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PLA.py
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155 lines (134 loc) · 4.08 KB
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#-*- coding: utf-8 -*-
####################################
#PLA的基本思路：
#每个特征都有权重wi，表示该特征的重要
#程度，当综合所有的特征和权重计算一个
#最终的分数，当这个分数超过某个阈值的
#时候，表示成功，否则表示失败。
####################################
####################################
#PLA的算法步骤:
#step1： 随便找一条直线，即随便找一个
#n维向量W0，赋初值w = w0
#step2： 如果这条直线正好把训练数据正
#确切分，训练结束。
#step3： 如果有任意一个样本没有被正确
#切分，即在权值计算中不等于正确的y值，
#此时我们就要对权值做一点简单的修正，
#令w(t+1) = w(t) + y'x'
#step4: 跳转到step2
####################################
import numpy as np
import matplotlib.pyplot as plt
import xlwt
import xlrd
import random
#生成数据函数，并将数据写入csv文件
def born_data():
workbook = xlwt.Workbook("pla_data.xls")
sheet = workbook.add_sheet("sheet1", cell_overwrite_ok=True)
sheet.write(0, 0, "age")
sheet.write(0, 1, "price")
sheet.write(0, 2, "click")
for i in range(1, 400):
price = random.randint(50, 1000)
age = random.randint(13, 60)
if age * 3.14 + price * 2.3 >= 1000:
click = 1
else:
click = -1
sheet.write(i, 0, age)
sheet.write(i, 1, price)
sheet.write(i, 2, click)
workbook.save("pla_data.xls")
#读取数据，返回一个列表
def get_data(filepath='pla_data.xls', sheet_name="sheet1"):
lines = []
workbook = xlrd.open_workbook(filepath)
table = workbook.sheet_by_name(sheet_name)
row = table.nrows
for i in range(1, row):
lines.append(table.row_values(i))
return lines
#初始化三维列表，进行随机赋值。
def init_weight():
vector = np.random.uniform(0, 10, size=3)
return list(vector)
#判断数据被切分的正确率
def cut_dot(currucy=1.0, weight_vector=[], data_list=[], learning_rate=0.8):
right = 0.0
for i, data in enumerate(data_list):
if weight_vector[0] + weight_vector[1]*data[0] + weight_vector[2]*data[1] > 0:
if data[2] == 1:
right = right + 1.0
else:
weight_vector[0] = weight_vector[0] - learning_rate*2*random.randrange(1, 10)
weight_vector[1] = weight_vector[1] - learning_rate*2*data[0]
weight_vector[2] = weight_vector[2] - learning_rate*2*data[1]
else:
if data[2] == -1:
right = right + 1.0
else:
weight_vector[0] = weight_vector[0] + learning_rate*2*random.randrange(1, 10)
weight_vector[1] = weight_vector[1] + learning_rate*2*data[0]
weight_vector[2] = weight_vector[2] + learning_rate*2*data[1]
print("The curracy is {}".format(right/len(data_list)))
if right/len(data_list) >= currucy:
return True
else:
return False
#生成数据
born_data()
#读取数据
data_list = get_data()
print("This is the data_list {}".format(data_list))
#在图形中表示数据
x_negative = []
y_negative = []
x_positive = []
y_positive = []
for data in data_list:
if data[2] == -1:
x_negative.append(data[0])
y_negative.append(data[1])
else:
x_positive.append(data[0])
y_positive.append(data[1])
plt.scatter(x_negative, y_negative, c='grey', marker='o')
#print("This is the x_negative {}".format(x_negative))
#print("This is the y_negative {}".format(y_negative))
plt.scatter(x_positive, y_positive, c='red', marker='x')
plt.xlabel("age")
plt.ylabel("price")
plt.ylim([0, 1000])
plt.xlim([0, 60])
plt.ion()
plt.pause(1)
#初始化权重
weight = init_weight()
#pla分类
inter = 0
while cut_dot(0.999, weight, data_list, learning_rate=0.8) == False:
cut_dot(0.999, weight, data_list, learning_rate=0.8)
inter += 1
print("This is the weight: {}".format(weight))
#划线
x1 = []
y1 = []
for i in range(60):
x = i
y = (weight[1]*x - weight[0])/weight[2]
x1.append(x)
y1.append(y)
plt.clf()
plt.scatter(x_negative, y_negative, c='grey', marker='o')
plt.scatter(x_positive, y_positive, c='red', marker='x')
plt.plot(x1, y1)
plt.ylim([0, 1000])
plt.xlim([0, 60])
plt.title("Interation {0}".format(inter))
plt.xlabel("age")
plt.ylabel("price")
plt.pause(0.01)
print("The pla get successed!")
plt.pause(1000)