Add weather temporal classifier example using ML

[Brqndon1]

Summary

Adds a new example demonstrating the TemporalLogicIntegratedClassifier using a weather-monitoring scenario.

Scenario

A neural classifier observes sky conditions (sunny / cloudy / rainy) from three simulated features: cloud coverage rate, humidity, and precipitation rate. Over multiple timesteps, temporal rules derive a storm warning and ultimately a cancel_voyage conclusion when rainy conditions persist.

Rules

• storm_warning(sky) <-1 rainy(sky) — warning derived one step after rain
• cancel_voyage(sky) <-1 rainy(sky), storm_warning(sky) — terminal state

Screenshots

Notes

• Observed duplicate-fact-name warnings on repeated classifier calls
• Used sys.setrecursionlimit(10000) - used to avoid a numba compile-time recursion limit on the environment

[ColtonPayne]

Hi Brandon - this looks good, but needs a few changes:

1. Please close this PR and make a new one from the main PyReason Repo, rather than your own fork. This way I can run the code on your branch without cloning your fork.
2. The tutorial in the docs is missing, please add this as well

I will add more comments after these changes so I can run your code

[ColtonPayne]

Also, please make your branch names more concise in the future