MeteoInfo/MeteoInfoLab/pylib/mipylib/numeric/optimize/minpack.py

#from org.apache.commons.math3.analysis import UnivariateFunction
from org.meteoinfo.math.optimize import OptimizeUtil, ParamUnivariateFunction
from org.apache.commons.math3.fitting.leastsquares import LeastSquaresBuilder, LevenbergMarquardtOptimizer

import warnings
from ..core import numeric as np
from ._lsq.least_squares import prepare_bounds
from ..linalg import cholesky, solve_triangular, svd

#__all__ = ['fsolve', 'leastsq', 'fixed_point', 'curve_fit']
__all__ = ['curve_fit']

def _check_func(checker, argname, thefunc, x0, args, numinputs,
                output_shape=None):
    res = np.atleast_1d(thefunc(*((x0[:numinputs],) + args)))
    if (output_shape is not None) and (np.shape(res) != output_shape):
        if (output_shape[0] != 1):
            if len(output_shape) > 1:
                if output_shape[1] == 1:
                    return shape(res)
            msg = "%s: there is a mismatch between the input and output " \
                  "shape of the '%s' argument" % (checker, argname)
            func_name = getattr(thefunc, '__name__', None)
            if func_name:
                msg += " '%s'." % func_name
            else:
                msg += "."
            msg += 'Shape should be %s but it is %s.' % (output_shape, np.shape(res))
            raise TypeError(msg)
    return np.shape(res), res.dtype

def _wrap_func(func, xdata, ydata, transform):
    if transform is None:
        def func_wrapped(params):
            return func(xdata, *params) - ydata
    elif transform.ndim == 1:
        def func_wrapped(params):
            return transform * (func(xdata, *params) - ydata)
    else:
        # Chisq = (y - yd)^T C^{-1} (y-yd)
        # transform = L such that C = L L^T
        # C^{-1} = L^{-T} L^{-1}
        # Chisq = (y - yd)^T L^{-T} L^{-1} (y-yd)
        # Define (y-yd)' = L^{-1} (y-yd)
        # by solving
        # L (y-yd)' = (y-yd)
        # and minimize (y-yd)'^T (y-yd)'
        def func_wrapped(params):
            return solve_triangular(transform, func(xdata, *params) - ydata, lower=True)
    return func_wrapped

def _wrap_jac(jac, xdata, transform):
    if transform is None:
        def jac_wrapped(params):
            return jac(xdata, *params)
    elif transform.ndim == 1:
        def jac_wrapped(params):
            return transform[:, np.newaxis] * np.asarray(jac(xdata, *params))
    else:
        def jac_wrapped(params):
            return solve_triangular(transform, np.asarray(jac(xdata, *params)), lower=True)
    return jac_wrapped

def _initialize_feasible(lb, ub):
    p0 = np.ones_like(lb)
    lb_finite = np.isfinite(lb)
    ub_finite = np.isfinite(ub)

    mask = lb_finite & ub_finite
    p0[mask] = 0.5 * (lb[mask] + ub[mask])

    mask = lb_finite & ~ub_finite
    p0[mask] = lb[mask] + 1

    mask = ~lb_finite & ub_finite
    p0[mask] = ub[mask] - 1

    return p0

class UniFunc(ParamUnivariateFunction):
    def __init__(self, f):
        '''
        Initialize

        :param f: Jython function
        '''
        self.f = f
        self._args = list(f.__code__.co_varnames)[1:]
        self._args = tuple(self._args)
        self.order = len(self._args)

    def value(self, x):
        args = tuple(self.getParameters())
        return self.f(x, *args)

def curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False,
              check_finite=True, bounds=(-np.inf, np.inf), method=None,
              jac=None, **kwargs):
    '''
    Use non-linear least squares to fit a function, f, to data.

    Assumes ``ydata = f(xdata, *params) + eps``

    :param 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.
    :param xdata: array_like or object
        The independent variable where the data is measured.
        Should usually be an M-length sequence or an (k,M)-shaped array for
        functions with k predictors, but can actually be any object.
    :param ydata: array_like
        The dependent data, a length M array - nominally ``f(xdata, ...)``.
    :param p0: array_like, optional
        Initial guess for the parameters (length N).  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).
    :return:
    '''
    if p0 is None:
        # determine number of parameters by inspecting the function
        from ..lib._util import getargspec_no_self as _getargspec
        args, varargs, varkw, defaults = _getargspec(f)
        if len(args) < 2:
            raise ValueError("Unable to determine number of fit parameters.")
        n = len(args) - 1
        p0 = np.ones(n)
    else:
        p0 = np.atleast_1d(p0)
        n = p0.size

    func = UniFunc(f)
    jac_func = OptimizeUtil.getJacobianFunction(func, xdata.asarray(), func.order, 5, 0.1)
    problem = LeastSquaresBuilder().start(p0.tojarray('double')). \
        model(jac_func). \
        target(ydata.tojarray('double')). \
        lazyEvaluation(False). \
        maxEvaluations(1000). \
        maxIterations(1000). \
        build()
    optimum = LevenbergMarquardtOptimizer().optimize(problem)
    r = tuple(optimum.getPoint().toArray())

    return r