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