def estimate_infcens(Y):
res = {}
params = np.array([[0, 0, 1, 0, 1]] * N).T
for _ in range(100000):
D = MultivariateNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 1 * grad
if np.linalg.norm(grad) < 1e-4:
break
print('Jointly Estimated E:', params[0, 0])
res['joint'] = params[0, 0]
params = np.array([[0, 0]] * N).T
for _ in range(100000):
D = LogNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 0.005 * grad
if np.linalg.norm(grad) < 1e-4:
break
print('Estimate E (assume non-inf):', params[0, 0])
res['lognorm'] = params[0, 0]
return res
def mvnorm_mle(Y, max_iter=1e4, lr=0.5, eps=1e-4):
N = Y.shape[0]
params = np.array([[0, 0, 1, 0, 1]] * N).T
for _ in range(max_iter):
D = MultivariateNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - lr * grad
if np.linalg.norm(grad) < eps:
break
def lognormal_mle(Y, max_iter=1e4, lr=0.05, eps=1e-4, verbose=False):
N = Y.shape[0]
params = np.array([[0, 0]] * N).T
for i in range(int(max_iter)):
if i % 500 == 1 and verbose:
print('Param: ', params[:, :2])
print('Grad: ', grad)
D = LogNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - lr * grad
if np.linalg.norm(grad) < eps:
break
mu = params[0, 0]
sigma = params[1, 0]
return mu, sigma
col_event = 'Event'
col_time = 'Time'
y = np.empty(dtype=[(col_event, np.bool), (col_time, np.float64)],
shape=T.shape[0])
y[col_event] = E
y[col_time] = np.exp(T)
return y
Y = Y_join(T, E)
params = np.array([[0, 0, 1, 0, 1]] * N).T
for _ in range(100000):
D = MultivariateNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 1 * grad
if np.linalg.norm(grad) < 1e-4:
break
print('Jointly Estimated E:', params[0, 0])
params = np.array([[0, 0]] * N).T
for _ in range(100000):
D = LogNormal(params)
S = MLE()
grad = np.mean(S.grad(D, Y, natural=True).T, axis=1, keepdims=True)
params = params - 0.1 * grad
if np.linalg.norm(grad) < 1e-4:
break
