Module « scipy.odr »

Classe « Model »

Informations générales

Héritage

builtins.object
    Model

Définition

class Model(builtins.object):

Description _{[extrait de Model.__doc__]}

    The Model class stores information about the function you wish to fit.

    It stores the function itself, at the least, and optionally stores
    functions which compute the Jacobians used during fitting. Also, one
    can provide a function that will provide reasonable starting values
    for the fit parameters possibly given the set of data.

    Parameters
    ----------
    fcn : function
          fcn(beta, x) --> y
    fjacb : function
          Jacobian of fcn wrt the fit parameters beta.

          fjacb(beta, x) --> @f_i(x,B)/@B_j
    fjacd : function
          Jacobian of fcn wrt the (possibly multidimensional) input
          variable.

          fjacd(beta, x) --> @f_i(x,B)/@x_j
    extra_args : tuple, optional
          If specified, `extra_args` should be a tuple of extra
          arguments to pass to `fcn`, `fjacb`, and `fjacd`. Each will be called
          by `apply(fcn, (beta, x) + extra_args)`
    estimate : array_like of rank-1
          Provides estimates of the fit parameters from the data

          estimate(data) --> estbeta
    implicit : boolean
          If TRUE, specifies that the model
          is implicit; i.e `fcn(beta, x)` ~= 0 and there is no y data to fit
          against
    meta : dict, optional
          freeform dictionary of metadata for the model

    Notes
    -----
    Note that the `fcn`, `fjacb`, and `fjacd` operate on NumPy arrays and
    return a NumPy array. The `estimate` object takes an instance of the
    Data class.

    Here are the rules for the shapes of the argument and return
    arrays of the callback functions:

    `x`
        if the input data is single-dimensional, then `x` is rank-1
        array; i.e., ``x = array([1, 2, 3, ...]); x.shape = (n,)``
        If the input data is multi-dimensional, then `x` is a rank-2 array;
        i.e., ``x = array([[1, 2, ...], [2, 4, ...]]); x.shape = (m, n)``.
        In all cases, it has the same shape as the input data array passed to
        `~scipy.odr.odr`. `m` is the dimensionality of the input data,
        `n` is the number of observations.
    `y`
        if the response variable is single-dimensional, then `y` is a
        rank-1 array, i.e., ``y = array([2, 4, ...]); y.shape = (n,)``.
        If the response variable is multi-dimensional, then `y` is a rank-2
        array, i.e., ``y = array([[2, 4, ...], [3, 6, ...]]); y.shape =
        (q, n)`` where `q` is the dimensionality of the response variable.
    `beta`
        rank-1 array of length `p` where `p` is the number of parameters;
        i.e. ``beta = array([B_1, B_2, ..., B_p])``
    `fjacb`
        if the response variable is multi-dimensional, then the
        return array's shape is `(q, p, n)` such that ``fjacb(x,beta)[l,k,i] =
        d f_l(X,B)/d B_k`` evaluated at the ith data point.  If `q == 1`, then
        the return array is only rank-2 and with shape `(p, n)`.
    `fjacd`
        as with fjacb, only the return array's shape is `(q, m, n)`
        such that ``fjacd(x,beta)[l,j,i] = d f_l(X,B)/d X_j`` at the ith data
        point.  If `q == 1`, then the return array's shape is `(m, n)`. If
        `m == 1`, the shape is (q, n). If `m == q == 1`, the shape is `(n,)`.

    

Constructeur(s)

Signature du constructeur	Description
__init__(self, fcn, fjacb=None, fjacd=None, extra_args=None, estimate=None, implicit=0, meta=None)

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __init_subclass__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__