def observe(self, data, random_indices=()):
"""
Summary
"""
data = pandas_frame2value(data, self.name)
if isinstance(data, RandomVariable):
self.dataset = data
self.has_random_dataset = True
else:
self._observed_value = coerce_to_dtype(data, is_observed=True)
self.has_observed_value = True
self._observed = True
def observe(self, data):
if isinstance(data, pd.DataFrame):
data = {var_name: pandas_frame2value(data, index=var_name) for var_name in data}
if isinstance(data, dict):
if all([isinstance(k, Variable) for k in data.keys()]):
data_dict = data
if all([isinstance(k, str) for k in data.keys()]):
data_dict = {self.get_variable(name): value for name, value in data.items()}
else:
raise ValueError("The input data should be either a dictionary of values or a pandas dataframe")
for var in data_dict:
if isinstance(var, RandomVariable):
var.observe(data_dict[var])
def observe(self, data):
"""
Method. It assigns an observed value to a RandomVariable.
Args:
data: torch.Tensor, numeric, or np.ndarray. Input observed data.
Returns:
None
"""
data = pandas_frame2value(data, self.name)
if isinstance(data, RandomVariable):
self.dataset = data
self.has_random_dataset = True
else:
self._observed_value = coerce_to_dtype(data, is_observed=True)
self.has_observed_value = True
self._observed = True