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MATLAB课业代写 AMAT 592代写

2022-07-08 11:11 星期五 所属: Matlab代写 浏览:223

AMAT 592 Assignment 3

MATLAB课业代写 This assignment is done by MATLAB. Compress all code files into a .zip file and submit through Blackboard. 1.The handwritten digit dataset mat is

  • This assignment is done by MATLAB. Compress all code files into a .zip file and submit through Blackboard.

1.   MATLAB课业代写

The handwritten digit dataset mat is modified from theoriginal MNIST gray-scale image dataset, where samples of digit 9 belong to class 1 and otherwise class 1. It contains a training set Xtr with labels Ytr and a testing set Xte with labels Yte. There are 5000 sample images in both training and testing sets and each sample is stored a vector of 784 gray-scale pixel values between 0 and 255. We do binary classification using logistic regression:




(a) Visualize the first 9 training images in Xtr using MATLAB built-in function imshow. Dispaly the 9 images as a 3 × 3 tabular in the same figure using subplot. Note that you need to reshape each sample into a 28 × 28 matrix before visualization.

(b) Write a function named as logit.m to implement gradient decent algorithm for the logistic regression problem (1) using a constant step size η. The input arguments of logit include Xtr, Ytr, Xte, Yte, the constant step size η, and the regularization parameter λ. The output of logit should be the training accuracy, test accuracy, and the objective value at each iteration (stored as a vector). You should adopt a proper stopping criterion for gradient decent implementation. Call your function logit.m by choosing proper values of η and λ. Note that your main file is supposed to be separated from logit.m.

For this part, you need to:

i. Print out the final training accuracy and test accuracy (A reasonable test accuracy should be > 94%)



2.  MATLAB课业代写

In this problem, we use PCA to reduce the dimension of raw face images. Load the data face.mat, and we will have the variable X which is the data matrix of size 400 × 10304, where each row vector represents a gray-scale image originally of 112 × 92 pixels.

First of all, center the data points (i.e., row vectors) in X by subtracting their mean mu from each row. Denote the preprocessed data matrix by variable X 0. Apply PCA to X 0 and reduce data’s dimension to k = 350. You can use the following command (taking the i-th image as an example) to recover the image:

Recon = X_0(i,:)V_kV_k+ mu,

where V_k= V(:,1:k) contains the first k principal components. Recall the set of principal components can be computed via built-in function svd for SVD. Remember to reshape the vector Recon into a 112 × 92 matrix to show the image.

Pick any image from X, and show the effect of PCA by comparing the original image and the recovered images side by side using subplot. Give a title to each subfigure.





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