CostL2 (least squared deviation)

Bases: BaseCost

Least squared deviation.

Source code in ruptures/costs/costl2.py

class CostL2(BaseCost):
    r"""Least squared deviation."""

    model = "l2"

    def __init__(self):
        """Initialize the object."""
        self.signal = None
        self.min_size = 1

    def fit(self, signal) -> "CostL2":
        """Set parameters of the instance.

        Args:
            signal (array): array of shape (n_samples,) or (n_samples, n_features)

        Returns:
            self
        """
        if signal.ndim == 1:
            self.signal = signal.reshape(-1, 1)
        else:
            self.signal = signal

        return self

    def error(self, start, end) -> float:
        """Return the approximation cost on the segment [start:end].

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

        Returns:
            segment cost

        Raises:
            NotEnoughPoints: when the segment is too short (less than `min_size` samples).
        """
        if end - start < self.min_size:
            raise NotEnoughPoints

        return self.signal[start:end].var(axis=0).sum() * (end - start)

__init__()

Initialize the object.

Source code in ruptures/costs/costl2.py

def __init__(self):
    """Initialize the object."""
    self.signal = None
    self.min_size = 1

error(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

Raises:

Type	Description
NotEnoughPoints	when the segment is too short (less than min_size samples).

Source code in ruptures/costs/costl2.py

def error(self, start, end) -> float:
    """Return the approximation cost on the segment [start:end].

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

    Returns:
        segment cost

    Raises:
        NotEnoughPoints: when the segment is too short (less than `min_size` samples).
    """
    if end - start < self.min_size:
        raise NotEnoughPoints

    return self.signal[start:end].var(axis=0).sum() * (end - start)

fit(signal)

Set parameters of the instance.

Parameters:

Name	Type	Description	Default
signal	array	array of shape (n_samples,) or (n_samples, n_features)	required

Returns:

Type	Description
CostL2	self

Source code in ruptures/costs/costl2.py

def fit(self, signal) -> "CostL2":
    """Set parameters of the instance.

    Args:
        signal (array): array of shape (n_samples,) or (n_samples, n_features)

    Returns:
        self
    """
    if signal.ndim == 1:
        self.signal = signal.reshape(-1, 1)
    else:
        self.signal = signal

    return self