lwlr fixed

workflows/big_data/db/package_data.json

@@ -1451,6 +1451,24 @@
       "description": "Locally weighted linear regression with MapReduce\r\n\r\nReference:\r\nMapReduce version of algorithm is proposed by Cheng-Tao Chu; Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary Bradski, Andrew Ng, and Kunle Olukotun. \"Map-Reduce for Machine Learning on Multicore\". NIPS 2006.   \r\n"
     }
   },
+  {
+    "pk": 184,
+    "model": "workflows.abstractinput",
+    "fields": {
+      "widget": 61,
+      "name": "Training dataset",
+      "short_name": "dst",
+      "uid": "15644754-4c1d-4367-a0a3-5d442b51ff89",
+      "default": "",
+      "required": true,
+      "multi": false,
+      "parameter_type": null,
+      "variable": "training_dataset",
+      "parameter": false,
+      "order": 1,
+      "description": "Define training dataset"
+    }
+  },
   {
     "pk": 183,
     "model": "workflows.abstractinput",
@@ -1465,7 +1483,7 @@
       "parameter_type": null,
       "variable": "fitting_dataset",
       "parameter": false,
-      "order": 1,
+      "order": 2,
       "description": "Define a dataset that will be fitted to training dataset."
     }
   },
@@ -1483,26 +1501,8 @@
       "parameter_type": "text",
       "variable": "tau",
       "parameter": true,
-      "order": 2,
-      "description": "Parameter Tau controls how quickly the weight of a training example falls off with distance of its x(i) from the query point x"
-    }
-  },
-  {
-    "pk": 184,
-    "model": "workflows.abstractinput",
-    "fields": {
-      "widget": 61,
-      "name": "Training dataset",
-      "short_name": "dst",
-      "uid": "15644754-4c1d-4367-a0a3-5d442b51ff89",
-      "default": "",
-      "required": true,
-      "multi": false,
-      "parameter_type": null,
-      "variable": "training_dataset",
-      "parameter": false,
       "order": 3,
-      "description": "Define training dataset"
+      "description": "Parameter Tau controls how quickly the weight of a training example falls off with distance of its x(i) from the query point x"
     }
   },
   {

workflows/big_data/library.py

@@ -36,7 +36,7 @@ def file_url(input_dict):
                             X_meta = X_meta,
                             generate_urls = True if input_dict["range"] == "true" else False,
                             **input_dict)
-    print data.params
+    
     return {"dataset" : data}
 
 def big_data_apply_classifier(input_dict):
@@ -70,7 +70,7 @@ def lwlr_fit_predict(input_dict):
 
 def dt_fit(input_dict):
     from discomll.ensemble import decision_trees
-    print input_dict
+    
 
     fitmodel_url = decision_trees.fit(input = input_dict["dataset"],
                                        max_tree_nodes = input_dict["tree_nodes"],

workflows/big_data/visualization_views.py

@@ -94,7 +94,9 @@ def results_to_file(request,input_dict,output_dict,widget):
     tag = input_dict["string"]
     folder = 'discomll_results'
     add = "add" if input_dict["add_params"] == "true" else ""
+    
     destination = MEDIA_ROOT+'/'+folder+"/"+tag[0][6:]+add+'.txt'
+    
     ensure_dir(destination)
     
     if not os.path.isfile(destination): #file doesnt exists
@@ -107,7 +109,7 @@ def results_to_file(request,input_dict,output_dict,widget):
             for k, v in result_iterator(tag):
                 f.writelines(str(k) + " " + str(v[0]) + "\n")
         f.close()
-    filename = folder+"/"+tag[0][6:]+'.txt'
+    filename = folder+"/"+tag[0][6:]+add+'.txt'
 
     output_dict['filename'] = filename
     return render(request, 'visualizations/string_to_file.html',{'widget':widget,'input_dict':input_dict,'output_dict':output_dict})