Start working on project assess learners.

assess_learners/DTLearner.py

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+import numpy as np
+
+class DTLearner(object):
+
+    def __init__(self, leaf_size = 1, verbose = False):
+        pass # move along, these aren't the drones you're looking for
+
+    def author(self):
+        return 'felixm' # replace tb34 with your Georgia Tech username
+
+    def addEvidence(self, dataX, dataY):
+        """
+        @summary: Add training data to learner
+        @param dataX: X values of data to add
+        @param dataY: the Y training values
+        """
+
+        # slap on 1s column so linear regression finds a constant term
+        newdataX = np.ones([dataX.shape[0], dataX.shape[1]+1])
+        newdataX[:,0:dataX.shape[1]] = dataX
+
+        # build and save the model
+        self.model_coefs, residuals, rank, s = np.linalg.lstsq(newdataX,
+                                                               dataY,
+                                                               rcond=None)
+
+    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 (self.model_coefs[:-1] * points).sum(axis = 1) + self.model_coefs[-1]
+
+if __name__=="__main__":
+    print("the secret clue is 'zzyzx'")