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

params (dict, optional): a dictionary of parameters for the cost instance.

        Returns:
            self
        """
        self.min_size = min_size
        self.jump = jump
        self.width = 2 * (width // 2)
        self.n_samples = None
        self.signal = None
        self.inds = None
        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.score = list()

Returns:
            self
        """
        self.min_size = min_size
        self.jump = jump
        self.width = 2 * (width // 2)
        self.n_samples = None
        self.signal = None
        self.inds = None
        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.score = list()

def test_costs_5D_noisy(signal_bkps_5D_noisy, cost_name):
    signal, bkps = signal_bkps_5D_noisy
    cost = cost_factory(cost_name)
    cost.fit(signal)
    cost.error(0, 100)
    cost.error(100, signal.shape[0])
    cost.error(10, 50)
    cost.sum_of_costs(bkps)
    with pytest.raises(NotEnoughPoints):
        cost.error(1, 2)

def test_costs_1D(signal_bkps_1D, cost_name):
    signal, bkps = signal_bkps_1D
    cost = cost_factory(cost_name)
    cost.fit(signal)
    cost.fit(signal.flatten())
    cost.error(0, 100)
    cost.error(100, signal.shape[0])
    cost.error(10, 50)
    cost.sum_of_costs(bkps)
    with pytest.raises(NotEnoughPoints):
        cost.error(1, 2)

def test_factory_exception():
    with pytest.raises(ValueError):
        cost_factory("bkd;s")

Args:
            model (str, optional): segment model, ["l1", "l2", "rbf"]. Not used if ``'custom_cost'`` is not None.
            custom_cost (BaseCost, optional): custom cost function. Defaults to None.
            min_size (int, optional): minimum segment length.
            jump (int, optional): subsample (one every *jump* points).
            params (dict, optional): a dictionary of parameters for the cost instance.

        Returns:
            self
        """
        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.min_size = max(min_size, self.cost.min_size)
        self.jump = jump
        self.n_samples = None

model (str, optional): segment model, ["l1", "l2", "rbf",...]. Not used if ``'custom_cost'`` is not None.
            custom_cost (BaseCost, optional): custom cost function. Defaults to None.
            min_size (int, optional): minimum segment length. Defaults to 2 samples.
            jump (int, optional): subsample (one every *jump* points). Defaults to 5 samples.
            params (dict, optional): a dictionary of parameters for the cost instance.


        Returns:
            self
        """

        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.min_size = max(min_size, self.cost.min_size)
        self.jump = jump
        self.n_samples = None
        self.signal = None
        # cache for intermediate results
        self.single_bkp = lru_cache(maxsize=None)(self._single_bkp)

min_size (int, optional): minimum segment length. Defaults to 2 samples.
            jump (int, optional): subsample (one every *jump* points). Defaults to 5 samples.
            params (dict, optional): a dictionary of parameters for the cost instance.


        Returns:
            self
        """

        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.min_size = max(min_size, self.cost.min_size)
        self.jump = jump
        self.n_samples = None
        self.signal = None
        # cache for intermediate results
        self.single_bkp = lru_cache(maxsize=None)(self._single_bkp)

custom_cost (BaseCost, optional): custom cost function. Defaults to None.
            min_size (int, optional): minimum segment length.
            jump (int, optional): subsample (one every *jump* points).
            params (dict, optional): a dictionary of parameters for the cost instance.

        Returns:
            self
        """
        self.seg = lru_cache(maxsize=None)(self._seg)  # dynamic programming
        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.min_size = max(min_size, self.cost.min_size)
        self.jump = jump
        self.n_samples = None

model (str, optional): segment model, ["l1", "l2", "rbf"]. Not used if ``'custom_cost'`` is not None.
            custom_cost (BaseCost, optional): custom cost function. Defaults to None.
            min_size (int, optional): minimum segment length. Defaults to 2 samples.
            jump (int, optional): subsample (one every *jump* points). Defaults to 5 samples.
            params (dict, optional): a dictionary of parameters for the cost instance.

        Returns:
            self
        """
        if custom_cost is not None and isinstance(custom_cost, BaseCost):
            self.cost = custom_cost
        else:
            if params is None:
                self.cost = cost_factory(model=model)
            else:
                self.cost = cost_factory(model=model, **params)
        self.min_size = max(min_size, self.cost.min_size)
        self.jump = jump
        self.n_samples = None
        self.signal = None
        self.leaves = None
        self.merge = lru_cache(maxsize=None)(self._merge)