Metadata-Version: 2.1
Name: quants
Version: 2.0.0
Summary: ANN based learning of quantifiers
Home-page: https://github.com/veltzerdoron/quants
Author: Veltzer Doron
Author-email: veltzerdoron@gmail.com
License: UNKNOWN
Description: 
        # quants package
        
        Package for Artifical Neural Network (**ANN**) based learning of quantifiers.
        
        It contains definition of scenes of elements and classes for both quantifier sampling and NN simple example package.
        
        
        ## Classes
        
        ### Quantifer classes
        These classes are a quantifier hierarchy with methods and definitions for:
        * *Scenes* that are composed of world *elements* pertaining to use of a sentence of the form "**q as are bs**".
        * Construction of quantifier if parameters (such as in the case of ExactlyN for instance) are required.
        * Static method for generation of a completely random scene.
        * Generation of permuted prototype scenes that are evaluated as true under the given quantifier q.
        * Evaluation of a given scene (returns **TRUE** for scenes generated by the same quantifier q in the previous method).
        
        ### Classifier class approach
        This approach assumes that quantifiers are learned as a group, essentially each quantifier q **TRUE** *scene* is a negative example for all other quantifiers q'.
        There is of course many scenes for which more than one quantifier is evaluated as **TRUE**, for instance if "Both students are eating" is **TRUE** then "Some students are eating" is also **TRUE**, this can be mitigated by implicatures which we do not address.
        
        The classifier is in effect a solver for which q makes the sentence "**q as are bs**" most likely given an input *scene* s.
        
        This enables us to use not only the quantifier quantify evaluation methods but the classifier in order to generate a teacher-student scheme.
        
        ### AE approach
        The AE approach tries to let an AE look at *scenes* where a given quantifier q was used by a teacher (language speaker), this is repeated many times till whatever structure typical to scenes **TRUE** under the quantifier q are encoded in the AE hidden values' represented structure. When learning is complete and when we are given a scene we use the AE as an anomaly detector to decide whether the scene is True under the quantifier q. The idea is that after seeing many q **TRUE** scenes a non q **TRUE** scene will have relatively high reconstruction errors.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
