极限学习机Python程序_极限学习机_

'''这种算法是针对 SLFNs （即含单个隐藏层的前馈型神经网络）的监督型学习算法， 其主要思想是：输入层与隐藏层之间的权值参数，以及隐藏层上的偏置向量参数是 once for all 的（不需要像其他基于梯度的学习算法一样通过迭代反复调整刷新）， 求解很直接，只需求解一个最小范数最小二乘问题（最终化归成求解一个矩阵的 Moore-Penrose 广义逆问题）。''' #class hpelm.elm.ELM(inputs, outputs, classification='', w=None, batch=1000, accelerator=None, precision='double', norm=None, tprint=5) #coding:utf-8 from __future__ import division from pylab import* from sklearn.svm import SVR mpl.rcParams['font.sans-serif']=['SimHei'] mpl.rcParams['axes.unicode_minus']=False from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split import numpy as np import math import csv from sklearn import metrics import csv from pylab import* mpl.rcParams['font.sans-serif']=['SimHei'] mpl.rcParams['axes.unicode_minus']=False from sklearn.ensemble import RandomForestRegressor from hpelm import ELM from sklearn import preprocessing from sklearn.preprocessing import MinMaxScaler from keras import metrics from sklearn.preprocessing import MinMaxScaler import numpy as np import csv from sklearn.model_selection import train_test_split from pylab import* from sklearn import preprocessing from sklearn.preprocessing import MinMaxScaler import time from matplotlib import pyplot import math from random import random from pylab import* from sklearn.utils import shuffle i=0 j=[] data = [] X = [] indicess = [] xback =24 with open(r'D:\晴天新.csv') as f: reader = csv.reader(f) for row in reader: if i == 0: i += 1 continue else: data.append(row[:])#提取出每一行中的2:14列 data = np.array(data) m,n = np.shape(data) for i in range(m): for j in range(n): data[i][j] = data[i][j].astype('float64')#是从第三列开始的 data = data.astype('float64') y = data[:,-1] y1 = data[:,-1] set1 = data[:,:] set2 = data[:,-1] def create_interval_dataset(dataset1, dataset2, xback): dataX, dataY = [], [] for i in range(0, len(dataset1)-xback,24): dataX.append(dataset1[i:i+xback]) dataY.append(dataset2[i+xback:i+2*xback]) return np.asarray(dataX), np.asarray(dataY) dataX, dataY = create_interval_dataset(set1, set2, 24) dataY=np.reshape(dataY, [-1,24]) X_tr = dataX[:26, :] X_te = dataX[26:, :] y_tr = dataY[:26] y_te= dataY[26:] X_tr = np.reshape(X_tr, [-1,168]) X_te= np.reshape(X_te, [-1,168]) y_tr = np.reshape(y_tr, [-1,24]) y_te = np.reshape(y_te, [-1,24]) scaler = MinMaxScaler(feature_range=(0, 1)) scaler1 = MinMaxScaler(feature_range=(0, 1)) X_tr= scaler.fit_transform(X_tr) X_te= scaler1.fit_transform(X_te) scaler2 = MinMaxScaler(feature_range=(0, 1)) y_tr= scaler2.fit_transform(y_tr.reshape(-1, 24)) scaler3 = MinMaxScaler(feature_range=(0, 1)) y_te= scaler3.fit_transform(y_te.reshape(-1,24)) X_tr = np.reshape(X_tr, [-1,168]) X_te= np.reshape(X_te, [-1,168]) y_tr = np.reshape(y_tr, [-1,24]) y_te= np.reshape(y_te, [-1,24]) y_te = y_te .astype('float64') y_tr = y_tr.astype('float64') y_tr = np.reshape(y_tr, [-1,24]) y_te= np.reshape(y_te, [-1,24]) y_tr = y_tr.astype('float64') X_tr = X_tr.astype('float64') X_te = X_te.astype('float64') y_te = y_te.astype('float64') y_tr = np.reshape(y_tr, [-1,24]) elm = ELM(168, y_tr.shape[1]) elm.add_neurons(5, "sigm") elm.add_neurons(6, "rbf_l2") elm.train(X_tr, y_tr, 'r') predicted = elm.predict(X_te) predicted1 = elm.predict(X_tr) predicted = scaler3.inverse_transform(predicted) predicted1 = scaler2.inverse_transform(predicted1) X_te= scaler1.inverse_transform(X_te) y_tr = scaler2.inverse_transform(y_tr) y_te = scaler3.inverse_transform(y_te) predicted= predicted.flatten() y_tr = y_tr.flatten() y_te = y_te.flatten() predicted1= predicted1.flatten() start=0 end =0 fei0=[] for i in range(len(y_tr)): if y_tr[i]!=0: fei0.append(i) xina=[] xinb=[] for i in fei0: xina.append(y_tr[i]) xinb.append(predicted1[i]) fei01=[] for i in range(len(y_te)): if y_te[i]!=0: fei01.append(i) xina1=[] xinb1=[] for i in fei01: xina1.append(y_te[i]) xinb1.append(predicted[i]) print("*****************训练集误差***********************") v = list(map(lambda x: (abs((x[0] - x[1]) / x[0])), zip(xina, xinb))) loss = sum(v) * 100 / len(y_tr) #print("v", v) print("MAPE loss",loss) v = list(map(lambda x: ((pow((x[0] - x[1]), 2))), zip(xina, xinb))) loss = math.sqrt(sum(v) / len(y_tr)) print("the RMSE is :",loss) #accuracy = metrics.mean_squared_error(y_true=y_te, y_pred=pred_X) #print('gcForest accuracy : {}'.format(accuracy)) v = list(map(lambda x: (abs((x[0] - x[1])) ), zip(xina, xinb))) loss = sum(v)/ len(y_tr) print("the MABE is :",loss) file.write('——————————ELM提前一小时测试误差————————————' + '\n') print("*****************测试集误差***********************") v = list(map(lambda x: (abs((x[0] - x[1]) / x[0])), zip(xina1, xinb1))) loss = sum(v) * 100 / len(y_te) print("the MAPE is : ",loss) v = list(map(lambda x: ((pow((x[0] - x[1]), 2))), zip(xina1, xinb1))) loss = math.sqrt(sum(v)/ len(y_te)) print("RMSE loss",loss) v = list(map(lambda x: (abs((x[0] - x[1])) ), zip(xina1, xinb1))) loss = sum(v)/ len(y_te) print("MABE loss",loss) plt.plot(y_te[:100], 'g--', lw=2, label='ELM真实值曲线') plt.plot(predicted[:100] ,'r', lw=2, label='ELM预测值曲线') plt.title('ELM',fontsize=18)#表的名称 plt.legend() plt.show() ———————————————— 版权声明：本文为CSDN博主「simple_hututu」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。 原文链接：https://blog.csdn.net/pwtd_huran/java/article/details/79729913