Update parm_dict.json

parm_dict.json

@@ -1,62 +1,62 @@
-{
-  "feature_file_name": "multi_features_data_simu5.pkl",
-  "Is_train_autoencoder": false,
-  "autoencoder_structure": 4,
-  "L1": {
-    "noise_threshold": 0.01,
-    "_noise_threshold_dsp": "the number less of cluster than the threshold, consider to be noise,BB11",
-    "choose_model_power": "Mix",
-    "_choose_model_power_dsp": "options: Mix, DBSCAN",
-    "choose_model_vib": "Hierarchical",
-    "_choose_model_vib_dsp": "options, Hierarchical, DBSCAN",
-    "DBSCAN_power_params": [
-      1.2,
-      50,
-      0.9
-    ],
-    "DBSCAN_params_dsp": "p1= eps, p2= min count in cluster, p3= standby threshold",
-    "Hierarchical_power_params": [
-      0.2,
-      1,
-      2,
-      0.2,
-      1
-    ],
-    "_param1_dsp": "param 1 is factor ,the stable indicattor (less than value)(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)",
-    "Is_save_model": true,
-    "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",
-    "_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": 3,
-    "_min_class_dsp": "minimum cluster number for search optimal option",
-    "max_class": 7,
-    "_max_class_dsp": "maximum cluster number (not include) for search optimal option",
-    "stable_indicator": 0.3,
-    "_stable_indicator_dsp": "when change less than the value, consider it's stable",
-    "covariance_type": "diag",
-    "_covariance_type_dsp": "GMM parameter, effect on model size"
-  }
+{
+  "feature_file_name": "multi_features_data_simu5.pkl",
+  "Is_train_autoencoder": false,
+  "autoencoder_structure": 4,
+  "L1": {
+    "noise_threshold": 0.01,
+    "_noise_threshold_dsp": "the number less of cluster than the threshold, consider to be noise,BB3",
+    "choose_model_power": "Mix",
+    "_choose_model_power_dsp": "options: Mix, DBSCAN",
+    "choose_model_vib": "Hierarchical",
+    "_choose_model_vib_dsp": "options, Hierarchical, DBSCAN",
+    "DBSCAN_power_params": [
+      1.2,
+      50,
+      0.9
+    ],
+    "DBSCAN_params_dsp": "p1= eps, p2= min count in cluster, p3= standby threshold",
+    "Hierarchical_power_params": [
+      0.2,
+      1,
+      2,
+      0.2,
+      1
+    ],
+    "_param1_dsp": "param 1 is factor ,the stable indicattor (less than value)(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)",
+    "Is_save_model": true,
+    "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",
+    "_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": 3,
+    "_min_class_dsp": "minimum cluster number for search optimal option",
+    "max_class": 7,
+    "_max_class_dsp": "maximum cluster number (not include) for search optimal option",
+    "stable_indicator": 0.3,
+    "_stable_indicator_dsp": "when change less than the value, consider it's stable",
+    "covariance_type": "diag",
+    "_covariance_type_dsp": "GMM parameter, effect on model size"
+  }
 }