diff options
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/dfatool.py | 47 | ||||
| -rw-r--r-- | lib/functions.py | 41 |
2 files changed, 41 insertions, 47 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 07aa7b3..47ce24e 100644 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -27,53 +27,6 @@ except ImportError: arg_support_enabled = True -def gplearn_to_function(function_str: str): - """ - Convert gplearn-style function string to Python function. - - Takes a function string like "mul(add(X0, X1), X2)" and returns - a Python function implementing the specified behaviour, - e.g. "lambda x, y, z: (x + y) * z". - - Supported functions: - add -- x + y - sub -- x - y - mul -- x * y - div -- x / y if |y| > 0.001, otherwise 1 - sqrt -- sqrt(|x|) - log -- log(|x|) if |x| > 0.001, otherwise 0 - inv -- 1 / x if |x| > 0.001, otherwise 0 - """ - eval_globals = { - "add": lambda x, y: x + y, - "sub": lambda x, y: x - y, - "mul": lambda x, y: x * y, - "div": lambda x, y: np.divide(x, y) if np.abs(y) > 0.001 else 1.0, - "sqrt": lambda x: np.sqrt(np.abs(x)), - "log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.0, - "inv": lambda x: 1.0 / x if np.abs(x) > 0.001 else 0.0, - } - - last_arg_index = 0 - for i in range(0, 100): - if function_str.find("X{:d}".format(i)) >= 0: - last_arg_index = i - - arg_list = [] - for i in range(0, last_arg_index + 1): - arg_list.append("X{:d}".format(i)) - - eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str) - logger.debug(eval_str) - return eval(eval_str, eval_globals) - - -def append_if_set(aggregate: dict, data: dict, key: str): - """Append data[key] to aggregate if key in data.""" - if key in data: - aggregate.append(data[key]) - - def mean_or_none(arr): """ Compute mean of NumPy array `arr`, return -1 if empty. diff --git a/lib/functions.py b/lib/functions.py index 99ba17d..94b1aaf 100644 --- a/lib/functions.py +++ b/lib/functions.py @@ -25,6 +25,47 @@ def powerset(iterable): return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1)) +def gplearn_to_function(function_str: str): + """ + Convert gplearn-style function string to Python function. + + Takes a function string like "mul(add(X0, X1), X2)" and returns + a Python function implementing the specified behaviour, + e.g. "lambda x, y, z: (x + y) * z". + + Supported functions: + add -- x + y + sub -- x - y + mul -- x * y + div -- x / y if |y| > 0.001, otherwise 1 + sqrt -- sqrt(|x|) + log -- log(|x|) if |x| > 0.001, otherwise 0 + inv -- 1 / x if |x| > 0.001, otherwise 0 + """ + eval_globals = { + "add": lambda x, y: x + y, + "sub": lambda x, y: x - y, + "mul": lambda x, y: x * y, + "div": lambda x, y: np.divide(x, y) if np.abs(y) > 0.001 else 1.0, + "sqrt": lambda x: np.sqrt(np.abs(x)), + "log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.0, + "inv": lambda x: 1.0 / x if np.abs(x) > 0.001 else 0.0, + } + + last_arg_index = 0 + for i in range(0, 100): + if function_str.find("X{:d}".format(i)) >= 0: + last_arg_index = i + + arg_list = [] + for i in range(0, last_arg_index + 1): + arg_list.append("X{:d}".format(i)) + + eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str) + logger.debug(eval_str) + return eval(eval_str, eval_globals) + + class ParamFunction: """ A one-dimensional model function, ready for least squares optimization and similar. |