""" Objects for dealing with Laguerre series. This module provides a number of objects (mostly functions) useful for dealing with Laguerre series, including a `Laguerre` 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 --------- - `lagdomain` -- Laguerre series default domain, [-1,1]. - `lagzero` -- Laguerre series that evaluates identically to 0. - `lagone` -- Laguerre series that evaluates identically to 1. - `lagx` -- Laguerre series for the identity map, ``f(x) = x``. Arithmetic ---------- - `lagmulx` -- multiply a Laguerre series in ``P_i(x)`` by ``x``. - `lagadd` -- add two Laguerre series. - `lagsub` -- subtract one Laguerre series from another. - `lagmul` -- multiply two Laguerre series. - `lagdiv` -- divide one Laguerre series by another. - `lagval` -- evaluate a Laguerre series at given points. Calculus -------- - `lagder` -- differentiate a Laguerre series. - `lagint` -- integrate a Laguerre series. Misc Functions -------------- - `lagfromroots` -- create a Laguerre series with specified roots. - `lagroots` -- find the roots of a Laguerre series. - `lagvander` -- Vandermonde-like matrix for Laguerre polynomials. - `lagfit` -- least-squares fit returning a Laguerre series. - `lagtrim` -- trim leading coefficients from a Laguerre series. - `lagline` -- Laguerre series of given straight line. - `lag2poly` -- convert a Laguerre series to a polynomial. - `poly2lag` -- convert a polynomial to a Laguerre series. Classes ------- - `Laguerre` -- A Laguerre series class. See also -------- `numpy.polynomial` """ from __future__ import division __all__ = ['lagzero', 'lagone', 'lagx', 'lagdomain', 'lagline', 'lagadd', 'lagsub', 'lagmulx', 'lagmul', 'lagdiv', 'lagval', 'lagder', 'lagint', 'lag2poly', 'poly2lag', 'lagfromroots', 'lagvander', 'lagfit', 'lagtrim', 'lagroots', 'Laguerre'] import numpy as np import numpy.linalg as la import polyutils as pu import warnings from polytemplate import polytemplate lagtrim = pu.trimcoef def poly2lag(pol) : """ poly2lag(pol) Convert a polynomial to a Laguerre 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 Laguerre 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 Laguerre series. See Also -------- lag2poly 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.laguerre import poly2lag >>> poly2lag(np.arange(4)) array([ 23., -63., 58., -18.]) """ [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 for i in range(deg, -1, -1) : res = lagadd(lagmulx(res), pol[i]) return res def lag2poly(cs) : """ Convert a Laguerre series to a polynomial. Convert an array representing the coefficients of a Laguerre 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 Laguerre 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 -------- poly2lag 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.laguerre import lag2poly >>> lag2poly([ 23., -63., 58., -18.]) array([ 0., 1., 2., 3.]) """ from polynomial import polyadd, polysub, polymulx [cs] = pu.as_series([cs]) n = len(cs) if n == 1: 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*(i - 1))/i) c1 = polyadd(tmp, polysub((2*i - 1)*c1, polymulx(c1))/i) return polyadd(c0, polysub(c1, polymulx(c1))) # # These are constant arrays are of integer type so as to be compatible # with the widest range of other types, such as Decimal. # # Laguerre lagdomain = np.array([0,1]) # Laguerre coefficients representing zero. lagzero = np.array([0]) # Laguerre coefficients representing one. lagone = np.array([1]) # Laguerre coefficients representing the identity x. lagx = np.array([1, -1]) def lagline(off, scl) : """ Laguerre 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 Laguerre series for ``off + scl*x``. See Also -------- polyline, chebline Examples -------- >>> from numpy.polynomial.laguerre import lagline, lagval >>> lagval(0,lagline(3, 2)) 3.0 >>> lagval(1,lagline(3, 2)) 5.0 """ if scl != 0 : return np.array([off + scl, -scl]) else : return np.array([off]) def lagfromroots(roots) : """ Generate a Laguerre 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 Laguerre 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 Laguerre (basis) polynomial over the domain `[-1,1]`. Note that, unlike `polyfromroots`, due to the nature of the Laguerre basis set, the above identity *does not* imply :math:`c_n = 1` identically (see Examples). Examples -------- >>> from numpy.polynomial.laguerre import lagfromroots, lagval >>> coef = lagfromroots((-1, 0, 1)) >>> lagval((-1, 0, 1), coef) array([ 0., 0., 0.]) >>> coef = lagfromroots((-1j, 1j)) >>> lagval((-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 = lagsub(lagmulx(prd), r*prd) return prd def lagadd(c1, c2): """ Add one Laguerre series to another. Returns the sum of two Laguerre 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 Laguerre series coefficients ordered from low to high. Returns ------- out : ndarray Array representing the Laguerre series of their sum. See Also -------- lagsub, lagmul, lagdiv, lagpow Notes ----- Unlike multiplication, division, etc., the sum of two Laguerre series is a Laguerre 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.laguerre import lagadd >>> lagadd([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 lagsub(c1, c2): """ Subtract one Laguerre series from another. Returns the difference of two Laguerre 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 Laguerre series coefficients ordered from low to high. Returns ------- out : ndarray Of Laguerre series coefficients representing their difference. See Also -------- lagadd, lagmul, lagdiv, lagpow Notes ----- Unlike multiplication, division, etc., the difference of two Laguerre series is a Laguerre 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.laguerre import lagsub >>> lagsub([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 lagmulx(cs): """Multiply a Laguerre series by x. Multiply the Laguerre series `cs` by x, where x is the independent variable. Parameters ---------- cs : array_like 1-d array of Laguerre 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 Laguerre polynomials in the form .. math:: xP_i(x) = (-(i + 1)*P_{i + 1}(x) + (2i + 1)P_{i}(x) - iP_{i - 1}(x)) Examples -------- >>> from numpy.polynomial.laguerre import lagmulx >>> lagmulx([1, 2, 3]) array([ -1., -1., 11., -9.]) """ # 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] prd[1] = -cs[0] for i in range(1, len(cs)): prd[i + 1] = -cs[i]*(i + 1) prd[i] += cs[i]*(2*i + 1) prd[i - 1] -= cs[i]*i return prd def lagmul(c1, c2): """ Multiply one Laguerre series by another. Returns the product of two Laguerre 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 Laguerre series coefficients ordered from low to high. Returns ------- out : ndarray Of Laguerre series coefficients representing their product. See Also -------- lagadd, lagsub, lagdiv, lagpow Notes ----- In general, the (polynomial) product of two C-series results in terms that are not in the Laguerre polynomial basis set. Thus, to express the product as a Laguerre 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.laguerre import lagmul >>> lagmul([1, 2, 3], [0, 1, 2]) array([ 8., -13., 38., -51., 36.]) """ # 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 = lagsub(cs[-i]*xs, (c1*(nd - 1))/nd) c1 = lagadd(tmp, lagsub((2*nd - 1)*c1, lagmulx(c1))/nd) return lagadd(c0, lagsub(c1, lagmulx(c1))) def lagdiv(c1, c2): """ Divide one Laguerre series by another. Returns the quotient-with-remainder of two Laguerre 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 Laguerre series coefficients ordered from low to high. Returns ------- [quo, rem] : ndarrays Of Laguerre series coefficients representing the quotient and remainder. See Also -------- lagadd, lagsub, lagmul, lagpow Notes ----- In general, the (polynomial) division of one Laguerre series by another results in quotient and remainder terms that are not in the Laguerre polynomial basis set. Thus, to express these results as a Laguerre series, it is necessary to "re-project" the results onto the Laguerre basis set, which may produce "un-intuitive" (but correct) results; see Examples section below. Examples -------- >>> from numpy.polynomial.laguerre import lagdiv >>> lagdiv([ 8., -13., 38., -51., 36.], [0, 1, 2]) (array([ 1., 2., 3.]), array([ 0.])) >>> lagdiv([ 9., -12., 38., -51., 36.], [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 = lagmul([0]*i + [1], c2) q = rem[-1]/p[-1] rem = rem[:-1] - q*p[:-1] quo[i] = q return quo, pu.trimseq(rem) def lagpow(cs, pow, maxpower=16) : """Raise a Laguerre series to a power. Returns the Laguerre 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 Laguerre 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 Laguerre series of power. See Also -------- lagadd, lagsub, lagmul, lagdiv Examples -------- >>> from numpy.polynomial.laguerre import lagpow >>> lagpow([1, 2, 3], 2) array([ 14., -16., 56., -72., 54.]) """ # 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 = lagmul(prd, cs) return prd def lagder(cs, m=1, scl=1) : """ Differentiate a Laguerre 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 Laguerre 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 Laguerre series of the derivative. See Also -------- lagint Notes ----- In general, the result of differentiating a Laguerre 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.laguerre import lagder >>> lagder([ 1., 1., 1., -3.]) array([ 1., 2., 3.]) >>> lagder([ 1., 0., 0., -4., 3.], 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] = -cs[j] cs[j - 1] += cs[j] cs = der return cs def lagint(cs, m=1, k=[], lbnd=0, scl=1): """ Integrate a Laguerre series. Returns a Laguerre series that is the Laguerre 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 Laguerre 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 Laguerre 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 Laguerre series coefficients of the integral. Raises ------ ValueError If ``m < 0``, ``len(k) > m``, ``np.isscalar(lbnd) == False``, or ``np.isscalar(scl) == False``. See Also -------- lagder 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.laguerre import lagint >>> lagint([1,2,3]) array([ 1., 1., 1., -3.]) >>> lagint([1,2,3], m=2) array([ 1., 0., 0., -4., 3.]) >>> lagint([1,2,3], k=1) array([ 2., 1., 1., -3.]) >>> lagint([1,2,3], lbnd=-1) array([ 11.5, 1. , 1. , -3. ]) >>> lagint([1,2], m=2, k=[1,2], lbnd=-1) array([ 11.16666667, -5. , -3. , 2. ]) """ 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] tmp[1] = -cs[0] for j in range(1, n): tmp[j] += cs[j] tmp[j + 1] = -cs[j] tmp[0] += k[i] - lagval(lbnd, tmp) cs = tmp return cs def lagval(x, cs): """Evaluate a Laguerre 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 Laguerre 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 -------- lagfit Examples -------- Notes ----- The evaluation uses Clenshaw recursion, aka synthetic division. Examples -------- >>> from numpy.polynomial.laguerre import lagval >>> coef = [1,2,3] >>> lagval(1, coef) -0.5 >>> lagval([[1,2],[3,4]], coef) array([[-0.5, -4. ], [-4.5, -2. ]]) """ # cs is a trimmed copy [cs] = pu.as_series([cs]) if isinstance(x, tuple) or isinstance(x, list) : x = np.asarray(x) 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*(nd - 1))/nd c1 = tmp + (c1*((2*nd - 1) - x))/nd return c0 + c1*(1 - x) def lagvander(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 Laguerre 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 Laguerre 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.laguerre import lagvander >>> x = np.array([0, 1, 2]) >>> lagvander(x, 3) array([[ 1. , 1. , 1. , 1. ], [ 1. , 0. , -0.5 , -0.66666667], [ 1. , -1. , -1. , -0.33333333]]) """ 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 : v[1] = 1 - x for i in range(2, ideg + 1) : v[i] = (v[i-1]*(2*i - 1 - x) - v[i-2]*(i - 1))/i return np.rollaxis(v, 0, v.ndim) def lagfit(x, y, deg, rcond=None, full=False, w=None): """ Least squares fit of Laguerre series to data. Fit a Laguerre 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) Laguerre 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 -------- lagval : Evaluates a Laguerre series. lagvander : Vandermonde matrix of Laguerre 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 Laguerre 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 Laguerre 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.laguerre import lagfit, lagval >>> x = np.linspace(0, 10) >>> err = np.random.randn(len(x))/10 >>> y = lagval(x, [1, 2, 3]) + err >>> lagfit(x, y, 2) array([ 0.96971004, 2.00193749, 3.00288744]) """ 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 = lagvander(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 lagroots(cs): """ Compute the roots of a Laguerre series. Return the roots (a.k.a "zeros") of the Laguerre 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 Laguerre 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 Laguerre 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.laguerre import lagroots, lagfromroots >>> coef = lagfromroots([0, 1, 2]) >>> coef array([ 2., -8., 12., -6.]) >>> lagroots(coef) array([ -4.44089210e-16, 1.00000000e+00, 2.00000000e+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([1 + cs[0]/cs[1]]) n = len(cs) - 1 cs /= cs[-1] cmat = np.zeros((n,n), dtype=cs.dtype) cmat[0, 0] = 1 cmat[1, 0] = -1 for i in range(1, n): cmat[i - 1, i] = -i cmat[i, i] = 2*i + 1 if i != n - 1: cmat[i + 1, i] = -(i + 1) else: cmat[:, i] += cs[:-1]*(i + 1) roots = la.eigvals(cmat) roots.sort() return roots # # Laguerre series class # exec polytemplate.substitute(name='Laguerre', nick='lag', domain='[-1,1]')