100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
import math
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from .synthetic_utils import TimeStamp
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from .synthetic_env import SyntheticDEnv
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from .math_core import LinearSFunc
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from .math_core import LinearDFunc
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from .math_core import QuadraticDFunc, SinQuadraticDFunc, BinaryQuadraticDFunc
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from .math_core import (
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ConstantFunc,
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ComposedSinSFunc as SinFunc,
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ComposedCosSFunc as CosFunc,
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)
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from .math_core import UniformDGenerator, GaussianDGenerator
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__all__ = ["TimeStamp", "SyntheticDEnv", "get_synthetic_env"]
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def get_synthetic_env(total_timestamp=1600, num_per_task=1000, mode=None, version="v1"):
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max_time = math.pi * 10
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if version.lower() == "v1":
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mean_generator = ConstantFunc(0)
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std_generator = ConstantFunc(1)
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data_generator = GaussianDGenerator(
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[mean_generator], [[std_generator]], (-3, 3)
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)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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)
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oracle_map = LinearDFunc(
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params={
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0: SinFunc(params={0: 2.0, 1: 1.0, 2: 2.2}), # 2 sin(t) + 2.2
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1: SinFunc(params={0: 1.5, 1: 0.6, 2: 1.8}), # 1.5 sin(0.6t) + 1.8
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}
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)
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dynamic_env = SyntheticDEnv(
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data_generator, oracle_map, time_generator, num_per_task, noise=0.1
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)
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dynamic_env.set_regression()
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elif version.lower() == "v2":
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mean_generator = ConstantFunc(0)
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std_generator = ConstantFunc(1)
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data_generator = GaussianDGenerator(
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[mean_generator], [[std_generator]], (-3, 3)
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)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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)
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oracle_map = QuadraticDFunc(
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params={
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0: LinearSFunc(params={0: 0.1, 1: 0}), # 0.1 * t
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1: ConstantFunc(0),
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2: CosFunc(params={0: 4.0, 1: 10, 2: 0}), # 4 * cos(10 * t)
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}
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)
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dynamic_env = SyntheticDEnv(
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data_generator, oracle_map, time_generator, num_per_task, noise=0.1
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)
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dynamic_env.set_regression()
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elif version.lower() == "v3":
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mean_generator = SinFunc(params={0: 1, 1: 1, 2: 0}) # sin(t)
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std_generator = CosFunc(params={0: 0.5, 1: 1, 2: 1}) # 0.5 cos(t) + 1
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data_generator = GaussianDGenerator(
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[mean_generator], [[std_generator]], (-3, 3)
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)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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)
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oracle_map = SinQuadraticDFunc(
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params={
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0: CosFunc(params={0: 0.5, 1: 1, 2: 1}), # 0.5 cos(t) + 1
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1: SinFunc(params={0: 1, 1: 1, 2: 0}), # sin(t)
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2: ConstantFunc(0),
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}
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)
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dynamic_env = SyntheticDEnv(
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data_generator, oracle_map, time_generator, num_per_task, noise=0.05
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)
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dynamic_env.set_regression()
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elif version.lower() == "v4":
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l_generator = ConstantFunc(-2)
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r_generator = ConstantFunc(2)
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data_generator = UniformDGenerator([l_generator] * 2, [r_generator] * 2)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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)
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oracle_map = BinaryQuadraticDFunc(
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params={
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0: SinFunc(params={0: 1, 1: 3, 2: 0}), # sin(3 * t)
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1: CosFunc(params={0: 1, 1: 6, 2: 0}), # cos(6 * t)
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2: ConstantFunc(0),
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}
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)
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dynamic_env = SyntheticDEnv(
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data_generator, oracle_map, time_generator, num_per_task, noise=None
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)
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dynamic_env.set_classification(2)
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else:
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raise ValueError("Unknown version: {:}".format(version))
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return dynamic_env
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