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Minor fixes to regression example with robot_wireless.
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2 changed files with 5 additions and 5 deletions
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@ -334,16 +334,16 @@ def robot_wireless(optim_iters=100):
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# optimize
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m.optimize(messages=True, max_f_eval=optim_iters)
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Ypredict = m.predict(data['Y'])[0]
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Xpredict = m.predict(data['Ytest'])[0]
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pb.plot(data['Xtest'][:, 0], data['Xtest'][:, 1], 'r-')
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pb.plot(Ypredict[:, 0], Ypredict[:, 1], 'b-')
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pb.plot(Xpredict[:, 0], Xpredict[:, 1], 'b-')
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pb.axis('equal')
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pb.title('WiFi Localization with Gaussian Processes')
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pb.legend(('True Location', 'Predicted Location'))
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sse = ((data['Ytest'] - Y.predict)**2).sum()
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sse = ((data['Xtest'] - Xpredict)**2).sum()
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print(m)
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print('Sum of squares error on test data: ', str(sse))
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print('Sum of squares error on test data: ' + str(sse))
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return m
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def sparse_GP_regression_1D(N=400, num_inducing=5, optim_iters=100):
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@ -404,7 +404,7 @@ def robot_wireless(data_set='robot_wireless'):
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Y = allY[0:215, :]
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Xtest = allX[215:, :]
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Ytest = allX[215:, :]
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Ytest = allY[215:, :]
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return data_details_return({'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'addresses' : addresses, 'times' : times}, data_set)
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def silhouette(data_set='ankur_pose_data'):
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