PCs using SVDS. You are given a python function: U,s, Vtr = svd (Z, full.matrices = False) which computes an “economy" SVD. Given a data matrix x, write python code that: (i) Finds the PCs and mean of the data. (ii) Finds the minimum number of PCs in order that the proportion of variance is at least 90%. (iii) Create an approximation Xhat of x using the number of PCs in part (ii).

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5. PCs using SVDs. You are given a python function:
U,s, Vtr = svd (Z, full.matrices = False)
which computes an "economy" SVD. Given a data matrix x, write python code that:
(i) Finds the PCs and mean of the data.
(ii) Finds the minimum number of PCs in order that the proportion of variance is at least
90%.
(iii) Create an approximation Xhat of x using the number of PCs in part (ii).
Transcribed Image Text:5. PCs using SVDs. You are given a python function: U,s, Vtr = svd (Z, full.matrices = False) which computes an "economy" SVD. Given a data matrix x, write python code that: (i) Finds the PCs and mean of the data. (ii) Finds the minimum number of PCs in order that the proportion of variance is at least 90%. (iii) Create an approximation Xhat of x using the number of PCs in part (ii).
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