调用SKlearn中的PCA算法将Iris数据降维 降维到二维 输出降维后的结果的形状 查看降维后前10个数据
时间: 2024-05-12 19:20:20 浏览: 13
下面是代码实现:
```python
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
# 加载数据
iris = load_iris()
X = iris.data
# 使用PCA进行降维
pca = PCA(n_components=2)
X_new = pca.fit_transform(X)
# 输出降维后的结果的形状
print(X_new.shape)
# 查看降维后前10个数据
print(X_new[:10])
```
输出结果为:
```
(150, 2)
[[-2.68420713 0.32660731]
[-2.71539062 -0.16955685]
[-2.88981954 -0.13734561]
[-2.7464372 -0.31112432]
[-2.72859298 0.33392456]
[-2.27989736 0.74778271]
[-2.82089068 -0.08210451]
[-2.62648199 0.17040535]
[-2.88795857 -0.57079803]
[-2.67384469 -0.1066917 ]]
```
可以看到,降维后的结果形状为`(150, 2)`,即150个样本,每个样本只有2个特征。并且输出了降维后的前10个数据。
相关问题
使用PCA降维后的鸢尾花数据集(load_iris)导入调用dbscan库用python实现dbscan聚类算法并将其可视化
好的,我来回答你的问题。
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
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.cluster import DBSCAN
iris = load_iris()
X = iris.data
y = iris.target
```
然后,我们使用PCA将数据集降维到二维:
```python
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
```
接下来,我们使用DBSCAN库调用DBSCAN聚类算法:
```python
eps = 0.5
min_samples = 5
dbscan = DBSCAN(eps=eps, min_samples=min_samples)
labels = dbscan.fit_predict(X_pca)
```
最后,我们将聚类结果可视化:
```python
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels)
plt.xlabel('PCA Component 1')
plt.ylabel('PCA Component 2')
plt.title('DBSCAN Clustering with PCA')
plt.show()
```
这样,我们就实现了对PCA降维后的鸢尾花数据集使用DBSCAN库进行聚类,并将其可视化的过程。
使用PCA降维后的鸢尾花数据集load_iris,不调用dbscan库用python实现dbscan聚类算法并将其可视化
好的,我来回答你的问题。
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
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
iris = load_iris()
X = iris.data
y = iris.target
```
然后,我们使用PCA将数据集降维到二维:
```python
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
```
接下来,我们实现DBSCAN聚类算法:
```python
def dbscan(X, eps, min_samples):
labels = np.zeros(len(X))
C = 0
visited = set()
for i in range(len(X)):
if i not in visited:
visited.add(i)
neighbors = find_neighbors(X, i, eps)
if len(neighbors) < min_samples:
labels[i] = -1
else:
C += 1
labels[i] = C
expand_cluster(X, labels, i, neighbors, C, eps, min_samples, visited)
return labels
def find_neighbors(X, i, eps):
neighbors = []
for j in range(len(X)):
if np.linalg.norm(X[i] - X[j]) < eps:
neighbors.append(j)
return neighbors
def expand_cluster(X, labels, i, neighbors, C, eps, min_samples, visited):
for j in neighbors:
if j not in visited:
visited.add(j)
neighbors_j = find_neighbors(X, j, eps)
if len(neighbors_j) >= min_samples:
neighbors.extend(neighbors_j)
if labels[j] == 0:
labels[j] = C
```
最后,我们将聚类结果可视化:
```python
eps = 0.5
min_samples = 5
labels = dbscan(X_pca, eps, min_samples)
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels)
plt.xlabel('PCA Component 1')
plt.ylabel('PCA Component 2')
plt.title('DBSCAN Clustering with PCA')
plt.show()
```
这样,我们就实现了对PCA降维后的鸢尾花数据集进行DBSCAN聚类,并将其可视化的过程。