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maths/probability_of_n_heads_in_m_tossing.py

@@ -1,58 +1,56 @@
-
-
 import numpy as np
 
 
 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.
+    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
+     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
+     If you toss 1 time -> 0 or 1 head
+     Distribution [0.5 0.5] -> Meaning: 0.5 in both indexes
 
-    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 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}
+     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}
 
-    Therefore,
-    Probability_distribution(N+1) =
-          [Probability_distribution(N) 0]/2 + [0 Probability_distribution(N)]/2
+     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)
+     I used that method in my paper
+     Titled: Uncertainty-aware Decisions in Cloud Computing:
+     Foundations and Future Directions
+     Journal: ACM Computing Surveys (CSUR)
 
-    >>> import numpy as np
-    >>> Probability_of_n_heads_in_m_tossing(.2,.5)
-    Traceback (most recent call last):
-        ...
-    ValueError: The function only accepts integer values
-    >>> Probability_of_n_heads_in_m_tossing(-1,5)
-    Traceback (most recent call last):
-        ...
-    ValueError: The function is not defined for negative values
-    >>> Probability_of_n_heads_in_m_tossing(3,2)
-    Traceback (most recent call last):
-        ...
-    ValueError: Head count should be smaller than toss count
+     >>> import numpy as np
+     >>> Probability_of_n_heads_in_m_tossing(.2,.5)
+     Traceback (most recent call last):
+         ...
+     ValueError: The function only accepts integer values
+     >>> Probability_of_n_heads_in_m_tossing(-1,5)
+     Traceback (most recent call last):
+         ...
+     ValueError: The function is not defined for negative values
+     >>> Probability_of_n_heads_in_m_tossing(3,2)
+     Traceback (most recent call last):
+         ...
+     ValueError: Head count should be smaller than toss count
 
-    >>> Probability_of_n_heads_in_m_tossing(1,1)
-    0.5
-    >>> Probability_of_n_heads_in_m_tossing(0,2)
-    0.25
-    >>> Probability_of_n_heads_in_m_tossing(2,3)
-    0.375
+     >>> Probability_of_n_heads_in_m_tossing(1,1)
+     0.5
+     >>> Probability_of_n_heads_in_m_tossing(0,2)
+     0.25
+     >>> Probability_of_n_heads_in_m_tossing(2,3)
+     0.375
     """
     if head_count != int(head_count) or toss_count != int(toss_count):
         raise ValueError("The function only accepts integer values")