Feature for ML

[faux66]

1. Mathematical Foundation Expansion

• Implemented 8 continuous probability distributions: Normal, Exponential, Weibull, Gamma, LogNormal, Student's t, Beta, and Uniform

2. Update GenericCriterion

• Implemented Dynamic Parameter Injection: The adapter now automatically maps distribution fitting results from scipy.stats (e.g., shape, mu) to specific engine attributes required by pysatl-criterion (e.g., a, loc, df) using a comprehensive alias map.
• Introduced Signature Introspection: Using the inspect module, the system now determines if a statistic requires cdf_vals. This allows for Lazy Evaluation, calculating the theoretical CDF only when explicitly requested by the test engine

3. Automated Discovery (DynamicCriterionSelector)

• Developed selection mechanism that dynamically scans the pysatl-criterion library at runtime. This eliminates manual class mapping.

4. Feature for ML (FeatureVector)

• Developed selection mechanism that dynamically scans the pysatl-criterion library at runtime. This eliminates manual class mapping and allows the system to automatically support new statistical tests as they are added to the external library.
• Implemented Missing Value Encoding: If a test is mathematically inapplicable (e.g., negative values for Exponential) or fails, it is assigned a value of -1.0.

[f1i3g3]

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