def gen_benchmarks(self, force=False):
if not os.path.isfile(self.filtering_benchmark) or force:
print('generating benchmark {}...'.format(
self.filtering_benchmark))
bench = []
for _ in range(100):
bench.append(
sample_mog(10,
1000,
4,
rand_N=True,
rand_K=True,
return_ll=True))
torch.save(bench, self.filtering_benchmark)
if not os.path.isfile(self.clustering_benchmark) or force:
print('generating benchmark {}...'.format(
self.clustering_benchmark))
bench = []
for _ in range(100):
bench.append(
sample_mog(10,
3000,
12,
rand_N=True,
rand_K=True,
return_ll=True))
torch.save(bench, self.clustering_benchmark)
def gen_benchmarks(self, force=False):
if not os.path.isfile(self.testfile) or force:
print('generating benchmark {}...'.format(self.testfile))
bench = []
for _ in range(100):
bench.append(sample_mog(10, 1000, 4,
rand_N=True, rand_K=True, return_ll=True))
torch.save(bench, self.testfile)
if not os.path.isfile(self.clusterfile) or force:
print('generating benchmark {}...'.format(self.clusterfile))
bench = []
for _ in range(100):
# bench.append(sample_mog(10, 3000, 12,
bench.append(sample_mog(10, 600, 12,
rand_N=True, rand_K=True, return_ll=True))
# bench.append(sample_mog_FP(B=10, N=-1, K=12, sample_K=False, det_per_cluster=4, dim=2,
# onehot=True, add_false_positives=False, FP_count=64, meas_std=.1))
torch.save(bench, self.clusterfile)
def sample(self, B, N, K, **kwargs):
return sample_mog(B, N, K, device='cuda', **kwargs)
def sample(self, B, N, K, **kwargs):
return sample_mog(B, N, K, device=torch.device('cuda'), **kwargs)