diff options
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/functions.py | 36 |
1 files changed, 17 insertions, 19 deletions
diff --git a/lib/functions.py b/lib/functions.py index c30a5f7..98b6317 100644 --- a/lib/functions.py +++ b/lib/functions.py @@ -39,18 +39,17 @@ class ParamFunction: (-> single float as model input) are used. However, n-dimensional functions (-> list of float as model input) are also supported. - arguments: - param_function -- regression function. Must have the signature - (reg_param, model_param) -> float. - reg_param is a list of regression variable values, - model_param is the model input value (float). - Example: lambda rp, mp: rp[0] + rp[1] * mp - validation_function -- function used to check whether param_function - is defined for a given model_param. Signature: - model_param -> bool - Example: lambda mp: mp > 0 - num_vars -- How many regression variables are used by this function, - i.e., the length of param_function's reg_param argument. + :param param_function: regression function. Must have the signature + (reg_param, model_param) -> float. + reg_param is a list of regression variable values, + model_param is the model input value (float). + Example: lambda rp, mp: rp[0] + rp[1] * mp + :param validation_function: function used to check whether param_function + is defined for a given model_param. Signature: + model_param -> bool + Example: lambda mp: mp > 0 + :param num_vars: How many regression variables are used by this function, + i.e., the length of param_function's reg_param argument. """ self._param_function = param_function self._validation_function = validation_function @@ -270,9 +269,9 @@ class AnalyticFunction: Evaluate model function with specified param/arg values. arguments: - param_list -- parameter values (list of float). First item + :param param_list: parameter values (list of float). First item corresponds to lexically first parameter, etc. - arg_list -- argument values (list of float), if arguments are used. + :param arg_list: argument values (list of float), if arguments are used. """ if len(self._regression_args) == 0: return self._function(param_list, arg_list) @@ -315,15 +314,14 @@ class analytic: """ Retrieve pre-defined set of regression function candidates. + :param safe_functions_enabled: Include "safe" variants of functions with + limited argument range, e.g. a safe + inverse which returns 1 when dividing by 0. + Returns a dict of functions which are typical for energy/timing behaviour of embedded hardware, e.g. linear, exponential or inverse dependency on a configuration setting/runtime variable. - arguments: - safe_functions_enabled -- Include "safe" variants of functions with - limited argument range, e.g. a safe - inverse which returns 1 when dividing by 0. - Each function is a ParamFunction object. In most cases, two regression variables are expected. """ |