Data Science, Machine Learning, Pattern Recognition, Pca Analysis, Data Reduction.
Principal axes theta = np.pi/6 # Rotate cloud by pi/3 R = np.array([[np.cos(theta), -np.sin(theta)], # Rotation matrix [np.sin(theta), np.cos(theta)]]) nPoints = 10000 # Create 10,000 points ### Data X = R @ np.diag(sig) @ np.random.randn(2,nPoints) + np.diag(xC) @ np.ones((2,nPoints)) Plot using