Gaussian Noise

I was wondering how I could add Gaussian noise with mean vector = 0 and identity covariance matrix. I'm fairly new to all of this and I've seen that numpy can generate noise, but I'm not sure if it is also identity covariance matrix.

2 responses

Hey Chris, you can generate Gaussian noise by using the numpy.random.normal() function. When you call that function, you pass in a parameter called 'size', which is the shape of your output numpy array, along with the mean, and the standard deviation. You can read more about it here https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html.

Furthermore, you can generate an identity matrix using the numpy.eye function, which will output an identity matrix with desired dimensions you can specify in the parameters. You can read more about that here https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.eye.html

As for generating the identity covariance matrix, could you clarify what you mean?

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Thanks for the help!
Following up on some research here, and it calls for inputting Gaussian noise to variables, with mean vector 0 and identity covariance matrix. Not quite sure where they were going with that, but the noise with numpy does work. Not sure if this noise is different than the one specified, but for now I'll continue.