利用KNN算法对鸢尾花数据进行分类
鸢尾花数据集下载：
https://blog.csdn.net/wolflikeinnocence/article/details/90140587
KNN代码
'''
Iris数据集三类标签分别为Iris-setosa、Iris-versicolor、Iris-virginica
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def k_nn(x):
accurary = 0
for i in range(150):
count1 = 0
count2 = 0
count3 = 0
prediction = 0
distance = np.zeros((149, 2))
test = x[i].reshape(1, 5)
train = np.delete(x, i, axis=0)
test1 = test[:, 0:4]
train1 = train[:, 0:4]
for t in range(149):
distance[t, 1] = np.linalg.norm(test1 - train1[t])
distance[t, 0] = train[t, 4]
order = distance[np.lexsort(distance.T)]
for n in range(k):
if order[n, 0] == 1:
count1 += 1
if order[n, 0] == 2:
count2 += 1
if order[n, 0] == 3:
count3 += 1
if count1 >= count2 and count1 >= count3:
prediction = 1
if count2 >= count1 and count2 >= count3:
prediction = 2
if count3 >= count1 and count3 >= count2:
prediction = 3
if prediction == test[0, 4]:
accurary += 1
Accuracy = accurary / 150
return Accuracy
iris = pd.read_csv('Iris.csv', header=None, sep=',')
x = iris.iloc[1:, 1:5]
x = np.array(x, dtype=float)
a = np.full((50, 1), 1)
b = np.full((50, 1), 2)
c = np.full((50, 1), 3)
Res = np.zeros(50)
d = np.vstack((a, b, c))
x = np.hstack((x, d))
for m in range(50):
k = m + 1
Res[m] = k_nn(x)
k_label = np.arange(1, 51, 1)
plt.xlabel('k Value')
plt.ylabel('Accuracy')
plt.ylim((.9, 1))
plt.plot(k_label, Res, 'b')
plt.show()
最后给大家看看不同的K值和正确率的关系图：
相关知识
Knn算法实现鸢尾花分类
实验一：鸢尾花数据集分类
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[机器学习基础][笔记] 一、鸢尾花分类
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