code stringlengths 3 6.57k |
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procedure('kernel_heat_3d') |
loop(0) |
known('n>3') |
tile(0,2,8,2) |
tile(0,4,64,3) |
tile(1,2,8,2) |
tile(1,4,64,3) |
flags.DEFINE_integer("resolution", 64, "Resolution of the video.") |
flags.DEFINE_string("output_file", None, "Path to the output file.") |
main(argv) |
len(argv) |
tf.app.UsageError("Too many command-line arguments.") |
tf.io.TFRecordWriter(FLAGS.output_file) |
range(20) |
six.StringIO() |
save(png_image) |
tf.train.BytesList(value=[png_image.getvalue() |
png_image.close() |
tf.train.FeatureList(feature=frame_list) |
tf.train.Int64List(value=[20]) |
tf.train.FloatList(value=[20.0]) |
tf.train.Int64List(value=[20]) |
tf.train.Int64List(value=[FLAGS.resolution, FLAGS.resolution]) |
tf.train.FeatureLists(feature_list=feature_list) |
tf.train.Features(feature=context_feature) |
writer.write(example.SerializeToString() |
writer.close() |
app.run(main) |
ndb
(base_models,) |
models.Registry.import_models([models.NAMES.base_model]) |
get_deletion_policy() |
get_export_policy() |
has_reference_to_user_id(cls, unused_user_id) |
The (unused) |
TopicSimilaritiesModel(base_models.BaseModel) |
ndb.JsonProperty(required=True) |
get_deletion_policy() |
get_export_policy() |
simplefilter(action='ignore', category=FutureWarning) |
np.random.seed(seed) |
gen_sample_func(sample_size=size_check, marginal=marginal) |
tp_func(theta_vec=theta_vec, x_vec=x_vec) |
np.average([np.log(true_prob_vec[kk]) |
np.log(1 - true_prob_vec[kk]) |
enumerate(bern_vec) |
tqdm(total=rep, desc='Toy Example for Simulations, n=%s, b=%s' % (sample_size_obs, b) |
gen_obs_func(sample_size=sample_size_obs, true_param=t0_val) |
sorted(classifier_dict_run.items() |
print('----- %s Trained' % clf_name) |
log_loss(y_true=bern_vec, y_pred=est_prob_vec) |
parameter (of size guided_sample) |
gen_param_fun(sample_size=guided_sample) |
np.apply_along_axis(arr=theta_mat_sample.reshape(-1, 1) |
np.apply_along_axis(arr=theta_mat_sample.reshape(-1, 1) |
np.apply_along_axis(arr=theta_mat_sample.reshape(-1, 1) |
np.exp(stats_sample) |
np.sum(stats_sample) |
np.random.choice(a=theta_mat_sample, p=stats_sample.reshape(-1, ) |
np.std(theta_mat_gaussian_fit) |
np.std(theta_mat_gaussian_fit) |
np.random.normal(size=b_prime, loc=np.mean(theta_mat_gaussian_fit) |
np.apply_along_axis(arr=theta_mat.reshape(-1, 1) |
msnh_sampling_func(b_prime=b_prime, sample_size=sample_size_obs) |
np.hstack((theta_mat.reshape(-1, 1) |
np.any(np.isnan(stats_mat_generated) |
np.all(np.isfinite(stats_mat_generated) |
np.any(np.isnan(stats_mat_observed) |
np.all(np.isfinite(stats_mat_observed) |
np.greater(stats_mat_observed, stats_mat_generated) |
astype(int) |
sorted(classifier_pvalue_dict.items() |
sum(indicator_vec) |
sum(indicator_vec) |
len(indicator_vec) |
np.repeat(sum(indicator_vec) |
len(indicator_vec) |
train_pvalue_clf(clf_model=clf_model_pvalue, X=theta_mat.reshape(-1, model_obj.d) |
indicator_vec.reshape(-1, ) |
clf_pvalue.predict_proba(t0_grid.reshape(-1, model_obj.d) |
clf_pvalue.predict_proba(theta_mat.reshape(-1, model_obj.d) |
log_loss(y_true=indicator_vec, y_pred=theta_mat_pred) |
clf_odds_fitted.items() |
items() |
np.mean((pvalue_val > alpha) |
astype(int) |
enumerate(t0_grid) |
int(t0_val == theta_0_current) |
int(pvalue_val[kk] > alpha) |
int(pvalue_val[kk] <= alpha) |
int(guided_sim) |
int(empirical_marginal) |
pbar.update(1) |
pd.DataFrame.from_records(data=out_val, index=range(len(out_val) |
str(alpha) |
replace('.', '-') |
str(t0_val) |
replace('.', '-') |
datetime.strftime(datetime.today() |
out_df.to_csv(out_dir + out_filename) |
print(cov_df.groupby(['classifier', 'classifier_pvalue']) |
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