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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) The perceptron algorithm is used on the above data set, and different initial vectors are used to initialize the parameter vector.
(c) Test the performance of each algorithm on and.
(d) Draw the data set and, as well as the classification surface.
(Click to check if it's the file you need, and recomment it at the bottom):
|L2_1.py|| 2068 || 2020-04-20
|L2_2.py|| 474 || 2020-04-21|