def generate_base_hyper_parameter_set(low_lr=1, high_lr=4, low_reg=1, high_reg=4):
    ' Generate a base set of hyperparameters that are necessary for any\n    model, but sufficient for none.\n\n    Parameters\n    ----------\n    low_lr : float\n        minimum of log range for learning rate: learning rate is sampled\n        between `10**(-low_reg)` and `10**(-high_reg)`\n    high_lr : float\n        maximum  of log range for learning rate: learning rate is sampled\n        between `10**(-low_reg)` and `10**(-high_reg)`\n    low_reg : float\n        minimum  of log range for regularization rate: regularization rate is\n        sampled between `10**(-low_reg)` and `10**(-high_reg)`\n    high_reg : float\n        maximum  of log range for regularization rate: regularization rate is\n        sampled between `10**(-low_reg)` and `10**(-high_reg)`\n\n    Returns\n    -------\n    hyperparameters : dict\n        basis hyperpameters\n    '
    hyperparameters = {}
    hyperparameters['learning_rate'] = get_learning_rate(low_lr, high_lr)
    hyperparameters['regularization_rate'] = get_regularization(low_reg, high_reg)
    return hyperparameters
Generate a base set of hyperparameters that are necessary for any
model, but sufficient for none.

Parameters
----------
low_lr : float
    minimum of log range for learning rate: learning rate is sampled
    between `10**(-low_reg)` and `10**(-high_reg)`
high_lr : float
    maximum  of log range for learning rate: learning rate is sampled
    between `10**(-low_reg)` and `10**(-high_reg)`
low_reg : float
    minimum  of log range for regularization rate: regularization rate is
    sampled between `10**(-low_reg)` and `10**(-high_reg)`
high_reg : float
    maximum  of log range for regularization rate: regularization rate is
    sampled between `10**(-low_reg)` and `10**(-high_reg)`

Returns
-------
hyperparameters : dict
    basis hyperpameters
def generate_base_hyper_parameter_set(low_lr=1, high_lr=4, low_reg=1, high_reg=4):
    ' Generate a base set of hyperparameters that are necessary for any\n    model, but sufficient for none.\n\n    Parameters\n    ----------\n    low_lr : float\n        minimum of log range for learning rate: learning rate is sampled\n        between `10**(-low_reg)` and `10**(-high_reg)`\n    high_lr : float\n        maximum  of log range for learning rate: learning rate is sampled\n        between `10**(-low_reg)` and `10**(-high_reg)`\n    low_reg : float\n        minimum  of log range for regularization rate: regularization rate is\n        sampled between `10**(-low_reg)` and `10**(-high_reg)`\n    high_reg : float\n        maximum  of log range for regularization rate: regularization rate is\n        sampled between `10**(-low_reg)` and `10**(-high_reg)`\n\n    Returns\n    -------\n    hyperparameters : dict\n        basis hyperpameters\n    '
    hyperparameters = {}
    hyperparameters['learning_rate'] = get_learning_rate(low_lr, high_lr)
    hyperparameters['regularization_rate'] = get_regularization(low_reg, high_reg)
    return hyperparameters