Resolve split_value issue in DTLearner and pass all tests.

assess_learners/BagLearner.py

@@ -1,18 +1,14 @@
 import numpy as np
+from AbstractTreeLearner import AbstractTreeLearner
 
 
-class BagLearner(object):
-    def __init__(self, learner, bags=20, boost=False, verbose=False, **kwargs):
+class BagLearner(AbstractTreeLearner):
+    def __init__(self, learner, bags=9, boost=False, verbose=False, kwargs={}):
         self.learner = learner
-        self.bags = bags
-        self.boost = boost
         self.verbose = verbose
-        self.kwargs = kwargs
+        self.bags = bags
         self.learners = [learner(**kwargs) for _ in range(bags)]
 
-    def author(self):
-        return 'felixm' # replace tb34 with your Georgia Tech username
-
     def get_bag(self, data_x, data_y):
         num_items = int(data_x.shape[0] * 0.5) # 50% of samples
         bag_x, bag_y = [], []
@@ -22,7 +18,6 @@ class BagLearner(object):
             bag_y.append(data_y[i])
         return np.array(bag_x), np.array(bag_y)
 
-
     def addEvidence(self, data_x, data_y):
         """
         @summary: Add training data to learner
@@ -36,10 +31,8 @@ class BagLearner(object):
     def query(self, points):
         """
         @summary: Estimate a set of test points given the model we built.
-        @param points: should be a numpy array with each row corresponding to a specific query.
+        @param points: numpy array with each row corresponding to a query.
         @returns the estimated values according to the saved model.
         """
         return np.mean([l.query(points) for l in self.learners], axis=0)
 
-if __name__=="__main__":
-    print("the secret clue is 'zzyzx'")