Welcome to Ex-Fuzzy’s documentation!

Ex-Fuzzy is a library to perform fuzzy logic inference using fuzzy logic, with a special focus on the explainable features of approximate reasoning.

Check out the Getting Started section for a few examples. If you want a quick overview, go to main_iris_demo.py in the demos folder. Other demos showcase different features of the library.

• Getting Started
• Creating fuzzy sets and fuzzy variables
  • Fuzzy Sets
  • Fuzzy Variables
• Using Fuzzy Rules
  • Fuzzy Rules
  • Rule Bases
  • Classification problems and Master Rule Bases
• Optimizing a Fuzzy rule base for a classification problem
  • Fuzzy rule based classifier
  • Training a fuzzy rule based classifier
  • Evaluation
• Visualize rules and results
  • Visualize Rules
  • Visualize Fuzzy Sets
• Computing fuzzy partitions
  • About the precomputed partitions
• Genetic algorithm details
  • Limitations of the optimization process
  • Fitness function
• General Type 2
• Temporal Fuzzy Sets
• Extending Ex-Fuzzy
• Persistence
• Advanced classifiers
  • Support, Confidence and lift
• Bootstrapping and rule robustness
  • Variability in the rulebases due to the genetic optimization
  • Bootstrapping
• API
  • Fuzzy Sets Functions
    • FS
      • FS.membership()
      • FS.shape()
      • FS.type()
    • GT2
      • GT2.alpha_reduction()
      • GT2.membership()
      • GT2.type()
    • IVFS
      • IVFS.membership()
      • IVFS.type()
    • categoricalFS
      • categoricalFS.membership()
      • categoricalFS.shape()
      • categoricalFS.type()
    • categoricalIVFS
      • categoricalIVFS.membership()
      • categoricalIVFS.shape()
      • categoricalIVFS.type()
    • fuzzyVariable
      • fuzzyVariable.append()
      • fuzzyVariable.compute_memberships()
      • fuzzyVariable.domain()
      • fuzzyVariable.fuzzy_type()
      • fuzzyVariable.get_linguistic_variables()
      • fuzzyVariable.linguistic_variable_names()
    • gaussianFS
      • gaussianFS.membership()
      • gaussianFS.shape()
      • gaussianFS.type()
    • gaussianIVFS
      • gaussianIVFS.membership()
      • gaussianIVFS.shape()
      • gaussianIVFS.type()
    • trapezoidal_membership()
    • triangularFS
      • triangularFS.shape()
  • Fuzzy Rules Functions
    • MasterRuleBase
      • MasterRuleBase.add_rule()
      • MasterRuleBase.add_rule_base()
      • MasterRuleBase.compute_association_degrees()
      • MasterRuleBase.compute_firing_strenghts()
      • MasterRuleBase.explainable_predict()
      • MasterRuleBase.fuzzy_type()
      • MasterRuleBase.get_antecedents()
      • MasterRuleBase.get_consequents()
      • MasterRuleBase.get_consequents_names()
      • MasterRuleBase.get_rulebase_matrix()
      • MasterRuleBase.get_rulebases()
      • MasterRuleBase.get_rules()
      • MasterRuleBase.get_scores()
      • MasterRuleBase.get_weights()
      • MasterRuleBase.n_linguistic_variables()
      • MasterRuleBase.predict()
      • MasterRuleBase.print_rule_bootstrap_results()
      • MasterRuleBase.print_rules()
      • MasterRuleBase.purge_rules()
      • MasterRuleBase.rename_cons()
      • MasterRuleBase.winning_rule_predict()
    • Rule
      • Rule.consequent_centroid()
      • Rule.membership()
    • RuleBase
      • RuleBase.add_rule()
      • RuleBase.add_rules()
      • RuleBase.compute_antecedents_memberships()
      • RuleBase.compute_rule_antecedent_memberships()
      • RuleBase.forward()
      • RuleBase.fuzzy_type()
      • RuleBase.get_rulebase_matrix()
      • RuleBase.get_rules()
      • RuleBase.get_scores()
      • RuleBase.get_weights()
      • RuleBase.inference()
      • RuleBase.n_linguistic_variables()
      • RuleBase.print_rule_bootstrap_results()
      • RuleBase.print_rules()
      • RuleBase.prune_bad_rules()
      • RuleBase.remove_rule()
      • RuleBase.remove_rules()
      • RuleBase.scores()
    • RuleBaseGT2
      • RuleBaseGT2.alpha_compute_rule_antecedent_memberships()
      • RuleBaseGT2.compute_rule_antecedent_memberships()
      • RuleBaseGT2.forward()
      • RuleBaseGT2.fuzzy_type()
      • RuleBaseGT2.inference()
    • RuleBaseT1
      • RuleBaseT1.forward()
      • RuleBaseT1.fuzzy_type()
      • RuleBaseT1.inference()
    • RuleBaseT2
      • RuleBaseT2.forward()
      • RuleBaseT2.fuzzy_type()
      • RuleBaseT2.inference()
    • RuleError
    • RuleSimple
    • compute_antecedents_memberships()
    • construct_rule_base()
    • generate_rule_string()
    • list_rules_to_matrix()
  • Rule Evaluation Functions
    • evalRuleBase
      • evalRuleBase.add_auxiliary_rule_weights()
      • evalRuleBase.add_classification_metrics()
      • evalRuleBase.add_full_evaluation()
      • evalRuleBase.add_rule_weights()
      • evalRuleBase.association_degree()
      • evalRuleBase.aux_dominance_scores()
      • evalRuleBase.bootstrap_confidence_confinterval()
      • evalRuleBase.bootstrap_confidence_rules()
      • evalRuleBase.bootstrap_support_confinterval()
      • evalRuleBase.bootstrap_support_rules()
      • evalRuleBase.classification_eval()
      • evalRuleBase.compute_antecedent_pattern_support()
      • evalRuleBase.compute_aux_pattern_confidence()
      • evalRuleBase.compute_aux_pattern_support()
      • evalRuleBase.compute_pattern_confidence()
      • evalRuleBase.compute_pattern_support()
      • evalRuleBase.dominance_scores()
      • evalRuleBase.effective_rulesize_eval()
      • evalRuleBase.p_permutation_classifier_validation()
      • evalRuleBase.size_antecedents_eval()
  • Classification evaluation tools
    • FuzzyEvaluator
      • FuzzyEvaluator.eval_fuzzy_model()
      • FuzzyEvaluator.get_metric()
    • eval_fuzzy_model()
  • Rule Visualization
    • choose_popular_rules()
    • connect_master_rulebase()
    • connect_rulebase()
    • create_graph_connection()
    • filter_useless_columns()
    • matrix_rule_base_form()
    • plot_fuzzy_variable()
    • rules_to_latex()
    • visualize_rulebase()
  • Evolutionary Algorithms to Fit the rules
    • BaseFuzzyRulesClassifier
      • BaseFuzzyRulesClassifier.customized_loss()
      • BaseFuzzyRulesClassifier.explainable_predict()
      • BaseFuzzyRulesClassifier.fit()
      • BaseFuzzyRulesClassifier.forward()
      • BaseFuzzyRulesClassifier.get_rulebase()
      • BaseFuzzyRulesClassifier.load_master_rule_base()
      • BaseFuzzyRulesClassifier.p_value_validation()
      • BaseFuzzyRulesClassifier.plot_fuzzy_variables()
      • BaseFuzzyRulesClassifier.predict()
      • BaseFuzzyRulesClassifier.predict_proba()
      • BaseFuzzyRulesClassifier.print_rule_bootstrap_results()
      • BaseFuzzyRulesClassifier.print_rules()
      • BaseFuzzyRulesClassifier.rename_fuzzy_variables()
      • BaseFuzzyRulesClassifier.reparametrice_loss()
    • ExploreRuleBases
      • ExploreRuleBases.fitness_func()
    • FitRuleBase
      • FitRuleBase.encode_rulebase()
      • FitRuleBase.fitness_func()
  • Rule Mining methods
    • generate_rules_from_itemsets()
    • mine_rulebase_support()
    • multiclass_mine_rulebase()
    • prune_rules_confidence_lift()
    • rule_search()
    • simple_mine_rulebase()
    • simple_multiclass_mine_rulebase()
  • Advanced Classifiers
    • FuzzyRulesClassifier
      • FuzzyRulesClassifier.fit()
      • FuzzyRulesClassifier.predict()
    • RuleFineTuneClassifier
      • RuleFineTuneClassifier.fit()
      • RuleFineTuneClassifier.predict()
    • RuleMineClassifier
      • RuleMineClassifier.fit()
      • RuleMineClassifier.predict()
  • Classification persistence
    • load_fuzzy_rules()
    • load_fuzzy_variables()
    • print_fuzzy_variable()
    • save_fuzzy_variables()
  • Fuzzy Cognitive Maps
    • attractors_report()
    • look_pattern_states()
    • study_attractors_FCM()
  • Partition utils
    • assign_time()
    • classify_temp()
    • compute_quantiles()
    • construct_conditional_frequencies()
    • construct_crisp_categorical_partition()
    • construct_partitions()
    • create_multi_tempVariables()
    • create_tempVariables()
    • extend_fuzzy_sets_enum()
    • fixed_quantile_compute()
    • gt2_fuzzy_partitions_dataset()
    • mcc_loss()
    • partition3_quantile_compute()
    • quartile_compute()
    • t1_fuzzy_partitions_dataset()
    • t1_n_gaussian_partition_parameters()
    • t1_n_partition_parameters()
    • t1_simple_gaussian_partition()
    • t1_simple_partition()
    • t1_simple_triangular_partition()
    • t1_simple_triangular_partition_parameters()
    • t1_three_partition()
    • t2_fuzzy_partitions_dataset()
    • t2_n_partition_parameters()
    • temporal_assemble()
    • temporal_cuts()
  • T2 Centroid compute
    • center_of_masses()
    • compute_centroid_iv()
    • compute_centroid_t2_l()
    • compute_centroid_t2_r()
    • consequent_centroid()
    • consequent_centroid_l()
    • consequent_centroid_r()
  • Temporal fuzzy sets
    • NEW_FUZZY_SETS
    • TMP_FUZZY_SETS
    • TemporalFuzzyRulesClassifier
      • TemporalFuzzyRulesClassifier.fit()
      • TemporalFuzzyRulesClassifier.forward()
      • TemporalFuzzyRulesClassifier.get_rulebase()
      • TemporalFuzzyRulesClassifier.plot_fuzzy_variables()
    • eval_temporal_fuzzy_model()
    • temporalFS
      • temporalFS.fix_time()
      • temporalFS.inside_type()
      • temporalFS.membership()
      • temporalFS.type()
    • temporalFuzzyVariable
      • temporalFuzzyVariable.compute_memberships()
      • temporalFuzzyVariable.fix_time()
      • temporalFuzzyVariable.n_time_moments()
    • temporalMasterRuleBase
      • temporalMasterRuleBase.add_rule()
      • temporalMasterRuleBase.add_rule_base()
      • temporalMasterRuleBase.compute_firing_strenghts()
      • temporalMasterRuleBase.fuzzy_type()
      • temporalMasterRuleBase.get_consequents()
      • temporalMasterRuleBase.get_rulebase_matrix()
      • temporalMasterRuleBase.get_rulebases()
      • temporalMasterRuleBase.get_rules()
      • temporalMasterRuleBase.get_scores()
      • temporalMasterRuleBase.print_rules()
      • temporalMasterRuleBase.purge_rules()
      • temporalMasterRuleBase.winning_rule_predict()
  • Pattern stability analysis

Indices and tables

• Index

• Module Index

• Search Page