import pandas as pd import numpy as np import matplotlib.pyplot as plt

y_hat = pd.read_csv('y_hat.csv') # len = 93 predict_Y = pd.read_csv('predict_Y.csv') # len = 92 original = pd.read_csv('original.csv') # len = 100 y_pred = pd.read_csv('y_pred_mnn.csv') # len = 100

plt.figure(figsize=(10, 10)) plt.xlabel('Longitude Error (degree)') plt.ylabel('Latitude Error (degree)')

LSTM

a_lat_LSTM = [] b_lon_LSTM = [] for i in range(len(y_hat)): long = original.iloc[i + 7, 1] - y_hat.iloc[i, 1] lat = original.iloc[i+7, 0] - y_hat.iloc[i, 0] a_lat_LSTM.append(long) b_lon_LSTM.append(lat)

GRU

a_lat_GRU = [] b_lon_GRU = [] for i in range(len(predict_Y)): long = original.iloc[i+8, 1] - predict_Y.iloc[i, 1] lat = original.iloc[i+8, 0] - predict_Y.iloc[i, 0] a_lat_GRU.append(long) b_lon_GRU.append(lat)

MNN

a_lat_MNN = [] b_lon_MNN = [] for i in range(len(y_pred)): long = original.iloc[i, 1] - y_pred.iloc[i, 2] lat = original.iloc[i, 0] - y_pred.iloc[i, 1] a_lat_MNN.append(long) b_lon_MNN.append(lat)

计算LSTM，MNN，GRU对经纬度的MAPE，内容：MAPE是指平均绝对百分比误差，计算公式为：

MAPE = (1/n) * Σ(|(预测值-真实值)/真实值|) * 100%

其中n为样本数量，Σ表示对所有样本求和。

计算LSTM，MNN，GRU对经度的MAPE：

MAPE_lon_LSTM = (1/len(a_lat_LSTM)) * np.sum(np.abs(np.array(a_lat_LSTM)/original.iloc[7:-1, 1]) * 100) MAPE_lon_GRU = (1/len(b_lon_GRU)) * np.sum(np.abs(np.array(b_lon_GRU)/original.iloc[8:-1, 1]) * 100) MAPE_lon_MNN = (1/len(b_lon_MNN)) * np.sum(np.abs(np.array(b_lon_MNN)/original.iloc[:, 1]) * 100)

计算LSTM，MNN，GRU对纬度的MAPE：

MAPE_lat_LSTM = (1/len(a_lat_LSTM)) * np.sum(np.abs(np.array(a_lat_LSTM)/original.iloc[7:-1, 0]) * 100) MAPE_lat_GRU = (1/len(b_lon_GRU)) * np.sum(np.abs(np.array(b_lon_GRU)/original.iloc[8:-1, 0]) * 100) MAPE_lat_MNN = (1/len(b_lon_MNN)) * np.sum(np.abs(np.array(b_lon_MNN)/original.iloc[:, 0]) * 100)

最后输出结果：

print('LSTM对经度的MAPE为：', MAPE_lon_LSTM) print('GRU对经度的MAPE为：', MAPE_lon_GRU) print('MNN对经度的MAPE为：', MAPE_lon_MNN)

print('LSTM对纬度的MAPE为：', MAPE_lat_LSTM) print('GRU对纬度的MAPE为：', MAPE_lat_GRU) print('MNN对纬度的MAPE为：', MAPE_lat_MNN)