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laguerre.py
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laguerre.py
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"""
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)
roots.sort()
p = [lagline(-r, 1) for r in roots]
n = len(p)
while n > 1:
m, r = divmod(n, 2)
tmp = [lagmul(p[i], p[i+m]) for i in range(m)]
if r:
tmp[0] = lagmul(tmp[0], p[-1])
p = tmp
n = m
return p[0]
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.
Return the coefficients of a Laguerre 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
.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),
where `n` is `deg`.
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
--------
chebfit, legfit, polyfit, hermfit, hermefit
lagval : Evaluates a Laguerre series.
lagvander : pseudo Vandermonde matrix of Laguerre series.
lagweight : Laguerre weight function.
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Laguerre series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where the :math:`w_j` are the weights. This problem is solved by