数据集iris 分类

• 花匠小妙招
• 2024-11-26 10:18

# 2021.01.15  By yangbocsu

import numpy as np

import matplotlib.pyplot as plt

import time

from sklearn.datasets import load_iris

plt.rcParams['font.sans-serif']=['SimHei']

plt.rcParams['axes.unicode_minus'] = False

iris = load_iris()

irisFeature = iris.data

irisTarget = iris.target

x = np.array([0,0])

for i in range(len(irisTarget)-50):

if irisTarget[i] == 0:

irisTarget[i] = -1

x = np.row_stack((x, irisFeature[i][:2]))

x = np.delete(x, 0, 0)

y = irisTarget[:100]

w = np.zeros( (1,2) )

b = np.array([0])

learning_rate = 1

def judge_class(x,y,w,b):

misclassification = False

cnt = 0

mis_index = 0

for i in range(len(x)):

if y[i]*(np.dot(w,x[i])+b) <= 0 :

cnt += 1

mis_index = i

break

if cnt > 0 :

misclassification = True

return misclassification, mis_index

def update(x, y, w1, b, i):

w1 = w1 + learning_rate*y[i]*x[i]

b = b + learning_rate*y[i]

return w1, b

t1 = time.time()

misclassification, mis_index = judge_class(x,y,w,b)

while misclassification:

w, b = update(x, y, w, b, mis_index)

misclassification, mis_index = judge_class(x,y,w,b)

t2 = time.time()

print("w={}".format(w))

print("b={}".format(b))

print("感知机学习算法的原始形式共用:{}ms".format((t2-t1)*1000))

for i in range(len(irisFeature)-50):

if y[i] == -1:

type11 = plt.scatter(irisFeature[i, 0], irisFeature[i, 1], c="r",marker="o")

elif y[i] == 1:

type22 = plt.scatter(irisFeature[i, 0], irisFeature[i, 1], c="g",marker="x")

plt.title("数据集Iris的前两个特征")

plt.xlabel("Sepal.Length花萼长度")

plt.ylabel("Sepal.Width花萼宽度")

plt.legend((type11, type22), ('Iris Setosa山鸢尾-0', 'Iris Versicolour杂色鸢尾-1'))

plt.xlim(xmax=7.5,xmin=3.5)

plt.ylim(ymax=5,ymin=1.5)

x2 = np.arange(0,7.5,0.1)

y2 = -((w[0][0])*x2 +b)/(w[0][1])

plt.plot(x2,y2,label="拟合直线",c='orange')

plt.show()

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