本关任务是使用sklearn库来实现线性支持向量机，然后使用癌细胞数据的训练集来训练模型，最后使用测试集的糖尿病数据来进行识别。

首先，导入所需的库和数据集：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score

# 导入数据集
data = pd.read_csv('cancer.csv')

# 将特征和标签分开
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

然后，使用LinearSVC来训练模型：

# 训练模型
classifier = LinearSVC(random_state=0, max_iter=10000)
classifier.fit(X_train, y_train)

最后，使用测试集进行识别并计算准确率：

# 预测测试集结果
y_pred = classifier.predict(X_test)

# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

完整代码如下：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score

# 导入数据集
data = pd.read_csv('cancer.csv')

# 将特征和标签分开
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 训练模型
classifier = LinearSVC(random_state=0, max_iter=10000)
classifier.fit(X_train, y_train)

# 预测测试集结果
y_pred = classifier.predict(X_test)

# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)