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Autoregressive model change (CostAutoregressive)#

CostAR #

Bases: BaseCost

Least-squares estimate for changes in autoregressive coefficients.

Source code in ruptures/costs/costautoregressive.py 
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class CostAR(BaseCost):
    r"""Least-squares estimate for changes in autoregressive coefficients."""

    model = "ar"

    def __init__(self, order=4):
        """Initialize the object.

        Args:
            order (int): autoregressive order
        """
        self.signal = None
        self.covar = None
        self.min_size = max(5, order + 1)
        self.order = order

    def fit(self, signal):
        """Set parameters of the instance. The signal must be 1D.

        Args:
            signal (array): 1d signal. Shape (n_samples, 1) or (n_samples,).

        Returns:
            self: the current object
        """
        self.signal = deepcopy(signal)
        if signal.ndim == 1:
            self.signal = self.signal.reshape(-1, 1)

        # lagged covariates
        n_samples, _ = self.signal.shape
        strides = (self.signal.itemsize, self.signal.itemsize)
        shape = (n_samples - self.order, self.order)
        lagged = as_strided(self.signal, shape=shape, strides=strides)
        # pad the first columns
        lagged_after_padding = np.pad(lagged, ((self.order, 0), (0, 0)), mode="edge")
        # add intercept
        self.covar = np.c_[lagged_after_padding, np.ones(n_samples)]
        # pad signal on the edges
        self.signal[: self.order] = self.signal[self.order]
        return self

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

__init__(order=4) #

Initialize the object.

Parameters:

Name Type Description Default
order int

autoregressive order

 4
Source code in ruptures/costs/costautoregressive.py 
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def __init__(self, order=4):
    """Initialize the object.

    Args:
        order (int): autoregressive order
    """
    self.signal = None
    self.covar = None
    self.min_size = max(5, order + 1)
    self.order = order

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:

Name 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/costautoregressive.py 
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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
    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 signal must be 1D.

Parameters:

Name Type Description Default
signal array

1d signal. Shape (n_samples, 1) or (n_samples,).

 required

Returns:

Name Type Description
self

the current object
Source code in ruptures/costs/costautoregressive.py 
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def fit(self, signal):
    """Set parameters of the instance. The signal must be 1D.

    Args:
        signal (array): 1d signal. Shape (n_samples, 1) or (n_samples,).

    Returns:
        self: the current object
    """
    self.signal = deepcopy(signal)
    if signal.ndim == 1:
        self.signal = self.signal.reshape(-1, 1)

    # lagged covariates
    n_samples, _ = self.signal.shape
    strides = (self.signal.itemsize, self.signal.itemsize)
    shape = (n_samples - self.order, self.order)
    lagged = as_strided(self.signal, shape=shape, strides=strides)
    # pad the first columns
    lagged_after_padding = np.pad(lagged, ((self.order, 0), (0, 0)), mode="edge")
    # add intercept
    self.covar = np.c_[lagged_after_padding, np.ones(n_samples)]
    # pad signal on the edges
    self.signal[: self.order] = self.signal[self.order]
    return self