prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Concatenate/consolidate all algorithms with different implementations
  ### Feature description
  
  There are lots of algorithms with the same concept but different implementations/methods in different files. All these should be moved into one file
  
  </issue>
  <code>
  [start of dynamic_programming/longest_sub_array.py]
  1 """
  2 Author  : Yvonne
  3 
  4 This is a pure Python implementation of Dynamic Programming solution to the
  5     longest_sub_array problem.
  6 
  7 The problem is  :
  8 Given an array, to find the longest and continuous sub array and get the max sum of the
  9     sub array in the given array.
  10 """
  11 
  12 
  13 class SubArray:
  14     def __init__(self, arr):
  15         # we need a list not a string, so do something to change the type
  16         self.array = arr.split(",")
  17 
  18     def solve_sub_array(self):
  19         rear = [int(self.array[0])] * len(self.array)
  20         sum_value = [int(self.array[0])] * len(self.array)
  21         for i in range(1, len(self.array)):
  22             sum_value[i] = max(
  23                 int(self.array[i]) + sum_value[i - 1], int(self.array[i])
  24             )
  25             rear[i] = max(sum_value[i], rear[i - 1])
  26         return rear[len(self.array) - 1]
  27 
  28 
  29 if __name__ == "__main__":
  30     whole_array = input("please input some numbers:")
  31     array = SubArray(whole_array)
  32     re = array.solve_sub_array()
  33     print(("the results is:", re))
  34
  [end of dynamic_programming/longest_sub_array.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/dynamic_programming/longest_sub_array.py b/dynamic_programming/longest_sub_array.py
  deleted file mode 100644
  --- a/dynamic_programming/longest_sub_array.py
  +++ /dev/null
  @@ -1,33 +0,0 @@
  -"""
  -Author  : Yvonne
  -
  -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.
  -"""
  -
  -
  -class SubArray:
  -    def __init__(self, arr):
  -        # we need a list not a string, so do something to change the type
  -        self.array = arr.split(",")
  -
  -    def solve_sub_array(self):
  -        rear = [int(self.array[0])] * len(self.array)
  -        sum_value = [int(self.array[0])] * len(self.array)
  -        for i in range(1, len(self.array)):
  -            sum_value[i] = max(
  -                int(self.array[i]) + sum_value[i - 1], int(self.array[i])
  -            )
  -            rear[i] = max(sum_value[i], rear[i - 1])
  -        return rear[len(self.array) - 1]
  -
  -
  -if __name__ == "__main__":
  -    whole_array = input("please input some numbers:")
  -    array = SubArray(whole_array)
  -    re = array.solve_sub_array()
  -    print(("the results is:", re))
  
verification_info: {"golden_diff": "diff --git a/dynamic_programming/longest_sub_array.py b/dynamic_programming/longest_sub_array.py\ndeleted file mode 100644\n--- a/dynamic_programming/longest_sub_array.py\n+++ /dev/null\n@@ -1,33 +0,0 @@\n-\"\"\"\n-Author  : Yvonne\n-\n-This is a pure Python implementation of Dynamic Programming solution to the\n-    longest_sub_array problem.\n-\n-The problem is  :\n-Given an array, to find the longest and continuous sub array and get the max sum of the\n-    sub array in the given array.\n-\"\"\"\n-\n-\n-class SubArray:\n-    def __init__(self, arr):\n-        # we need a list not a string, so do something to change the type\n-        self.array = arr.split(\",\")\n-\n-    def solve_sub_array(self):\n-        rear = [int(self.array[0])] * len(self.array)\n-        sum_value = [int(self.array[0])] * len(self.array)\n-        for i in range(1, len(self.array)):\n-            sum_value[i] = max(\n-                int(self.array[i]) + sum_value[i - 1], int(self.array[i])\n-            )\n-            rear[i] = max(sum_value[i], rear[i - 1])\n-        return rear[len(self.array) - 1]\n-\n-\n-if __name__ == \"__main__\":\n-    whole_array = input(\"please input some numbers:\")\n-    array = SubArray(whole_array)\n-    re = array.solve_sub_array()\n-    print((\"the results is:\", re))\n", "issue": "Concatenate/consolidate all algorithms with different implementations\n### Feature description\n\nThere are lots of algorithms with the same concept but different implementations/methods in different files. All these should be moved into one file\n", "before_files": [{"content": "\"\"\"\nAuthor  : Yvonne\n\nThis is a pure Python implementation of Dynamic Programming solution to the\n    longest_sub_array problem.\n\nThe problem is  :\nGiven an array, to find the longest and continuous sub array and get the max sum of the\n    sub array in the given array.\n\"\"\"\n\n\nclass SubArray:\n    def __init__(self, arr):\n        # we need a list not a string, so do something to change the type\n        self.array = arr.split(\",\")\n\n    def solve_sub_array(self):\n        rear = [int(self.array[0])] * len(self.array)\n        sum_value = [int(self.array[0])] * len(self.array)\n        for i in range(1, len(self.array)):\n            sum_value[i] = max(\n                int(self.array[i]) + sum_value[i - 1], int(self.array[i])\n            )\n            rear[i] = max(sum_value[i], rear[i - 1])\n        return rear[len(self.array) - 1]\n\n\nif __name__ == \"__main__\":\n    whole_array = input(\"please input some numbers:\")\n    array = SubArray(whole_array)\n    re = array.solve_sub_array()\n    print((\"the results is:\", re))\n", "path": "dynamic_programming/longest_sub_array.py"}]}