from sklearn import datasets
iris = datasets.load_iris() # 辨識花朵
digits = datasets.load_digits() # 辨識手寫數字
print(digits.data)
# [[ 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.]]
print(digits.target)
# [0, 1, 2, ..., 8, 9, 8]
from sklearn import svm
X, y = digits.data, digits.target
clf = svm.SVC(gamma=0.001, C=100.) # 利用 SVM 提供的 support vector classification
clf.fit(X, y) # clf 即為訓練好的 model (hypothesis)
print(clf)
# SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,
# decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',
# max_iter=-1, probability=False, random_state=None, shrinking=True,
# tol=0.001, verbose=False)
ans = clf.predict(X[-1:]) # 試著預測最後一個 data
print(ans)
# [8]
import pickle
s = pickle.dumps(clf) # save
clf2 = pickle.loads(s)
clf2.predict(X[-1:]) # 8
from joblib import dump, load
dump(clf, 'myModel.joblib')
clf3 = load('myModel.joblib')
clf3.predict(X[-1:]) # 8
clf = svm.SVC()
clf.set_params(kernel='linear').fit(X, y)
clf.predict(X[:1])
clf.set_params(kernel='rbf', gamma='scale').fit(X, y)
clf.predict(X[:1])