关于 SVM 一些疑问

我现在有这样的数据： x: [[1,2,3,...,15],[16,17,18,...,30],... * n ] y: [0,1,... * n] 我想对这个 15 维的数组求出来一个范围致使在里面取值得到的结果为 1 ，我搜了很久，想用 SVM 去实现，这是我的代码：

import numpy as np from matplotlib import colors from sklearn import svm from sklearn import model_selection import matplotlib.pyplot as plt import matplotlib as mpl # 加载 data = np.loadtxt('./data.csv',dtype=float,delimiter=',') # 切分 x, y = np.split(data, (15, ), axis=1) x_train, x_test, y_train, y_test=model_selection.train_test_split(x, y, random_state=1, test_size=0.2) # 构建 def classifier(): clf = svm.SVC(C=0.8,kernel='linear',decision_function_shape='ovr') return clf # 训练 def train(clf, x_train, y_train): clf.fit(x_train, y_train.ravel()) # 定义 clf = classifier() # 调用 train(clf, x_train, y_train) # 判断 a,b 是否相等 计算 acc 的均值 def show_accuracy(a, b, tip): acc = a.ravel() == b.ravel() print('%s Accuracy:%.3f' %(tip, np.mean(acc))) # 分别打印训练集和测试集的准确率 score(x_train, y_train)表示输出 x_train,y_train 在模型上的准确率 def print_accuracy(clf, x_train, y_train, x_test, y_test): print('training prediction:%.3f' %(clf.score(x_train, y_train))) print('test data prediction:%.3f' %(clf.score(x_test, y_test))) # 原始结果和预测结果进行对比 predict() 表示对 x_train 样本进行预测,返回样本类别 show_accuracy(clf.predict(x_train), y_train, 'traing data') show_accuracy(clf.predict(x_test), y_test, 'testing data') print_accuracy(clf, x_train, y_train, x_test, y_test) 

佬们可以帮我斧正一下吗，有哪里需要改一下？我一直感觉前面有一层雾蒙蒙的东西我理解不上来