Update probability_of_n_heads_in_m_tossing.py

Author: dipuk0506

maths/probability_of_n_heads_in_m_tossing.py

@@ -1,43 +1,39 @@
-"""
-The probability of getting exactly n heads in m tossing.
 
-Algorithm Explanation:
-If you toss 0 time -> 0 head
-Distribution [1] -> Meaning: 1 in the 0-index
 
-If you toss 1 time -> 0 or 1 head
-Distribution [0.5 0.5] -> Meaning: 0.5 in both indexes
+import numpy as np
 
-If you toss 2 times -> 0 to 2 heads
-Distribution [0.25 0.5 0.25] ->
-Meaning:  probability of n heads from the distribution
-{HH, HT, TH, TT}
 
-If you toss 3 times -> 0 to 3 heads
-Distribution [0.125 0.375 0.375 0.125] ->
-Meaning:  probability of n heads from the distribution
-{HHH, HHT, HTH, HTT, THH, THT, TTH, TTT}
+def probability_of_n_heads_in_m_tossing(head_count: int, toss_count: int) -> int:
+    """
+   The probability of getting exactly n heads in m tossing.
 
-Therefore,
-Probability_distribution(N+1) =
-      [Probability_distribution(N) 0]/2 + [0 Probability_distribution(N)]/2
+    Algorithm Explanation:
+    If you toss 0 time -> 0 head
+    Distribution [1] -> Meaning: 1 in the 0-index
 
-I used that method in my paper
-Titled: Uncertainty-aware Decisions in Cloud Computing:
-Foundations and Future Directions
-Journal: ACM Computing Surveys (CSUR)
-"""
+    If you toss 1 time -> 0 or 1 head
+    Distribution [0.5 0.5] -> Meaning: 0.5 in both indexes
 
-import numpy as np
+    If you toss 2 times -> 0 to 2 heads
+    Distribution [0.25 0.5 0.25] ->
+    Meaning:  probability of n heads from the distribution
+    {HH, HT, TH, TT}
 
+    If you toss 3 times -> 0 to 3 heads
+    Distribution [0.125 0.375 0.375 0.125] ->
+    Meaning:  probability of n heads from the distribution
+    {HHH, HHT, HTH, HTT, THH, THT, TTH, TTT}
 
-def probability_of_n_heads_in_m_tossing(head_count: int, toss_count: int) -> int:
-    """
-    Calculate the factorial of specified number (n!).
+    Therefore,
+    Probability_distribution(N+1) =
+          [Probability_distribution(N) 0]/2 + [0 Probability_distribution(N)]/2
+
+    I used that method in my paper
+    Titled: Uncertainty-aware Decisions in Cloud Computing:
+    Foundations and Future Directions
+    Journal: ACM Computing Surveys (CSUR)
 
-    >>> import math
-    >>> all(factorial(i) == math.factorial(i) for i in range(20))
-    True
+    >>> import numpy as np
     >>> Probability_of_n_heads_in_m_tossing(.2,.5)
     Traceback (most recent call last):
         ...