A Python program to calculate the probability of drawing a specific combination of items from a collection using Monte Carlo simulations. This project leverages object-oriented programming, random sampling, and statistical analysis to estimate probabilities in complex scenarios.
Features
Define a collection of items with specified quantities.
Randomly draw a specific number of items from the collection.
Run multiple experiments to estimate the probability of obtaining a desired combination.
Handles large numbers of experiments efficiently while preserving the original collection.
Flexible: works for any number of item types, quantities, and expected outcomes.
How It Works
Collection Class
Represents a set of items and their quantities.
draw(num_items) method randomly removes a specified number of items from the collection.
Probability Calculation Function
Simulates drawing items multiple times to estimate the probability of a specific combination.
Compares drawn items with the expected combination using a counter to count successful draws.
Returns the estimated probability as a float between 0 and 1.
Example from probability_calculator import Collection, experiment
my_collection = Collection(blue=5, red=4, green=2)
probability = experiment( collection=my_collection, expected_items={'red': 1, 'green': 2}, num_items_drawn=4, num_experiments=20000 )
print(probability) # Output: Approximate probability
Installation
No external libraries are required beyond Python’s standard library.
git clone <repository_url> python probability_calculator.py
Applications
Calculating probabilities in games, experiments, and real-life scenarios.
Educational tool for learning probability and statistics.
Useful for simulations in research or teaching.