SVM:SVC真实案例,写出python代码
时间: 2023-07-22 12:06:51 浏览: 47
以下是一个使用SVM(SVC)进行二元分类的Python代码示例:
```python
import numpy as np
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 生成随机数据集
np.random.seed(0)
X = np.random.randn(200, 2)
y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
y = np.where(y, 1, -1)
# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# 创建支持向量机分类器
clf = svm.SVC(kernel='linear')
# 训练模型
clf.fit(X_train, y_train)
# 预测测试集
y_pred = clf.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("准确率:", acc)
```
在这个例子中,我们生成了一个随机的二元分类数据集,并将其拆分为训练集和测试集。然后,我们创建了一个支持向量机分类器,使用线性核函数进行训练,并使用测试集进行预测。最后,我们计算了模型的准确率。
相关问题
基于SVM实现股票趋势预测,写出python代码
以下是一个简单的基于SVM的股票趋势预测的Python代码示例:
首先,我们需要导入必要的库:
```python
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
```
然后,我们可以读取股票数据,例如:
```python
df = pd.read_csv('stock_data.csv')
```
接下来,我们需要对数据进行预处理,包括特征提取和标签编码等。例如,我们可以将每天的收盘价作为特征,将涨跌幅度编码为1或-1作为标签:
```python
X = df['Close'].values.reshape(-1, 1)
y = np.where(df['Change'] > 0, 1, -1)
```
然后,我们可以将数据集分为训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,我们可以训练一个SVM分类器:
```python
svm = SVC(kernel='linear', C=1.0, random_state=42)
svm.fit(X_train, y_train)
```
最后,我们可以使用测试集评估模型的性能:
```python
y_pred = svm.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)
```
完整代码如下:
```python
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df = pd.read_csv('stock_data.csv')
X = df['Close'].values.reshape(-1, 1)
y = np.where(df['Change'] > 0, 1, -1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
svm = SVC(kernel='linear', C=1.0, random_state=42)
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)
```
给定一个网络,如何利用svm进行链路预测,写出Python代码
链路预测是指在网络中预测两个节点之间是否存在边。支持向量机(SVM)是一种常用的机器学习算法,可以用于链路预测任务。
下面是一个使用Python和sklearn库实现SVM进行链路预测的示例代码:
```python
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import networkx as nx
import numpy as np
# 构建一个网络图
G = nx.karate_club_graph()
# 生成特征矩阵和标签矩阵
X = np.array([list(nx.jaccard_coefficient(G, e)) for e in nx.non_edges(G)])
y = np.array([int(G.has_edge(e[0], e[1])) for e in nx.non_edges(G)])
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 构建SVM模型
svm = SVC(kernel='linear', probability=True, random_state=42)
# 模型训练
svm.fit(X_train, y_train)
# 预测测试集
y_pred = svm.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)
```
这段代码中,我们先构建了一个Karate Club网络图,并使用NetworkX库计算了每条非边的Jaccard系数作为特征矩阵X。标签矩阵y则是根据边的存在与否生成的。然后我们用sklearn库的train_test_split函数将数据集划分为训练集和测试集,使用SVM模型进行训练和预测,并计算准确率。