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Start working on defeat learners assignment.

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Felix Martin
2020-09-27 16:22:16 -04:00
parent 8ee47c9a1d
commit db537d7043
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defeat_learners/DTLearner.py Normal file
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"""
A simple wrapper for linear regression. (c) 2015 Tucker Balch
Note, this is NOT a correct DTLearner; Replace with your own implementation.
Copyright 2018, Georgia Institute of Technology (Georgia Tech)
Atlanta, Georgia 30332
All Rights Reserved
Template code for CS 4646/7646
Georgia Tech asserts copyright ownership of this template and all derivative
works, including solutions to the projects assigned in this course. Students
and other users of this template code are advised not to share it with others
or to make it available on publicly viewable websites including repositories
such as github and gitlab. This copyright statement should not be removed
or edited.
We do grant permission to share solutions privately with non-students such
as potential employers. However, sharing with other current or future
students of CS 7646 is prohibited and subject to being investigated as a
GT honor code violation.
-----do not edit anything above this line---
Student Name: Tucker Balch (replace with your name)
GT User ID: tb34 (replace with your User ID)
GT ID: 900897987 (replace with your GT ID)
"""
import numpy as np
import warnings
class DTLearner(object):
def __init__(self, leaf_size=1, verbose = False):
warnings.warn("\n\n WARNING! THIS IS NOT A CORRECT DTLearner IMPLEMENTATION! REPLACE WITH YOUR OWN CODE\n")
pass # move along, these aren't the drones you're looking for
def author(self):
return 'tb34' # 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'")