Update parm_dict.json

parm_dict.json

@@ -1,37 +1,75 @@
 {
   "root_path": "C:/tmp_data/extract_feature/",
+  "upload_path": "C:/tmp_data/extract_feature/upload_models/",
   "feature_file_name": "multi_features_data_simu5.pkl",
   "Is_train_autoencoder": false,
   "autoencoder_structure": 4,
   "L1": {
-    "choose_model": "Hierarchical",
-    "_param_threshold_dsp": "the number less than the threshold of all count according cluster",
     "noise_threshold": 0.01,
+    "_noise_threshold_dsp": "the number less of cluster than the threshold, consider to be noise",
+    "choose_model": "Hierarchical",
+    "_choose_model_dsp": "options, Hierarchical, DBSCAN",
+    "Hierarchical_power_params": [
+      [
+        0.3,
+        4,
+        10,
+        0.5
+      ],
+      [
+        0.2,
+        4,
+        10,
+        0.5
+      ],
+      [
+        0.15,
+        4,
+        10,
+        0.5
+      ],
+      [
+        0.1,
+        4,
+        10,
+        0.5
+      ]
+    ],
     "_param1_dsp": "param 1 is factor ,the stable indicattor (percentage of flucuation)(difference from the reverse previous 2 level)",
     "_param2_dsp": "param 2 is min number of cluster",
     "_param3_dsp": "param 3 is max number of cluster",
     "_param4_dsp": "param 4 is standby threshold (percentage)",
-    "Hierarchical_power_params": [
-      [0.3 , 4, 10, 0.5],
-      [0.2 , 4, 10, 0.5],
-      [0.15, 4, 10, 0.5],
-      [0.1 , 4, 10, 0.5]
-    ],
     "Is_save_model": true,
-    "Hierarchical_vib_params": [ [0.2, 4, 10, 0.5] ],
+    "Hierarchical_vib_params": [
+      [
+        0.2,
+        4,
+        10,
+        0.5
+      ]
+    ],
+    "_Hierarchical_vib_params_dsp": "For vibration, the format is same",
     "L1_models_name": "L1_models.pkl"
   },
   "L2": {
     "strategy": "consensus",
+    "_strategy_dsp": "options: consensus, feature_fusion, meta_clustering, multi_view_cluster",
     "Is_save_model": true,
     "consensus_model": "GMM",
-    "reslut_type": "prob",
+    "_consensus_model_dsp": "model using on each type of sensor clusting: KMeans, GMM",
+    "result_type": "prob",
+    "_result_type_dsp": "The input the consensus matrix is each submodel result:  prob (probability), one-hot",
     "feature_fusion_pca_factor": 0.95,
+    "_feature_fusion_pca_factor_dsp": "how much the PCA catch of the variance\uff0cfor feature_fusion, meta_clustering, multi_view_cluster",
     "feature_fusion_model": "GMM",
     "meta_clustering_model": "GMM",
     "number_class": 0,
+    "_number_class_dsp": "The spicify cluster number of each type of sensor. 0 mean search optimal number base on certain score",
     "min_class": 2,
+    "_min_class_dsp": "minimum cluster number for search optimal option",
     "max_class": 7,
-    "covariance_type": "diag"
+    "_max_class_dsp": "maximum cluster number (not include) for search optimal option",
+    "covariance_type": "diag",
+    "_covariance_type_dsp": "GMM parameter, effect on model size"
   }
 }