import numpy as np
import torch
from torch.autograd import Variable

torch.manual_seed(42)
np.random.seed(42)

poisson_mean = 4
poisson_numsample = 200

samples = np.random.poisson(poisson_mean, poisson_numsample)

param = Variable(torch.zeros(1), requires_grad=True)
torch_samples = Variable(torch.from_numpy(samples).float(), requires_grad=False)

optimizer = torch.optim.RMSprop([param], lr=0.1)
loss = torch.nn.PoissonNLLLoss(log_input=False, full=True)

for i in range(5000):
    optimizer.zero_grad()
    output = loss(param, torch_samples)
    output.backward()

    if i % 1000 == 0:
        print('Loss:', output.data[0])

    optimizer.step()

print('Sample mean: ', torch_samples.mean().data.numpy())
print('Mean is : ', param.data.numpy())