# Exploring Distinct Island Shapes: 5 Python Methods to Analyze Matrix Data

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π‘ Problem Formulation: In computational geometry and computer science, a common problem is the identification of distinct shapes within a 2D grid or matrix. For instance, we may represent a map where ‘1’s indicate land and ‘0’s denote water. The challenge is to determine the number of unique island shapes in this matrix. An island is defined as a group of adjacent lands, connected horizontally or vertically, but not diagonally. The task is to write a Python program that can process such a matrix and output the count of distinct island configurations.

## Method 1: Depth-First Search (DFS) with Coordinate Transformation

The first method involves using Depth-First Search to explore each island and transforming the coordinates of each visited cell into a positional hash relative to the top-leftmost point of the island. This ensures that shapes are compared irrespective of their location in the grid. It is a robust approach, that tends to work well for most cases and can handle large matrices efficiently.

Here’s an example:

```def distinct_islands(grid):
def dfs(r, c, origin):
if 0 <= r < len(grid) and 0 <= c < len(grid[0]) and grid[r][c]:
grid[r][c] = 0
shape.add((r - origin[0], c - origin[1]))
dfs(r + 1, c, origin)
dfs(r - 1, c, origin)
dfs(r, c + 1, origin)
dfs(r, c - 1, origin)

shapes = set()
for r in range(len(grid)):
for c in range(len(grid[0])):
if grid[r][c]:
shape = set()
dfs(r, c, (r,c))
if shape:
return len(shapes)

# Example matrix:
grid = [[1, 1, 0, 0, 0],
[1, 1, 0, 0, 1],
[0, 0, 0, 1, 1],
[0, 0, 0, 1, 1]]
print(distinct_islands(grid))  # Output: 2
```

The output of this code snippet is `2`, meaning there are two distinct island shapes in the provided matrix.

In this code, the `distinct_islands` function explores the matrix using DFS and records the relative positions of ‘1’s forming an island shape. Each distinct shape is stored as a unique entry in the `shapes` set, leveraging the immutability of `frozenset` objects to handle uniqueness. The return value is the count of unique shapes found.

## Method 2: Breadth-First Search (BFS) with Normalization

Breadth-First Search is an alternative iterative method that can be used to explore islands. This method normalizes the shapes by offsetting their coordinates, which ensures shape comparison is location invariant. While typically slower than DFS for this purpose, BFS can be more intuitive and easier to debug because of its queue-based nature.

Here’s an example:

```from collections import deque

def distinct_islands(grid):
def bfs(r, c):
q = deque([(r, c)])
island = []
while q:
r, c = q.popleft()
island.append((r - r0, c - c0))
for dr, dc in ((1, 0), (-1, 0), (0, 1), (0, -1)):
rr, cc = r + dr, c + dc
if 0 <= rr < len(grid) and 0 <= cc < len(grid[0]) and grid[rr][cc]:
grid[rr][cc] = 0
q.append((rr, cc))
return frozenset(island)

islands = set()
for r0 in range(len(grid)):
for c0 in range(len(grid[0])):
if grid[r0][c0]:
grid[r0][c0] = 0
return len(islands)

# Example matrix:
grid = [[1, 0, 0, 1, 1],
[1, 0, 0, 1, 1],
[0, 0, 0, 0, 0],
[0, 0, 1, 1, 1]]
print(distinct_islands(grid))  # Output: 2
```

The output of this code snippet is `2`, indicating there are two distinct island shapes within the matrix.

Here, the `distinct_islands` function uses a queue to perform BFS for island nodes, tracking their relative positions to the first node encountered. The result is a set of coordinates normalized for each island, which are hashed and then added to the overall set of shapes to count distinct formations.

## Method 3: Union-Find Data Structure for Shape Identification

Using Union-Find, also known as the Disjoint-Set data structure, we can group nodes of the same island and later assign a unique hash to each island shape. This method is efficient for large matrices and can help in dynamic scenarios where the matrix updates and we have to maintain the island counts.

Here’s an example:

```class UnionFind:
def __init__(self, grid):
self.parent = {}
for r in range(len(grid)):
for c in range(len(grid[r])):
if grid[r][c] == 1:
self.parent[(r, c)] = (r, c)

def find(self, position):
if self.parent[position] != position:
self.parent[position] = self.find(self.parent[position])
return self.parent[position]

def union(self, a, b):
ar = self.find(a)
br = self.find(b)
if ar != br:
self.parent[br] = ar

def distinct_islands(grid):
def neighbors(r, c):
for nr, nc in [(r-1, c), (r+1, c), (r, c-1), (r, c+1)]:
if 0 <= nr < len(grid) and 0 <= nc < len(grid[0]) and grid[nr][nc] == 1:
yield (nr, nc)

uf = UnionFind(grid)
for r in range(len(grid)):
for c in range(len(grid[0])):
if grid[r][c] == 1:
for nr, nc in neighbors(r, c):
uf.union((r, c), (nr, nc))

shapes = set()
for r in range(len(grid)):
for c in range(len(grid[0])):
if grid[r][c] == 1:
return len(shapes)

# Example matrix:
grid = [[1, 0, 0, 1, 1],
[1, 0, 1, 0, 1],
[0, 1, 0, 0, 0],
[0, 0, 1, 1, 1]]
print(distinct_islands(grid))  # Output: 3
```

The output of this code snippet is `3`, suggesting the presence of three distinct island shapes in the matrix.

This snippet implements the Union-Find algorithm to categorize the cells into distinct islands. We initialize a Union-Find instance and perform union operations for adjacent land cells. Finally, we hash the root parent for each island to generate a shapes set, count the unique shapes.

## Method 4: Recursive Backtracking with String Encoding

We can explore each island via recursive backtracking and encode the path taken as a string pattern, which becomes a unique identifier for the island’s shape. This approach works well for small to medium-sized island shapes and has the advantage of a more intuitive string comparison to distinguish unique shapes.

Here’s an example:

```def distinct_islands(grid):
def explore(r, c, direction):
if 0 <= r < len(grid) and 0 <= c < len(grid[0]) and grid[r][c]:
grid[r][c] = 0
path.append(direction)
explore(r + 1, c, 'D')  # Down
explore(r - 1, c, 'U')  # Up
explore(r, c + 1, 'R')  # Right
explore(r, c - 1, 'L')  # Left
path.append('B')  # Backtrack

shapes = set()
for r in range(len(grid)):
for c in range(len(grid[0])):
path = []
if grid[r][c]:
explore(r, c, 'S')  # Start
return len(shapes)

# Example matrix:
grid = [[1, 1, 0, 1, 0],
[0, 1, 0, 0, 1],
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 0]]
print(distinct_islands(grid))  # Output: 3
```

The output of this code snippet is `3`, representing three distinct island shapes in the given matrix.

This function defines an `explore` method which recursively ventures through the grid to mark the land cells and record the direction of each move. Each complete path is saved as a string, providing us with a unique identifier for the shape of the islands. Finally, we use the length of the `shapes` set to determine the number of distinct shapes.

## Bonus One-Liner Method 5: Using Python’s Powerful Set and Functional Programming

For a concise and elegant solution, we can leverage Python’s set comprehension and functional programming capabilities by writing a one-liner that incorporates the methods above. This is largely an academic or trivial compression of the problem, but it demonstrates the power of Python’s expressive syntax for simple cases.

Here’s an example:

```# The one-liner would be too complex and not advised for real usage, as it hides the logic of the shape identification.
```

In this one-liner, you might imagine combining the operations of marking, encoding, and hashing island shapes by using an advanced comprehension and functional abstraction. However, due to complexity reasons, the code is omitted, as it would not reflect best programming practices.

## Summary/Discussion

• Method 1: Depth-First Search (DFS) with Coordinate Transformation. Strengths: Efficient and handles large matrices well. Weaknesses: Requires a good understanding of recursion and hashing.
• Method 2: Breadth-First Search (BFS) with Normalization. Strengths: Intuitive and easier to debug. Weaknesses: It may be slower than DFS and can be less efficient for very large datasets.
• Method 3: Union-Find for Shape Identification. Strengths: Highly efficient for dynamic scenarios. Weaknesses: The complexity of understanding and implementing Disjoint-Set operations.
• Method 4: Recursive Backtracking with String Encoding. Strengths: Provides an intuitive path representation of island shapes. Weaknesses: Can rapidly increase in complexity with larger island shapes.
• Method 5: Python One-Liner. Strengths: Demonstrates the elegance of Python for simple cases. Weaknesses: Not practical for real-world applications; readability and maintenance issues.