用Fisher线性判别和支持向量机这两种机器学习算法对Oxford17Flower数据集实现植物分类，附上代码

好的，以下是用Fisher线性判别和支持向量机进行Oxford17Flower数据集分类的代码示例：

首先，我们需要加载Oxford17Flower数据集，这里我们使用sklearn中的datasets库：

from sklearn.datasets import load_sample_images
from sklearn.model_selection import train_test_split

# 加载Oxford17Flower数据集
data = load_sample_images()
X = data.images
y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]

# 将数据集分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们使用Fisher线性判别对数据集进行分类：

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import accuracy_score

# 使用Fisher线性判别进行分类
lda = LinearDiscriminantAnalysis()
X_train_lda = lda.fit_transform(X_train.reshape(len(X_train), -1), y_train)
X_test_lda = lda.transform(X_test.reshape(len(X_test), -1))
clf_lda = SVC(kernel='linear')
clf_lda.fit(X_train_lda, y_train)

# 计算分类准确率
y_pred_lda = clf_lda.predict(X_test_lda)
acc_lda = accuracy_score(y_test, y_pred_lda)
print("Fisher Linear Discriminant Accuracy: {:.2f}%".format(acc_lda*100))

最后，我们使用支持向量机对数据集进行分类：

from sklearn.svm import SVC

# 使用支持向量机进行分类
clf_svm = SVC(kernel='rbf')
clf_svm.fit(X_train.reshape(len(X_train), -1), y_train)

# 计算分类准确率
y_pred_svm = clf_svm.predict(X_test.reshape(len(X_test), -1))
acc_svm = accuracy_score(y_test, y_pred_svm)
print("SVM Accuracy: {:.2f}%".format(acc_svm*100))

完整代码如下：

from sklearn.datasets import load_sample_images
from sklearn.model_selection import train_test_split
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载Oxford17Flower数据集
data = load_sample_images()
X = data.images
y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]

# 将数据集分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 使用Fisher线性判别进行分类
lda = LinearDiscriminantAnalysis()
X_train_lda = lda.fit_transform(X_train.reshape(len(X_train), -1), y_train)
X_test_lda = lda.transform(X_test.reshape(len(X_test), -1))
clf_lda = SVC(kernel='linear')
clf_lda.fit(X_train_lda, y_train)

# 计算分类准确率
y_pred_lda = clf_lda.predict(X_test_lda)
acc_lda = accuracy_score(y_test, y_pred_lda)
print("Fisher Linear Discriminant Accuracy: {:.2f}%".format(acc_lda*100))

# 使用支持向量机进行分类
clf_svm = SVC(kernel='rbf')
clf_svm.fit(X_train.reshape(len(X_train), -1), y_train)

# 计算分类准确率
y_pred_svm = clf_svm.predict(X_test.reshape(len(X_test), -1))
acc_svm = accuracy_score(y_test, y_pred_svm)
print("SVM Accuracy: {:.2f}%".format(acc_svm*100))

这样，我们就完成了用Fisher线性判别和支持向量机这两种机器学习算法对Oxford17Flower数据集实现植物分类的任务。