2 files changed, 32 insertions(+), 7 deletions(-)

M 3optimization.py
M shell.nix

@@ 6,25 6,50 @@ feature = pd.read_excel('./data/feature.xlsx', sheet_name='Sheet1')['name'].toli
 df = pd.read_excel('./data/325.xlsx', index_col=0, sheet_name='Sheet2').drop(['时间'] ,axis=1)
 
 import re
+normalzation_record = [
+    {}, # min
+    {}  # max
+]
 def get_var_range(index):
     str_range = action_data[action_data['位号'] == index].iloc[0]['取值范围']
     str_range = re.sub(r'\(|\)|（|）', '', str_range)
     str_range = re.sub(r'(?<=-)?(?<=\d)+-', '/', str_range, 1)
     num_range = list(map(int, str_range.split('/')))
+    def norm_range(idx):
+        num = num_range[idx]
+        df_ = df[index].copy()
+        df_.loc[len(df) + 1] = num
+        mean = df_.mean()
+        std = df_.std()
+        normalzation_record[idx][index] = {
+            'mean': mean,
+            'std':  std
+        }
+        return (num - mean) / std
+    num_range = list(map(norm_range , range(0, len(num_range))))
     return num_range
 
 from keras.models import load_model
 model = load_model('./data/modelweight.model')
+
+df_ron_loss = df['RON损失']
+min_ron_loss = (df_ron_loss.min() - df_ron_loss.mean()) / df_ron_loss.std()
+
 def fitness(args):
     # step1: calculate target value
-    # step2: compute RON LOSS RATE
-    # step3: get the max/min target values as our fitness value
-    return
+    pred_ron_loss = model.predict(np.array([args]))[0][0]
+    # step2: check sulfur
+    # TODO?
+    # step3: compute RON LOSS RATE
+    loss_reduce_rate = abs(pred_ron_loss - min_ron_loss / min_ron_loss)
+    return -loss_reduce_rate
 
 from sko.GA import GA
-# TODO set lb and ub to each variables
-ga = GA(func=schaffer, n_dim=11, size_pop=50, max_iter=800, lb=[-1, -1],
-        ub=[1, 1], precision=1e-7)
+feature_range = list(map(get_var_range, feature))
+ga = GA(func=fitness, n_dim=11, size_pop=50, max_iter=800,
+        lb=[r[0] for r in feature_range],
+        ub=[r[1] for r in feature_range],
+        precision=1e-7)
 best_x, best_y = ga.run()
 print('best_x:', best_x, '\n', 'best_y:', best_y)
 

@@ 14,7 14,7 @@ let tex-combined = texlive.combine {
     pythonPackages = python3Packages;
     pythonDeps = with pythonPackages; [ python numpy ipython pandas
                                         matplotlib # scikit-opt
-                                        tensorflow Keras
+                                        tensorflow Keras scikitlearn
                                         python3Packages.venvShellHook
                                       ];
 in mkShell {