题解 | 单神经元

import math
import numpy as np
def single_neuron_model(features, labels, weights, bias):
    def sigmoid(x):
        return 1 / (1 + np.exp(-x))
    probabilities_np = sigmoid(weights @ features.T + bias)
    probabilities = []
    mse = 0
    for i in range(len(probabilities_np)):
        probabilities.append(round(probabilities_np[i], 4))
        mse += (probabilities_np[i] - labels[i])**2
    mse = round(mse / len(probabilities_np), 4)
    return probabilities, mse


if __name__ == "__main__":
    features = np.array(eval(input()))
    labels = np.array(eval(input()))
    weights = np.array(eval(input()))
    bias = float(input())
    print(single_neuron_model(features, labels, weights, bias))