Set the Python file maximum line length to 88 characters (#2122)

* flake8 --max-line-length=88

* fixup! Format Python code with psf/black push

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

dynamic_programming/fibonacci.py

@@ -1,5 +1,6 @@
 """
-This is a pure Python implementation of Dynamic Programming solution to the fibonacci sequence problem.
+This is a pure Python implementation of Dynamic Programming solution to the fibonacci
+sequence problem.
 """
 
 

dynamic_programming/integer_partition.py

@@ -1,7 +1,8 @@
 """
-The number of partitions of a number n into at least k parts equals the number of partitions into exactly k parts
-plus the number of partitions into at least k-1 parts. Subtracting 1 from each part of a partition of n into k parts
-gives a partition of n-k into k parts. These two facts together are used for this algorithm.
+The number of partitions of a number n into at least k parts equals the number of
+partitions into exactly k parts plus the number of partitions into at least k-1 parts.
+Subtracting 1 from each part of a partition of n into k parts gives a partition of n-k
+into k parts. These two facts together are used for this algorithm.
 """
 
 

dynamic_programming/iterating_through_submasks.py

@@ -12,7 +12,8 @@ def list_of_submasks(mask: int) -> List[int]:
 
     """
     Args:
-        mask : number which shows mask ( always integer > 0, zero does not have any submasks )
+        mask : number which shows mask ( always integer > 0, zero does not have any
+            submasks )
 
     Returns:
         all_submasks : the list of submasks of mask (mask s is called submask of mask

dynamic_programming/knapsack.py

@@ -9,8 +9,8 @@ Note that only the integer weights 0-1 knapsack problem is solvable
 
 def MF_knapsack(i, wt, val, j):
     """
-    This code involves the concept of memory functions. Here we solve the subproblems which are needed
-    unlike the below example
+    This code involves the concept of memory functions. Here we solve the subproblems
+    which are needed unlike the below example
     F is a 2D array with -1s filled up
     """
     global F  # a global dp table for knapsack

dynamic_programming/longest_common_subsequence.py

@@ -1,6 +1,7 @@
 """
-LCS Problem Statement: Given two sequences, find the length of longest subsequence present in both of them.
-A subsequence is a sequence that appears in the same relative order, but not necessarily continuous.
+LCS Problem Statement: Given two sequences, find the length of longest subsequence
+present in both of them.  A subsequence is a sequence that appears in the same relative
+order, but not necessarily continuous.
 Example:"abc", "abg" are subsequences of "abcdefgh".
 """
 

dynamic_programming/longest_sub_array.py

@@ -1,10 +1,12 @@
 """
 Author  : Yvonne
 
-This is a pure Python implementation of Dynamic Programming solution to the longest_sub_array problem.
+This is a pure Python implementation of Dynamic Programming solution to the
+    longest_sub_array problem.
 
 The problem is  :
-Given an array, to find the longest and continuous sub array and get the max sum of the sub array in the given array.
+Given an array, to find the longest and continuous sub array and get the max sum of the
+    sub array in the given array.
 """
 
 

dynamic_programming/rod_cutting.py

@@ -13,8 +13,9 @@ pieces separately or not cutting it at all if the price of it is the maximum obt
 
 def naive_cut_rod_recursive(n: int, prices: list):
     """
-        Solves the rod-cutting problem via naively without using the benefit of dynamic programming.
-        The results is the same sub-problems are solved several times leading to an exponential runtime
+        Solves the rod-cutting problem via naively without using the benefit of dynamic
+        programming. The results is the same sub-problems are solved several times
+        leading to an exponential runtime
 
         Runtime: O(2^n)
 
@@ -26,7 +27,8 @@ def naive_cut_rod_recursive(n: int, prices: list):
 
         Returns
         -------
-        The maximum revenue obtainable for a rod of length n given the list of prices for each piece.
+        The maximum revenue obtainable for a rod of length n given the list of prices
+        for each piece.
 
         Examples
         --------
@@ -50,8 +52,9 @@ def naive_cut_rod_recursive(n: int, prices: list):
 
 def top_down_cut_rod(n: int, prices: list):
     """
-        Constructs a top-down dynamic programming solution for the rod-cutting problem
-        via memoization. This function serves as a wrapper for _top_down_cut_rod_recursive
+        Constructs a top-down dynamic programming solution for the rod-cutting
+        problem via memoization. This function serves as a wrapper for
+        _top_down_cut_rod_recursive
 
         Runtime: O(n^2)
 
@@ -63,12 +66,13 @@ def top_down_cut_rod(n: int, prices: list):
 
         Note
         ----
-        For convenience and because Python's lists using 0-indexing, length(max_rev) = n + 1,
-        to accommodate for the revenue obtainable from a rod of length 0.
+        For convenience and because Python's lists using 0-indexing, length(max_rev) =
+        n + 1, to accommodate for the revenue obtainable from a rod of length 0.
 
         Returns
         -------
-        The maximum revenue obtainable for a rod of length n given the list of prices for each piece.
+        The maximum revenue obtainable for a rod of length n given the list of prices
+        for each piece.
 
         Examples
         -------
@@ -99,7 +103,8 @@ def _top_down_cut_rod_recursive(n: int, prices: list, max_rev: list):
 
         Returns
         -------
-        The maximum revenue obtainable for a rod of length n given the list of prices for each piece.
+        The maximum revenue obtainable for a rod of length n given the list of prices
+        for each piece.
         """
     if max_rev[n] >= 0:
         return max_rev[n]
@@ -144,7 +149,8 @@ def bottom_up_cut_rod(n: int, prices: list):
         """
     _enforce_args(n, prices)
 
-    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of length 0.
+    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
+    # length 0.
     max_rev = [float("-inf") for _ in range(n + 1)]
     max_rev[0] = 0
 
@@ -167,7 +173,8 @@ def _enforce_args(n: int, prices: list):
 
         Throws ValueError:
 
-        if n is negative or there are fewer items in the price list than the length of the rod
+        if n is negative or there are fewer items in the price list than the length of
+        the rod
         """
     if n < 0:
         raise ValueError(f"n must be greater than or equal to 0. Got n = {n}")

dynamic_programming/subset_generation.py

@@ -1,4 +1,4 @@
-# Python program to print all subset combinations of n element in given set of r element.
+# Print all subset combinations of n element in given set of r element.
 
 
 def combination_util(arr, n, r, index, data, i):

dynamic_programming/sum_of_subset.py

@@ -9,7 +9,8 @@ def isSumSubset(arr, arrLen, requiredSum):
     # initially no subsets can be formed hence False/0
     subset = [[False for i in range(requiredSum + 1)] for i in range(arrLen + 1)]
 
-    # for each arr value, a sum of zero(0) can be formed by not taking any element hence True/1
+    # for each arr value, a sum of zero(0) can be formed by not taking any element
+    # hence True/1
     for i in range(arrLen + 1):
         subset[i][0] = True