|
1193 | 1193 | }, |
1194 | 1194 | { |
1195 | 1195 | "cell_type": "code", |
1196 | | - "execution_count": 7, |
1197 | | - "metadata": {}, |
1198 | | - "outputs": [ |
1199 | | - { |
1200 | | - "data": { |
1201 | | - "text/plain": [ |
1202 | | - "array([0.01087865, 1.63221335, 4.28792526, 4.19397442, 5.9202309 ])" |
1203 | | - ] |
1204 | | - }, |
1205 | | - "execution_count": 7, |
1206 | | - "metadata": {}, |
1207 | | - "output_type": "execute_result" |
1208 | | - } |
1209 | | - ], |
| 1196 | + "execution_count": null, |
| 1197 | + "metadata": {}, |
| 1198 | + "outputs": [], |
1210 | 1199 | "source": [ |
1211 | 1200 | "from spotPython.fun.objectivefunctions import analytical\n", |
1212 | 1201 | "import numpy as np\n", |
|
1217 | 1206 | }, |
1218 | 1207 | { |
1219 | 1208 | "cell_type": "code", |
1220 | | - "execution_count": 1, |
1221 | | - "metadata": {}, |
1222 | | - "outputs": [ |
1223 | | - { |
1224 | | - "name": "stdout", |
1225 | | - "output_type": "stream", |
1226 | | - "text": [ |
1227 | | - "[1 2 3 4 5]\n" |
1228 | | - ] |
1229 | | - } |
1230 | | - ], |
| 1209 | + "execution_count": null, |
| 1210 | + "metadata": {}, |
| 1211 | + "outputs": [], |
1231 | 1212 | "source": [ |
1232 | 1213 | "from spotPython.fun.objectivefunctions import analytical\n", |
1233 | 1214 | "import numpy as np\n", |
1234 | 1215 | "print(np.array([1, 2, 3, 4, 5]))\n", |
1235 | 1216 | "\n" |
1236 | 1217 | ] |
1237 | 1218 | }, |
| 1219 | + { |
| 1220 | + "cell_type": "code", |
| 1221 | + "execution_count": null, |
| 1222 | + "metadata": {}, |
| 1223 | + "outputs": [], |
| 1224 | + "source": [ |
| 1225 | + "import numpy as np\n", |
| 1226 | + "from math import inf\n", |
| 1227 | + "from spotPython.fun.objectivefunctions import analytical\n", |
| 1228 | + "from spotPython.spot import spot\n", |
| 1229 | + "from scipy.optimize import shgo\n", |
| 1230 | + "from scipy.optimize import direct\n", |
| 1231 | + "from scipy.optimize import differential_evolution\n", |
| 1232 | + "import matplotlib.pyplot as plt\n", |
| 1233 | + "from spotPython.utils.init import fun_control_init\n", |
| 1234 | + "fun_control = fun_control_init(seed=4321, sigma=0.1)\n", |
| 1235 | + "fun = analytical(seed=222, sigma=0.0).fun_sphere" |
| 1236 | + ] |
| 1237 | + }, |
| 1238 | + { |
| 1239 | + "cell_type": "code", |
| 1240 | + "execution_count": null, |
| 1241 | + "metadata": {}, |
| 1242 | + "outputs": [], |
| 1243 | + "source": [ |
| 1244 | + "spot_1 = spot.Spot(fun=fun,\n", |
| 1245 | + " lower = np.array([-10]),\n", |
| 1246 | + " upper = np.array([100]),\n", |
| 1247 | + " fun_evals = 100,\n", |
| 1248 | + " fun_repeats = 3,\n", |
| 1249 | + " max_time = inf,\n", |
| 1250 | + " noise = True,\n", |
| 1251 | + " tolerance_x = np.sqrt(np.spacing(1)),\n", |
| 1252 | + " var_type=[\"num\"],\n", |
| 1253 | + " infill_criterion = \"y\",\n", |
| 1254 | + " n_points = 1,\n", |
| 1255 | + " seed=111,\n", |
| 1256 | + " log_level = 10,\n", |
| 1257 | + " show_models=False,\n", |
| 1258 | + " fun_control = fun_control,\n", |
| 1259 | + " design_control={\"init_size\": 5,\n", |
| 1260 | + " \"repeats\": 1},\n", |
| 1261 | + " surrogate_control={\"noise\": True,\n", |
| 1262 | + " \"cod_type\": \"norm\",\n", |
| 1263 | + " \"min_theta\": -4,\n", |
| 1264 | + " \"max_theta\": 3,\n", |
| 1265 | + " \"n_theta\": 1,\n", |
| 1266 | + " \"model_optimizer\": differential_evolution,\n", |
| 1267 | + " \"model_fun_evals\": 1000,\n", |
| 1268 | + " })\n", |
| 1269 | + "spot_1.run()" |
| 1270 | + ] |
| 1271 | + }, |
1238 | 1272 | { |
1239 | 1273 | "cell_type": "code", |
1240 | 1274 | "execution_count": null, |
|
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