prediction_generation/abed_conf.py [106:155]:
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]


# many of these combinations will be invalid for the changepoint package, but
# it's easier to do it this way than to generate only the valid configurations.
R_changepoint_params = {
    "function": ["mean", "var", "meanvar"],
    "penalty": [
        "None",
        "SIC",
        "BIC",
        "MBIC",
        "AIC",
        "Hannan-Quinn",
        "Asymptotic",
    ],
    "statistic": ["Normal", "CUSUM", "CSS", "Gamma", "Exponential", "Poisson"],
}
R_changepoint_params_seg = copy.deepcopy(R_changepoint_params)
R_changepoint_params_seg["Q"] = ["max", "default"]

PARAMS = {
    "best_bocpd": {
        "intensity": [50, 100, 200],
        "prior_a": [0.01, 1.0, 100],
        "prior_b": [0.01, 1.0, 100],
        "prior_k": [0.01, 1.0, 100],
    },
    "best_cpnp": {
        "penalty": [
            "None",
            "SIC",
            "BIC",
            "MBIC",
            "AIC",
            "Hannan-Quinn",
            "Asymptotic",
        ],
        "quantiles": [10, 20, 30, 40],
    },
    "best_pelt": R_changepoint_params,
    "best_amoc": R_changepoint_params,
    "best_segneigh": R_changepoint_params_seg,
    "best_binseg": R_changepoint_params_seg,
    "best_rfpop": {"loss": ["L1", "L2", "Huber", "Outlier"]},
    "best_ecp": {
        "algorithm": ["e.agglo", "e.divisive"],
        "siglvl": [0.01, 0.05],
        "minsize": [2, 30],
        "alpha": [0.5, 1.0, 1.5],
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prediction_generation/original-project/abed_conf.py [99:147]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
]

# many of these combinations will be invalid for the changepoint package, but
# it's easier to do it this way than to generate only the valid configurations.
R_changepoint_params = {
    "function": ["mean", "var", "meanvar"],
    "penalty": [
        "None",
        "SIC",
        "BIC",
        "MBIC",
        "AIC",
        "Hannan-Quinn",
        "Asymptotic",
    ],
    "statistic": ["Normal", "CUSUM", "CSS", "Gamma", "Exponential", "Poisson"],
}
R_changepoint_params_seg = copy.deepcopy(R_changepoint_params)
R_changepoint_params_seg["Q"] = ["max", "default"]

PARAMS = {
    "best_bocpd": {
        "intensity": [50, 100, 200],
        "prior_a": [0.01, 1.0, 100],
        "prior_b": [0.01, 1.0, 100],
        "prior_k": [0.01, 1.0, 100],
    },
    "best_cpnp": {
        "penalty": [
            "None",
            "SIC",
            "BIC",
            "MBIC",
            "AIC",
            "Hannan-Quinn",
            "Asymptotic",
        ],
        "quantiles": [10, 20, 30, 40],
    },
    "best_pelt": R_changepoint_params,
    "best_amoc": R_changepoint_params,
    "best_segneigh": R_changepoint_params_seg,
    "best_binseg": R_changepoint_params_seg,
    "best_rfpop": {"loss": ["L1", "L2", "Huber", "Outlier"]},
    "best_ecp": {
        "algorithm": ["e.agglo", "e.divisive"],
        "siglvl": [0.01, 0.05],
        "minsize": [2, 30],
        "alpha": [0.5, 1.0, 1.5],
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