Add one more synthetic env
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@ -5,7 +5,7 @@ from .get_dataset_with_transform import get_datasets, get_nas_search_loaders
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from .SearchDatasetWrap import SearchDataset
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from .math_base_funcs import QuadraticFunc, CubicFunc, QuarticFunc
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from .math_dynamic_funcs import DynamicQuadraticFunc
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from .math_dynamic_funcs import DynamicQuadraticFunc, DynamicLinearFunc
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from .math_adv_funcs import ConstantFunc
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from .math_adv_funcs import ComposedSinFunc
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@ -59,18 +59,24 @@ class ComposedSinFunc(FitFunc):
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max_amplitude = kwargs.get("max_amplitude", 4)
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phase_shift = kwargs.get("phase_shift", 0.0)
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# create parameters
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amplitude_scale = QuadraticFunc(
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[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
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)
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fitting_data = []
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temp_max_scalar = 2 ** (num_sin_phase - 1)
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for i in range(num_sin_phase):
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value = (2 ** i) / temp_max_scalar
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next_value = (2 ** (i + 1)) / temp_max_scalar
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for _phase in (0, 0.25, 0.5, 0.75):
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inter_value = value + (next_value - value) * _phase
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fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
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period_phase_shift = QuarticFunc(fitting_data)
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if kwargs.get("amplitude_scale", None) is None:
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amplitude_scale = QuadraticFunc(
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[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
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)
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else:
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amplitude_scale = kwargs.get("amplitude_scale")
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if kwargs.get("period_phase_shift", None) is None:
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fitting_data = []
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temp_max_scalar = 2 ** (num_sin_phase - 1)
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for i in range(num_sin_phase):
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value = (2 ** i) / temp_max_scalar
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next_value = (2 ** (i + 1)) / temp_max_scalar
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for _phase in (0, 0.25, 0.5, 0.75):
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inter_value = value + (next_value - value) * _phase
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fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
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period_phase_shift = QuarticFunc(fitting_data)
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else:
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period_phase_shift = kwargs.get("period_phase_shift")
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self.set(
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dict(amplitude_scale=amplitude_scale, period_phase_shift=period_phase_shift)
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)
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@ -37,6 +37,33 @@ class DynamicFunc(FitFunc):
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return noise_y
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class DynamicLinearFunc(DynamicFunc):
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"""The dynamic linear function that outputs f(x) = a * x + b.
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The a and b is a function of timestamp.
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"""
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def __init__(self, params=None):
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super(DynamicLinearFunc, self).__init__(3, params)
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def __call__(self, x, timestamp=None):
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self.check_valid()
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if timestamp is None:
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timestamp = self._timestamp
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a = self._params[0](timestamp)
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b = self._params[1](timestamp)
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convert_fn = lambda x: x[-1] if isinstance(x, (tuple, list)) else x
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a, b = convert_fn(a), convert_fn(b)
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return a * x + b
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def __repr__(self):
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return "{name}({a} * x + {b}, timestamp={timestamp})".format(
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name=self.__class__.__name__,
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a=self._params[0],
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b=self._params[1],
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timestamp=self._timestamp,
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)
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class DynamicQuadraticFunc(DynamicFunc):
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"""The dynamic quadratic function that outputs f(x) = a * x^2 + b * x + c.
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The a, b, and c is a function of timestamp.
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@ -3,11 +3,20 @@
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#####################################################
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import copy
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from .math_dynamic_funcs import DynamicQuadraticFunc
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from .math_dynamic_funcs import DynamicLinearFunc, DynamicQuadraticFunc
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from .math_adv_funcs import ConstantFunc, ComposedSinFunc
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from .synthetic_env import SyntheticDEnv
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def create_example(timestamp_config=None, num_per_task=5000, indicator="v1"):
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if indicator == "v1":
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return create_example_v1(timestamp_config, num_per_task)
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elif indicator == "v2":
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return create_example_v2(timestamp_config, num_per_task)
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else:
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raise ValueError("Unkonwn indicator: {:}".format(indicator))
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def create_example_v1(
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timestamp_config=None,
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num_per_task=5000,
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@ -35,3 +44,29 @@ def create_example_v1(
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dynamic_env.set_oracle_map(copy.deepcopy(function))
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return dynamic_env, function
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def create_example_v2(
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timestamp_config=None,
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num_per_task=5000,
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):
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mean_generator = ConstantFunc(0)
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std_generator = ConstantFunc(1)
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dynamic_env = SyntheticDEnv(
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[mean_generator],
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[[std_generator]],
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num_per_task=num_per_task,
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timestamp_config=timestamp_config,
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)
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function = DynamicLinearFunc()
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function_param = dict()
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function_param[0] = ComposedSinFunc(
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amplitude_scale=ConstantFunc(1.0), period_phase_shift=ConstantFunc(1.0)
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)
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function_param[1] = ConstantFunc(constant=0.9)
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function.set(function_param)
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dynamic_env.set_oracle_map(copy.deepcopy(function))
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return dynamic_env, function
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@ -15,6 +15,7 @@ if str(lib_dir) not in sys.path:
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from datasets import QuadraticFunc
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from datasets import ConstantFunc
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from datasets import DynamicLinearFunc
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from datasets import DynamicQuadraticFunc
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from datasets import ComposedSinFunc
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@ -50,3 +51,20 @@ class TestDynamicFunc(unittest.TestCase):
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function.set_timestamp(1)
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print(function(2))
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def test_simple_linear(self):
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timestamps = 30
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function = DynamicLinearFunc()
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function_param = dict()
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function_param[0] = ComposedSinFunc(
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num=timestamps, num_sin_phase=4, phase_shift=1.0, max_amplitude=1.0
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)
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function_param[1] = ConstantFunc(constant=0.9)
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function.set(function_param)
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print(function)
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with self.assertRaises(TypeError) as context:
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function(0)
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function.set_timestamp(1)
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print(function(2))
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