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