Finish all learners, but they don't pass tests. I have to figure out why they perform so bad.

assess_learners/BagLearner.py

@@ -0,0 +1,45 @@
+import numpy as np
+
+
+class BagLearner(object):
+    def __init__(self, learner, bags=20, boost=False, verbose=False, **kwargs):
+        self.learner = learner
+        self.bags = bags
+        self.boost = boost
+        self.verbose = verbose
+        self.kwargs = kwargs
+        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 = [], []
+        for _ in range(num_items):
+            i = np.random.randint(0, data_x.shape[0])
+            bag_x.append(data_x[i,:])
+            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
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+        for learner in self.learners:
+            x, y = self.get_bag(data_x, data_y)
+            learner.addEvidence(x, y)
+
+    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.
+        @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'")