Method __init__

numpy_ml/preprocessing/general.py:276–299

Convert a collection of features to a fixed-dimensional matrix using the hashing trick. Notes ----- Uses the md5 hash. Parameters ---------- n_dim : int The dimensionality of each example in the output feature matrix.

(self, n_dim=256, sparse=True)

class FeatureHasher:
    def __init__(self, n_dim=256, sparse=True):
        """
        Convert a collection of features to a fixed-dimensional matrix using
        the hashing trick.

        Notes
        -----
        Uses the md5 hash.

        Parameters
        ----------
        n_dim : int
            The dimensionality of each example in the output feature matrix.
            Small numbers of features are likely to cause hash collisions, but
            large numbers will cause larger overall parameter dimensions for
            any (linear) learning agent. Default is 256.
        sparse : bool
            Whether the resulting feature matrix should be a sparse
            :py:class:`csr_matrix <scipy.sparse.csr_matrix>` or dense
            :py:class:`ndarray <numpy.ndarray>`. Default is True.
        """
        self.n_dim = n_dim
        self.hash = hashlib.md5
        self.sparse = sparse and _SCIPY

    def encode(self, examples):
        """