def from_samples_bqm(cls, samples_like, bqm, **kwargs):
        """Build a SampleSet from raw samples using a BinaryQuadraticModel to get energies and vartype.

        Args:
            samples_like:
                A collection of raw samples. 'samples_like' is an extension of NumPy's array_like.
                See :func:`.as_samples`.

            bqm (:obj:`.BinaryQuadraticModel`):
                A binary quadratic model. It is used to calculate the energies
                and set the vartype.

            info (dict, optional):
                Information about the :class:`SampleSet` as a whole formatted as a dict.

            num_occurrences (array_like, optional):
                Number of occurrences for each sample. If not provided, defaults to a vector of 1s.

            aggregate_samples (bool, optional, default=False):
                If true, returned :obj:`.SampleSet` will have all unique samples.

            sort_labels (bool, optional, default=True):
                If true, :attr:`.SampleSet.variables` will be in sorted-order.
                Note that mixed types are not sortable in which case the given
                order will be maintained.

            **vectors (array_like):
                Other per-sample data.

        Returns:
            :obj:`.SampleSet`

        Examples:

            >>> bqm = dimod.BinaryQuadraticModel.from_ising({}, {('a', 'b'): -1})
            >>> samples = dimod.SampleSet.from_samples_bqm({'a': -1, 'b': 1}, bqm)

        """
        # more performant to do this once, here rather than again in bqm.energies
        # and in cls.from_samples
        samples_like = as_samples(samples_like)

        energies = bqm.energies(samples_like)

        return cls.from_samples(samples_like, energy=energies, vartype=bqm.vartype, **kwargs)
def from_samples_bqm ( cls , samples_like , bqm , ** kwargs ) : samples_like = as_samples ( samples_like ) energies = bqm . energies ( samples_like ) return cls . from_samples ( samples_like , energy = energies , vartype = bqm . vartype , ** kwargs )
Build a SampleSet from raw samples using a BinaryQuadraticModel to get energies and vartype.

        Args:
            samples_like:
                A collection of raw samples. 'samples_like' is an extension of NumPy's array_like.
                See :func:`.as_samples`.

            bqm (:obj:`.BinaryQuadraticModel`):
                A binary quadratic model. It is used to calculate the energies
                and set the vartype.

            info (dict, optional):
                Information about the :class:`SampleSet` as a whole formatted as a dict.

            num_occurrences (array_like, optional):
                Number of occurrences for each sample. If not provided, defaults to a vector of 1s.

            aggregate_samples (bool, optional, default=False):
                If true, returned :obj:`.SampleSet` will have all unique samples.

            sort_labels (bool, optional, default=True):
                If true, :attr:`.SampleSet.variables` will be in sorted-order.
                Note that mixed types are not sortable in which case the given
                order will be maintained.

            **vectors (array_like):
                Other per-sample data.

        Returns:
            :obj:`.SampleSet`

        Examples:

            >>> bqm = dimod.BinaryQuadraticModel.from_ising({}, {('a', 'b'): -1})
            >>> samples = dimod.SampleSet.from_samples_bqm({'a': -1, 'b': 1}, bqm)