RBF.rar_RBF_python_python RBF_rbf分类_rbf神经网络

# -*- coding: utf-8 -*- """ Created on Tue Oct 8 12:45:32 2019 @author: Winston """ from numpy import * from math import sqrt def load_data(file_name): '''导入数据 input: file_name(string):文件的存储位置 output: feature_data(mat):特征 label_data(mat):标签 n_class(int):类别的个数 ''' # 1、获取特征 f = open(file_name) # 打开文件 feature_data = [] label = [] for line in f.readlines(): feature_tmp = [] lines = line.strip().split("\t") for i in range(len(lines) - 1): feature_tmp.append(float(lines[i])) label.append(int(lines[-1])) feature_data.append(feature_tmp) f.close() # 关闭文件 n_output = 1 return mat(feature_data), mat(label).transpose(), n_output def linear(x): '''Sigmoid函数（输出层神经元激活函数） input: x(mat/float):自变量，可以是矩阵或者是任意实数 output: Sigmoid值(mat/float):Sigmoid函数的值 ''' return x def hidden_out(feature,center,delta): '''rbf函数（隐含层神经元输出函数） input:feature(mat):数据特征 center(mat):rbf函数中心 delta(mat)：rbf函数扩展常数 output：hidden_output（mat）隐含层输出 ''' m,n = shape(feature) m1,n1 = shape(center) hidden_out = mat(zeros((m,m1))) for i in range(m): for j in range(m1): hidden_out[i,j] = exp(-1.0 * (feature[i,:]-center[j,:]) * (feature[i,:]-center[j,:]).T/(2*delta[0,j]*delta[0,j])) return hidden_out def predict_in(hidden_out, w): '''计算输出层的输入 input: hidden_out(mat):隐含层的输出 w1(mat):隐含层到输出层之间的权重 b1(mat):隐含层到输出层之间的偏置 output: predict_in(mat):输出层的输入 ''' m = shape(hidden_out)[0] predict_in = hidden_out * w return predict_in def predict_out(predict_in): '''输出层的输出 input: predict_in(mat):输出层的输入 output: result(mat):输出层的输出 ''' result = linear(predict_in) return result def bp_train(feature, label, n_hidden, maxCycle, alpha, n_output): '''计算隐含层的输入 input: feature(mat):特征 label(mat):标签 n_hidden(int):隐含层的节点个数 maxCycle(int):最大的迭代次数 alpha(float):学习率 n_output(int):输出层的节点个数 output: center(mat):rbf函数中心 delta(mat):rbf函数扩展常数 w(mat):隐含层到输出层之间的权重 ''' m, n = shape(feature) # 1、初始化 center = mat(random.rand(n_hidden,n)) center = center * (8.0 * sqrt(6) / sqrt(n + n_hidden)) - mat(ones((n_hidden,n))) * (4.0 * sqrt(6) / sqrt(n + n_hidden)) delta = mat(random.rand(1,n_hidden)) delta = delta * (8.0 * sqrt(6) / sqrt(n + n_hidden)) - mat(ones((1,n_hidden))) * (4.0 * sqrt(6) / sqrt(n + n_hidden)) w = mat(random.rand(n_hidden, n_output)) w = w * (8.0 * sqrt(6) / sqrt(n_hidden + n_output)) - mat(ones((n_hidden, n_output))) * (4.0 * sqrt(6) / sqrt(n_hidden + n_output)) # 2、训练 iter = 0 while iter <= maxCycle: # 2.1、信号正向传播 # 2.1.1、计算隐含层的输出 hidden_output = hidden_out(feature,center,delta) # 2.1.3、计算输出层的输入 output_in = predict_in(hidden_output, w) # 2.1.4、计算输出层的输出 output_out = predict_out(output_in) # 2.2、误差的反向传播 error = mat(label - output_out) for j in range(n_hidden): sum1 = 0.0 sum2 = 0.0 sum3 = 0.0 for i in range(m): sum1 += error[i,:] * exp(-1.0 * (feature[i]-center[j]) * (feature[i]-center[j]).T / (2 * delta[0,j]*delta[0,j])) * (feature[i] - center[j]) sum2 += error[i,:] * exp(-1.0 * (feature[i]-center[j]) * (feature[i]-center[j]).T / (2 * delta[0,j]*delta[0,j])) * (feature[i] - center[j]) * (feature[i] - center[j]).T sum3 += error[i,:] * exp(-1.0 * (feature[i]-center[j]) * (feature[i]-center[j]).T / (2 * delta[0,j]*delta[0,j])) delta_center = (w[j,:]/(delta[0,j]*delta[0,j])) * sum1 delta_delta = (w[j,:]/(delta[0,j]*delta[0,j]*delta[0,j])) * sum2 delta_w = sum3 # 2.3、 修正权重和rbf函数中心和扩展常数 center[j,:] = center[j,:] + alpha * delta_center delta[0,j] = delta[0,j] + alpha * delta_delta w[j,:] = w[j,:] + alpha * delta_w if iter % 10 == 0: cost = (1.0/2) * get_cost(get_predict(feature, center, delta, w) - label) print ("\t-------- iter: ", iter, " ,cost: ", cost) if cost < 3: ###如果损失函数值小于3则停止迭 break iter += 1 return center, delta, w def get_cost(cost): '''计算当前损失函数的值 input: cost(mat):预测值与标签之间的差 output: cost_sum / m (double):损失函数的值 ''' m,n = shape(cost) cost_sum = 0.0 for i in range(m): for j in range(n): cost_sum += cost[i,j] * cost[i,j] return cost_sum / 2 def get_predict(feature, center, delta, w): '''计算最终的预测 input: feature(mat):特征 w0(mat):输入层到隐含层之间的权重 b0(mat):输入层到隐含层之间的偏置 w1(mat):隐含层到输出层之间的权重 b1(mat):隐含层到输出层之间的偏置 output: 预测值 ''' return predict_out(predict_in(hidden_out(feature,center,delta), w)) def save_model_result(center, delta, w, result): '''保存最终的模型 input: w0(mat):输入层到隐含层之间的权重 b0(mat):输入层到隐含层之间的偏置 w1(mat):隐含层到输出层之间的权重 b1(mat):隐含层到输出层之间的偏置 output: ''' def write_file(file_name, source): f = open(file_name, "w") m, n = shape(source) for i in range(m): tmp = [] for j in range(n): tmp.append(str(source[i, j])) f.write("\t".join(tmp) + "\n") f.close() write_file("center.txt", center) write_file("delta.txt", delta) write_file("weight.txt", w) write_file('train_result.txt',result) def err_rate(label, pre): '''计算训练样本上的错误率 input: label(mat):训练样本的标签 pre(mat):训练样本的预测值 output: rate[0,0](float):错误率 ''' m = shape(label)[0] for j in range(m): if pre[j,0] > 0.5: pre[j,0] = 1.0 else: pre[j,0] = 0.0 err = 0.0 for i in range(m): if float(label[i, 0]) != float(pre[i, 0]): err += 1 rate = err / m return rate if __name__ == "__main__": # 1、导入数据 print ("--------- 1.load data ------------") feature, label, n_output = load_data("22-20.txt") # 2、训练网络模型 print ("--------- 2.training ------------") center, delta, w = bp_train(feature, label, 20, 5000, 0.008, n_output) # 3、得到最终的预测结果 print ("--------- 3.get prediction ------------") result = get_predict(feature, center, delta, w) print ("训练准确性为：", (1 - err_rate(label, result))) # 4、保存最终的模型 print ("--------- 4.save model and result ------------") save_model_result(center, delta, w, result)