def generate_test_data(sensors):
events = generate_random_events(sensors,
num_test_instances,
at_least_one_per_setting=True)
data = events_to_dataset(events,
name="synthetic",
excluded_sensors=[],
excluded_actions=[])
return dataset_to_sklearn(data)
def generate_synthetic_dataset(num_sensors, nominal_values_per_sensor, num_instances):
"""
Generate a random dataset in the format required by scikit-learn, see dataset.dataset_to_sklearn for more details.
@param num_sensors: Number of different sensors in the dataset.
@param nominal_values_per_sensor: Number of possible settings for each sensor.
@param num_instances: Number of data instances in the dataset.
@return: A random dataset in scikit-learn format.
"""
#generate the desired number of sensors, all have the same number of possible settings
sensor_settings = set("v%d" % id for id in range(nominal_values_per_sensor))
sensor_name = lambda id: "s%d" % id
sensors = {sensor_name(id): sensor_settings for id in range(num_sensors)}
events = generate_random_events(sensors, num_instances)
dataset = events_to_dataset(events, name="synthetic", excluded_sensors=[], excluded_actions=[])
return dataset_to_sklearn(dataset)
def generate_synthetic_dataset(num_sensors, nominal_values_per_sensor,
num_instances):
"""
Generate a random dataset in the format required by scikit-learn, see dataset.dataset_to_sklearn for more details.
@param num_sensors: Number of different sensors in the dataset.
@param nominal_values_per_sensor: Number of possible settings for each sensor.
@param num_instances: Number of data instances in the dataset.
@return: A random dataset in scikit-learn format.
"""
#generate the desired number of sensors, all have the same number of possible settings
sensor_settings = set("v%d" % id
for id in range(nominal_values_per_sensor))
sensor_name = lambda id: "s%d" % id
sensors = {sensor_name(id): sensor_settings for id in range(num_sensors)}
events = generate_random_events(sensors, num_instances)
dataset = events_to_dataset(events,
name="synthetic",
excluded_sensors=[],
excluded_actions=[])
return dataset_to_sklearn(dataset)
def generate_test_data(sensors):
events = generate_random_events(sensors, num_test_instances, at_least_one_per_setting=True)
data = events_to_dataset(events, name="synthetic", excluded_sensors=[], excluded_actions=[])
return dataset_to_sklearn(data)