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data_hp.py
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data_hp.py
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import numpy as np
data_size_train = 1080000
data_size_test = 100000
dict_n1_train = {
"m_array": np.array(np.hstack([np.tile(2, 4), np.tile(2, 10), np.tile(1, 16)])),
"eta": 7.29,
"Pt": 0.28183815,
"alpha": 2,
"d_0":100,
"d_array": np.arange(10, 301, 10, dtype=np.int32),
# control 'low', and 'high' will prevent the train dataset to have some extreme values.
# Thus, it will help the model to perform better. + Same for n2 train.
"noise": 0.1,
"low": 0.05,
"high": 0.999,
}
dict_n1_test = {
"m_array": dict_n1_train["m_array"],
"eta": dict_n1_train["eta"],
"Pt": dict_n1_train["Pt"],
"alpha": dict_n1_train["alpha"],
"d_0": dict_n1_train["d_0"],
"d_array": dict_n1_train["d_array"],
"noise": dict_n1_train["noise"],
"low": 0,
"high": 1.0,
}
dict_n2_train = {
"m_array": np.array(np.hstack([np.tile(1.5, 14), np.tile(1, 16)])),
"eta": 7.29,
"Pt": 0.28183815,
"alpha": 2.5,
"d_0":100,
"d_array": np.arange(10, 301, 10, dtype=np.int32),
"noise": 0.1,
"low": 0.05,
"high": 0.999,
}
dict_n2_test = {
"m_array": dict_n2_train["m_array"],
"eta": dict_n2_train["eta"],
"Pt": dict_n2_train["Pt"],
"alpha": dict_n2_train["alpha"],
"d_0": dict_n2_train["d_0"],
"d_array": dict_n2_train["d_array"],
"noise": dict_n2_train["noise"],
"low": 0,
"high": 1.0,
}
dict_ln1 = {
"eta": 7.29e-10,
"Pt": 0.28183815,
"alpha_1": 2.56,
"alpha_2": 6.34,
"delta_1": 3.9,
"delta_2": 5.2,
"d_0": 1,
"d_c": 102,
"d_array": np.arange(10, 301, 10, dtype=np.int32),
}
dict_ln2 = {
"eta": 7.29e-10,
"Pt": 0.28183815,
"alpha_1": 1.89,
"alpha_2": 5.86,
"delta_1": 3.1,
"delta_2": 3.6,
"d_0": 1,
"d_c": 182,
"d_array": np.arange(10, 301, 10, dtype=np.int32),
}