第二维度输入矩阵，是模型预测出来的概率，是小数值，大于0.5 的判断为1，小于0.5的判断为0.
import numpy as np
def calculate_ber(signal, received):
"""
Calculates the bit error rate (BER) of two NumPy matrices representing
a binary signal and the received signal, respectively.
Parameters:
-----------
signal : numpy.ndarray
A matrix of shape (m, n) representing the binary signal.
received : numpy.ndarray
A matrix of shape (m, n) representing the received signal.
Returns:
--------
ber : float
The bit error rate between the two signals.
"""
# Ensure the two matrices have the same shape
assert signal.shape == received.shape, "Error: matrices must have the same shape."
# Calculate the number of bit errors
num_errors = np.count_nonzero(signal != (received > 0.5))
# Calculate the total number of bits
total_bits = signal.size
# Calculate the bit error rate (BER)
ber = num_errors / total_bits
return ber
例子
import numpy as np
# Define the two matrices
signal = np.array([[0, 1, 1, 0],
[1, 0, 1, 1],
[0, 1, 0, 0]])
received = np.array([[0.2, 0.8, 0.9, 0.3],
[0.7, 0.4, 0.6, 0.8],
[0.1, 0.6, 0.4, 0.2]])
# Calculate the BER
ber = calculate_ber(signal, received)
# Print the result
print("Bit error rate:", ber)
