(self)
| 27 | assert spikes.size() == torch.Size((t, *shape)) |
| 28 | |
| 29 | def test_bernoulli_loader(self): |
| 30 | for s in [1, 100]: # number of data samples |
| 31 | for n in [1, 100]: # number of nodes in layer |
| 32 | for m in [0.1, 1.0]: # maximum spiking probability |
| 33 | for t in [1, 100]: # number of timesteps |
| 34 | data = torch.empty(s, n).uniform_(0, 1) |
| 35 | spike_loader = bernoulli_loader(data, time=t, max_prob=m) |
| 36 | |
| 37 | for i, spikes in enumerate(spike_loader): |
| 38 | assert spikes.size() == torch.Size((t, n)) |
| 39 | |
| 40 | def test_poisson(self): |
| 41 | for n in [1, 100]: # number of nodes in layer |
nothing calls this directly
no test coverage detected