def __init__(self,
model="l2",
custom_cost=None,
min_size=2,
jump=5,
ensembling=1,
params=None):
"""Initialize a Binseg instance.
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. 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)
self.ensembling = ensembling
def __init__(self, kernel="linear", min_size=2, jump=1, params=None):
r"""Creates a KernelCPD instance.
Available kernels:
- `linear`: $k(x,y) = x^T y$.
- `rbf`: $k(x, y) = exp(\gamma \|x-y\|^2)$ where $\gamma>0$
(`gamma`) is a user-defined parameter.
- `cosine`: $k(x,y)= (x^T y)/(\|x\|\|y\|)$.
Args:
kernel (str, optional): name of the kernel, ["linear", "rbf", "cosine"]
min_size (int, optional): minimum segment length.
jump (int, optional): not considered, set to 1.
params (dict, optional): a dictionary of parameters for the kernel instance
Raises:
AssertionError: if the kernel is not implemented.
"""
self.kernel_name = kernel
err_msg = "Kernel not found: {}.".format(self.kernel_name)
assert self.kernel_name in ["linear", "rbf", "cosine"], err_msg
self.model_name = "l2" if self.kernel_name == "linear" else self.kernel_name
self.params = params
# load the associated cost function
if self.params is None:
self.cost = cost_factory(model=self.model_name)
else:
self.cost = cost_factory(model=self.model_name, **self.params)
self.min_size = max(min_size, self.cost.min_size)
self.jump = 1 # set to 1
self.n_samples = None
self.segmentations_dict = dict() # {n_bkps: bkps_list}
def __init__(self,
model="l2",
custom_cost=None,
min_size=2,
jump=5,
params=None):
"""Initialize a Pelt instance.
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.
"""
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
def __init__(self,
model="l2",
custom_cost=None,
min_size=2,
jump=5,
ensembling=1,
params=None):
"""Creates a Dynp instance.
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
"""
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
self.ensembling = ensembling
def __init__(self,
width=100,
model="l2",
custom_cost=None,
min_size=2,
jump=5,
params=None):
"""Instanciate with window length.
Args:
width (int, optional): window length. Defaults to 100 samples.
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
"""
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_5D_noisy_names(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):
if cost_name == "cosine":
cost.min_size = 4
cost.error(1, 2)
else:
cost.error(1, 2)
def test_factory_exception():
with pytest.raises(ValueError):
cost_factory("bkd;s")
