Cronym

import numpy as np

# === Transformation Matrices ===
T_flip = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
T_rotate_90 = np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]])
T_rotate_45 = np.array([
    [np.cos(np.pi/4), -np.sin(np.pi/4), 0],
    [np.sin(np.pi/4),  np.cos(np.pi/4), 0],
    [0, 0, 1]
])
T_rotate_180 = np.array([[-1, 0, 0], [0, -1, 0], [0, 0, 1]])

# === Simplified Letter Positions ===
letter_positions = {
    'A': np.array([1, 1, 2]),
    'B': np.array([1, 1, 3]),
    'C': np.array([1, 1, 1]),
    'D': np.array([1, 1, 4]),
    'E': np.array([0, 1, 2]),
    'F': np.array([1, 1, 3]),
    'H': np.array([1, 0, 1]),
    'I': np.array([0, 0, 2]),
    'M': np.array([1, 0, 3]),
    'N': np.array([1, 0, 4]),
    'O': np.array([0, 0, 1]),
    'R': np.array([0, 1, 3]),
    'L': np.array([0, 1, 4]),
    'W': np.array([1, 0, 5])
}

# === Helpers ===
def get_scaling_matrix(word):
    vowel_count = sum(1 for letter in word.upper() if letter in "AEIOU")
    scale = 1.5 if vowel_count > 2 else 1.0
    return np.diag([scale, scale, scale])

def extract_acronym(word):
    return ''.join([c.upper() for c in word if c.isalpha()])

def transform_word(word, matrix):
    result = ""
    for letter in word:
        if letter.upper() in letter_positions:
            pos = matrix @ letter_positions[letter.upper()]
            closest = min(letter_positions, key=lambda k: np.linalg.norm(letter_positions[k] - pos))
            result += closest
        else:
            result += letter
    return result

def transform_and_report(word, label, matrix):
    transformed = transform_word(word, matrix)
    acronym = extract_acronym(transformed)
    print(f"[{label}] {word} → {transformed} → {acronym}")
    return (word, transformed, acronym)

# === Main Execution ===
words_to_test = ["CAB", "LIME", "HEAR", "WORLD", "EXAMPLE", "COMPUTER"]

transformations = {
    "Rotate180": T_rotate_180,
    "Rotate90": T_rotate_90,
    "Flip": T_flip,
    "Rotate45": T_rotate_45
}

for word in words_to_test:
    scaling_matrix = get_scaling_matrix(word)
    for name, matrix in transformations.items():
        full_matrix = matrix @ scaling_matrix
        transform_and_report(word, name, full_matrix)