fsdcats
Machine Learning Practicals — Ultra-Short & Clean Exam Codes.
Zero terminal output, direct clipboard copy, automatic Python file creation (prac1.py, etc.), cross-platform (Windows & macOS).
Prerequisites
Run once in terminal before starting:
pip install numpy pandas matplotlib scikit-learn minisom gymnasium tensorflow
CLI Usage
npx fsdcats <practical-number-or-name>
Each run automatically:
- Copies the practical guide & setup directly to your clipboard.
- Creates the Python code file directly in your folder (
prac1.py,csv_loader.py, etc.). - Generates the
practical-X.txtfile. - Outputs 0 terminal logs (completely silent).
- Does not open popup windows.
Practical Quick Reference & Access Names
| Practical | Topic | Logical File | Access Names |
|---|---|---|---|
| 0 | CSV Loader (Auto-Delimiter, Scale, Split) | csv_loader.py | 0, csv, csv_loader |
| 1 | ANN for Linear Regression | prac1.py | 1, ann, linear-regression |
| 2 | Sigmoid Activation Function Plot | prac2.py | 2, sigmoid, activation |
| 3 | Self-Organizing Map (SOM U-Matrix) | prac3.py | 3, som, minisom |
| 4 | Reinforcement Learning (Q-Learning) | prac4.py | 4, qlearning, ql |
| 5 | Reinforcement Learning with SARSA | prac5.py | 5, sarsa |
| 6 | Deep Q-Network (DQN with Keras) | prac6.py | 6, dqn |
| 7 | Radial Basis Function (RBF for XOR) | prac7.py | 7, rbf, xor |
| 8 | Genetic Algorithm I (Target String) | prac8.py | 8, ga1, ga |
| 9 | Genetic Algorithm II (Tournament Selection) | prac9.py | 9, ga2, tournament |
| 10 | Convolutional Neural Network (CNN) | prac10.py | 10, cnn |
| 11 | Particle Swarm Optimization (PSO) | prac11.py | 11, pso |
| 12 | Ant Colony Optimization (ACO) | prac12.py | 12, aco |
| All | All 13 Practicals | all-practicals.txt | all |
Examples
npx fsdcats 1 # Practical 1: ANN
npx fsdcats qlearning # Practical 4: Q-Learning
npx fsdcats dqn # Practical 6: DQN
npx fsdcats all # All practicals
Node.js Module Usage
const ml = require('fsdcats');
// Access by logical name
console.log(ml.ann.components[0].code);
console.log(ml.qlearning.components[0].code);
// Generate files programmatically
ml.generatePracticalFile('ann');
ml.generateAllFiles();