def weights(self, value):
from pyemma.coordinates.data import DataInMemory
import types
if is_float_vector(value):
value = DataInMemory(value)
elif isinstance(value, (list, tuple)):
value = DataInMemory(value)
elif isinstance(value, numbers.Integral):
value = float(value) if value is not None else 1.0
elif hasattr(value, 'weights') and type(getattr(
value, 'weights')) == types.MethodType:
from pyemma.coordinates.data._base.transformer import StreamingTransformer
class compute_weights_streamer(StreamingTransformer):
def __init__(self, func):
super(compute_weights_streamer, self).__init__()
self.func = func
def dimension(self):
return 1
def _transform_array(self, X):
return self.func.weights(X)
def describe(self):
pass
value = compute_weights_streamer(value)
self._weights = value
def __init__(self,
c00=True,
c0t=False,
ctt=False,
remove_constant_mean=None,
remove_data_mean=False,
reversible=False,
bessel=True,
sparse_mode='auto',
modify_data=False,
lag=0,
weights=None,
stride=1,
skip=0,
chunksize=None,
ncov_max=float('inf')):
super(LaggedCovariance, self).__init__(chunksize=chunksize)
if (c0t or ctt) and lag == 0:
raise ValueError("lag must be positive if c0t=True or ctt=True")
if is_float_vector(weights):
weights = ensure_float_vector(weights)
if remove_constant_mean is not None and remove_data_mean:
raise ValueError(
'Subtracting the data mean and a constant vector simultaneously is not supported.'
)
if remove_constant_mean is not None:
remove_constant_mean = ensure_float_vector(remove_constant_mean)
self.set_params(c00=c00,
c0t=c0t,
ctt=ctt,
remove_constant_mean=remove_constant_mean,
remove_data_mean=remove_data_mean,
reversible=reversible,
sparse_mode=sparse_mode,
modify_data=modify_data,
lag=lag,
bessel=bessel,
weights=weights,
stride=stride,
skip=skip,
ncov_max=ncov_max)
self._rc = None
self._used_data = 0
