def __init__(self, name, array, conditions=None): self.data = array if conditions is not None and isinstance(conditions, list): self.conditions = conditions Dataset.__init__(self, '{}'.format(name))
def __init__(self, name, garray, gindexer, alphabetsize, channel_last): self.garray = garray self.gindexer = gindexer self._alphabetsize = alphabetsize self._rcindex = [_complement_index(idx, garray.order) for idx in range(pow(alphabetsize, garray.order))] self._channel_last = channel_last Dataset.__init__(self, '{}'.format(name))
def __init__(self, name, array, conditions=None): if conditions is not None: assert array.shape[-1] == len(conditions), \ "array shape and condition number does not agree: {} != {}" \ .format(array.shape[-1], len(conditions)) self.data = copy.copy(array) self.conditions = conditions Dataset.__init__(self, '{}'.format(name))
def __init__(self, name, garray, gindexer, alphabet): self.garray = garray self.gindexer = gindexer self._alphabet = alphabet self.conditions = [ ''.join(item) for item in product(sorted(self._alphabet), repeat=self.garray.order) ] self._alphabetsize = len(self._alphabet) self._rcindex = [ _complement_index(idx, garray.order) for idx in range(pow(self._alphabetsize, garray.order)) ] Dataset.__init__(self, '{}'.format(name))
def __init__( self, name, garray, gindexer, # indices of pointing to region start padding_value, dimmode, channel_last): # padding value self.garray = garray self.gindexer = gindexer self.padding_value = padding_value self.dimmode = dimmode self._channel_last = channel_last Dataset.__init__(self, name)
def __init__(self, name, filename, conditions=None, dtype='int8', sep=','): self.filename = filename if conditions is None: conditions = [ os.path.splitext(os.path.basename(f))[0] for f in filename ] self.conditions = conditions data = [] for _file in self.filename: data.append(pd.read_csv(_file, header=None, sep=sep, dtype=dtype)) self.data = pd.concat(data, axis=1, ignore_index=True).values Dataset.__init__(self, name)
def __init__(self, array, aggregator=None, axis=None): self.data = copy.copy(array) if aggregator is None: aggregator = 'sum' def _get_aggregator(name): if callable(name): return name if name == 'sum': return np.sum elif name == 'mean': return np.mean elif name == 'max': return np.max raise ValueError('ReduceDim aggregator="{}" not known.'.format(name) + 'Must be "sum", "mean" or "max" or a callable.') self.aggregator = _get_aggregator(aggregator) self.axis = axis if axis is not None else (1, 2) Dataset.__init__(self, array.name)
def __init__(self, array): self.data = copy.copy(array) Dataset.__init__(self, array.name)
def __init__(self, array, deviance): self.data = copy.copy(array) self.deviance = deviance Dataset.__init__(self, array.name)
def __init__(self, array, axis): self.data = copy.copy(array) self.axis = axis Dataset.__init__(self, array.name)