使用分布式对iris数据集进行knn分类
时间: 2023-11-26 22:55:04 浏览: 34
首先,我们需要加载iris数据集并将其拆分为训练集和测试集:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
```
接下来,我们需要定义一个函数来计算两个数据点之间的欧几里得距离:
```python
import numpy as np
def euclidean_distance(x1, x2):
return np.sqrt(np.sum((x1 - x2) ** 2))
```
然后,我们可以定义一个knn分类器,该分类器使用分布式计算来找到最近的k个邻居:
```python
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
def knn(X_train, y_train, x, k):
distances = []
for i in range(X_train.shape[0]):
dist = euclidean_distance(X_train[i], x)
distances.append((dist, y_train[i]))
distances.sort()
# 同时找到所有进程中最近的k个邻居
all_distances = comm.allgather(distances[:k])
# 统计每种标签出现的次数
votes = {}
for distances in all_distances:
for distance in distances:
label = distance[1]
if label not in votes:
votes[label] = 0
votes[label] += 1
# 找到票数最多的标签
max_votes = 0
max_label = None
for label, vote_count in votes.items():
if vote_count > max_votes:
max_votes = vote_count
max_label = label
return max_label
```
最后,我们可以使用上述函数对测试集进行分类,并计算分类准确率:
```python
k = 5
correct = 0
for i in range(X_test.shape[0]):
x = X_test[i]
y = y_test[i]
pred = knn(X_train, y_train, x, k)
if pred == y:
correct += 1
accuracy = correct / X_test.shape[0]
print(f"Accuracy: {accuracy:.2f}")
```
这样,我们就完成了对iris数据集的分布式knn分类。