code stringlengths 3 6.57k |
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open('pp30days_views_edits.csv', 'w') |
csv.writer(f) |
writer.writerow(('date', 'edits', 'views') |
writer.writerow((n[0], n[1]['total'], n[1]['views'],) |
test_instantiates_without_error() |
FakeSite() |
RoutingResource(fake_site) |
test_how_head_requests_are_handled(dummy_request:RequestRetval) |
App(__name__) |
app.add("/foo", methods=["POST"]) |
handle_foo(request) |
dummy_request.request.requestReceived(b"HEAD", b"/foo", b"HTTP/1.1") |
test_ensure_blows_up_with_a_bad_add() |
App(__name__) |
pytest.raises(ValueError) |
app.add("/trash") |
str(excinfo.value) |
test_ensure_blowsup_with_a_class_that_has_no_way_to_render() |
App(__name__) |
pytest.raises(Unrenderable) |
app.add("/trash") |
BaseClass(object) |
test_ensure_a_classic_like_class_is_routed() |
App(__name__) |
app.add("/trash") |
GoodClass(object) |
render(self, request) |
next(iter(app.router.iter_rules() |
isinstance(endpoint, ViewClassResource) |
test_ensure_resource_is_added() |
App(__name__) |
app.add_resource("/404", resource=NoResource() |
next(iter(app.router.iter_rules() |
isinstance(endpoint, NoResource) |
test_handle_add_slashes(dummy_request:RequestRetval) |
App(__name__) |
MagicMock() |
app.route("/js/") |
dummy_request.request.requestReceived(b"GET", b"/js", b"HTTP/1.1") |
dummy_request.request.responseHeaders.getRawHeaders(b"location") |
np.random.seed(1) |
mx.random.seed(1) |
inv_softplus(y: NPArrayLike) |
log(1 + exp(x) |
log(exp(y) |
np.log(np.exp(y) |
PositiveInt(5) |
PositiveFloat(1e-2) |
mx.cpu() |
distr_output.get_args_proj() |
arg_proj.initialize() |
arg_proj.hybridize() |
zip(arg_proj.proj, init_biases) |
mx.initializer.Constant(bias) |
arg_proj.collect_params() |
mx.nd.array(np.ones((len(samples) |
mx.gluon.data.ArrayDataset(dummy_data, samples) |
range(num_epochs) |
enumerate(train_data) |
data.as_in_context(model_ctx) |
sample_label.as_in_context(model_ctx) |
mx.autograd.record() |
arg_proj(data) |
distr_output.distribution(distr_args) |
distr.loss(sample_label) |
loss.backward() |
trainer.step(BATCH_SIZE) |
mx.nd.mean(loss) |
asscalar() |
print("Epoch %s, loss: %s" % (e, cumulative_loss / num_batches) |
asnumpy() |
arg_proj(mx.nd.array(np.ones((1, 1) |
pytest.mark.parametrize("mu, sigma, nu", [(2.3, 0.7, 6.0) |
pytest.mark.parametrize("hybridize", [True, False]) |
mx.nd.zeros((NUM_SAMPLES,) |
mx.nd.zeros((NUM_SAMPLES,) |
mx.nd.zeros((NUM_SAMPLES,) |
StudentT(mus, sigmas, nus) |
distr.sample() |
softplus(x) |
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma) |
inv_softplus(nu - 2) |
StudentTOutput() |
PositiveInt(10) |
PositiveFloat(1e-2) |
np.abs(mu_hat - mu) |
np.abs(sigma_hat - sigma) |
np.abs(nu_hat - nu) |
pytest.mark.parametrize("mu, sigma", [(1.0, 0.1) |
pytest.mark.parametrize("hybridize", [True, False]) |
test_gaussian_likelihood(mu: float, sigma: float, hybridize: bool) |
mx.nd.zeros((NUM_SAMPLES,) |
mx.nd.zeros((NUM_SAMPLES,) |
Gaussian(mus, sigmas) |
distr.sample() |
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma) |
GaussianOutput() |
PositiveFloat(0.001) |
PositiveInt(5) |
np.abs(mu_hat - mu) |
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