基于机器学习(ANN XGBOOST随机森林等)的CFST承载力预测源码.zip

import numpy as np import pandas as pd from matplotlib import pyplot as plt def sigmoid(x): # 第一层到第二层的激活函数 return 1 / (1 + np.exp(-x)) def deriv_sigmoid(x): # 第一层到第二层的激活函数的求导函数 fx = sigmoid(x) return fx * (1 - fx) def mse_loss(y_true, y_pred): # 使用方差作为损失函数 return ((y_true - y_pred) ** 2).mean() class OurNeuralNetwork: def __init__(self): # 第一层到第二层的函数 self.w11 = np.random.normal() self.w12 = np.random.normal() self.w13 = np.random.normal() self.w14 = np.random.normal() self.w21 = np.random.normal() self.w22 = np.random.normal() self.w23 = np.random.normal() self.w24 = np.random.normal() # 第二层到第三层的函数 self.w1 = np.random.normal() self.w2 = np.random.normal() # 截距项，Biases self.b1 = np.random.normal() self.b2 = np.random.normal() self.b3 = np.random.normal() def feedforward(self, x): # 前向传播学习 h1 = sigmoid(self.w11 * x[0] + self.w12 * x[1] + self.w13 * x[2] + self.w14 * x[3] + self.b1) h2 = sigmoid(self.w21 * x[0] + self.w22 * x[1] + self.w23 * x[2] + self.w24 * x[3] + self.b1) o1 = self.w1 * h1 + self.w2 * h2 + self.b3 return o1 #训练函数 def train(self, data, all_y_trues): learn_rate = 0.01 # 学习率 epochs = 1000 # 训练的次数 # 画图数据 self.loss = np.zeros(100) self.sum = 0; # 开始训练 for epoch in range(epochs): for x, y_true in zip(data, all_y_trues): # 计算h1 h1 = sigmoid(self.w11 * x[0] + self.w12 * x[1] + self.w13 * x[2] + self.w14 * x[3] + self.b1) # 计算h2 h2 = sigmoid(self.w21 * x[0] + self.w22 * x[1] + self.w23 * x[2] + self.w24 * x[3] + self.b2) #计算输出节点 y_pred = self.w1 * h1 + self.w2 * h2 + self.b3 # 反向传播计算导数 d_L_d_ypred = -2 * (y_true - y_pred) d_ypred_d_w1 = h1 d_ypred_d_w2 = h2 d_ypred_d_b3 = 0 d_ypred_d_h1 = self.w1 d_ypred_d_h2 = self.w2 sum_1=self.w11 * x[0] + self.w12 * x[1] + self.w13 * x[2] + self.w14 * x[3] + self.b1 d_h1_d_w11 = x[0] * deriv_sigmoid(sum_1) d_h1_d_w12 = x[1] * deriv_sigmoid(sum_1) d_h1_d_w13 = x[2] * deriv_sigmoid(sum_1) d_h1_d_w14 = x[3] * deriv_sigmoid(sum_1) d_h1_d_b1 = deriv_sigmoid(sum_1) sum_2 = self.w21 * x[0] + self.w22 * x[1] + self.w23 * x[2] + self.w24 * x[3] + self.b2 d_h1_d_w21 = x[0] * deriv_sigmoid(sum_2) d_h1_d_w22 = x[1] * deriv_sigmoid(sum_2) d_h1_d_w23 = x[2] * deriv_sigmoid(sum_2) d_h1_d_w24 = x[3] * deriv_sigmoid(sum_2) d_h1_d_b2 = deriv_sigmoid(sum_2) # 梯度下降法 self.w11 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w11 self.w12 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w12 self.w13 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w13 self.w14 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w14 self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1 self.w21 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h1_d_w21 self.w22 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h1_d_w22 self.w23 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h1_d_w23 self.w24 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h1_d_w24 self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h1_d_b2 self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_w1 self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_w2 self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3 if epoch % 10 == 0: y_preds = np.apply_along_axis(self.feedforward, 1, data) loss = mse_loss(all_y_trues, y_preds) print("Epoch %d loss: %.3f" % (epoch, loss)) self.loss[self.sum] = loss self.sum = self.sum + 1 # 文件的名字 FILENAME = "E:\\database\\cfst\\cacfst.csv" # 禁用科学计数法 pd.set_option('float_format', lambda x: '%.3f' % x) # 得到的DataFrame分别为总价、面积、房间、客厅、年份 data = pd.read_csv(FILENAME) # DataFrame转化为array DataArray = data.values Y = DataArray[:, 5] X = DataArray[:, 0:5] X = np.array(X)#转化为array,自变量 Y = np.array(Y)#转化为array，因变量房价 # 处理数据 data = np.array(X) data_mean = np.sum(data, axis=0) / np.size(data, 0) data = (data - data_mean) / np.max(data) all_y_trues = np.array(Y) all_y_trues_mean = np.sum(all_y_trues) / np.size(all_y_trues) all_y_trues = (all_y_trues - all_y_trues_mean) / np.max(all_y_trues) # 训练数据 network = OurNeuralNetwork() network.train(data, all_y_trues) # 输出神经网络参数 print("w11-->%.3f" % network.w11) print("w12-->%.3f" % network.w12) print("w13-->%.3f" % network.w13) print("w14-->%.3f" % network.w14) print("w21-->%.3f" % network.w21) print("w22-->%.3f" % network.w22) print("w23-->%.3f" % network.w23) print("w24-->%.3f" % network.w24) print("w1-->%.3f" % network.w1) print("w2-->%.3f" % network.w2) print("b1-->%.3f" % network.b1) print("b2-->%.3f" % network.b2) print("b3-->%.3f" % network.b3) # 标题显示中文 plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus'] = False # 测试数据 testData = np.array([152, 1.7, 900, 270,73]) testPrice = network.feedforward(testData) # 损失函数曲线图 plt.plot(np.arange(100), network.loss) plt.show() # 真实值与预测值对比 y_preds = np.apply_along_axis(network.feedforward, 1, data) plt.plot(np.arange(380), all_y_trues,"r^") plt.plot(np.arange(380),y_preds,"bs") plt.title("红色为真实值，蓝色为预测值") plt.show()