I have a basic understanding using numpy to create a matrix, but the context in which I have to create one confuses me. For example, I need to create a 2X1000 matrix with normally distributed values with mean 0 and standard deviation of 1. I'm not sure what it means to make a matrix with these conditions.
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2 Answers
Besides what was writeen above by CoDEmanX, From numpy.random.normal we can read about the generic Normal Distribution in numpy: numpy.random.normal(loc=0.0, scale=1.0, size=None) Where: loc is the Mean of the distribution and scale is the standard deviation (square root of variance).
import numpy as np
A = np.random.normal(loc =0, scale =1, size=(2, 1000))
print(A)
But if these examples are confusing, then consider that
np.random.normal()
simply gives you a random number, and you can create your own custom matrix:
import numpy as np
A = [ [np.random.normal() for i in range(1000)] for j in range(2) ]
A = np.array(A)
Comments
If you refer to the numpy docs, whether there are utility functions that facilitate you reaching your goal, you'll come across a normal distribution function:
numpy.random.standard_normal(size=None)
standard_normal(size=None)
Returns samples from a Standard Normal distribution (mean=0, stdev=1).
The simple average is 0, the standard deviation 1.
arr = numpy.random.standard_normal((2, 1000))
print(arr.mean()) # -0.027...
print(arr.std()) # 1.0272...
Note that it's not exactly 0 or 1.
I still recommend to read about normal distributation and standard deviation / variance, eventhough numpy offers a simple solution.