I even turned off the bot, thinking that the winner would go through Mara's slipstream.
We’ll see next time if that person is mocking anyone. Next time, all my bots will be ON.
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import numpy as np
from mpmath import mp
import argparse
# Set higher decimal precision for mpmath
mp.dps = 60 # Adjust precision as needed
# Target numbers (ordinal, value)
target_numbers = [
(1, 1), (2, 3), (3, 7), (4, 8), (5, 21), (6, 49), (7, 76), (8, 224), (9, 467), (10, 514),
(11, 1155), (12, 2683), (13, 5216), (14, 10544), (15, 26867), (16, 51510),
(17, 95823), (18, 198669), (19, 357535), (20, 863317), (21, 1811764),
(22, 3007503), (23, 5598802), (24, 14428676), (25, 33185509),
(26, 54538862), (27, 111949941), (28, 227634408), (29, 400708894),
(30, 1033162084), (31, 2102388551), (32, 3093472814), (33, 7137437912),
(34, 14133072157), (35, 20112871792), (36, 42387769980), (37, 100251560595),
(38, 146971536592), (39, 323724968937), (40, 1003651412950),
(41, 1458252205147), (42, 2895374552463), (43, 7409811047825),
(44, 15404761757071), (45, 19996463086597), (46, 51408670348612),
(47, 119666659114170), (48, 191206974700443), (49, 409118905032525),
(50, 611140496167764), (51, 2058769515153876), (52, 4216495639600700),
(53, 6763683971478124), (54, 9974455244496707), (55, 30045390491869460),
(56, 44218742292676575), (57, 138245758910846492), (58, 199976667976342049),
(59, 525070384258266191), (60, 1135041350219496382), (61, 1425787542618654982),
(62, 3908372542507822062), (63, 8993229949524469768),
(64, 17799667357578236628), (65, 30568377312064202855),
(66, 46346217550346335726)
]
def main(puzzle_number):
# Extracting the ordinal (x) and values (y) up to the puzzle before the target puzzle
if puzzle_number in [x[0] for x in target_numbers]:
ordinals = np.array([x[0] for x in target_numbers if x[0] < puzzle_number], dtype=float)
values = np.array([x[1] for x in target_numbers if x[0] < puzzle_number], dtype=float)
else:
# Use all available data for prediction if puzzle_number is not in the list
ordinals = np.array([x[0] for x in target_numbers], dtype=float)
values = np.array([x[1] for x in target_numbers], dtype=float)
# Compute log of the values using mpmath for high precision
log_values = np.array([float(mp.log(val)) for val in values], dtype=float)
# Introduce weights to give more importance to recent values
weights = np.linspace(0.5, 1, len(ordinals)) # Linear weighting
# Perform linear regression on log(values) to fit the exponential model, using weights
coefficients = np.polyfit(ordinals, log_values, 1, w=weights)
# Extracting the coefficients
log_a = coefficients[1]
log_b = coefficients[0]
# Convert coefficients back to the exponential form
a = mp.exp(log_a)
b = mp.exp(log_b)
# Correction factor
alpha = 2.7931615407112251695923857484645003617613050861
# Predict the value for the given puzzle using the exponential model
predicted_value = (a * mp.power(b, puzzle_number)) - ((((2 ** puzzle_number) - 1) - (2 ** (puzzle_number - 1))) / alpha)
# Check if the real value is available
real_value = None
if puzzle_number in [x[0] for x in target_numbers]:
real_value = [x[1] for x in target_numbers if x[0] == puzzle_number][0]
# Function to format mpmath values to avoid scientific notation
def format_mp(value, decimals=None):
if decimals is not None:
# Use string formatting to keep decimal places without thousands separators
return f"{float(value):,.{decimals}f}".replace(',', '')
return mp.nstr(value, mp.dps)
# Compare the corrected value with the real value for the given puzzle
if real_value is not None:
print(f"Puzzle {puzzle_number} Real Value: {format_mp(real_value)}")
print(f"Puzzle {puzzle_number} Predicted Value: {format_mp(predicted_value, 2)}")
# Calculate the percentage error in the corrected prediction if real value is available
if real_value is not None:
error_percentage = abs((predicted_value - real_value) / real_value) * 100
print(f"Percentage error in the corrected prediction for Puzzle {puzzle_number}: {format_mp(error_percentage, 2)}%")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Predict puzzle values based on an exponential model.")
parser.add_argument("puzzle_number", type=int, help="Puzzle number to predict")
args = parser.parse_args()
main(args.puzzle_number)