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Linear model change (CostLinear)#

Bases: BaseCost

Least-square estimate for linear changes.

Source code in ruptures/costs/costlinear.py 
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class CostLinear(BaseCost):
    r"""Least-square estimate for linear changes."""

    model = "linear"

    def __init__(self):
        """Initialize the object."""
        self.signal = None
        self.covar = None
        self.min_size = 2

    def fit(self, signal) -> "CostLinear":
        """Set parameters of the instance. The first column contains the
        observed variable. The other columns contains the covariates.

        Args:
            signal (array): signal of shape (n_samples, n_regressors+1)

        Returns:
            self
        """
        assert signal.ndim > 1, "Not enough dimensions"

        self.signal = signal[:, 0].reshape(-1, 1)
        self.covar = signal[:, 1:]
        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
        y, X = self.signal[start:end], self.covar[start:end]
        _, residual, _, _ = lstsq(X, y, rcond=None)
        return residual.sum()

__init__() #

Initialize the object.

Source code in ruptures/costs/costlinear.py 
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def __init__(self):
    """Initialize the object."""
    self.signal = None
    self.covar = None
    self.min_size = 2

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/costlinear.py 
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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
    y, X = self.signal[start:end], self.covar[start:end]
    _, residual, _, _ = lstsq(X, y, rcond=None)
    return residual.sum()

fit(signal) #

Set parameters of the instance. The first column contains the observed variable. The other columns contains the covariates.

Parameters:

Name Type Description Default
signal array

signal of shape (n_samples, n_regressors+1)

 required

Returns:

Type Description
CostLinear

self
Source code in ruptures/costs/costlinear.py 
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def fit(self, signal) -> "CostLinear":
    """Set parameters of the instance. The first column contains the
    observed variable. The other columns contains the covariates.

    Args:
        signal (array): signal of shape (n_samples, n_regressors+1)

    Returns:
        self
    """
    assert signal.ndim > 1, "Not enough dimensions"

    self.signal = signal[:, 0].reshape(-1, 1)
    self.covar = signal[:, 1:]
    return self