updating markov documentation

markov/markov.py

@@ -19,12 +19,20 @@ states = np.array(['L', 'w', 'W'])
 
 # Possible sequences of events
 transitionName = np.array(
-    [['LL', 'Lw', 'LW'], ['wL', 'ww', 'wW'], ['WL', 'Ww', 'WW']]
+    [
+        ['LL', 'Lw', 'LW'],
+        ['wL', 'ww', 'wW'],
+        ['WL', 'Ww', 'WW'],
+    ]
 )
 
 # Probabilities Matrix (transition matrix)
 transitionMatrix = np.array(
-    [[0.6, 0.1, 0.3], [0.1, 0.7, 0.2], [0.2, 0.2, 0.6]]
+    [
+        [0.6, 0.1, 0.3],
+        [0.1, 0.7, 0.2],
+        [0.2, 0.2, 0.6],
+    ]
 )
 
 # Starting state - Given as a list of probabilities of starting
@@ -57,17 +65,41 @@ stat_dist = True
 
 # A class that implements the Markov chain to forecast the state/mood:
 class markov(object):
-    """simulates a markov chain given its states, current state and
+    """Simulates a markov chain given its states, current state and
     transition matrix.
 
-    Parameters:
-    states: 1-d array containing all the possible states
-    transitionName: 2-d array containing a list
-                   of the all possible state directions
-    transitionMatrix: 2-d array containing all
-                     the probabilites of moving to each state
-    currentState: a string indicating the starting state
-    steps: an integer determining how many steps (or times) to simulate"""
+    Attributes
+    ----------
+    change : str
+        Random choice from transitionName
+    currentState : str
+        Current state of the Markov Chain
+    prob : float
+        Step probability
+    stateList : list
+        A list that tracks how `currentState` evolves over time
+    states : np.array
+        An array containing the possible states of the chain
+    steps : int
+        See below
+    transitionMatrix : np.array
+        Transition matrix
+    transitionName : np.array
+        Step representation of the probailities of the transisition matrix
+
+    Parameters
+    ----------
+    states : np.array
+        See above
+    transitionName : np.array
+        See above
+    transitionMatrix : np.array
+        See above
+    currentState : str
+        See above
+    steps : int
+        How many steps to take
+    """
 
     def __init__(
         self,
@@ -77,9 +109,9 @@ class markov(object):
         currentState: str,
         steps: int,
     ):
+
         super(markov, self).__init__()
         self.states = states
-        self.list = list
         self.transitionName = transitionName
         self.transitionMatrix = transitionMatrix
         self.currentState = currentState
@@ -108,6 +140,13 @@ class markov(object):
 
     @functools.lru_cache(maxsize=128)
     def forecast(self):
+        """Simulate the Markov chain
+        
+        Returns
+        -------
+        np.array
+            Description
+        """
         print(f'Start state: {self.currentState}\n')
         # Shall store the sequence of states taken
         self.stateList = [self.currentState]
@@ -115,7 +154,6 @@ class markov(object):
         # To calculate the probability of the stateList
         self.prob = 1
         while i != self.steps:
-            import pdb; pdb.set_trace()  # breakpoint 24e62119 //
             if self.currentState == self.states[0]:
                 self.change = np.random.choice(
                     self.transitionName[0], replace=True, p=transitionMatrix[0]
@@ -265,7 +303,9 @@ def main(*args, **kwargs):
     if stat_dist:
         checker(initial_dist, 'initial distribution')
         print(
-            f'Stat dist is {markov.stationary_dist(transitionMatrix,initial_dist, stepTime)}'
+            f"""Stat dist is {
+                markov.stationary_dist(transitionMatrix,initial_dist, stepTime)
+            }"""
         )