Working on a generic ml algorithm

create.py

@@ -43,3 +43,7 @@ def create(text,score,prompt_string,model_path):
 
     return results
 
+
+def create_generic(numeric_values, textual_values, target, model_path):
+    pass
+

grade.py

@@ -100,6 +100,4 @@ def grade(grader_data,grader_config,submission):
 
     return results
 
-
-
     

predictor_set.py

+import numpy
+import nltk
+import sys
+import random
+import os
+import logging
+
+base_path = os.path.dirname(__file__)
+sys.path.append(base_path)
+import util_functions
+
+if not base_path.endswith("/"):
+    base_path=base_path+"/"
+
+log=logging.getLogger(__name__)
+
+
+class AlgorithmTypes(object):
+    regression = "regression"
+    classification = "classifiction"
+
+
+class PredictorSet(object):
+    def __init__(self, type="train", algorithm = AlgorithmTypes.regression):
+        """
+        Initialize variables and check essay set type
+        """
+        if(type != "train" and type != "test"):
+            type = "train"
+
+        if(algorithm not in [AlgorithmTypes.regression, AlgorithmTypes.classification]):
+            algorithm = AlgorithmTypes.regression
+
+        self._type = type
+        self._target=[]
+        self._textual_features=[]
+        self._numeric_features=[]
+        self.essay_sets=[]
+
+    def add_row(self, numeric_features, textual_features, target):
+        pass