def test_log_binom_coeff_many(self):
for _ in range(100):
n = randint(1, 10)
k = randint(0, n)
self.assertEqual(round(exp(HelperFunctions.log_bin_coeff(n, k))), round(binom(n, k)))
def log_pdf(self, X):
if not type(X) is numpy.ndarray:
raise TypeError("X must be a numpy array")
if not len(X.shape) is 2:
raise TypeError("X must be a 2D numpy array")
# this also enforces correct data ranges
if X.dtype != numpy.bool8:
raise ValueError("X must be a bool8 numpy array")
if not X.shape[1] == self.dimension:
raise ValueError("Dimension of X does not match own dimension")
num_active_self = sum(self.mu)
#max_possible_change = min(num_active_self, self.dimension - num_active_self)
# result vector
log_liks = zeros(len(X))
# compute action dependent log likelihood parts
for i in range(len(X)):
x = X[i]
num_active_x = sum(x)
# hamming distances using numpy broadcasting
# divide by two, integer division is always fine since even number of differences
num_diff = sum(self.mu != x)
if num_active_self == num_active_x:
num_diff / 2
if num_diff > self.N:
log_liks[i]=-inf
continue
if num_active_self != num_active_x:
action = num_active_x < num_active_self
if not all(x[self.mu==action]==action):
log_liks[i]=-inf
continue
else:
action = 2
#shared-terms
log_liks[i] = HelperFunctions.log_bin_coeff(self.N - 1, num_diff - 1) \
+ (num_diff - 1) * log(self.spread) \
+ (self.N - num_diff) * log(1 - self.spread)
# if there was a freedom of action, use factor 1/3
if num_diff <= min(num_active_self,self.dimension-num_active_self):
log_liks[i] -= log(3)
# action-specific terms
if action == 0:
# add
log_liks[i] -= HelperFunctions.log_bin_coeff(self.dimension - num_active_self, num_diff)
elif action == 1:
# del
log_liks[i] -= HelperFunctions.log_bin_coeff(num_active_self, num_diff)
elif action == 2:
# swap
log_liks[i] -= HelperFunctions.log_bin_coeff(num_active_self, num_diff) \
- HelperFunctions.log_bin_coeff(self.dimension - num_active_self, num_diff)
return log_liks
def test_log_binom_coeff_5(self):
n = 2
k = 3
self.assertEqual(round(exp(HelperFunctions.log_bin_coeff(n, k))), binom(n, k))
