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import warnings
import _minpack
from numpy import atleast_1d, dot, take, triu, shape, eye, \
transpose, zeros, product, greater, array, \
all, where, isscalar, asarray, inf
error = _minpack.error
__all__ = ['fsolve', 'leastsq', 'fixed_point', 'bisection', 'curve_fit']
def check_func(thefunc, x0, args, numinputs, output_shape=None):
res = atleast_1d(thefunc(*((x0[:numinputs],)+args)))
if (output_shape is not None) and (shape(res) != output_shape):
if (output_shape[0] != 1):
if len(output_shape) > 1:
if output_shape[1] == 1:
return shape(res)
msg = "There is a mismatch between the input and output " \
"shape of %s." % thefunc.func_name
raise TypeError(msg)
return shape(res)
def fsolve(func, x0, args=(), fprime=None, full_output=0,
col_deriv=0, xtol=1.49012e-8, maxfev=0, band=None,
epsfcn=0.0, factor=100, diag=None, warning=True):
"""
Find the roots of a function.
Return the roots of the (non-linear) equations defined by
``func(x) = 0`` given a starting estimate.
Parameters
----------
func : callable f(x, *args)
A function that takes at least one (possibly vector) argument.
x0 : ndarray
The starting estimate for the roots of ``func(x) = 0``.
args : tuple
Any extra arguments to `func`.
fprime : callable(x)
A function to compute the Jacobian of `func` with derivatives
across the rows. By default, the Jacobian will be estimated.
full_output : bool
If True, return optional outputs.
col_deriv : bool
Specify whether the Jacobian function computes derivatives down
the columns (faster, because there is no transpose operation).
warning : bool
Whether to print a warning message when the call is unsuccessful.
This option is deprecated, use the warnings module instead.
Returns
-------
x : ndarray
The solution (or the result of the last iteration for
an unsuccessful call).
infodict : dict
A dictionary of optional outputs with the keys::
* 'nfev': number of function calls
* 'njev': number of Jacobian calls
* 'fvec': function evaluated at the output
* 'fjac': the orthogonal matrix, q, produced by the QR
factorization of the final approximate Jacobian
matrix, stored column wise
* 'r': upper triangular matrix produced by QR factorization of same
matrix
* 'qtf': the vector (transpose(q) * fvec)
ier : int
An integer flag. Set to 1 if a solution was found, otherwise refer
to `mesg` for more information.
mesg : str
If no solution is found, `mesg` details the cause of failure.
Other Parameters
----------------
xtol : float
The calculation will terminate if the relative error between two
consecutive iterates is at most `xtol`.
maxfev : int
The maximum number of calls to the function. If zero, then
``100*(N+1)`` is the maximum where N is the number of elements
in `x0`.
band : tuple
If set to a two-sequence containing the number of sub- and
super-diagonals within the band of the Jacobi matrix, the
Jacobi matrix is considered banded (only for ``fprime=None``).
epsfcn : float
A suitable step length for the forward-difference
approximation of the Jacobian (for ``fprime=None``). If
`epsfcn` is less than the machine precision, it is assumed
that the relative errors in the functions are of the order of
the machine precision.
factor : float
A parameter determining the initial step bound
(``factor * || diag * x||``). Should be in the interval
``(0.1, 100)``.
diag : sequence
N positive entries that serve as a scale factors for the
variables.
Notes
-----
``fsolve`` is a wrapper around MINPACK's hybrd and hybrj algorithms.
From scipy 0.8.0 `fsolve` returns an array of size one instead of a scalar
when solving for a single parameter.
"""
if not warning :
msg = "The warning keyword is deprecated. Use the warnings module."
warnings.warn(msg, DeprecationWarning)
x0 = array(x0,ndmin=1)
n = len(x0)
if type(args) != type(()): args = (args,)
check_func(func,x0,args,n,(n,))
Dfun = fprime
if Dfun is None:
if band is None:
ml,mu = -10,-10
else:
ml,mu = band[:2]
if (maxfev == 0):
maxfev = 200*(n+1)
retval = _minpack._hybrd(func, x0, args, full_output, xtol,
maxfev, ml, mu, epsfcn, factor, diag)
else:
check_func(Dfun,x0,args,n,(n,n))
if (maxfev == 0):
maxfev = 100*(n+1)
retval = _minpack._hybrj(func, Dfun, x0, args, full_output,
col_deriv, xtol, maxfev, factor,diag)
errors = {0:["Improper input parameters were entered.",TypeError],
1:["The solution converged.", None],
2:["The number of calls to function has "
"reached maxfev = %d." % maxfev, ValueError],
3:["xtol=%f is too small, no further improvement "
"in the approximate\n solution "
"is possible." % xtol, ValueError],
4:["The iteration is not making good progress, as measured "
"by the \n improvement from the last five "
"Jacobian evaluations.", ValueError],
5:["The iteration is not making good progress, "
"as measured by the \n improvement from the last "
"ten iterations.", ValueError],
'unknown': ["An error occurred.", TypeError]}
info = retval[-1] # The FORTRAN return value
if (info != 1 and not full_output):
if info in [2,3,4,5]:
msg = errors[info][0]
warnings.warn(msg, RuntimeWarning)
else:
try:
raise errors[info][1](errors[info][0])
except KeyError:
raise errors['unknown'][1](errors['unknown'][0])
if full_output:
try:
return retval + (errors[info][0],) # Return all + the message
except KeyError:
return retval + (errors['unknown'][0],)
else:
return retval[0]
def leastsq(func, x0, args=(), Dfun=None, full_output=0,
col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8,
gtol=0.0, maxfev=0, epsfcn=0.0, factor=100, diag=None,warning=True):
"""Minimize the sum of squares of a set of equations.
::
x = arg min(sum(func(y)**2,axis=0))
y
Parameters
----------
func : callable
should take at least one (possibly length N vector) argument and
returns M floating point numbers.
x0 : ndarray
The starting estimate for the minimization.
args : tuple
Any extra arguments to func are placed in this tuple.
Dfun : callable
A function or method to compute the Jacobian of func with derivatives
across the rows. If this is None, the Jacobian will be estimated.
full_output : bool
non-zero to return all optional outputs.
col_deriv : bool
non-zero to specify that the Jacobian function computes derivatives
down the columns (faster, because there is no transpose operation).
ftol : float
Relative error desired in the sum of squares.
xtol : float
Relative error desired in the approximate solution.
gtol : float
Orthogonality desired between the function vector and the columns of
the Jacobian.
maxfev : int
The maximum number of calls to the function. If zero, then 100*(N+1) is
the maximum where N is the number of elements in x0.
epsfcn : float
A suitable step length for the forward-difference approximation of the
Jacobian (for Dfun=None). If epsfcn is less than the machine precision,
it is assumed that the relative errors in the functions are of the
order of the machine precision.
factor : float
A parameter determining the initial step bound
(``factor * || diag * x||``). Should be in interval ``(0.1, 100)``.
diag : sequence
N positive entries that serve as a scale factors for the variables.
warning : bool
Whether to print a warning message when the call is unsuccessful.
Deprecated, use the warnings module instead.
Returns
-------
x : ndarray
The solution (or the result of the last iteration for an unsuccessful
call).
cov_x : ndarray
Uses the fjac and ipvt optional outputs to construct an
estimate of the jacobian around the solution. ``None`` if a
singular matrix encountered (indicates very flat curvature in
some direction). This matrix must be multiplied by the
residual standard deviation to get the covariance of the
parameter estimates -- see curve_fit.
infodict : dict
a dictionary of optional outputs with the keys::
- 'nfev' : the number of function calls
- 'fvec' : the function evaluated at the output
- 'fjac' : A permutation of the R matrix of a QR
factorization of the final approximate
Jacobian matrix, stored column wise.
Together with ipvt, the covariance of the
estimate can be approximated.
- 'ipvt' : an integer array of length N which defines
a permutation matrix, p, such that
fjac*p = q*r, where r is upper triangular
with diagonal elements of nonincreasing
magnitude. Column j of p is column ipvt(j)
of the identity matrix.
- 'qtf' : the vector (transpose(q) * fvec).
mesg : str
A string message giving information about the cause of failure.
ier : int
An integer flag. If it is equal to 1, 2, 3 or 4, the solution was
found. Otherwise, the solution was not found. In either case, the
optional output variable 'mesg' gives more information.
Notes
-----
"leastsq" is a wrapper around MINPACK's lmdif and lmder algorithms.
From scipy 0.8.0 `leastsq` returns an array of size one instead of a scalar
when solving for a single parameter.
"""
if not warning :
msg = "The warning keyword is deprecated. Use the warnings module."
warnings.warn(msg, DeprecationWarning)
x0 = array(x0,ndmin=1)
n = len(x0)
if type(args) != type(()): args = (args,)
m = check_func(func,x0,args,n)[0]
if n>m:
raise TypeError('Improper input: N=%s must not exceed M=%s' % (n,m))
if Dfun is None:
if (maxfev == 0):
maxfev = 200*(n+1)
retval = _minpack._lmdif(func, x0, args, full_output,
ftol, xtol, gtol,
maxfev, epsfcn, factor, diag)
else:
if col_deriv:
check_func(Dfun,x0,args,n,(n,m))
else:
check_func(Dfun,x0,args,n,(m,n))
if (maxfev == 0):
maxfev = 100*(n+1)
retval = _minpack._lmder(func,Dfun,x0,args,full_output,col_deriv,ftol,xtol,gtol,maxfev,factor,diag)
errors = {0:["Improper input parameters.", TypeError],
1:["Both actual and predicted relative reductions "
"in the sum of squares\n are at most %f" % ftol, None],
2:["The relative error between two consecutive "
"iterates is at most %f" % xtol, None],
3:["Both actual and predicted relative reductions in "
"the sum of squares\n are at most %f and the "
"relative error between two consecutive "
"iterates is at \n most %f" % (ftol,xtol), None],
4:["The cosine of the angle between func(x) and any "
"column of the\n Jacobian is at most %f in "
"absolute value" % gtol, None],
5:["Number of calls to function has reached "
"maxfev = %d." % maxfev, ValueError],
6:["ftol=%f is too small, no further reduction "
"in the sum of squares\n is possible.""" % ftol, ValueError],
7:["xtol=%f is too small, no further improvement in "
"the approximate\n solution is possible." % xtol, ValueError],
8:["gtol=%f is too small, func(x) is orthogonal to the "
"columns of\n the Jacobian to machine "
"precision." % gtol, ValueError],
'unknown':["Unknown error.", TypeError]}
info = retval[-1] # The FORTRAN return value
if (info not in [1,2,3,4] and not full_output):
if info in [5,6,7,8]:
warnings.warn(errors[info][0], RuntimeWarning)
else:
try:
raise errors[info][1](errors[info][0])
except KeyError:
raise errors['unknown'][1](errors['unknown'][0])
mesg = errors[info][0]
if full_output:
cov_x = None
if info in [1,2,3,4]:
from numpy.dual import inv
from numpy.linalg import LinAlgError
perm = take(eye(n),retval[1]['ipvt']-1,0)
r = triu(transpose(retval[1]['fjac'])[:n,:])
R = dot(r, perm)
try:
cov_x = inv(dot(transpose(R),R))
except LinAlgError:
pass
return (retval[0], cov_x) + retval[1:-1] + (mesg,info)
else:
return (retval[0], info)
def _general_function(params, xdata, ydata, function):
return function(xdata, *params) - ydata
def _weighted_general_function(params, xdata, ydata, function, weights):
return weights * (function(xdata, *params) - ydata)
def curve_fit(f, xdata, ydata, p0=None, sigma=None, **kw):
"""
Use non-linear least squares to fit a function, f, to data.
Assumes ``ydata = f(xdata, *params) + eps``
Parameters
----------
f : callable
The model function, f(x, ...). It must take the independent
variable as the first argument and the parameters to fit as
separate remaining arguments.
xdata : An N-length sequence or an (k,N)-shaped array
for functions with k predictors.
The independent variable where the data is measured.
ydata : N-length sequence
The dependent data --- nominally f(xdata, ...)
p0 : None, scalar, or M-length sequence
Initial guess for the parameters. If None, then the initial
values will all be 1 (if the number of parameters for the function
can be determined using introspection, otherwise a ValueError
is raised).
sigma : None or N-length sequence
If not None, it represents the standard-deviation of ydata.
This vector, if given, will be used as weights in the
least-squares problem.
Returns
-------
popt : array
Optimal values for the parameters so that the sum of the squared error
of ``f(xdata, *popt) - ydata`` is minimized
pcov : 2d array
The estimated covariance of popt. The diagonals provide the variance
of the parameter estimate.
Notes
-----
The algorithm uses the Levenburg-Marquardt algorithm:
scipy.optimize.leastsq. Additional keyword arguments are passed directly
to that algorithm.
Examples
--------
>>> import numpy as np
>>> from scipy.optimize import curve_fit
>>> def func(x, a, b, c):
... return a*np.exp(-b*x) + c
>>> x = np.linspace(0,4,50)
>>> y = func(x, 2.5, 1.3, 0.5)
>>> yn = y + 0.2*np.random.normal(size=len(x))
>>> popt, pcov = curve_fit(func, x, yn)
"""
if p0 is None or isscalar(p0):
# determine number of parameters by inspecting the function
import inspect
args, varargs, varkw, defaults = inspect.getargspec(f)
if len(args) < 2:
msg = "Unable to determine number of fit parameters."
raise ValueError(msg)
if p0 is None:
p0 = 1.0
p0 = [p0]*(len(args)-1)
args = (xdata, ydata, f)
if sigma is None:
func = _general_function
else:
func = _weighted_general_function
args += (1.0/asarray(sigma),)
res = leastsq(func, p0, args=args, full_output=1, **kw)
(popt, pcov, infodict, errmsg, ier) = res
if ier not in [1,2,3,4]:
msg = "Optimal parameters not found: " + errmsg
raise RuntimeError(msg)
if (len(ydata) > len(p0)) and pcov is not None:
s_sq = (func(popt, *args)**2).sum()/(len(ydata)-len(p0))
pcov = pcov * s_sq
else:
pcov = inf
return popt, pcov
def check_gradient(fcn,Dfcn,x0,args=(),col_deriv=0):
"""Perform a simple check on the gradient for correctness.
"""
x = atleast_1d(x0)
n = len(x)
x=x.reshape((n,))
fvec = atleast_1d(fcn(x,*args))
m = len(fvec)
fvec=fvec.reshape((m,))
ldfjac = m
fjac = atleast_1d(Dfcn(x,*args))
fjac=fjac.reshape((m,n))
if col_deriv == 0:
fjac = transpose(fjac)
xp = zeros((n,), float)
err = zeros((m,), float)
fvecp = None
_minpack._chkder(m,n,x,fvec,fjac,ldfjac,xp,fvecp,1,err)
fvecp = atleast_1d(fcn(xp,*args))
fvecp=fvecp.reshape((m,))
_minpack._chkder(m,n,x,fvec,fjac,ldfjac,xp,fvecp,2,err)
good = (product(greater(err,0.5),axis=0))
return (good,err)
# Newton-Raphson method
def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50):
"""Find a zero using the Newton-Raphson or secant method.
Find a zero of the function `func` given a nearby starting point `x0`.
The Newton-Rapheson method is used if the derivative `fprime` of `func`
is provided, otherwise the secant method is used.
Parameters
----------
func : function
The function whose zero is wanted. It must be a function of a
single variable of the form f(x,a,b,c...), where a,b,c... are extra
arguments that can be passed in the `args` parameter.
x0 : float
An initial estimate of the zero that should be somewhere near the
actual zero.
fprime : {None, function}, optional
The derivative of the function when available and convenient. If it
is None, then the secant method is used. The default is None.
args : tuple, optional
Extra arguments to be used in the function call.
tol : float, optional
The allowable error of the zero value.
maxiter : int, optional
Maximum number of iterations.
Returns
-------
zero : float
Estimated location where function is zero.
See Also
--------
brentq, brenth, ridder, bisect -- find zeroes in one dimension.
fsolve -- find zeroes in n dimensions.
Notes
-----
The convergence rate of the Newton-Rapheson method is quadratic while
that of the secant method is somewhat less. This means that if the
function is well behaved the actual error in the estimated zero is
approximatly the square of the requested tolerance up to roundoff
error. However, the stopping criterion used here is the step size and
there is no quarantee that a zero has been found. Consequently the
result should be verified. Safer algorithms are brentq, brenth, ridder,
and bisect, but they all require that the root first be bracketed in an
interval where the function changes sign. The brentq algorithm is
recommended for general use in one dimemsional problems when such an
interval has been found.
"""
msg = "minpack.newton is moving to zeros.newton"
warnings.warn(msg, DeprecationWarning)
if fprime is not None:
# Newton-Rapheson method
p0 = x0
for iter in range(maxiter):
myargs = (p0,) + args
fval = func(*myargs)
fder = fprime(*myargs)
if fder == 0:
msg = "derivative was zero."
warnings.warn(msg, RuntimeWarning)
return p0
p = p0 - func(*myargs)/fprime(*myargs)
if abs(p - p0) < tol:
return p
p0 = p
else:
# Secant method
p0 = x0
if x0 >= 0:
p1 = x0*(1 + 1e-4) + 1e-4
else:
p1 = x0*(1 + 1e-4) - 1e-4
q0 = func(*((p0,) + args))
q1 = func(*((p1,) + args))
for iter in range(maxiter):
if q1 == q0:
if p1 != p0:
msg = "Tolerance of %s reached" % (p1 - p0)
warnings.warn(msg, RuntimeWarning)
return (p1 + p0)/2.0
else:
p = p1 - q1*(p1 - p0)/(q1 - q0)
if abs(p - p1) < tol:
return p
p0 = p1
q0 = q1
p1 = p
q1 = func(*((p1,) + args))
msg = "Failed to converge after %d iterations, value is %s" % (maxiter, p)
raise RuntimeError(msg)
# Steffensen's Method using Aitken's Del^2 convergence acceleration.
def fixed_point(func, x0, args=(), xtol=1e-8, maxiter=500):
"""Find the point where func(x) == x
Given a function of one or more variables and a starting point, find a
fixed-point of the function: i.e. where func(x)=x.
Uses Steffensen's Method using Aitken's Del^2 convergence acceleration.
See Burden, Faires, "Numerical Analysis", 5th edition, pg. 80
Example
-------
>>> from numpy import sqrt, array
>>> from scipy.optimize import fixed_point
>>> def func(x, c1, c2):
return sqrt(c1/(x+c2))
>>> c1 = array([10,12.])
>>> c2 = array([3, 5.])
>>> fixed_point(func, [1.2, 1.3], args=(c1,c2))
array([ 1.4920333 , 1.37228132])
"""
if not isscalar(x0):
x0 = asarray(x0)
p0 = x0
for iter in range(maxiter):
p1 = func(p0, *args)
p2 = func(p1, *args)
d = p2 - 2.0 * p1 + p0
p = where(d == 0, p2, p0 - (p1 - p0)*(p1-p0) / d)
relerr = where(p0 == 0, p, (p-p0)/p0)
if all(relerr < xtol):
return p
p0 = p
else:
p0 = x0
for iter in range(maxiter):
p1 = func(p0, *args)
p2 = func(p1, *args)
d = p2 - 2.0 * p1 + p0
if d == 0.0:
return p2
else:
p = p0 - (p1 - p0)*(p1-p0) / d
if p0 == 0:
relerr = p
else:
relerr = (p-p0)/p0
if relerr < xtol:
return p
p0 = p
msg = "Failed to converge after %d iterations, value is %s" % (maxiter, p)
raise RuntimeError(msg)
def bisection(func, a, b, args=(), xtol=1e-10, maxiter=400):
"""Bisection root-finding method. Given a function and an interval with
func(a) * func(b) < 0, find the root between a and b.
"""
msg = "minpack.bisection is deprecated, use zeros.bisect instead"
warnings.warn(msg, DeprecationWarning)
i = 1
eva = func(a,*args)
evb = func(b,*args)
if eva*evb >= 0:
msg = "Must start with interval where func(a) * func(b) < 0"
raise ValueError(msg)
while i <= maxiter:
dist = (b - a)/2.0
p = a + dist
if dist < xtol:
return p
ev = func(p,*args)
if ev == 0:
return p
i += 1
if ev*eva > 0:
a = p
eva = ev
else:
b = p
msg = "Failed to converge after %d iterations, value is %s" % (maxiter, p)
raise RuntimeError(msg)
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