46 lines
1.4 KiB
Plaintext
46 lines
1.4 KiB
Plaintext
from math import fabs as fabs
|
|
from math import sqrt as sqrt
|
|
from scipy.special import erfc as erfc
|
|
import numpy as np
|
|
from scipy import stats
|
|
|
|
|
|
class StartUPTest:
|
|
|
|
@staticmethod
|
|
def monobit_test(binary_data: str):
|
|
|
|
length_of_bit_string = len(binary_data)
|
|
|
|
# Variable for S(n)
|
|
count = 0
|
|
# Iterate each bit in the string and compute for S(n)
|
|
for bit in binary_data:
|
|
if bit == 48:
|
|
# If bit is 0, then -1 from the S(n)
|
|
count -= 1
|
|
elif bit == 49:
|
|
# If bit is 1, then +1 to the S(n)
|
|
count += 1
|
|
|
|
# Compute the test statistic
|
|
sObs = count / sqrt(length_of_bit_string)
|
|
|
|
# Compute p-Value
|
|
p_value = erfc(fabs(sObs) / sqrt(2))
|
|
|
|
# return a p_value and randomness result
|
|
return p_value, (p_value >= 0.01)
|
|
|
|
@staticmethod
|
|
def chi_square(binary_data: str):
|
|
|
|
observed_frequencies = [binary_data.count(48), binary_data.count(49)]
|
|
expected_probabilities = [0.5, 0.5] # Assuming equal probability for each bit value
|
|
total_observations = len(binary_data)
|
|
expected_frequencies = np.array(expected_probabilities) * total_observations
|
|
|
|
chi2_statistic, p_value = stats.chisquare(observed_frequencies, f_exp=expected_frequencies)
|
|
|
|
return p_value, p_value >= 0.01, chi2_statistic
|