Hey there, let's break down why you're running into this error and how to fix it quickly.

The Root Cause #

The problem comes down to using math.exp() with a NumPy array. The math module's functions are designed to work with single Python scalar values—not arrays. When you calculate (x[t,:]- 99)**2, you're producing a 1D NumPy array (shape (196,)), and passing that to math.exp() throws the error because it can't handle array inputs.

The Solution #

Replace math.exp() with NumPy's np.exp() instead. NumPy's mathematical functions are vectorized, meaning they can operate directly on entire arrays, applying the function to each element individually. This is exactly what you need for your 196-element array.

Here's your corrected code:

import numpy as np

# Initialize your array as before
x = np.zeros((13, 196), dtype=np.float64)
x[0,:] = 106 * np.ones((1, 196))

for t in range(0,12):
    # Use np.exp() instead of math.exp()
    x[t+1,:] = 100 + (0.7 + (0.4/(1 + np.exp(0.1*((x[t,:]- 99)**2)))))

A Quick Tip #

Whenever you're working with NumPy arrays, stick to NumPy's built-in math functions (like np.sin(), np.log(), np.sqrt()) instead of the standard math module. Not only does this avoid scalar/array mismatch errors, but vectorized operations are also significantly faster than looping through each element manually.

内容的提问来源于stack exchange，提问作者Ja123