code stringlengths 3 6.57k |
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autofix_lib.Commit('message!', 'test-branch', None) |
testing.git.revparse(file_config_files.dir1) |
testing.git.revparse(file_config_files.dir2) |
assert (rev_before1, rev_before2) |
test_fix_non_default_branch(file_config_non_default) |
clone.main(('--config-filename', str(file_config_non_default.cfg) |
str(file_config_non_default.output_dir.join('repo1') |
load_config(file_config_non_default.cfg) |
autofix_lib.Commit('message!', 'test-branch', 'A B <a@a.a>') |
file_config_non_default.dir1.join('f') |
read() |
NameGenerator(object) |
__init__(self, names=None) |
__call__(self) |
self.names.pop(random.randrange(len(self.names) |
__iter__(self) |
self() |
expired_token(auth_token) |
timezone.now() |
timezone.timedelta(hours=24) |
create_auth_token(user) |
Token.objects.get_or_create(user=user) |
timezone.now() |
token.save() |
sys.exit(1) |
dependency_checker.check_deps() |
dependency_installer.install_deps() |
dependency_checker.check_deps() |
sys.exit(1) |
dependency_updater.update_deps() |
VortexWindow() |
pyglet.app.run() |
GPTTextField(TransformerTextField) |
GPTEmbedder(TransformerEmbedder) |
GPT2TextField(TransformerTextField) |
GPT2Embedder(TransformerEmbedder) |
LSTMSeq2Seq(BaseModel) |
__init__(self, check_optional_config=True, future_seq_len=2) |
_build_train(self, mc=False, **config) |
super() |
_check_config(**config) |
config.get('metric', 'mean_squared_error') |
config.get('latent_dim', 128) |
config.get('dropout', 0.2) |
config.get('lr', 0.001) |
config.get('batch_size', 64) |
Input(shape=(None, self.feature_num) |
encoder(self.encoder_inputs, training=training) |
Input(shape=(None, self.target_col_num) |
Dense(self.target_col_num, name="decoder_dense") |
self.decoder_dense(decoder_outputs) |
Model([self.encoder_inputs, self.decoder_inputs], decoder_outputs) |
keras.optimizers.RMSprop(lr=self.lr) |
_restore_model(self) |
_build_inference(self, mc=False) |
Model(self.encoder_inputs, self.encoder_states) |
Input(shape=(self.latent_dim,) |
Input(shape=(self.latent_dim,) |
self.decoder_dense(decoder_outputs) |
_decode_sequence(self, input_seq, mc=False) |
self._build_inference(mc=mc) |
encoder_model.predict(input_seq) |
np.zeros((len(input_seq) |
np.zeros((len(input_seq) |
range(self.future_seq_len) |
decoder_model.predict([target_seq] + states_value) |
sequence (of length 1) |
np.zeros((len(input_seq) |
_get_decoder_inputs(self, x, y) |
of (sample_num, past_sequence_len, feature_num) |
of (sample_num, future_sequence_len, target_col_num) |
np.zeros(y.shape) |
_get_len(self, x, y) |
_expand_y(self, y) |
len(y.shape) |
np.expand_dims(y, axis=2) |
_pre_processing(self, x, y, validation_data) |
self._expand_y(y) |
self._get_len(x, y) |
self._get_decoder_inputs(x, y) |
self._expand_y(val_y) |
self._get_decoder_inputs(val_x, val_y) |
fit_eval(self, data, validation_data=None, mc=False, verbose=0, **config) |
form (x, y) |
format (no. of samples, past sequence length, 2+feature length) |
value (data type should be numeric) |
format (no. of samples, future sequence length) |
format (no. of samples, ) |
format (x_test,y_test) |
self._pre_processing(x, y, validation_data) |
self._build_train(mc=mc, **config) |
config.get('batch_size', 64) |
format(batch_size, epochs, lr, time() |
TensorBoard(log_dir="logs/" + name) |
config.get("epochs", 10) |
print(hist.history) |
self.model.evaluate(x, y) |
hist.history.get(self.metric) |
hist.history.get('val_' + str(self.metric) |
evaluate(self, x, y, metric=['mse']) |
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