can add positive and negative examples to learner files

dtree_learner.py

 from typing import List, Tuple
 import numpy as np
-
+import os
 from learner_base import LearnerBase
-
-
-def construct_sample_to_feature_func(a_mat: np.ndarray, b_vec: np.ndarray):
-    perc_dim, state_dim = a_mat.shape
-
-    def sample_to_feature_vec(sample):
-        assert len(sample) == state_dim + perc_dim
-        state = sample[0: state_dim]
-        perc = sample[state_dim: state_dim+perc_dim]
-        perc_bar = perc - (np.dot(state, a_mat.T) + b_vec)
-        return perc_bar
-    return sample_to_feature_vec
-
+import csv
 
 class DTreeLearner(LearnerBase):
     def __init__(self, state_dim: int, perc_dim: int,
                  timeout: int = 10000) -> None:
         super().__init__()
-
+        #learners/C50exact/c5.0dbg  -I 1 -m 1 -f FileLocation/pre
         self._state_dim: int = state_dim
         self._perc_dim: int = perc_dim
 
         self._s2f_func_list: List = []
 
+        dir_name = "tempLocation"
+        check_temp_dir: bool = os.path.isdir(dir_name)
+        if not check_temp_dir:
+            os.makedirs(dir_name)
+
+        self.data_file = dir_name+"/pre.data"
+        self.names_file = dir_name+"/pre.names"
+        open(self.data_file,'w').close() # create empty file
+        open(self.names_file,'w').close()
+
     @property
     def state_dim(self) -> int:
         return self._state_dim
@@ -51,8 +49,7 @@ class DTreeLearner(LearnerBase):
             feature_vec_list.append(feature_vec)
 
         print("Positive feature vectors (dbar, psibar):", feature_vec_list)
-
-        # TODO save list of feature vectors to data as positive examples
+        self.write_to_file(self.data_file,feature_vec_list, "true")
 
     def add_negative_examples(self, *args) -> None:
         feature_vec_list = []
@@ -63,14 +60,54 @@ class DTreeLearner(LearnerBase):
             feature_vec_list.append(feature_vec)
 
         print("Negative feature vectors (dbar, psibar):", feature_vec_list)
+        self.write_to_file(self.data_file, feature_vec_list, "false")
+
+    def write_to_file(self, file:str, feature_vec_list, label:str ):
+        with open(file, 'a') as d_file:
+            data_out = csv.writer(d_file)
+            for f in feature_vec_list:
+                print(f)
+                data_out.writerow(f+[label])
 
-        # TODO save list of feature vectors to data as negative examples
 
     def learn(self) -> Tuple:
         # TODO read result from Dtree learner
 
         pass
 
+def construct_sample_to_feature_func(a_mat: np.ndarray, b_vec: np.ndarray):
+    perc_dim, state_dim = a_mat.shape
+
+    def sample_to_feature_vec(sample):
+        assert len(sample) == state_dim + perc_dim
+        state = sample[0: state_dim]
+        perc = sample[state_dim: state_dim+perc_dim]
+        perc_bar = perc - (np.dot(state, a_mat.T) + b_vec)
+        return perc_bar
+    return sample_to_feature_vec
+
+def test_sample_to_feature():
+    # tuple
+    a_mat = np.array([[0., -1., 0.],
+                      [0., 0., -1]])
+    b_vec = np.zeros(2)
+
+    #construct_sample_to_feature_func: returns a function
+    #map: lin_trans(a_mat and b_vec pair) -> func
+    sample_to_feature_func = construct_sample_to_feature_func(a_mat, b_vec)
+                                
+    #map = {name1:sample_to_feature_func}
+    sample = np.array([1., 2., 3., -2., -3.])
+    # sample_to_feature_func will compute dBar and psiBar
+    feature_vec = sample_to_feature_func(sample)
+    print("sample: "+ str(feature_vec))
+    assert np.array_equal(feature_vec, np.array([0., 0.]))
+
+    sample = np.array([1., 2., 3., -1., -2.])
+    feature_vec = sample_to_feature_func(sample)
+    print("sample: "+ str(feature_vec))
+    assert np.array_equal(feature_vec, np.array([1., 1.]))
+
 
 def test_dtree_learner():
     a_mat = np.array([[0., -1., 0.],
@@ -85,7 +122,7 @@ def test_dtree_learner():
         (1., 2., 3., -1., -2.)
     ]
     learner.add_positive_examples(*pos_examples)
-
+    
     neg_examples = [
         (10., 1.0, 1.0, 0.5, 0.5),
         (10., 1.0, 1.0, 1.5, 1.5),
@@ -93,23 +130,9 @@ def test_dtree_learner():
     ]
     learner.add_negative_examples(*neg_examples)
 
-    learner.learn()
-
-
-def test_sample_to_feature():
-    a_mat = np.array([[0., -1., 0.],
-                      [0., 0., -1]])
-    b_vec = np.zeros(2)
-    sample_to_feature_func = construct_sample_to_feature_func(a_mat, b_vec)
-
-    sample = np.array([1., 2., 3., -2., -3.])
-    feature_vec = sample_to_feature_func(sample)
-    assert np.array_equal(feature_vec, np.array([0., 0.]))
-
-    sample = np.array([1., 2., 3., -1., -2.])
-    feature_vec = sample_to_feature_func(sample)
-    assert np.array_equal(feature_vec, np.array([1., 1.]))
+    # learner.learn()
 
 
 if __name__ == "__main__":
+    #test_sample_to_feature()
     test_dtree_learner()