How to use the ruptures.costs.Cost function in ruptures

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github deepcharles / ruptures / ruptures / search_methods / window.py View on Github external
def __init__(self, width=100):
        """Instanciate with window length.

        Args:
            width (int): window lenght.

        Returns:
            self
        """
        self.width = 2 * (width // 2)
        # window
        self.win = np.sign(np.linspace(-1, 1, self.width)).reshape(-1, 1)
        self.n_samples = None
        self.signal = None
        self.cost = Cost(model="constantl2")
        self.score = None
github deepcharles / ruptures / ruptures / search_methods / dynp.py View on Github external
Detailled description

        Args:
            model (str): constantl1|constantl2|rbf
            min_size (int, optional): minimum segment length
            jump (int, optional): subsample (one every "jump" points)

        Returns:
            self
        """
        self.model = model
        self.min_size = min_size
        self.jump = jump
        self.seg = lru_cache(maxsize=None)(
            self._seg)  # dynamic programming dans cette fonction.
        self.cost = Cost(model=self.model)
        self.n_samples = None
github deepcharles / ruptures / ruptures / search_methods / binseg.py View on Github external
"""One line description

        Detailled description

        Args:
            model (str): constantl1|constantl2|rbf, defaults to constantl2
            min_size (int, optional): minimum segment length
            jump (int, optional): subsample (one every "jump" points)

        Returns:
            self
        """
        self.model = model  # not used
        self.min_size = min_size
        self.jump = jump
        self.cost = Cost(model="constantl2")
        self.n_samples = None
        self.signal = None
github deepcharles / ruptures / ruptures / search_methods / pelt.py View on Github external
"""One line description

        Detailled description

        Args:
            model (str): constantl1|constantl2|rbf
            min_size (int, optional): minimum segment length
            jump (int, optional): subsample (one every "jump" points)

        Returns:
            self
        """
        self.model = model
        self.min_size = min_size
        self.jump = jump
        self.cost = Cost(model=self.model)
        self.n_samples = None