I was looking through some codes and saw this line, numpy.random.normal(size=x.shape). Where, x = numpy.linspace(1, 2, 100). I don't understand what this does. I've only come across, np.random.normal(size=1) before. Can someone please explain the difference in both the cases and their use.
From numpy.random.normal
size: int or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if loc and scale are both scalars. Otherwise, np.broadcast(loc, scale).size samples are drawn.
shape return a tuple. If you send it to the size parameter the size will be multiplication of the values (the result array will have the same shape)
arr = numpy.array([1, 2, 3])
print(arr.shape)
random_arr = numpy.random.normal(size=arr.shape)
print(random_arr)
# Output
# (3,)
# [ 0.02756549 -0.52115646 -2.32361849]
arr = numpy.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)
random_arr = numpy.random.normal(size=arr.shape)
print(random_arr)
# Output
# (2, 3)
# [[ 1.10564417 0.32478606 -1.71487667]
# [ 0.5461406 0.51505975 0.2158163 ]]
arr = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr.shape)
random_arr = numpy.random.normal(size=arr.shape)
print(random_arr)
# Output
# (3, 3)
# [[ 0.75194289 -1.1696558 1.05238044]
# [-0.68043824 0.98258701 3.48030454]
# [-0.84775259 -0.49676403 1.33367409]]