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Coolsnippetbossbro

Coolsnippetbossbro

bossbro

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Comprehensive AI algorithm and data preprocessing snippets for exam preparation (Python, NumPy, Pandas).
Installation
Launch VS Code Quick Open (Ctrl+P), paste the following command, and press enter.
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AI Exam Snippets

A comprehensive snippet extension for VS Code containing data preprocessing boilerplate and 13 essential search/machine learning/optimization algorithms tailored for exams. All code is written in Python using basic, C++-level explicit loops (no complex list comprehensions) for maximum readability and ease of tracing.

Triggers and Customization Tips

Trigger Description Exam Customization Tips
!lib Imports all required libraries Contains numpy, pandas, heapq, random, and Counter.
!preprocess Data preprocessing, scaling, splitting Adjust train/test split ratio (e.g., 0.7), feature column selections, missing value imputation strategy (median/mode), or outlier Winsorization clipping bounds.
!adversarial Minimax, Expectimax, Alpha-Beta Change evaluation get_score(state) or comment/uncomment Alpha-Beta lines to switch to standard Minimax or Expectimax chance nodes.
!kmeans K-Means Clustering Change distance calculation loop to np.sum(np.abs(point - c)) for Manhattan distance.
!knn K-Nearest Neighbors Classifier Change distance calculation loop to np.sum(np.abs(x_test_point - x)) for Manhattan distance. Contains automatic tie-breaker (defaults to closest neighbor class).
!linreg Linear Regression via GD Modify learning_rate or prediction function shapes.
!logreg Logistic Regression Classifier Change decision threshold (default 0.5) in predict_logistic.
!hierarchical Hierarchical Clustering Single linkage by default. Swap minimum distance loop for complete linkage (maximum distance) if requested.
!genetic Genetic Algorithms Modify tournament size k, crossover split point selection, or bit-flip probability.
!csp CSP Backtracking Solver Update constraint_satisfied(var, val, neighbor, neighbor_val) to match the specific problem rules.
!ac3 AC-3 Arc Consistency Update constraint checker function to check consistency of variable values across arcs.
!astar A* Search Change get_heuristic(neighbor, goal) and get_cost(curr, neighbor) logic per graph/grid.
!greedy Greedy Best-First Search Change get_heuristic(neighbor, goal) per graph/grid.
!beam Beam Search Adjust beam width, change sorting order for minimization.
!hill Hill Climbing Change inequality check directions for minimization.
!anneal Simulated Annealing Modify cooling schedule, change acceptance probability calculation for minimization.
!cosine Cosine Similarity Computes dot product and norms from scratch. Avoids division-by-zero.
!metrics Model Evaluation Metrics Manual TP, FP, TN, FN computation. Change printed output format if needed.

How to Use

  1. Open a Python file (.py).
  2. Type any of the triggers above (e.g., !preprocess or !ac3).
  3. Press Tab or Enter to expand the snippet.
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