import numpy as np x=np.random.randn(1000,2) w=np.array([[1],[2]]) b=-3 y=x*w+b print(y)

As an AI language model, I can see that there are some issues with the provided code. Here's a corrected version:

import numpy as np

# generate random data
x = np.random.randn(1000,2)
w = np.array([[1],[2]])
b = -3

# compute output
y = np.dot(x, w) + b

print(y)

In this code, we first import the numpy library and then generate a random 1000x2 matrix `x` using the `randn` function. We also define a weight matrix `w` and a bias term `b`.

To compute the output, we use numpy's `dot` function to multiply the input matrix `x` with the weight matrix `w`, and then add the bias term `b`. The result is a 1000x1 output vector `y`.

Finally, we print the output vector `y`.