variables=list() #The element type is: dict:variable
variable
{
User-provided attributes:
- 'var_id': Variable ID, int type, counting from 0
- 'is_easy': Whether this variable is a easy instance, the value is True or False
- 'is_evidence': Whether this variable is an evidence variable, the value is True or False
- 'label': Inferred labels: 0 is negative, 1 is positive, -1 is unknown
- 'true_label': The true label of this variable
- 'feature_set': All feature information owned by this variable
{
feature_id1: [theta1,feature_value1],
feature_id2: [theta2,feature_value2],
...
}
Attributes that may be automatically generated while the code is running
- 'inferenced_probability': Inferred probability
- 'probability': Inferred probability
- 'evidential_support': Evidence support
- 'entropy': Entropy
- 'approximate_weight':Approximate weight
- 'approximate_probability': Approximate probability
...
}
features = list() #The element type is: dict:feature
feature
{
User-provided attributes
- 'feature_id': The id of this feature, int type, counting from 0
- 'feature_type': Whether this feature is a single factor feature or a dual factor feature, currently supports both unary_feature and binary_feature
- 'feature_name': Feature name, optional
- 'alpha_bound':[bound0,bound1] alpha's Upper and lower bounds
- 'tau_bound':[bound0,bound1] tau's Upper and lower bounds
- 'parameterize':type of int , indicating whether the feature of this type is parameterized
- 'weight': Information about all relevant variables of this feature
{
var_id1: [weight_value1,feature_value1],
(varid3,varid4): [weight_value2,feature_value2],
...
}
Attributes that may be automatically generated while the code is running
- 'tau': tau value
- 'alpha':alpha value
- 'regerssion'： Linear regression results
}