1.使用MINIST数据集分类算法(SVM)编写程序,最后附上混淆矩阵预测结果; 2.使用flexclust 包的nutrient数据集任选聚类算法(K-means、层次聚类等)编写程序,最后附上plot可视化结果。
时间: 2024-10-27 17:05:48 浏览: 51
首先,关于使用MINIST数据集进行手写数字分类,我们可以利用Python的Scikit-learn库,其中包含支持向量机(SVM)算法。以下是简化的步骤:
1. 导入必要的库和数据集:
```python
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
# 加载MNIST数据
mnist = fetch_openml('mnist_784', version=1)
X, y = mnist.data, mnist.target
```
2. 数据预处理,划分训练集和测试集,标准化特征:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# 使用SVM模型训练并预测
svm = SVC(kernel='linear')
svm.fit(X_train_scaled, y_train)
y_pred = svm.predict(X_test_scaled)
```
3. 计算混淆矩阵并可视化:
```python
conf_mat = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(10, 7))
sns.heatmap(conf_mat, annot=True, fmt="d", cmap="Blues")
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('SVM Confusion Matrix on MINST')
plt.show()
```
对于使用flexclust包的nutrient数据集进行聚类分析,这里我们以K-means为例:
1. 首先安装flexclust库(如果尚未安装):
```bash
pip install flexclust
```
2. 导入所需模块,加载数据集:
```python
import flexclust
import pandas as pd
data = pd.read_csv('nutrient_data.csv') # 假设数据集中名为'nutrient_data.csv'
```
3. 对数据进行预处理,选择K-means:
```python
X = data.drop("label_column", axis=1) # 假设"label_column"是目标列
kmeans = flexclust.KMeans(n_clusters=3) # 选择3个簇
kmeans.fit(X)
labels = kmeans.labels_
```
4. 可视化聚类结果:
```python
plt.scatter(X.iloc[:, 0], X.iloc[:, 1], c=labels, cmap='viridis')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('Nutrient Data K-Means Clustering')
plt.show()
```
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