code stringlengths 3 6.57k |
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print(lerp(100, 200, 1.) |
print(lerp(100, 200, .5) |
print(lerp(100, 200, .25) |
logging.getLogger(__name__) |
TimerProtocol(Protocol) |
start(self) |
NotImplementedError() |
stop(self) |
NotImplementedError() |
GaugeProtocol(Protocol) |
set(self, value: Union[int, float]) |
NotImplementedError() |
CounterProtocol(Protocol) |
count(self) |
NotImplementedError() |
BaseMetrics(ABC) |
__init__(self, base_name: str) |
create_timer(self, name: str, **kwargs: Any) |
NotImplementedError() |
create_gauge(self, name: str, **kwargs: Any) |
NotImplementedError() |
create_counter(self, name: str, **kwargs: Any) |
NotImplementedError() |
get_metrics_interface(base_name: str) |
load_system_paasta_config() |
get_metrics_provider() |
register_metrics_interface(name: Optional[str]) |
outer(func: Type[BaseMetrics]) |
register_metrics_interface('meteorite') |
MeteoriteMetrics(BaseMetrics) |
__init__(self, base_name: str) |
ImportError("yelp_meteorite not imported, pleast try another metrics provider") |
create_timer(self, name: str, **kwargs: Any) |
yelp_meteorite.create_timer(self.base_name + '.' + name, kwargs) |
create_gauge(self, name: str, **kwargs: Any) |
yelp_meteorite.create_gauge(self.base_name + '.' + name, kwargs) |
create_counter(self, name: str, **kwargs: Any) |
yelp_meteorite.create_counter(self.base_name + '.' + name, kwargs) |
Timer(TimerProtocol) |
__init__(self, name: str) |
start(self) |
log.debug("timer {} start at {}".format(self.name, time.time() |
stop(self) |
log.debug("timer {} stop at {}".format(self.name, time.time() |
Gauge(GaugeProtocol) |
__init__(self, name: str) |
set(self, value: Union[int, float]) |
log.debug(f"gauge {self.name} set to {value}") |
Counter(GaugeProtocol) |
__init__(self, name: str) |
count(self) |
log.debug(f"counter {self.name} incremented to {self.counter}") |
register_metrics_interface(None) |
NoMetrics(BaseMetrics) |
__init__(self, base_name: str) |
create_timer(self, name: str, **kwargs: Any) |
Timer(self.base_name + '.' + name) |
create_gauge(self, name: str, **kwargs: Any) |
Gauge(self.base_name + '.' + name) |
create_counter(self, name: str, **kwargs: Any) |
Counter(self.base_name + '.' + name) |
__init__(self, n_actions, frame_height=63, frame_width=113, stacked_frames=4, learning_rate=0.00001) |
self.conv_layer(self.input, 32, [8, 8], 4, 'conv1') |
self.conv_layer(self.conv1, 64, [4, 4], 2, 'conv2') |
self.conv_layer(self.conv2, 64, [3, 3], 1, 'conv3') |
Flatten() |
self.dense_layer(self.flat, 512, 'dense1', relu) |
tf.split(self.dense1, 2, 1) |
self.dense_layer(self.v_stream, 1, 'value') |
self.dense_layer(self.a_stream, self.n_actions, 'advantage') |
tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True) |
tf.argmax(self.q_values, 1) |
tf.placeholder(shape=[None], dtype=tf.float32) |
tf.placeholder(shape=[None], dtype=tf.uint8) |
tf.one_hot(self.action, self.n_actions, dtype=tf.float32) |
tf.reduce_sum(tf.multiply(self.q_values, self.action_one_hot) |
logcosh(self.target_q, self.Q) |
tf.reduce_mean(self.error) |
tf.train.AdamOptimizer(learning_rate=self.learning_rate) |
self.optimizer.minimize(self.loss) |
conv_layer(_inputs, _filters, _kernel_size, _strides, _name) |
VarianceScaling(scale=2.0) |
dense_layer(_inputs, _units, _name, _activation=None) |
VarianceScaling(scale=2.0) |
__init__(self, main_vars, target_vars) |
update_target_vars(self) |
enumerate(self.main_vars) |
assign(var.value() |
update_ops.append(copy_op) |
update_networks(self, sess) |
self.update_target_vars() |
sess.run(copy_op) |
StrEnum(str, Enum) |
_create_activation(activation_type) |
torch.sigmoid(x) |
ValueError('invalid activation_type.') |
_create_activation(activation_type) |
len(observation_shape) |
len(observation_shape) |
ValueError('observation_shape must be 1d or 3d.') |
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