Add brute force solution of Rain Terraces problem.

README.md

@@ -128,7 +128,7 @@ a set of rules that precisely define a sequence of operations.
   * `B` [Square Matrix Rotation](src/algorithms/uncategorized/square-matrix-rotation) - in-place algorithm
   * `B` [Jump Game](src/algorithms/uncategorized/jump-game) - backtracking, dynamic programming (top-down + bottom-up) and greedy examples 
   * `B` [Unique Paths](src/algorithms/uncategorized/unique-paths) - backtracking, dynamic programming and Pascal's Triangle based examples 
-  * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
+  * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem (dynamic programming and brute force versions)
   * `A` [N-Queens Problem](src/algorithms/uncategorized/n-queens)
   * `A` [Knight's Tour](src/algorithms/uncategorized/knight-tour)
 
@@ -140,6 +140,7 @@ algorithm is an abstraction higher than a computer program.
 
 * **Brute Force** - look at all the possibilities and selects the best solution
   * `B` [Linear Search](src/algorithms/search/linear-search)
+  * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
   * `A` [Maximum Subarray](src/algorithms/sets/maximum-subarray)
   * `A` [Travelling Salesman Problem](src/algorithms/graph/travelling-salesman) - shortest possible route that visits each city and returns to the origin city
 * **Greedy** - choose the best option at the current time, without any consideration for the future
@@ -164,6 +165,7 @@ algorithm is an abstraction higher than a computer program.
   * `B` [Fibonacci Number](src/algorithms/math/fibonacci)
   * `B` [Jump Game](src/algorithms/uncategorized/jump-game)
   * `B` [Unique Paths](src/algorithms/uncategorized/unique-paths)
+  * `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
   * `A` [Levenshtein Distance](src/algorithms/string/levenshtein-distance) - minimum edit distance between two sequences
   * `A` [Longest Common Subsequence](src/algorithms/sets/longest-common-subsequence) (LCS)
   * `A` [Longest Common Substring](src/algorithms/string/longest-common-substring)

src/algorithms/uncategorized/rain-terraces/README.md

@@ -62,16 +62,58 @@ be stored in every element of array. For example, consider the array
 `[3, 0, 0, 2, 0, 4]`, We can trap "3*2 units" of water between 3 an 2, "1 unit" 
 on top of bar 2 and "3 units" between 2 and 4. See below diagram also.
 
-A **simple solution** is to traverse every array element and find the highest 
+### Approach 1: Brute force
-bars on left and right sides. Take the smaller of two heights. The difference 
-between smaller height and height of current element is the amount of water 
-that can be stored in this array element. Time complexity of this solution 
-is `O(n2)`.
 
-An **efficient solution** is to pre-compute highest bar on left and right of 
+**Intuition**
-every bar in `O(n)` time. Then use these pre-computed values to find the 
+
-amount of water in every array element.
+For each element in the array, we find the maximum level of water it can trap 
+after the rain, which is equal to the minimum of maximum height of bars on both 
+the sides minus its own height.
+
+**Steps**
+
+- Initialize `answer = 0`
+- Iterate the array from left to right:
+  - Initialize `max_left = 0  and `max_right = 0`
+  - Iterate from the current element to the beginning of array updating: `max_left = max(max_left, height[j])`
+  - Iterate from the current element to the end of array updating: `max_right = max(max_right, height[j])`
+  - Add `min(max_left, max_right) − height[i]` to `answer`
+
+**Complexity Analysis**
+
+Time complexity: `O(n^2)`. For each element of array, we iterate the left and right parts.
+
+Auxiliary space complexity: `O(1)` extra space.
+
+### Approach 2: Dynamic Programming
+
+**Intuition**
+
+In brute force, we iterate over the left and right parts again and again just to 
+find the highest bar size up to that index. But, this could be stored. Voila, 
+dynamic programming.
+
+So we may pre-compute highest bar on left and right of every bar in `O(n)` time.
+Then use these pre-computed values to find the amount of water in every array element.
+
+The concept is illustrated as shown:
+
+![DP Trapping Rain Water](https://leetcode.com/problems/trapping-rain-water/Figures/42/trapping_rain_water.png)
+
+**Steps**
+
+- Find maximum height of bar from the left end up to an index `i` in the array `left_max`.
+- Find maximum height of bar from the right end up to an index `i` in the array `right_max`.
+- Iterate over the `height` array and update `answer`:
+  - Add `min(max_left[i], max_right[i]) − height[i]` to `answer`.
+
+**Complexity Analysis**
+
+Time complexity: `O(n)`.
+
+Auxiliary space complexity: `O(n)` extra space.
 
 ## References
 
 - [GeeksForGeeks](https://www.geeksforgeeks.org/trapping-rain-water/)
+- [LeetCode](https://leetcode.com/problems/trapping-rain-water/solution/)

src/algorithms/uncategorized/rain-terraces/__test__/bfRainTerraces.test.js

@@ -0,0 +1,21 @@
+import bfRainTerraces from '../bfRainTerraces';
+
+describe('bfRainTerraces', () => {
+  it('should find the amount of water collected after raining', () => {
+    expect(bfRainTerraces([1])).toBe(0);
+    expect(bfRainTerraces([1, 0])).toBe(0);
+    expect(bfRainTerraces([0, 1])).toBe(0);
+    expect(bfRainTerraces([0, 1, 0])).toBe(0);
+    expect(bfRainTerraces([0, 1, 0, 0])).toBe(0);
+    expect(bfRainTerraces([0, 1, 0, 0, 1, 0])).toBe(2);
+    expect(bfRainTerraces([0, 2, 0, 0, 1, 0])).toBe(2);
+    expect(bfRainTerraces([2, 0, 2])).toBe(2);
+    expect(bfRainTerraces([2, 0, 5])).toBe(2);
+    expect(bfRainTerraces([3, 0, 0, 2, 0, 4])).toBe(10);
+    expect(bfRainTerraces([0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1])).toBe(6);
+    expect(bfRainTerraces([1, 1, 1, 1, 1])).toBe(0);
+    expect(bfRainTerraces([1, 2, 3, 4, 5])).toBe(0);
+    expect(bfRainTerraces([4, 1, 3, 1, 2, 1, 2, 1])).toBe(4);
+    expect(bfRainTerraces([0, 2, 4, 3, 4, 2, 4, 0, 8, 7, 0])).toBe(7);
+  });
+});

src/algorithms/uncategorized/rain-terraces/__test__/dpRainTerraces.test.js

@@ -0,0 +1,21 @@
+import dpRainTerraces from '../dpRainTerraces';
+
+describe('dpRainTerraces', () => {
+  it('should find the amount of water collected after raining', () => {
+    expect(dpRainTerraces([1])).toBe(0);
+    expect(dpRainTerraces([1, 0])).toBe(0);
+    expect(dpRainTerraces([0, 1])).toBe(0);
+    expect(dpRainTerraces([0, 1, 0])).toBe(0);
+    expect(dpRainTerraces([0, 1, 0, 0])).toBe(0);
+    expect(dpRainTerraces([0, 1, 0, 0, 1, 0])).toBe(2);
+    expect(dpRainTerraces([0, 2, 0, 0, 1, 0])).toBe(2);
+    expect(dpRainTerraces([2, 0, 2])).toBe(2);
+    expect(dpRainTerraces([2, 0, 5])).toBe(2);
+    expect(dpRainTerraces([3, 0, 0, 2, 0, 4])).toBe(10);
+    expect(dpRainTerraces([0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1])).toBe(6);
+    expect(dpRainTerraces([1, 1, 1, 1, 1])).toBe(0);
+    expect(dpRainTerraces([1, 2, 3, 4, 5])).toBe(0);
+    expect(dpRainTerraces([4, 1, 3, 1, 2, 1, 2, 1])).toBe(4);
+    expect(dpRainTerraces([0, 2, 4, 3, 4, 2, 4, 0, 8, 7, 0])).toBe(7);
+  });
+});

src/algorithms/uncategorized/rain-terraces/__test__/rainTerraces.test.js

@@ -1,21 +0,0 @@
-import rainTerraces from '../rainTerraces';
-
-describe('rainTerraces', () => {
-  it('should find the amount of water collected after raining', () => {
-    expect(rainTerraces([1])).toBe(0);
-    expect(rainTerraces([1, 0])).toBe(0);
-    expect(rainTerraces([0, 1])).toBe(0);
-    expect(rainTerraces([0, 1, 0])).toBe(0);
-    expect(rainTerraces([0, 1, 0, 0])).toBe(0);
-    expect(rainTerraces([0, 1, 0, 0, 1, 0])).toBe(2);
-    expect(rainTerraces([0, 2, 0, 0, 1, 0])).toBe(2);
-    expect(rainTerraces([2, 0, 2])).toBe(2);
-    expect(rainTerraces([2, 0, 5])).toBe(2);
-    expect(rainTerraces([3, 0, 0, 2, 0, 4])).toBe(10);
-    expect(rainTerraces([0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1])).toBe(6);
-    expect(rainTerraces([1, 1, 1, 1, 1])).toBe(0);
-    expect(rainTerraces([1, 2, 3, 4, 5])).toBe(0);
-    expect(rainTerraces([4, 1, 3, 1, 2, 1, 2, 1])).toBe(4);
-    expect(rainTerraces([0, 2, 4, 3, 4, 2, 4, 0, 8, 7, 0])).toBe(7);
-  });
-});

src/algorithms/uncategorized/rain-terraces/bfRainTerraces.js

@@ -0,0 +1,33 @@
+/**
+ * BRUTE FORCE approach of solving Trapping Rain Water problem.
+ *
+ * @param {number[]} terraces
+ * @return {number}
+ */
+export default function bfRainTerraces(terraces) {
+  let waterAmount = 0;
+
+  for (let terraceIndex = 0; terraceIndex < terraces.length; terraceIndex += 1) {
+    // Get left most high terrace.
+    let leftHighestLevel = 0;
+    for (let leftIndex = terraceIndex - 1; leftIndex >= 0; leftIndex -= 1) {
+      leftHighestLevel = Math.max(leftHighestLevel, terraces[leftIndex]);
+    }
+
+    // Get right most high terrace.
+    let rightHighestLevel = 0;
+    for (let rightIndex = terraceIndex + 1; rightIndex < terraces.length; rightIndex += 1) {
+      rightHighestLevel = Math.max(rightHighestLevel, terraces[rightIndex]);
+    }
+
+    // Add current terrace water amount.
+    const terraceBoundaryLevel = Math.min(leftHighestLevel, rightHighestLevel);
+    if (terraceBoundaryLevel > terraces[terraceIndex]) {
+      // Terrace will be able to store the water if the lowest of two left and right highest
+      // terraces are still higher than the current one.
+      waterAmount += Math.min(leftHighestLevel, rightHighestLevel) - terraces[terraceIndex];
+    }
+  }
+
+  return waterAmount;
+}

src/algorithms/uncategorized/rain-terraces/dpRainTerraces.js

@@ -1,8 +1,10 @@
 /**
+ * DYNAMIC PROGRAMMING approach of solving Trapping Rain Water problem.
+ *
  * @param {number[]} terraces
  * @return {number}
  */
-export default function rainTerraces(terraces) {
+export default function dpRainTerraces(terraces) {
   /*
    * STEPS
    *