题解 | 单神经元

import math
import numpy as np
def sigmoid(x):
    return 1/(1+math.exp(-1*x))


def single_neuron_model(features, labels, weights, bias):
    probabilities = []
    diff = 0
    for i in range(len(features)):
        z = np.dot(features[i], weights) + bias
        z = round(sigmoid(z), 4)
        probabilities.append(z)
        diff += (z-labels[i])**2
    mse = round(diff / len(features), 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))