translation: Add Python and Java code for EN version (#1345)

* Add the intial translation of code of all the languages

* test

* revert

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* Add Python and Java code for EN version

en/codes/python/chapter_computational_complexity/time_complexity.py

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+"""
+File: time_complexity.py
+Created Time: 2022-11-25
+Author: krahets (krahets@163.com)
+"""
+
+
+def constant(n: int) -> int:
+    """Constant complexity"""
+    count = 0
+    size = 100000
+    for _ in range(size):
+        count += 1
+    return count
+
+
+def linear(n: int) -> int:
+    """Linear complexity"""
+    count = 0
+    for _ in range(n):
+        count += 1
+    return count
+
+
+def array_traversal(nums: list[int]) -> int:
+    """Linear complexity (traversing an array)"""
+    count = 0
+    # Loop count is proportional to the length of the array
+    for num in nums:
+        count += 1
+    return count
+
+
+def quadratic(n: int) -> int:
+    """Quadratic complexity"""
+    count = 0
+    # Loop count is squared in relation to the data size n
+    for i in range(n):
+        for j in range(n):
+            count += 1
+    return count
+
+
+def bubble_sort(nums: list[int]) -> int:
+    """Quadratic complexity (bubble sort)"""
+    count = 0  # Counter
+    # Outer loop: unsorted range is [0, i]
+    for i in range(len(nums) - 1, 0, -1):
+        # Inner loop: swap the largest element in the unsorted range [0, i] to the right end of the range
+        for j in range(i):
+            if nums[j] > nums[j + 1]:
+                # Swap nums[j] and nums[j + 1]
+                tmp: int = nums[j]
+                nums[j] = nums[j + 1]
+                nums[j + 1] = tmp
+                count += 3  # Element swap includes 3 individual operations
+    return count
+
+
+def exponential(n: int) -> int:
+    """Exponential complexity (loop implementation)"""
+    count = 0
+    base = 1
+    # Cells split into two every round, forming the sequence 1, 2, 4, 8, ..., 2^(n-1)
+    for _ in range(n):
+        for _ in range(base):
+            count += 1
+        base *= 2
+    # count = 1 + 2 + 4 + 8 + .. + 2^(n-1) = 2^n - 1
+    return count
+
+
+def exp_recur(n: int) -> int:
+    """Exponential complexity (recursive implementation)"""
+    if n == 1:
+        return 1
+    return exp_recur(n - 1) + exp_recur(n - 1) + 1
+
+
+def logarithmic(n: int) -> int:
+    """Logarithmic complexity (loop implementation)"""
+    count = 0
+    while n > 1:
+        n = n / 2
+        count += 1
+    return count
+
+
+def log_recur(n: int) -> int:
+    """Logarithmic complexity (recursive implementation)"""
+    if n <= 1:
+        return 0
+    return log_recur(n / 2) + 1
+
+
+def linear_log_recur(n: int) -> int:
+    """Linear logarithmic complexity"""
+    if n <= 1:
+        return 1
+    count: int = linear_log_recur(n // 2) + linear_log_recur(n // 2)
+    for _ in range(n):
+        count += 1
+    return count
+
+
+def factorial_recur(n: int) -> int:
+    """Factorial complexity (recursive implementation)"""
+    if n == 0:
+        return 1
+    count = 0
+    # From 1 split into n
+    for _ in range(n):
+        count += factorial_recur(n - 1)
+    return count
+
+
+"""Driver Code"""
+if __name__ == "__main__":
+    # Can modify n to experience the trend of operation count changes under various complexities
+    n = 8
+    print("Input data size n =", n)
+
+    count: int = constant(n)
+    print("Constant complexity operation count =", count)
+
+    count: int = linear(n)
+    print("Linear complexity operation count =", count)
+    count: int = array_traversal([0] * n)
+    print("Linear complexity (traversing an array) operation count =", count)
+
+    count: int = quadratic(n)
+    print("Quadratic complexity operation count =", count)
+    nums = [i for i in range(n, 0, -1)]  # [n, n-1, ..., 2, 1]
+    count: int = bubble_sort(nums)
+    print("Quadratic complexity (bubble sort) operation count =", count)
+
+    count: int = exponential(n)
+    print("Exponential complexity (loop implementation) operation count =", count)
+    count: int = exp_recur(n)
+    print("Exponential complexity (recursive implementation) operation count =", count)
+
+    count: int = logarithmic(n)
+    print("Logarithmic complexity (loop implementation) operation count =", count)
+    count: int = log_recur(n)
+    print("Logarithmic complexity (recursive implementation) operation count =", count)
+
+    count: int = linear_log_recur(n)
+    print("Linear logarithmic complexity (recursive implementation) operation count =", count)
+
+    count: int = factorial_recur(n)
+    print("Factorial complexity (recursive implementation) operation count =", count)