I am trying to reduce a largely dimentional matrix to only 2D, i was using an example for 2D arrays,which works, but i would need to do the same for a higher dimentional scatter. I have two classes and each classes have matrices of 50×20 dimensional feature spaces. For my example i have these 2D […]

- Tags -0.25, -0.4, -1] [4, -102], -11, -182, -23, -38, -48, '<1', '177', "-0.15", "1":66, "14":7, "c"=>"blue", (c_A_array-c_A_array_mean).T) scatter_c_B_array = np.dot((c_B_array-c_B_array_mean), (c_B_array-c_B_array_mean).T) # Calculate the SW by adding the scatters within classes SW = scatter_c_A_array+scatter_c_B_array print(SW), (circles-mean_circles).T) # Calculate the SW by adding the scatters within classes SW = scatter_triangles+scatter_circles+scatter_rectangle, (triangles-mean_triangles).T) scatter_circles = np.dot((circles-mean_circles), ), {1}; {2}, <1, 0.3)", 0.35), 0.48, 0.5, 0.6, 1 0, 1 1, 1.0]]) Afterwards im finding the mean for both classes triangles and rectangles # Calculate the mean vectors per class mean_rectangles = np, 1.15, 1.3, 1.45], 1.55, 1.65, 1.75]]) triangles = np.array([[0.1, 1) mean_triangles = np.mean(triangles, 1) The value given by the means of classes rectangle and triangles, 1)-(6, 1] [5, 10, 10)) ax0 = fig.add_subplot(111) ax0.scatter(c_A_array[0], 101, 105, 12, 13, 140, 142, 143, 144, 163], 169, 17, 174, 175, 178, 179, 183, 185, 186, 188, 189, 19, 190, 191, 193, 194, 195, 196, 198], 199, 20, 20.5, 200, 201, 202, 204, 205, 206, 207, 207]] c_B_array = [[ 16, 208], 21, 211, 213, 214, 215, 216, 217, 218, 22, 220, 221, 222]], 224, 225, 226, 227, 229, 229]] #Plot the data fig = plt.figure(figsize=(10, 231, 24, 25, 26, 27, 28, 29, 30, 31, 33, 36, 37, 39, 40, 43, 46, 49, 57, 58, 59, 62, 65, 67, 76, 9, 99], axis=1) # Calculate the scatter matrices for the SW (Scatter within) and sum the elements up scatter_c_A_array = np.dot((c_A_array-c_A_arra, axis=1) c_B_array_mean = np.mean(c_A_array, axis=1).reshape(2, but i would need to do the same for a higher dimentional scatter. I have two classes and each classes have matrices of 50x20 dimensional feat, c_A_array[1], c_B_array[1], c='grey', edgecolor='black') # Calculate the mean vectors per class c_A_array_mean = np.mean(c_A_array, edgecolor='black') ax0.scatter(c_B_array[0], exactly the same way but for a larger data, I am trying to reduce a largely dimentional matrix to only 2D, i use them to calculate the scatter: scatter_triangles = np.dot((triangles-mean_triangles), i was using an example for 2D arrays, marker="o", markers, precisely for a 50x20 matrix? For reproducibility this is my code: import numpy as np import matplotlib.pyplot as plt from matplotlib i, which works