Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé

Module « scipy.stats.mstats »

Fonction hmean - module scipy.stats.mstats

Signature de la fonction hmean

def hmean(a, axis=0, dtype=None, *, weights=None, nan_policy='propagate', keepdims=False) 

Description

help(scipy.stats.mstats.hmean)

    


Calculate the weighted harmonic mean along the specified axis.

The weighted harmonic mean of the array :math:`a_i` associated to weights
:math:`w_i` is:

.. math::

    \frac{ \sum_{i=1}^n w_i }{ \sum_{i=1}^n \frac{w_i}{a_i} } \, ,

and, with equal weights, it gives:

.. math::

    \frac{ n }{ \sum_{i=1}^n \frac{1}{a_i} } \, .

Parameters
----------
a : array_like
    Input array, masked array or object that can be converted to an array.
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
dtype : dtype, optional
    Type of the returned array and of the accumulator in which the
    elements are summed. If `dtype` is not specified, it defaults to the
    dtype of `a`, unless `a` has an integer `dtype` with a precision less
    than that of the default platform integer. In that case, the default
    platform integer is used.
weights : array_like, optional
    The weights array can either be 1-D (in which case its length must be
    the size of `a` along the given `axis`) or of the same shape as `a`.
    Default is None, which gives each value a weight of 1.0.
    
    .. versionadded:: 1.9
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

Returns
-------
hmean : ndarray
    See `dtype` parameter above.

See Also
--------

:func:`numpy.mean`
    Arithmetic average
:func:`numpy.average`
    Weighted average
:func:`gmean`
    Geometric mean


Notes
-----
The sample harmonic mean is the reciprocal of the mean of the reciprocals
of the observations.

The harmonic mean is computed over a single dimension of the input
array, axis=0 by default, or all values in the array if axis=None.
float64 intermediate and return values are used for integer inputs.

The harmonic mean is only defined if all observations are non-negative;
otherwise, the result is NaN.

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

References
----------
.. [1] "Weighted Harmonic Mean", *Wikipedia*,
       https://en.wikipedia.org/wiki/Harmonic_mean#Weighted_harmonic_mean
.. [2] Ferger, F., "The nature and use of the harmonic mean", Journal of
       the American Statistical Association, vol. 26, pp. 36-40, 1931

Examples
--------
>>> from scipy.stats import hmean
>>> hmean([1, 4])
1.6000000000000001
>>> hmean([1, 2, 3, 4, 5, 6, 7])
2.6997245179063363
>>> hmean([1, 4, 7], weights=[3, 1, 3])
1.9029126213592233

Vous êtes un professionnel et vous avez besoin d'une formation ? Sensibilisation à
l'Intelligence Artificielle Voir le programme détaillé