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Description: (a) Generate the sum of two datasets with 200 two-dimensional vectors (Note: before generating the dataset, it is better to initialize the Gaussian random generator to 0 (or any given value) with the command randn ("seed", 0), which is important for the repeatability of the results). The first half of the vector comes from the normal distribution of the mean vector and the covariance matrix. The second half of the vector comes from the normal distribution of the mean vector and the covariance matrix. Where is a 2 * 2 identity matrix.
(b) On the above datasets, and belong to + 1 and - 1 classes respectively. Please use Fisher linear discriminant algorithm to classify, draw the best projection vector, and give the classification threshold.
(Click to check if it's the file you need, and recomment it at the bottom):
|L4_1.py|| 2928 || 2020-04-20
|L4_2.py|| 1171 || 2020-04-20|