Continuous linear change (CostCLinear)

Bases: BaseCost

Piecewise linear approximation with a continuity constraint.

Source code in ruptures/costs/costclinear.py

class CostCLinear(BaseCost):
    r"""Piecewise linear approximation with a continuity constraint."""

    model = "clinear"

    def __init__(self):
        """Initialize the object."""
        self.signal = None
        self.min_size = 3

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

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

        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 (float)

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

        if start == 0:
            start = 1

        sub = self.signal[start:end]
        slope = (self.signal[end - 1] - self.signal[start - 1]) / (end - start)
        intercept = self.signal[start - 1]
        approx = slope.reshape(-1, 1) * np.arange(
            1, end - start + 1
        ) + intercept.reshape(-1, 1)
        return np.sum((sub - approx.transpose()) ** 2)

__init__()

Initialize the object.

Source code in ruptures/costs/costclinear.py

def __init__(self):
    """Initialize the object."""
    self.signal = None
    self.min_size = 3

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 (float)

Raises:

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

Source code in ruptures/costs/costclinear.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 (float)

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

    if start == 0:
        start = 1

    sub = self.signal[start:end]
    slope = (self.signal[end - 1] - self.signal[start - 1]) / (end - start)
    intercept = self.signal[start - 1]
    approx = slope.reshape(-1, 1) * np.arange(
        1, end - start + 1
    ) + intercept.reshape(-1, 1)
    return np.sum((sub - approx.transpose()) ** 2)

fit(signal)

Set parameters of the instance.

Parameters:

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

Returns:

Type	Description
CostCLinear	self

Source code in ruptures/costs/costclinear.py

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

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

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

    return self