg) if ideg != deg: raise ValueError("deg must be integer") if ideg < 0: raise ValueError("deg must be non-negative") x = np.array(x, copy=0, ndmin=1) + 0.0 v = np.empty((ideg + 1,) + x.shape, dtype=x.dtype) v[0] = x*0 + 1 if ideg > 0 : x2 = x*2 v[1] = x2 for i in range(2, ideg + 1) : v[i] = (v[i-1]*x2 - v[i-2]*(2*(i - 1))) return np.rollaxis(v, 0, v.ndim) def hermfit(x, y, deg, rcond=None, full=False, w=None): """ Least squares fit of Hermite series to data. Fit a Hermite series ``p(x) = p[0] * P_{0}(x) + ... + p[deg] * P_{deg}(x)`` of degree `deg` to points `(x, y)`. Returns a vector of coefficients `p` that minimises the squared error. 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 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. Returns ------- coef : ndarray, 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] : present when `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`. 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', RankWarning) See Also -------- hermval : Evaluates a Hermite series. hermvander : Vandermonde matrix of Hermite series. polyfit : least squares fit using polynomials. chebfit : least squares fit using Chebyshev series. linalg.lstsq : Computes a least-squares fit from the matrix. scipy.interpolate.UnivariateSpline : Computes spline fits. Notes ----- The solution are the coefficients ``c[i]`` of the Hermite series ``P(x)`` that minimizes the squared error ``E = \\sum_j |y_j - P(x_j)|^2``. This problem is solved by setting up as the overdetermined matrix equation ``V(x)*c = y``, where ``V`` is the Vandermonde matrix of `x`, the elements of ``c`` are the coefficients to be solved for, and the elements of `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 coeficient 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 usually better conditioned than fits using power series, but much can 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. References ---------- .. [1] Wikipedia, "Curve fitting", http://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([ 0.97902637, 1.99849131, 3.00006 ]) """ order = int(deg) + 1 x = np.asarray(x) + 0.0 y = np.asarray(y) + 0.0 # check arguments. if deg < 0 : raise ValueError, "expected deg >= 0" if x.ndim != 1: raise TypeError, "expected 1D vector for x" if x.size == 0: raise TypeError, "expected non-empty vector for x" if y.ndim < 1 or y.ndim > 2 : raise TypeError, "expected 1D or 2D array for y" if len(x) != len(y): raise TypeError, "expected x and y to have same length" # set up the least squares matrices lhs = hermvander(x, deg) rhs = y if w is not None: w = np.asarray(w) + 0.0 if w.ndim != 1: raise TypeError, "expected 1D vector for w" if len(x) != len(w): raise TypeError, "expected x and w to have same length" # apply weights if rhs.ndim == 2: lhs *= w[:, np.newaxis] rhs *= w[:, np.newaxis] else: lhs *= w[:, np.newaxis] rhs *= w # set rcond if rcond is None : rcond = len(x)*np.finfo(x.dtype).eps # scale the design matrix and solve the least squares equation scl = np.sqrt((lhs*lhs).sum(0)) c, resids, rank, s = la.lstsq(lhs/scl, rhs, rcond) c = (c.T/scl).T # warn on rank reduction if rank != order and not full: msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) if full : return c, [resids, rank, s, rcond] else : return c def hermroots(cs): """ Compute the roots of a Hermite series. Return the roots (a.k.a "zeros") of the Hermite series represented by `cs`, which is the sequence of coefficients from lowest order "term" to highest, e.g., [1,2,3] is the series ``L_0 + 2*L_1 + 3*L_2``. Parameters ---------- cs : array_like 1-d array of Hermite series coefficients ordered from low to high. Returns ------- out : ndarray Array of the roots. If all the roots are real, then so is the dtype of ``out``; otherwise, ``out``'s dtype is complex. See Also -------- polyroots chebroots Notes ----- Algorithm(s) used: Remember: because the Hermite series basis set is different from the "standard" basis set, the results of this function *may* not be what one is expecting. Examples -------- >>> from numpy.polynomial.hermite import hermroots, hermfromroots >>> coef = hermfromroots([-1, 0, 1]) >>> coef array([ 0. , 0.25 , 0. , 0.125]) >>> hermroots(coef) array([ -1.00000000e+00, -1.38777878e-17, 1.00000000e+00]) """ # cs is a trimmed copy [cs] = pu.as_series([cs]) if len(cs) <= 1 : return np.array([], dtype=cs.dtype) if len(cs) == 2 : return np.array([-.5*cs[0]/cs[1]]) n = len(cs) - 1 cs /= cs[-1] cmat = np.zeros((n,n), dtype=cs.dtype) cmat[1, 0] = .5 for i in range(1, n): cmat[i - 1, i] = i if i != n - 1: cmat[i + 1, i] = .5 else: cmat[:, i] -= cs[:-1]*.5 roots = la.eigvals(cmat) roots.sort() return roots # # Hermite series class # exec polytemplate.substitute(name='Hermite', nick='herm', domain='[-1,1]')