def fst_derivative_log_pdf(self, y, f, s=None):
#sigma = sigmoid(f, 1e-10)
sigma = sigmoid(f)
A = poisson_link(f)
res = sigma * (np.divide(y, A) - 1.0)
res = np.nan_to_num(res)
#res = np.maximum(res, 1e-8)
return res
def snd_derivative_pdf(self, y, m, s=None):
pdf = self.pdf(y, poisson_link(m))
sigm = sigmoid(m)
A = poisson_link(m)
pmf_prime = self.fst_derivative_pdf(y, m)
assert(sigm.shape == m.shape)
assert(A.shape == m.shape)
# temp1 = (y/A - 1) * (pmf_prime * sigm + sigm*(1-sigm)*pmf)
# temp2 = pmf * sigm * (-y)/np.square(A) * sigm
# return 1/factorial(y) * (temp1 + temp2)
temp1 = np.multiply(pmf_prime, np.multiply(sigm, np.divide(y, A) - 1))
temp = np.multiply(-np.divide(y, np.square(A)), np.square(sigm)) \
+ np.multiply(y / A - 1, np.multiply(sigm, 1 - sigm))
temp2 = np.multiply(pdf, temp)
res = temp1 + temp2
assert(not np.any(pds.isnull(res)))
return res
def snd_derivative_log_pdf(self, y, f, s=None):
sigma = sigmoid(f)
A = poisson_link(f)
temp1 = sigma * (1 - sigma) * (np.divide(y, A) - 1)
temp2 = y * np.square(sigma) / np.square(A)
res = temp1 - temp2
res = np.nan_to_num(res)
#return np.minimum(0, res)
return res
def pdf(self, y, m, S=None):
A = poisson_link(m)
# res = np.divide(np.multiply(np.power(A, y), np.exp(-A)), factorial(y))
#res = poisson.pmf(y, A)
res = np.power(A, y) / (1 + np.exp(m)) / np.math.factorial(y)
res = np.maximum(res, self.epsilon)
# print("res: ", res)
assert(not np.any(pds.isnull(res)))
return res
def log_pdf(self, y, m, s=None):
#f = poisson_link(m)
#t1 = y * np.log(f)
#t2 = -f
#t3 = -math.log(np.math.factorial(y))
res = poisson.logpmf(y, poisson_link(m))
res = np.nan_to_num(res)
#return np.maximum(res, 1e-8)
#return t1 + t2 + t3
return res
def fst_derivative_pdf(self, y, m, S=None):
# pdf * sigmoid * (y/A(f) - 1)
A = poisson_link(m)
pdf = self.pdf(y, m, S)
sigm = sigmoid(m)
assert(sigm.shape == m.shape)
assert(A.shape == m.shape)
temp = np.subtract(np.divide(y, A), 1)
res = np.multiply(pdf, np.multiply(sigm, temp))
assert(not np.any(pds.isnull(res)))
return res
def sample(self, m, s=None, k=1):
f = poisson_link(m)
return np.random.poisson(f, size=k)