(self, parameter)
| 1216 | return params.get("param") |
| 1217 | |
| 1218 | def chooseRandomValueForParameter(self, parameter): |
| 1219 | if parameter.config.get("mode", "uniform") == "uniform": |
| 1220 | minVal = parameter.config["min"] |
| 1221 | maxVal = parameter.config["max"] |
| 1222 | |
| 1223 | if parameter.config.get("scaling", "linear") == "logarithmic": |
| 1224 | minVal = math.log(minVal) |
| 1225 | maxVal = math.log(maxVal) |
| 1226 | |
| 1227 | value = random.uniform(minVal, maxVal) |
| 1228 | |
| 1229 | if parameter.config.get("scaling", "linear") == "logarithmic": |
| 1230 | value = math.exp(value) |
| 1231 | |
| 1232 | if "rounding" in parameter.config: |
| 1233 | value = ( |
| 1234 | round(value / parameter.config["rounding"]) |
| 1235 | * parameter.config["rounding"] |
| 1236 | ) |
| 1237 | elif parameter.config.get("mode", "uniform") == "normal": |
| 1238 | meanVal = parameter.config["mean"] |
| 1239 | stddevVal = parameter.config["stddev"] |
| 1240 | |
| 1241 | if parameter.config.get("scaling", "linear") == "logarithmic": |
| 1242 | meanVal = math.log(meanVal) |
| 1243 | stddevVal = math.log(stddevVal) |
| 1244 | |
| 1245 | value = random.gauss(meanVal, stddevVal) |
| 1246 | |
| 1247 | if parameter.config.get("scaling", "linear") == "logarithmic": |
| 1248 | value = math.exp(value) |
| 1249 | |
| 1250 | if "rounding" in parameter.config: |
| 1251 | value = ( |
| 1252 | round(value / parameter.config["rounding"]) |
| 1253 | * parameter.config["rounding"] |
| 1254 | ) |
| 1255 | elif parameter.config.get("mode", "uniform") == "randint": |
| 1256 | min = parameter.config["min"] |
| 1257 | max = parameter.config["max"] |
| 1258 | value = random.randint(min, max) |
| 1259 | |
| 1260 | return value |
| 1261 | |
| 1262 | def computePartialResultStatistics(self, hyperparameterSpace, results): |
| 1263 | losses = numpy.array( |
no test coverage detected