if you use code, please use MATLAB.

2. Generating training samples: In two dimensional feature space x: (x1, x2,1), generate 20 random samples for different values of (x1,x2), that belong to two different classes CI (1) and C2 (0). The label of each feature vector is assigned so that the samples are linearly separable, i.e., can be separated by a linear model with a soft threshold (sigmoid) function. Plot the samples you generate in a two dimensional plane of (x1,x2). Hint: You may construct an underlying linear model to cut the plane in two halves. Then generate random samples at either side with proper labels. 2. Generating training samples: In two dimensional feature space x: (x1, x2,1), generate 20 random samples for different values of (x1,x2), that belong to two different classes CI (1) and C2 (0). The label of each feature vector is assigned so that the samples are linearly separable, i.e., can be separated by a linear model with a soft threshold (sigmoid) function. Plot the samples you generate in a two dimensional plane of (x1,x2). Hint: You may construct an underlying linear model to cut the plane in two halves. Then generate random samples at either side with proper labels