def predict(<EOL>self,<EOL>peptides,<EOL>alleles=None,<EOL>allele=None,<EOL>throw=True,<EOL>centrality_measure=DEFAULT_CENTRALITY_MEASURE):
df = self.predict_to_dataframe(<EOL>peptides=peptides,<EOL>alleles=alleles,<EOL>allele=allele,<EOL>throw=throw,<EOL>include_percentile_ranks=False,<EOL>include_confidence_intervals=False,<EOL>centrality_measure=centrality_measure,<EOL>)<EOL>return df.prediction.values<EOL>
Predict nM binding affinities.

If multiple predictors are available for an allele, the predictions are
the geometric means of the individual model predictions.

One of 'allele' or 'alleles' must be specified. If 'allele' is specified
all predictions will be for the given allele. If 'alleles' is specified
it must be the same length as 'peptides' and give the allele
corresponding to each peptide.

Parameters
----------
peptides : `EncodableSequences` or list of string
alleles : list of string
allele : string
throw : boolean
    If True, a ValueError will be raised in the case of unsupported
    alleles or peptide lengths. If False, a warning will be logged and
    the predictions for the unsupported alleles or peptides will be NaN.
centrality_measure : string or callable
    Measure of central tendency to use to combine predictions in the
    ensemble. Options include: mean, median, robust_mean.

Returns
-------
numpy.array of predictions