Skip to content

approximate_probability_estimation.md

Latest commit

 

History

History
66 lines (51 loc) · 3.06 KB

approximate_probability_estimation.md

File metadata and controls

66 lines (51 loc) · 3.06 KB

the class for Approximate Probability Estimation：

class ApproximateProbabilityEstimation(variables, features, method) [source]
Methods for Approximate Probability Estimation class

Parameters:

  • variables - variables
  • features - features
  • method - The method used to calculate the approximate probability, the default value is 'relation'

This class currently provides the following methods:

  1. init_binary_feature_weight(value1,value2) [source]

    Function：Set initial value of binary feature weight
    Parameters:
    · value1 - binary feature initial weight value 1
    · value2 - binary feature initial weight value 2
    Returns：binary feature weight initial value
    Return type：dictionary

  2. labeling_conflict_with_ds(mass_functions) [source]

    Function：based on (D-S) theory Evidence support measurement
    Parameters：
    · mass_functions - Returns：Evidence support value
    Return type：float

  3. get_pos_prob_based_relation(var_id, weight) [source]

    Function：Calculate the proportion of positive instances in marked instances with a feature
    Parameters：
    · var_id - Target variable id
    · weight - feature weight
    Returns：Proportion of positive instances in marked instances with a feature
    Return type：float

  4. construct_mass_function_for_confict(uncertain_degree, pos_prob, neg_prob) [source]

    Function：Evidence support for calculating each feature connected to an unlabeled variable
    Parameters：
    · theta - Uncertainty of a feature
    Returns：MassFunction function
    Return type：function

  5. construct_mass_function_for_ER(alpha,tau,featureValue) [source]

    Function：Evidence support for calculating each feature connected to an unlabeled variable
    Parameters：
    · theta - Uncertainty of a feature
    · alpha - Parameters after unaryfactor linear regression · tau - Parameters after unaryfactor linear regression · featureValue - featurevalue Returns：MassFunction function
    Return type：function

  6. approximate_probability_estimation(variable_set) [source]

    Function：Calculate the approximate probability of the selected topm hidden variables, used to select topk, suitable for ER
    Parameters：
    · variable_set - Latent variable data set

  7. approximate_probability_estimation_by_custom(variable_set) [source]

    Function：Calculate the approximate probability of the selected topm hidden variables, used to select topk, user-defined calculation rules
    Parameters：
    · variable_set - Latent variable data set