msaf/utils.py
def normalize(X, norm_type, floor=0.0, min_db=-80):
    """Normalizes the given matrix of features.

    Parameters
    ----------
    X: np.array
        Each row represents a feature vector.
    norm_type: {"min_max", "log", np.inf, -np.inf, 0, float > 0, None}
        - `"min_max"`: Min/max scaling is performed
        - `"log"`: Logarithmic scaling is performed
        - `np.inf`: Maximum absolute value
        - `-np.inf`: Mininum absolute value
        - `0`: Number of non-zeros
        - float: Corresponding l_p norm.
        - None : No normalization is performed

    Returns
    -------
    norm_X: np.array
        Normalized `X` according the the input parameters.
    """
    if isinstance(norm_type, six.string_types):
        if norm_type == "min_max":
            return min_max_normalize(X, floor=floor)
        if norm_type == "log":
            return lognormalize(X, floor=floor, min_db=min_db)
    return librosa.util.normalize(X, norm=norm_type, axis=1)
def normalize(X, norm_type, floor=0.0, min_db=-80):
    if isinstance(norm_type, six.string_types):
        if norm_type == "min_max":
            return min_max_normalize(X, floor=floor)
        if norm_type == "log":
            return lognormalize(X, floor=floor, min_db=min_db)
    return librosa.util.normalize(X, norm=norm_type, axis=1)
def normalize ( X , norm_type , floor = 0.0 , min_db = - 80 ) : if isinstance ( norm_type , six . string_types ) : if norm_type == "min_max" : return min_max_normalize ( X , floor = floor ) if norm_type == "log" : return lognormalize ( X , floor = floor , min_db = min_db ) return librosa . util . normalize ( X , norm = norm_type , axis = 1 )
Normalizes the given matrix of features.

    Parameters
    ----------
    X: np.array
        Each row represents a feature vector.
    norm_type: {"min_max", "log", np.inf, -np.inf, 0, float > 0, None}
        - `"min_max"`: Min/max scaling is performed
        - `"log"`: Logarithmic scaling is performed
        - `np.inf`: Maximum absolute value
        - `-np.inf`: Mininum absolute value
        - `0`: Number of non-zeros
        - float: Corresponding l_p norm.
        - None : No normalization is performed

    Returns
    -------
    norm_X: np.array
        Normalized `X` according the the input parameters.
Normalizes the given matrix of features .