TheAlgorithms
Add t-SNE algorithm
Feature description
""" The Algorithms - Python A comprehensive collection of algorithms implemented in Python for educational purposes. All implementations are designed for learning and may not be optimized for production use. """
import math from typing import List, Optional, Tuple, Any from collections import deque, defaultdict import heapq
==================== SORTING ALGORITHMS ====================
def bubble_sort(arr: List[int]) -> List[int]: """ Bubble Sort Algorithm Time Complexity: O(n²), Space Complexity: O(1) """ n = len(arr) for i in range(n): swapped = False for j in range(0, n - i - 1): if arr[j] > arr[j + 1]: arr[j], arr[j + 1] = arr[j + 1], arr[j] swapped = True if not swapped: break return arr
def quick_sort(arr: List[int]) -> List[int]: """ Quick Sort Algorithm Time Complexity: O(n log n) average, O(n²) worst Space Complexity: O(log n) """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right)
def merge_sort(arr: List[int]) -> List[int]: """ Merge Sort Algorithm Time Complexity: O(n log n), Space Complexity: O(n) """ if len(arr) <= 1: return arr
mid = len(arr) // 2
left = merge_sort(arr[:mid])
right = merge_sort(arr[mid:])
return merge(left, right)
def merge(left: List[int], right: List[int]) -> List[int]: """Helper function for merge sort""" result = [] i = j = 0
while i < len(left) and j < len(right):
if left[i] <= right[j]:
result.append(left[i])
i += 1
else:
result.append(right[j])
j += 1
result.extend(left[i:])
result.extend(right[j:])
return result
def heap_sort(arr: List[int]) -> List[int]: """ Heap Sort Algorithm Time Complexity: O(n log n), Space Complexity: O(1) """ def heapify(arr, n, i): largest = i left = 2 * i + 1 right = 2 * i + 2
if left < n and arr[left] > arr[largest]:
largest = left
if right < n and arr[right] > arr[largest]:
largest = right
if largest != i:
arr[i], arr[largest] = arr[largest], arr[i]
heapify(arr, n, largest)
n = len(arr)
for i in range(n // 2 - 1, -1, -1):
heapify(arr, n, i)
for i in range(n - 1, 0, -1):
arr[0], arr[i] = arr[i], arr[0]
heapify(arr, i, 0)
return arr
==================== SEARCHING ALGORITHMS ====================
def binary_search(arr: List[int], target: int) -> int: """ Binary Search Algorithm (iterative) Time Complexity: O(log n), Space Complexity: O(1) Returns: index of target or -1 if not found """ left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
def binary_search_recursive(arr: List[int], target: int, left: int = 0, right: int = None) -> int: """ Binary Search Algorithm (recursive) Time Complexity: O(log n), Space Complexity: O(log n) """ if right is None: right = len(arr) - 1
if left > right:
return -1
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
return binary_search_recursive(arr, target, mid + 1, right)
else:
return binary_search_recursive(arr, target, left, mid - 1)
def linear_search(arr: List[int], target: int) -> int: """ Linear Search Algorithm Time Complexity: O(n), Space Complexity: O(1) """ for i, val in enumerate(arr): if val == target: return i return -1
def jump_search(arr: List[int], target: int) -> int: """ Jump Search Algorithm Time Complexity: O(√n), Space Complexity: O(1) """ n = len(arr) step = int(math.sqrt(n)) prev = 0
while arr[min(step, n) - 1] < target:
prev = step
step += int(math.sqrt(n))
if prev >= n:
return -1
while arr[prev] < target:
prev += 1
if prev == min(step, n):
return -1
if arr[prev] == target:
return prev
return -1
==================== GRAPH ALGORITHMS ====================
class Graph: """Graph representation using adjacency list"""
def __init__(self):
self.graph = defaultdict(list)
def add_edge(self, u: int, v: int):
"""Add an edge to the graph"""
self.graph[u].append(v)
def bfs(self, start: int) -> List[int]:
"""
Breadth-First Search
Time Complexity: O(V + E), Space Complexity: O(V)
"""
visited = set()
queue = deque([start])
result = []
while queue:
vertex = queue.popleft()
if vertex not in visited:
visited.add(vertex)
result.append(vertex)
queue.extend([v for v in self.graph[vertex] if v not in visited])
return result
def dfs(self, start: int) -> List[int]:
"""
Depth-First Search (iterative)
Time Complexity: O(V + E), Space Complexity: O(V)
"""
visited = set()
stack = [start]
result = []
while stack:
vertex = stack.pop()
if vertex not in visited:
visited.add(vertex)
result.append(vertex)
stack.extend(reversed(self.graph[vertex]))
return result
def dfs_recursive(self, start: int, visited: Optional[set] = None, result: Optional[List[int]] = None) -> List[int]:
"""
Depth-First Search (recursive)
Time Complexity: O(V + E), Space Complexity: O(V)
"""
if visited is None:
visited = set()
if result is None:
result = []
visited.add(start)
result.append(start)
for neighbor in self.graph[start]:
if neighbor not in visited:
self.dfs_recursive(neighbor, visited, result)
return result
def dijkstra(graph: dict, start: int) -> dict: """ Dijkstra's Shortest Path Algorithm Time Complexity: O((V + E) log V) graph format: {node: [(neighbor, weight), ...]} """ distances = {node: float('inf') for node in graph} distances[start] = 0 pq = [(0, start)]
while pq:
current_dist, current = heapq.heappop(pq)
if current_dist > distances[current]:
continue
for neighbor, weight in graph.get(current, []):
distance = current_dist + weight
if distance < distances[neighbor]:
distances[neighbor] = distance
heapq.heappush(pq, (distance, neighbor))
return distances
==================== DYNAMIC PROGRAMMING ====================
def fibonacci(n: int) -> int: """ Fibonacci Number (Dynamic Programming) Time Complexity: O(n), Space Complexity: O(n) """ if n <= 1: return n
dp = [0] * (n + 1)
dp[1] = 1
for i in range(2, n + 1):
dp[i] = dp[i - 1] + dp[i - 2]
return dp[n]
def fibonacci_optimized(n: int) -> int: """ Fibonacci Number (Space Optimized) Time Complexity: O(n), Space Complexity: O(1) """ if n <= 1: return n
a, b = 0, 1
for _ in range(2, n + 1):
a, b = b, a + b
return b
def knapsack_01(weights: List[int], values: List[int], capacity: int) -> int: """ 0/1 Knapsack Problem Time Complexity: O(n * capacity), Space Complexity: O(n * capacity) """ n = len(weights) dp = [[0] * (capacity + 1) for _ in range(n + 1)]
for i in range(1, n + 1):
for w in range(1, capacity + 1):
if weights[i - 1] <= w:
dp[i][w] = max(values[i - 1] + dp[i - 1][w - weights[i - 1]], dp[i - 1][w])
else:
dp[i][w] = dp[i - 1][w]
return dp[n][capacity]
def longest_common_subsequence(text1: str, text2: str) -> int: """ Longest Common Subsequence Time Complexity: O(m * n), Space Complexity: O(m * n) """ m, n = len(text1), len(text2) dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
if text1[i - 1] == text2[j - 1]:
dp[i][j] = dp[i - 1][j - 1] + 1
else:
dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])
return dp[m][n]
def coin_change(coins: List[int], amount: int) -> int: """ Coin Change Problem (minimum coins) Time Complexity: O(amount * len(coins)) Returns: minimum coins needed or -1 if impossible """ dp = [float('inf')] * (amount + 1) dp[0] = 0
for coin in coins:
for x in range(coin, amount + 1):
dp[x] = min(dp[x], dp[x - coin] + 1)
return dp[amount] if dp[amount] != float('inf') else -1
==================== STRING ALGORITHMS ====================
def is_palindrome(s: str) -> bool: """ Check if string is palindrome Time Complexity: O(n), Space Complexity: O(1) """ left, right = 0, len(s) - 1
while left < right:
if s[left] != s[right]:
return False
left += 1
right -= 1
return True
def kmp_search(text: str, pattern: str) -> List[int]: """ Knuth-Morris-Pratt Pattern Matching Time Complexity: O(n + m), Space Complexity: O(m) Returns: list of starting indices where pattern is found """ def compute_lps(pattern): lps = [0] * len(pattern) length = 0 i = 1
while i < len(pattern):
if pattern[i] == pattern[length]:
length += 1
lps[i] = length
i += 1
else:
if length != 0:
length = lps[length - 1]
else:
lps[i] = 0
i += 1
return lps
if not pattern:
return []
lps = compute_lps(pattern)
result = []
i = j = 0
while i < len(text):
if pattern[j] == text[i]:
i += 1
j += 1
if j == len(pattern):
result.append(i - j)
j = lps[j - 1]
elif i < len(text) and pattern[j] != text[i]:
if j != 0:
j = lps[j - 1]
else:
i += 1
return result
def longest_palindrome_substring(s: str) -> str: """ Find longest palindromic substring Time Complexity: O(n²), Space Complexity: O(1) """ if not s: return ""
def expand_around_center(left, right):
while left >= 0 and right < len(s) and s[left] == s[right]:
left -= 1
right += 1
return right - left - 1
start = end = 0
for i in range(len(s)):
len1 = expand_around_center(i, i)
len2 = expand_around_center(i, i + 1)
max_len = max(len1, len2)
if max_len > end - start:
start = i - (max_len - 1) // 2
end = i + max_len // 2
return s[start:end + 1]
==================== MATHEMATICAL ALGORITHMS ====================
def gcd(a: int, b: int) -> int: """ Greatest Common Divisor (Euclidean Algorithm) Time Complexity: O(log(min(a, b))) """ while b: a, b = b, a % b return a
def lcm(a: int, b: int) -> int: """ Least Common Multiple Time Complexity: O(log(min(a, b))) """ return abs(a * b) // gcd(a, b)
def is_prime(n: int) -> bool: """ Prime Number Check Time Complexity: O(√n) """ if n < 2: return False if n == 2: return True if n % 2 == 0: return False
for i in range(3, int(math.sqrt(n)) + 1, 2):
if n % i == 0:
return False
return True
def sieve_of_eratosthenes(n: int) -> List[int]: """ Find all primes up to n Time Complexity: O(n log log n) """ if n < 2: return []
is_prime = [True] * (n + 1)
is_prime[0] = is_prime[1] = False
for i in range(2, int(math.sqrt(n)) + 1):
if is_prime[i]:
for j in range(i * i, n + 1, i):
is_prime[j] = False
return [i for i in range(n + 1) if is_prime[i]]
def power(base: int, exp: int, mod: int = None) -> int: """ Fast Exponentiation (Binary Exponentiation) Time Complexity: O(log exp) """ result = 1 base = base % mod if mod else base
while exp > 0:
if exp % 2 == 1:
result = (result * base) % mod if mod else result * base
exp = exp >> 1
base = (base * base) % mod if mod else base * base
return result
==================== DATA STRUCTURES ====================
class Stack: """Stack implementation using list"""
def __init__(self):
self.items = []
def push(self, item: Any):
self.items.append(item)
def pop(self) -> Any:
if not self.is_empty():
return self.items.pop()
raise IndexError("Pop from empty stack")
def peek(self) -> Any:
if not self.is_empty():
return self.items[-1]
raise IndexError("Peek from empty stack")
def is_empty(self) -> bool:
return len(self.items) == 0
def size(self) -> int:
return len(self.items)
class Queue: """Queue implementation using deque"""
def __init__(self):
self.items = deque()
def enqueue(self, item: Any):
self.items.append(item)
def dequeue(self) -> Any:
if not self.is_empty():
return self.items.popleft()
raise IndexError("Dequeue from empty queue")
def is_empty(self) -> bool:
return len(self.items) == 0
def size(self) -> int:
return len(self.items)
==================== MACHINE LEARNING ALGORITHMS ====================
class TSNE: """ t-SNE (t-Distributed Stochastic Neighbor Embedding) Dimensionality reduction algorithm for visualization
Reference: Van der Maaten & Hinton (2008)
Time Complexity: O(n² * iterations)
"""
def __init__(self, n_components: int = 2, perplexity: float = 30.0,
learning_rate: float = 200.0, n_iter: int = 1000):
"""
Initialize t-SNE
Args:
n_components: Target dimensionality (typically 2 or 3)
perplexity: Balance between local and global aspects (5-50)
learning_rate: Learning rate for gradient descent
n_iter: Number of iterations
"""
self.n_components = n_components
self.perplexity = perplexity
self.learning_rate = learning_rate
self.n_iter = n_iter
self.embedding_ = None
def _euclidean_distance(self, X: List[List[float]]) -> List[List[float]]:
"""Compute pairwise Euclidean distances"""
n = len(X)
distances = [[0.0] * n for _ in range(n)]
for i in range(n):
for j in range(i + 1, n):
dist = sum((X[i][k] - X[j][k]) ** 2 for k in range(len(X[0])))
dist = math.sqrt(dist)
distances[i][j] = dist
distances[j][i] = dist
return distances
def _compute_perplexity(self, D: List[float], beta: float) -> Tuple[float, List[float]]:
"""Compute perplexity and probabilities for a given beta"""
P = [math.exp(-D[j] * beta) for j in range(len(D))]
sum_P = sum(P)
if sum_P == 0:
sum_P = 1e-8
P = [p / sum_P for p in P]
H = -sum(p * math.log2(p + 1e-8) for p in P if p > 0)
return H, P
def _binary_search_perplexity(self, distances_i: List[float], target_perplexity: float,
tol: float = 1e-5, max_iter: int = 50) -> List[float]:
"""Binary search for sigma that produces target perplexity"""
beta_min = -float('inf')
beta_max = float('inf')
beta = 1.0
for _ in range(max_iter):
H, P = self._compute_perplexity(distances_i, beta)
H_diff = H - math.log2(target_perplexity)
if abs(H_diff) < tol:
break
if H_diff > 0:
beta_min = beta
beta = (beta + beta_max) / 2 if beta_max != float('inf') else beta * 2
else:
beta_max = beta
beta = (beta + beta_min) / 2 if beta_min != -float('inf') else beta / 2
return P
def _compute_joint_probabilities(self, X: List[List[float]]) -> List[List[float]]:
"""Compute joint probability matrix P from high-dimensional data"""
n = len(X)
distances = self._euclidean_distance(X)
P = [[0.0] * n for _ in range(n)]
# Compute conditional probabilities
for i in range(n):
distances_i = [distances[i][j] if j != i else float('inf') for j in range(n)]
P_i = self._binary_search_perplexity(distances_i, self.perplexity)
for j in range(n):
if i != j:
P[i][j] = P_i[j]
# Symmetrize and normalize
for i in range(n):
for j in range(n):
P[i][j] = (P[i][j] + P[j][i]) / (2 * n)
P[i][j] = max(P[i][j], 1e-12) # Numerical stability
return P
def _compute_low_dim_affinities(self, Y: List[List[float]]) -> List[List[float]]:
"""Compute Student t-distribution affinities in low-dimensional space"""
n = len(Y)
Q = [[0.0] * n for _ in range(n)]
for i in range(n):
for j in range(n):
if i != j:
dist_sq = sum((Y[i][k] - Y[j][k]) ** 2 for k in range(len(Y[0])))
Q[i][j] = 1.0 / (1.0 + dist_sq)
# Normalize
sum_Q = sum(sum(row) for row in Q)
if sum_Q > 0:
Q = [[q / sum_Q for q in row] for row in Q]
# Ensure numerical stability
for i in range(n):
for j in range(n):
Q[i][j] = max(Q[i][j], 1e-12)
return Q
def _compute_gradient(self, P: List[List[float]], Q: List[List[float]],
Y: List[List[float]]) -> List[List[float]]:
"""Compute gradient of KL divergence"""
n = len(Y)
n_components = len(Y[0])
gradient = [[0.0] * n_components for _ in range(n)]
for i in range(n):
for j in range(n):
if i != j:
dist_sq = sum((Y[i][k] - Y[j][k]) ** 2 for k in range(n_components))
mult = (P[i][j] - Q[i][j]) / (1.0 + dist_sq)
for d in range(n_components):
gradient[i][d] += 4 * mult * (Y[i][d] - Y[j][d])
return gradient
def fit_transform(self, X: List[List[float]]) -> List[List[float]]:
"""
Fit t-SNE and return embedded coordinates
Args:
X: High-dimensional data (n_samples × n_features)
Returns:
Y: Low-dimensional embedding (n_samples × n_components)
"""
n = len(X)
# Initialize low-dimensional embedding randomly
import random
Y = [[random.gauss(0, 0.0001) for _ in range(self.n_components)] for _ in range(n)]
# Compute pairwise affinities in high-dimensional space
P = self._compute_joint_probabilities(X)
# Early exaggeration
for i in range(n):
for j in range(n):
P[i][j] *= 4.0
# Gradient descent with momentum
Y_momentum = [[0.0] * self.n_components for _ in range(n)]
for iteration in range(self.n_iter):
# Remove early exaggeration after 250 iterations
if iteration == 250:
for i in range(n):
for j in range(n):
P[i][j] /= 4.0
# Compute Q matrix (low-dimensional affinities)
Q = self._compute_low_dim_affinities(Y)
# Compute gradient
gradient = self._compute_gradient(P, Q, Y)
# Update with momentum
momentum = 0.5 if iteration < 250 else 0.8
for i in range(n):
for d in range(self.n_components):
Y_momentum[i][d] = momentum * Y_momentum[i][d] - self.learning_rate * gradient[i][d]
Y[i][d] += Y_momentum[i][d]
# Center the embedding
means = [sum(Y[i][d] for i in range(n)) / n for d in range(self.n_components)]
for i in range(n):
for d in range(self.n_components):
Y[i][d] -= means[d]
# Print progress every 100 iterations
if (iteration + 1) % 100 == 0:
# Compute KL divergence
kl_div = sum(P[i][j] * math.log((P[i][j] + 1e-12) / (Q[i][j] + 1e-12))
for i in range(n) for j in range(n) if i != j)
print(f"Iteration {iteration + 1}/{self.n_iter}, KL divergence: {kl_div:.4f}")
self.embedding_ = Y
return Y
==================== DEMO USAGE ====================
if name == "main": print("=" * 50) print("THE ALGORITHMS - PYTHON DEMO") print("=" * 50)
# Sorting Demo
print("\n--- SORTING ALGORITHMS ---")
arr = [64, 34, 25, 12, 22, 11, 90]
print(f"Original: {arr}")
print(f"Bubble Sort: {bubble_sort(arr.copy())}")
print(f"Quick Sort: {quick_sort(arr.copy())}")
print(f"Merge Sort: {merge_sort(arr.copy())}")
# Searching Demo
print("\n--- SEARCHING ALGORITHMS ---")
sorted_arr = [1, 3, 5, 7, 9, 11, 13, 15]
target = 7
print(f"Array: {sorted_arr}, Target: {target}")
print(f"Binary Search: Index {binary_search(sorted_arr, target)}")
# Dynamic Programming Demo
print("\n--- DYNAMIC PROGRAMMING ---")
print(f"Fibonacci(10): {fibonacci(10)}")
print(f"LCS('ABCDGH', 'AEDFHR'): {longest_common_subsequence('ABCDGH', 'AEDFHR')}")
# Math Demo
print("\n--- MATHEMATICAL ALGORITHMS ---")
print(f"GCD(48, 18): {gcd(48, 18)}")
print(f"Is 17 prime? {is_prime(17)}")
print(f"Primes up to 30: {sieve_of_eratosthenes(30)}")
print("\n" + "=" * 50)
print("Implementations complete! Visit TheAlgorithms/Python on GitHub for more!")
print("=" * 50)
Hi 👋
I noticed that a complete implementation of t-SNE (t-Distributed Stochastic Neighbor Embedding) already exists in this repository under
machine_learning/t_stochastic_neighbour_embedding.py.
That file includes all the main steps — high-dimensional affinities, low-dimensional affinities, gradient updates, and even a demo using the Iris dataset.
This issue might therefore be a duplicate of the existing implementation.
If helpful, I’d be happy to contribute by adding pytest-based tests or improving the documentation for the current t-SNE module.
Title: Fix if name == "main" and Indentation Errors
Comment: Hi maintainers ,
I noticed that several files contain invalid if name == "main": checks and inconsistent indentation (especially in functions like bubble_sort). These break code execution and affect readability.
I’d like to fix the following:
Replace all invalid if name == "main": with the correct if name == "main":
Correct indentation issues across affected files (e.g., bubble_sort and similar functions)
Kindly assign this issue to me. I’ll ensure the formatting and structure fully comply with PEP8 and existing repository standards.
Thank you
