svm加载数据集并预测

mac2024-08-06  61

ris Data Set(鸢尾属植物数据集)

from sklearn import datasets iris=datasets.load_iris() print(iris.data) print(iris.target)

[[5.1 3.5 1.4 0.2] [4.9 3. 1.4 0.2] [4.7 3.2 1.3 0.2] [4.6 3.1 1.5 0.2] [5. 3.6 1.4 0.2] [5.4 3.9 1.7 0.4] …] [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]

from sklearn import datasets digits=datasets.load_digits() print(digits.data) print(digits.target)

[[ 0. 0. 5. … 0. 0. 0.] [ 0. 0. 0. … 10. 0. 0.] [ 0. 0. 0. … 16. 9. 0.] … … [ 0. 0. 1. … 6. 0. 0.] [ 0. 0. 2. … 12. 0. 0.] [ 0. 0. 10. … 12. 1. 0.]] [0 1 2 … 8 9 8]

from sklearn import datasets print(digits.images[0]) print(digits.data[0])

[[ 0. 0. 5. 13. 9. 1. 0. 0.] [ 0. 0. 13. 15. 10. 15. 5. 0.] [ 0. 3. 15. 2. 0. 11. 8. 0.] [ 0. 4. 12. 0. 0. 8. 8. 0.] [ 0. 5. 8. 0. 0. 9. 8. 0.] [ 0. 4. 11. 0. 1. 12. 7. 0.] [ 0. 2. 14. 5. 10. 12. 0. 0.] [ 0. 0. 6. 13. 10. 0. 0. 0.]] [ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5. 0. 0. 3. 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8. 8. 0. 0. 5. 8. 0. 0. 9. 8. 0. 0. 4. 11. 0. 1. 12. 7. 0. 0. 2. 14. 5. 10. 12. 0. 0. 0. 0. 6. 13. 10. 0. 0. 0.]

from sklearn import datasets from sklearn import svm svmLearn=svm.SVC(gamma=0.01,C=90) print(svmLearn.fit(digits.data[:-2],digits.target[:-2])) print(svmLearn.predict(digits.data[-2:])) print(digits.target[-2:])

SVC(C=90, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape=‘ovr’, degree=3, gamma=0.01, kernel=‘rbf’, max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) [9 8] [9 8]

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