Module autostreamtree.aggregators

Functions

def adjusted_harmonic_mean(stuff)

Computes an adjusted harmonic mean that is corrected for non-positive values.

Args

stuff (array-like): The input array.

Returns

float

The adjusted harmonic mean.

Expand source code

def adjusted_harmonic_mean(stuff):
    """
    Computes an adjusted harmonic mean that is corrected for non-positive
    values.

    Args:
        stuff (array-like): The input array.

    Returns:
        float: The adjusted harmonic mean.
    """
    s = np.array(stuff)
    vals = s[s > 0.0]
    bads = s[s <= 0.0]
    mu = (1.0 / (np.sum([1.0/x for x in vals]) /
                 (len(vals)-len(bads)))) * ((len(vals)-len(bads))/len(vals))
    return mu

def aggregate_dist(method, stuff)

Aggregates the given array according to the specified method.

Args

method : str

The aggregation method to use (e.g. "HARM", "ARITH", "GEOM", etc.).

stuff (array-like): The input array to be aggregated.

Returns

float

The aggregated result.

Expand source code

def aggregate_dist(method, stuff):
    """
    Aggregates the given array according to the specified method.

    Args:
        method (str): The aggregation method to use (e.g. "HARM", "ARITH",
                      "GEOM", etc.).
        stuff (array-like): The input array to be aggregated.

    Returns:
        float: The aggregated result.
    """
    if method == "HARM":
        stuff = [x for x in stuff if x > 0]
        return scipy.stats.hmean(stuff) if len(stuff) > 0 else 0.0
    elif method == "ARITH":
        return np.mean(stuff)
    elif method == "GEOM":
        return scipy.stats.mstats.gmean(stuff)
    elif method == "MEDIAN":
        return np.median(stuff)
    elif method == "MAX":
        return np.max(stuff)
    elif method == "MIN":
        return np.min(stuff)
    elif method == "ADJHARM":
        return adjusted_harmonic_mean(stuff)
    elif method == "SD":
        return np.std(stuff)
    elif method == "VAR":
        return np.var(stuff)