latools/filtering/classifier_obj.py
def fit_meanshift(self, data, bandwidth=None, bin_seeding=False, **kwargs):
        """
        Fit MeanShift clustering algorithm to data.

        Parameters
        ----------
        data : array-like
            A dataset formatted by `classifier.fitting_data`.
        bandwidth : float
            The bandwidth value used during clustering.
            If none, determined automatically. Note:
            the data are scaled before clutering, so
            this is not in the same units as the data.
        bin_seeding : bool
            Whether or not to use 'bin_seeding'. See
            documentation for `sklearn.cluster.MeanShift`.
        **kwargs
            passed to `sklearn.cluster.MeanShift`.

        Returns
        -------
        Fitted `sklearn.cluster.MeanShift` object.
        """
        if bandwidth is None:
            bandwidth = cl.estimate_bandwidth(data)
        ms = cl.MeanShift(bandwidth=bandwidth, bin_seeding=bin_seeding)
        ms.fit(data)
        return ms
def fit_meanshift(self, data, bandwidth=None, bin_seeding=False, **kwargs):
        if bandwidth is None:
            bandwidth = cl.estimate_bandwidth(data)
        ms = cl.MeanShift(bandwidth=bandwidth, bin_seeding=bin_seeding)
        ms.fit(data)
        return ms
def fit_meanshift ( self , data , bandwidth = None , bin_seeding = False , * * kwargs ) : if bandwidth is None : bandwidth = cl . estimate_bandwidth ( data ) ms = cl . MeanShift ( bandwidth = bandwidth , bin_seeding = bin_seeding ) ms . fit ( data ) return ms
Fit MeanShift clustering algorithm to data.

        Parameters
        ----------
        data : array-like
            A dataset formatted by `classifier.fitting_data`.
        bandwidth : float
            The bandwidth value used during clustering.
            If none, determined automatically. Note:
            the data are scaled before clutering, so
            this is not in the same units as the data.
        bin_seeding : bool
            Whether or not to use 'bin_seeding'. See
            documentation for `sklearn.cluster.MeanShift`.
        **kwargs
            passed to `sklearn.cluster.MeanShift`.

        Returns
        -------
        Fitted `sklearn.cluster.MeanShift` object.
Fit MeanShift clustering algorithm to data .