TOTAL_VECTORS
8
LEVEL_1
3
LEVEL_5
2
LEVEL_9
3
CONSTANTS
Pi (π)
3.14159...
Circle proportionality
Euler's (e)
2.71828...
Natural logarithmic growth
Golden (φ)
1.61803...
Divine aesthetic ratio
Imaginary (i)
√-1
Complex domain pivot
MODULES_
Σ
medium
Discrete Math
❯ Logic, sets, recursion, and graph structures
[x]
hard
Linear Algebra
❯ Matrices, vectors, and eigen-transformations
dy/dx
hard
Calculus
❯ Limits, derivatives, and integral theory
%
medium
Stats & Prob
❯ Distributions, bayesian and variance mapping
mod
hard
Number Theory
❯ Primes, modular arithmetic, and cryptography
nCr
easy
Combinatorics
❯ Permutations, combinations, and counting trees
sin
easy
Trigonometry
❯ Unit circles, sine waves, and radial patterns
0|1
easy
Boolean Logic
❯ Gates, truth tables, and binary operations
SYNTAX_LOG
bayes_theorem.py
# ============================================
# BAYES' THEOREM P(A|B) = [P(B|A) * P(A)] / P(B)
# ============================================
def calc_bayes(p_a, p_b_given_a, p_b):
"""
Evidence-based probability update
"""
p_a_given_b = (p_b_given_a * p_a) / p_b
return p_a_given_b
# TEST CASE:
# Target detection probability given sensor input
print(f"PROBABILITY_UPDATED: {calc_bayes(0.01, 0.99, 0.05)}")