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Mahalanobis-type change (CostMl)#

ruptures.costs.costml.CostMl #

Mahalanobis-type cost function.

__init__(self, metric=None) special #

Create a new instance.

Parameters:

Name Type Description Default
metric ndarray

PSD matrix that defines a Mahalanobis-type pseudo distance. If None, defaults to the Mahalanobis matrix. Shape (n_features, n_features).

None
Source code in ruptures/costs/costml.py
def __init__(self, metric=None):
    """Create a new instance.

    Args:
        metric (ndarray, optional): PSD matrix that defines a Mahalanobis-type pseudo distance. If None, defaults to the Mahalanobis matrix. Shape (n_features, n_features).
    """
    self.metric = metric
    self.gram = None
    self.min_size = 2

error(self, start, end) #

Return the approximation cost on the segment [start:end].

Parameters:

Name Type Description Default
start int

start of the segment

required
end int

end of the segment

required

Returns:

Type Description
float

segment cost

Exceptions:

Type Description
NotEnoughPoints

when the segment is too short (less than 'min_size' samples).

Source code in ruptures/costs/costml.py
def error(self, start, end):
    """Return the approximation cost on the segment [start:end].

    Args:
        start (int): start of the segment
        end (int): end of the segment

    Returns:
        float: segment cost

    Raises:
        NotEnoughPoints: when the segment is too short (less than ``'min_size'`` samples).
    """
    if end - start < self.min_size:
        raise NotEnoughPoints
    sub_gram = self.gram[start:end, start:end]
    val = np.diagonal(sub_gram).sum()
    val -= sub_gram.sum() / (end - start)
    return val

fit(self, signal) #

Set parameters of the instance.

Parameters:

Name Type Description Default
signal array

signal. Shape (n_samples,) or (n_samples, n_features)

required

Returns:

Type Description
CostMl

self

Source code in ruptures/costs/costml.py
def fit(self, signal) -> "CostMl":
    """Set parameters of the instance.

    Args:
        signal (array): signal. Shape (n_samples,) or (n_samples, n_features)

    Returns:
        self
    """

    s_ = signal.reshape(-1, 1) if signal.ndim == 1 else signal

    # Mahalanobis metric if self.metric is None
    if self.metric is None:
        covar = np.cov(s_.T)
        self.metric = inv(covar.reshape(1, 1) if covar.size == 1 else covar)

    self.gram = s_.dot(self.metric).dot(s_.T)
    return self