code stringlengths 3 6.57k |
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assert_allclose(pars["x"].value, 2, rtol=1e-3) |
assert_allclose(pars["y"].value, 3e5, rtol=1e-3) |
poor (0.040488) |
assert_allclose(pars["z"].value, 4e-5, rtol=2e-2) |
assert_allclose(factors, [2, 3, 4], rtol=1e-3) |
assert_allclose(minuit.values["par_000_x"], 2, rtol=1e-3) |
assert_allclose(minuit.values["par_001_y"], 3, rtol=1e-3) |
assert_allclose(minuit.values["par_002_z"], 4, rtol=1e-3) |
test_iminuit_frozen(pars) |
optimize_iminuit(function=fcn, parameters=pars) |
assert_allclose(pars["x"].value, 2, rtol=1e-4) |
assert_allclose(pars["y"].value, 3.1e5) |
assert_allclose(pars["z"].value, 4.e-5, rtol=1e-4) |
assert_allclose(fcn(pars) |
minuit.list_of_fixed_param() |
test_iminuit_limits(pars) |
optimize_iminuit(function=fcn, parameters=pars) |
assert_allclose(pars["x"].value, 2, rtol=1e-2) |
assert_allclose(pars["y"].value, 301000, rtol=1e-3) |
minuit.get_param_states() |
assert_allclose(y["lower_limit"], 3.01) |
int(input() |
print('00') |
str(int(10 * m) |
len(m) |
print(m) |
print(int(m) |
print((int(m) |
print('89') |
Flow(nn.Module) |
torch.manual_seed(123) |
st.Flow(st.UnitNormal(dim) |
st.Affine(dim) |
torch.rand(1, dim) |
flow(x) |
flow.inverse(y) |
base_dist (Type[torch.distributions]) |
transforms (List[st.flows]) |
__init__(self, base_dist=None, transforms=[]) |
super() |
__init__() |
nn.ModuleList(transforms) |
forward(self, x, latent=None, mask=None, t=None, reverse=False, **kwargs) |
x (tensor) |
shape (..., dim) |
latent (tensor, optional) |
shape (..., latent_dim) |
mask (tensor) |
shape (..., 1) |
t (tensor, optional) |
reverse (bool, optional) |
y (tensor) |
density (..., dim) |
log_jac_diag (tensor) |
diagonal (..., dim) |
torch.zeros_like(x) |
to(x) |
f.inverse(x * _mask, latent=latent, mask=mask, t=t, **kwargs) |
f.forward(x * _mask, latent=latent, mask=mask, t=t, **kwargs) |
inverse(self, y, latent=None, mask=None, t=None, **kwargs) |
self.forward(y, latent=latent, mask=mask, t=t, reverse=True, **kwargs) |
log_prob(self, x, **kwargs) |
x (tensor) |
shape (..., dim) |
log_prob (tensor) |
shape (..., 1) |
ValueError('Please define `base_dist` if you need log-probability') |
self.inverse(x, **kwargs) |
self.base_dist.log_prob(x) |
log_jac_diag.sum(-1) |
log_prob.unsqueeze(-1) |
sample(self, num_samples, latent=None, mask=None, **kwargs) |
num_samples (tuple or int) |
latent (tensor) |
shape (..., latent_dim) |
x (tensor) |
shape (*num_samples, dim) |
ValueError('Please define `base_dist` if you need sampling') |
isinstance(num_samples, int) |
self.base_dist.rsample(num_samples) |
self.forward(x, **kwargs) |
Copyright (c) |
RPCSignerTest(BlinkhashTestFramework) |
mock_signer_path(self) |
os.path.join(os.path.dirname(os.path.realpath(__file__) |
platform.system() |
set_test_params(self) |
self.mock_signer_path() |
self.mock_signer_path() |
skip_test_if_missing_module(self) |
self.skip_if_no_external_signer() |
set_mock_result(self, node, res) |
open(os.path.join(node.cwd, "mock_result") |
f.write(res) |
clear_mock_result(self, node) |
os.remove(os.path.join(node.cwd, "mock_result") |
run_test(self) |
self.log.debug(f"-signer={self.mock_signer_path() |
self.set_mock_result(self.nodes[1], "2") |
self.clear_mock_result(self.nodes[1]) |
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