docs: Visitor pattern example

.gitignore

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 dist/
 *.egg-info
 *.whl
+__pycache__/
 
 # C artifacts
 *.a

examples/visitor/README.md

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+# Visitor Pattern Example
+This example showcases how a visitor pattern can be built to use the MAL parser.
+
+# Running
+1. Install [tree-sitter-cli](https://github.com/tree-sitter/tree-sitter/tree/master/cli)
+2. Build the language in the project root folder using `tree-sitter build`
+4. Navigate to `./examples/visitor/`
+  1. Create a virtual environment `python -m venv venv`
+  2. Activate the environment (e.g. `source ./venv/bin/activate`)
+  3. Install dependencies `pip install -r requirements.txt`
+6. Build the python dependency using `pip install -e ../..` (Path should direct to project root)
+7. (Optional) swap between fast or simple visitor patterns by editing `main.py` imports
+8. Execute the main file `python main.py`

examples/visitor/distributions.py

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+class DistributionsException(Exception):
+    def __init__(self, error_message):
+        self._error_message = error_message
+        super().__init__(self._error_message)
+
+class Distributions:
+    def validate(distribution_name: str, params: list) -> None:
+        match distribution_name:
+            case 'Bernoulli':
+                Bernoulli.validate(params)
+            case 'Binomial':
+                Binomial.validate(params)
+            case 'Exponential':
+                Exponential.validate(params)
+            case 'Gamma':
+                Gamma.validate(params)
+            case 'LogNormal':
+                LogNormal.validate(params)
+            case 'Pareto':
+                Pareto.validate(params)
+            case 'TruncatedNormal':
+                TruncatedNormal.validate(params)
+            case 'Uniform':
+                Uniform.validate(params)
+            case 'Enabled' | 'Disabled' | 'Zero' | 'Infinity' | 'EasyAndCertain' | \
+                'EasyAndUncertain' | 'HardAndCertain' | 'HardAndUncertain' | \
+                'VeryHardAndCertain' | 'VeryHardAndUncertain':
+                Combination.validate(params)
+            case _:
+                err_msg = f"Distribution {distribution_name} is not supported"
+                raise(DistributionsException(err_msg))
+
+class Bernoulli:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=1):
+            err_msg = "Expected exactly one parameter (probability), for Bernoulli distribution" 
+            raise(DistributionsException(err_msg))
+        if not 0<=params[0]<=1:
+            err_msg = f"{params[0]} is not in valid range '0 <= probability <= 1', for Bernoulli distribution"
+            raise(DistributionsException(err_msg))
+
+class Binomial:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=2):
+            err_msg = "Expected exactly two parameters (trials, probability), for Binomial distribution" 
+            raise(DistributionsException(err_msg))
+        if not 0<=params[1]<=1:
+            err_msg = f"{params[1]} is not in valid range '0 <= probability <= 1', for Binomial distribution"
+            raise(DistributionsException(err_msg))
+
+class Exponential:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=1):
+            err_msg = "Expected exactly one parameter (lambda), for Exponential distribution"
+            raise(DistributionsException(err_msg))
+        if params[0]<=0:
+            err_msg = f"{params[0]} is not in valid range 'lambda > 0', for Exponential distribution"
+            raise(DistributionsException(err_msg))
+
+class Gamma:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=2):
+            err_msg = "Expected exactly two parameters (shape, scale), for Gamma distribution"
+            raise(DistributionsException(err_msg))
+        if params[0]<=0:
+            err_msg = f"{params[0]} is not in valid range 'shape > 0', for Gamma distribution"
+            raise(DistributionsException(err_msg))
+        if params[1]<=0:
+            err_msg = f"{params[1]} is not in valid range 'scale > 0', for Gamma distribution"
+            raise(DistributionsException(err_msg))
+
+class LogNormal:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=2):
+            err_msg = "Expected exactly two parameters (mean, standardDeviation), for LogNormal distribution"
+            raise(DistributionsException(err_msg))
+        if params[1]<=0:
+            err_msg = f"{params[1]} is not in valid range 'standardDeviation > 0', for LogNormal distribution"
+            raise(DistributionsException(err_msg))
+
+class Pareto:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=2):
+            err_msg = "Expected exactly two parameters (min, shape), for Pareto distribution"
+            raise(DistributionsException(err_msg))
+        if params[0]<=0:
+            err_msg = f"{params[0]} is not in valid range 'min > 0', for Pareto distribution"
+            raise(DistributionsException(err_msg))
+        if params[1]<=0:
+            err_msg = f"{params[1]} is not in valid range 'shape > 0', for Pareto distribution"
+            raise(DistributionsException(err_msg))
+
+class TruncatedNormal:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=2):
+            err_msg = "Expected exactly two parameters (mean, standardDeviation), for TruncatedNormal distribution" 
+            raise(DistributionsException(err_msg))
+        if params[1]<=0:
+            err_msg = f"{params[1]} is not in valid range 'standardDeviation > 0', for TruncatedNormal distribution"
+            raise(DistributionsException(err_msg))
+
+class Uniform:
+    def validate(params: list) -> None:
+        if (not params or len(params)!=2):
+            err_msg = "Expected exactly two parameters (min, max), for Uniform distribution" 
+            raise(DistributionsException(err_msg))
+        if params[0] > params[1]:
+            err_msg = f"({params[0]}, {params[1]}) does not meet requirement 'min <= max', for Uniform distribution"
+            raise(DistributionsException(err_msg))
+
+class Combination:
+    def validate(params: list) -> None:
+        if (params and len(params)!=0):
+            err_msg = "Expected exactly zero parameters, for combination distributions" 
+            raise(DistributionsException(err_msg))