import numpy as np import pandas as pd from sklearn.svm import SVC from sklearn.datasets import load_iris data = load_iris() X = data['data'] y = data['target'] X = X[y != 2, 0:2] y = y[y != 2] X -= np.mean(X, axis=0) X /= np.std(X, axis=0, ddof=1) m = len(X) data = int(0.8 * m) X_train, X_test = np.split(X, [data]) y_train, y_test = np.split(y, [data]) model_svm = SVC(C=1.0, kernel='linear') model_svm.fit(X_train, y_train) Accuracy = model_svm.score(X_test, y_test) print('Linear Accuracy = ', Accuracy) X_test_h = model_svm.predict(X_test)
时间: 2024-04-17 15:29:02 浏览: 17
这段代码是一个简单的示例,展示了如何使用支持向量机(SVM)对鸢尾花数据集进行分类。
首先,导入所需的库:numpy、pandas、SVC(来自sklearn.svm)以及load_iris(来自sklearn.datasets)。
然后,加载鸢尾花数据集并将其分为特征矩阵X和目标向量y。在这个例子中,我们只选择了前两个特征,并将目标类别2过滤掉。
接下来,对特征矩阵进行标准化处理,将每个特征的均值减去,并除以标准差。
然后,计算样本数量m,并将80%的数据用作训练集,剩余的20%用作测试集。
创建一个SVC模型,使用线性核函数,并将其拟合到训练集上。
计算模型在测试集上的准确率,并打印出来。
最后,使用训练好的模型对测试集进行预测,并将结果保存在X_test_h中。
相关问题
在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作
要在Python中运行这些代码,你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。
首先,在命令行中运行以下命令来安装所需的包:
```bash
pip install scikit-learn pandas matplotlib numpy
```
然后,在你的Python脚本中导入所需的库:
```python
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
from sklearn import svm
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.model_selection import train_test_split
from sklearn import datasets
from matplotlib.colors import ListedColormap
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
import random
```
请确保在运行这些代码之前,已经安装了所需的Python库。如果遇到任何问题,请确保已正确安装这些库,并且版本与代码兼容。
import numpy as np import pandas as pd from sklearn.svm import SVC from sklearn.datasets import load_iris data = load_iris() X = data['data'] y = data['target'] X = X[y != 2, 0:2] y = y[y != 2] X -= np.mean(X, axis=0) X /= np.std(X, axis=0, ddof=1) m = len(X) data = int(0.8 * m) X_train, X_test = np.split(X, [data]) y_train, y_test = np.split(y, [data]) model_svm = SVC(C=1.0, kernel='linear') model_svm.fit(X_train, y_train) Accuracy = model_svm.score(X_test, y_test) print('Linear Accuracy = ', Accuracy) X_test_h = model_svm.predict(X_test) 将每一段的代码含义解释出来
这段代码使用了Scikit-learn库来训练一个支持向量机(SVM)模型并评估其准确性。下面是每段代码的含义解释:
1. `import numpy as np`:导入NumPy库,用于处理数组和数值计算。
2. `import pandas as pd`:导入Pandas库,用于数据处理和分析。
3. `from sklearn.svm import SVC`:从Scikit-learn库中导入支持向量机模型。
4. `from sklearn.datasets import load_iris`:从Scikit-learn库中导入鸢尾花数据集。
5. `data = load_iris()`:加载鸢尾花数据集。
6. `X = data['data']`:将数据集的特征存储在X变量中。
7. `y = data['target']`:将数据集的目标变量存储在y变量中。
8. `X = X[y != 2, 0:2]`:选择特征矩阵X中类别不为2的样本,并只保留前两个特征。
9. `y = y[y != 2]`:选择目标变量y中类别不为2的样本。
10. `X -= np.mean(X, axis=0)`:对特征矩阵X进行均值归一化,即减去每个特征的均值。
11. `X /= np.std(X, axis=0, ddof=1)`:对特征矩阵X进行标准差归一化,即除以每个特征的标准差。
12. `m = len(X)`:计算样本数量m。
13. `data = int(0.8 * m)`:计算训练集的大小,这里选择80%的样本作为训练集。
14. `X_train, X_test = np.split(X, [data])`:将特征矩阵X按照给定索引位置data进行分割,分成训练集X_train和测试集X_test。
15. `y_train, y_test = np.split(y, [data])`:将目标变量y按照给定索引位置data进行分割,分成训练集y_train和测试集y_test。
16. `model_svm = SVC(C=1.0, kernel='linear')`:创建一个线性核的支持向量机模型,并设置正则化参数C为1.0。
17. `model_svm.fit(X_train, y_train)`:使用训练集训练支持向量机模型。
18. `Accuracy = model_svm.score(X_test, y_test)`:计算测试集上的准确性得分。
19. `print('Linear Accuracy = ', Accuracy)`:打印线性核支持向量机模型在测试集上的准确性得分。
20. `X_test_h = model_svm.predict(X_test)`:使用训练好的模型对测试集进行预测。