docstring fix

bamt/core/node_models/continuous_distribution.py

@@ -31,12 +31,10 @@ class ContinuousDistribution:
     however, any custom continuous distribution can be used, as long as it implements
     `scipy.stats` interface.
     Example Usage:
-    ```python
-    data = np.random.normal(0, 1, 1000)
-    dist = ContinuousDistribution()
-    dist.fit(data, distributions_pool=DistributionPool.SMALL)
-    samples = dist.sample(10)
-    ```
+    >>> data = np.random.normal(0, 1, 1000)
+    >>> dist = ContinuousDistribution()
+    >>> dist.fit(data, distributions_pool=DistributionPool.SMALL)
+    >>> samples = dist.sample(10)
     """
 
     SMALL_POOL: Tuple[Type[stats.rv_continuous], ...] = (

bamt/core/node_models/empirical_distribution.py

@@ -10,15 +10,14 @@ class EmpiricalDistribution(Distribution):
     This class fits an empirical distribution to the provided categorical or discrete data by calculating
     the probabilities of unique values and allows sampling from it.
     Usage example:
-    ```python
-    data = ['apple', 'banana', 'apple', 'orange', 'banana', 'banana', 'orange', 'apple']
-    emp_dist = EmpiricalDistribution()
-    emp_dist.fit(data)
-    print(emp_dist)
-    samples = emp_dist.sample(10)
-    print(samples)
-    print(emp_dist.pmf('banana'))
-    ```
+    >>> data = ['apple', 'banana', 'apple', 'orange', 'banana', 'banana', 'orange', 'apple']
+    >>> emp_dist = EmpiricalDistribution()
+    >>> emp_dist.fit(data)
+    >>> print(emp_dist)
+    >>> samples = emp_dist.sample(10)
+    >>> print(samples)
+    >>> print(emp_dist.pmf('banana'))
+
     """
 
     def __init__(self) -> None:

bamt/core/nodes/root_nodes/continuous_node.py

@@ -11,16 +11,15 @@ class ContinuousNode(RootNode):
     These distributions are wrapped in the `ContinuousDistribution` class.
     Example Usage:
 
-    ```python
-        data = np.random.normal(0, 1, 1000)
-        dist = ContinuousDistribution()
-        node = ContinuousNode(distribution=dist)
-        node.fit(data)
-        print(node)
-        samples = node.sample(10)
-        print(samples)
-        print(node.get_parents())
-    ```
+
+    >>> data = np.random.normal(0, 1, 1000)
+    >>> dist = ContinuousDistribution()
+    >>> node = ContinuousNode(distribution=dist)
+    >>> node.fit(data)
+    >>> print(node)
+    >>> samples = node.sample(10)
+    >>> print(samples)
+    >>> print(node.get_parents())
     """
 
     def __init__(self, distribution: Optional[ContinuousDistribution] = None):