Stanford Cs 229 Homework Helper

CS229 Problem Set #1 Solutions 2 The − λ 2 θ T θ here is what is known as a regularization parameter, which will be discussed in a future lecture, but which we include here because it is needed for Newton’s method to perform well on this task. For the entirety of this problem you can use the value λ = 0 . 0001. Using this de±nition, the gradient of ℓ ( θ ) is given by ∇ θ ℓ ( θ ) = X T z − λθ where z ∈ R m is de±ned by z i = w ( i ) ( y ( i ) − h θ ( x ( i ) )) and the Hessian is given by H = X T DX − λI where D ∈ R m × m is a diagonal matrix with D ii = − w ( i ) h θ ( x ( i ) )(1 − h θ ( x ( i ) )) For the sake of this problem you can just use the above formulas, but you should try to derive these results for yourself as well. Given a query point x , we choose compute the weights w ( i ) = exp p − || x − x ( i ) || 2 2 τ 2 P . Much like the locally weighted linear regression that was discussed in class, this weighting scheme gives more when the “nearby” points when predicting the class of a new example. (a) Implement the Newton-Raphson algorithm for optimizing ℓ ( θ ) for a new query point x , and use this to predict the class of x . The q2/ directory contains data and code for this problem. You should implement the y = lwlr(X train, y train, x, tau) function in the lwlr.m ±le. This func-tion takes as input the training set (the X train and y train matrices, in the form described in the class notes), a new query point x and the weight bandwitdh tau .

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