_x = []
for j in range(n):
_x.append(((X[i, j] - Min[j]) / (Max[j] - Min[j]) - 0.5) * 2)
# 此处将feature值归一化压缩到 [-1,1] 区间内
_X.append(_x)
return mat(_X)
#--------------------------------------------------主函数---------------------------------------------------------
if __name__ == "__main__":
# 1. 导入训练数据
f = 'train.txt'
raw_X, raw_Y = load_data(f)
#print(raw_X.shape,raw_Y.shape)
dataSet = Normalization(raw_X)
labels = transformLabel(raw_Y)
# 2. 训练SVM模型
C = 1.2
toler = 0.001
svm_model = SVM.SVM_training(dataSet, labels, C, toler)
# 3. 计算训练的准确性
accuracy = SVM.cal_accuracy(svm_model, dataSet, labels)
predict = get_predict(svm_model, dataSet)
print("accuracy =", accuracy)
print(predict.shape)
show_plot(raw_X, predict)
