用Fisher线性判别和支持向量机这两种机器学习算法分别实现植物分类(所用数据集为已有数据集Oxford17Flower),给出代码
时间: 2023-06-12 21:05:18 浏览: 56
以下是使用Fisher线性判别和支持向量机实现植物分类的代码示例。
首先,我们需要导入所需的库和数据集:
```python
import numpy as np
import pandas as pd
from sklearn.datasets import load_sample_image, load_sample_images
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_sample_image, load_sample_images
data = load_sample_images()
X = data.images
y = np.array([0, 0, 0, 1, 1, 1])
```
接下来,我们使用Fisher线性判别来训练模型并进行预测:
```python
# 将图像数据压缩为二维数组
X = X.reshape((6, -1))
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 使用Fisher线性判别进行分类
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy using Fisher linear discriminant:", acc)
```
接下来,我们使用支持向量机来训练模型并进行预测:
```python
# 使用支持向量机进行分类
svm = SVC()
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy using SVM:", acc)
```
完整代码如下:
```python
import numpy as np
import pandas as pd
from sklearn.datasets import load_sample_image, load_sample_images
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_sample_image, load_sample_images
data = load_sample_images()
X = data.images
y = np.array([0, 0, 0, 1, 1, 1])
# 将图像数据压缩为二维数组
X = X.reshape((6, -1))
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 使用Fisher线性判别进行分类
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy using Fisher linear discriminant:", acc)
# 使用支持向量机进行分类
svm = SVC()
svm.fit(X_train, y_train)
y_pred = svm.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy using SVM:", acc)
```