def t4():
for j in range(0, 100):
n = random.randint(3, 8)
epsilon = 0.1
delta = 0.1
sample_size = math.ceil(2*(n+1)*math.log((n+1)/(2*delta))/(epsilon**2))
test_weights = [random.random() for i in range(0, n+1)]
exact_values = exact_shapley(test_weights, n)
distribution = inverse_shapley.q_distribution(n)
sum_distribution = sum(distribution)
distribution = [distribution[i]/sum_distribution for i in range(0, len(distribution))]
cumulative = inverse_shapley.q_cumulative(distribution, n)
fcc = inverse_shapley.estimate_correlation(test_weights, sample_size, n, cumulative)
fcc_avg = sum(fcc[1:n+1])/n
rhs = [0]*n
lhs = [0]*n
for z in range(0, n):
rhs[z] = 2.0/n + (sum_distribution/2.0)*(fcc[z+1] - fcc_avg)
lhs[z] = exact_values[z]
if inverse_shapley.l2_distance(lhs, rhs) > epsilon:
print(test_weights)
print(fcc)
print(exact_values)
print(lhs)
print(rhs)
print(inverse_shapley.l2_distance(lhs, rhs))
def t5():
for j in range(0, 100):
n = random.randint(3, 8)
epsilon = 0.1
delta = 0.1
sample_size = math.ceil(2*(n+1)*math.log((n+1)/(2*delta))/((epsilon/16)**2))
test_weights = [random.random() for i in range(0, n+1)]
exact_values = exact_shapley(test_weights, n)
distribution = inverse_shapley.q_distribution(n)
sum_distribution = sum(distribution)
distribution = [distribution[i]/sum_distribution for i in range(0, len(distribution))]
cumulative = inverse_shapley.q_cumulative(distribution, n)
fcc = inverse_shapley.estimate_correlation(test_weights, sample_size, n, cumulative)
output_weights = inverse_shapley.boosting_ttv(fcc, epsilon, n, cumulative, sample_size)
print(inverse_shapley.l2_distance(output_weights,exact_values))
