Created
May 24, 2020 01:24
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Fit new value
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| new_x = 15 | |
| data_x = np.array([new_x**i for i in range(0,13)]) | |
| new_y = 4*new_x+1+np.random.normal(0, 5, 1) | |
| idx = 0 | |
| for degree in [1, 2, 4, 6, 8, 10]: | |
| y_pred = models[idx].predict(data_x[0:degree+1].reshape(1,-1)) | |
| rss = (y_pred-new_y)**2 | |
| idx += 1 | |
| print "The residual error for X={} is {}".format(new_x, rss) |
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