def good_function(x = 0.0): print(x) x = np.random.randn() why this function do not have return but could still run

This function `good_function` will run without any error even though it does not have a return statement because it does not require any output to be returned.

The function takes an optional argument `x` with a default value of 0.0. When the function is called, it prints the value of `x` and then assigns a random number generated using the NumPy library to `x`.

Since there is no return statement, the function does not return any value, but it does successfully print the value of `x` and update its value.

However, if you try to use the value of `x` after calling the function, it will be the updated random number generated by NumPy and not the original value passed to the function.

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y_data = 9.0 * x_data + 2.0 * x_data + 8.0 * x_data + 1.0 + np.random.randn( *x_data.shape) * 0.4

This code is not complete and contains errors. It appears to be attempting to create a set of y values based on a set of x values. However, it is not clear what the intended operation is for each term in the equation.

Here is an example of how the code could be rewritten to create a set of y values that are a combination of the x values and some random noise:

import numpy as np

x_data = np.linspace(-10, 10, 100)
noise = np.random.randn(*x_data.shape) * 0.4
y_data = 9.0 * x_data + 2.0 * x_data - 8.0 * x_data + 1.0 + noise

In this version, the y values are created by multiplying the x values by some constants (9.0, 2.0, and -8.0), adding a constant term (1.0), and adding some random noise. The noise is generated using the `numpy.random.randn()` function, which creates an array of random numbers with a normal distribution. The `*x_data.shape` syntax is used to ensure that the noise array has the same shape as the x data.

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`.