sgdml/predict.py
def _set_batch_size(
        self, batch_size=None
    ):  # TODO: complain if chunk or worker parameters do not fit training data (this causes issues with the caching)!!
        """
        Set chunk size for each process.

        The chunk size determines how much of a processes workload will
        be passed to Python's underlying low-level routines at once.
        This parameter is highly hardware dependent. A chunk is a subset
        of the training set of the model.

        Note
        ----
                This parameter can be optimally determined using
                `prepare_parallel`.

        Parameters
        ----------
                batch_size : int
                        Chunk size (maximum value is set if `None`).
        """

        if batch_size is None:
            batch_size = self.n_train

        self._chunk_size = batch_size

        Set chunk size for each process.

        The chunk size determines how much of a processes workload will
        be passed to Python's underlying low-level routines at once.
        This parameter is highly hardware dependent. A chunk is a subset
        of the training set of the model.

        Note
        ----
                This parameter can be optimally determined using
                `prepare_parallel`.

        Parameters
        ----------
                batch_size : int
                        Chunk size (maximum value is set if `None`).
        
Set chunk size for each process.
The chunk size determines how much of a processes workload will
be passed to Python's underlying low-level routines at once.
This parameter is highly hardware dependent. A chunk is a subset
of the training set of the model.

Note

This parameter can be optimally determined using
`prepare_parallel`.

Parameters

batch_size : int
Chunk size (maximum value is set if `None`).
Set chunk size for each process . The chunk size determines how much of a processes workload will be passed to Python ' s underlying low - level routines at once . This parameter is highly hardware dependent . A chunk is a subset of the training set of the model . Note This parameter can be optimally determined using ` prepare_parallel ` . Parameters batch_size : int Chunk size ( maximum value is set if ` None ` ) .
def _set_batch_size(
        self, batch_size=None
    ):  
        if batch_size is None:
            batch_size = self.n_train
        self._chunk_size = batch_size
def _set_batch_size ( self , batch_size = None ) : if batch_size is None : batch_size = self . n_train self . _chunk_size = batch_size
Set chunk size for each process.
Set chunk size for each process .
# TODO: complain if chunk or worker parameters do not fit training data (this causes issues with the caching)!! """
        Set chunk size for each process.

        The chunk size determines how much of a processes workload will
        be passed to Python's underlying low-level routines at once.
        This parameter is highly hardware dependent. A chunk is a subset
        of the training set of the model.

        Note
        ----
                This parameter can be optimally determined using
                `prepare_parallel`.

        Parameters
        ----------
                batch_size : int
                        Chunk size (maximum value is set if `None`).
        """
self
batch_size