import numpy as np
import matplotlib.pyplot as plt
import pandas
import wx
class logistic:
def __sigmoid(x):
return 1.0/(1+np.exp(-x))
def Loaddata(name ="new.csv"):
with open(name,'r',newline='') as f:
lines=np.array(pandas.read_csv(f)).astype(np.float) #矩阵化
label=lines[:,4] #切片
label=np.expand_dims(label,axis=-1)
data=lines[:,0:2]/129
N,W=np.shape(data)
data=np.hstack((np.ones([N,1]),data))
return label,data
def __WEight_updata(data,label,w,alpha=0.01):
z=np.dot(data,w)
h=logistic.__sigmoid(z)
Error=label-h
w=w+alpha*np.dot(data.T,Error)
return w
def train_LR(data,label,n_epoch=2,alpha=0.01):
N,D=np.shape(data)
w=np.ones([D,1]) #生成w
for i in range (n_epoch):
w=logistic.__WEight_updata(data,label,w,alpha)
#print(w)
error_rate=logistic.__test_accuracy(data,label,w)
shuchu2.SetValue(str(error_rate))
return w
def __test_accuracy(data,label,w):
N,D=np.shape(data)
z=np.dot(data,w)
h=logistic.__sigmoid(z)
lab_det=(h>0.5).astype(np.float)
error_rate=np.sum(np.abs(label-lab_det))/N
#print(N)
return error_rate
def huitu(data,label,w,*z):
# print(w)
z=np.array(z)/129
dic_colors={0:(.8,0,0),1:(0,.8,0),2:(0,0,0)}
for i in range (2):
index=np.where(label==i)[0]
sub_data=data[index]
plt.scatter(sub_data[:,1],sub_data[:,2],color=dic_colors[i])
plt.scatter(z[:,0],z[:,1],color=dic_colors[2])
min_x=np.min(data[:,1])
max_x=np.max(data[:,1])
w=w[:,0]
x=np.arange(min_x,max_x,0.01)
y=(-x*w[1]-w[0])/w[2]
plt.plot(x,y)
plt.show()
def huitu2(data,label,w):
# print(w)
dic_colors={0:(.8,0,0),1:(0,.8,0),2:(0,0,0)}
for i in range (2):
index=np.where(label==i)[0]
sub_data=data[index]
plt.scatter(sub_data[:,1],sub_data[:,2],color=dic_colors[i])
min_x=np.min(data[:,1])
max_x=np.max(data[:,1])
w=w[:,0]
x=np.arange(min_x,max_x,0.01)
y=(-x*w[1]-w[0])/w[2]
plt.plot(x,y)
plt.show()
def ceshi1(even):
label,data=logistic.Loaddata()
weights=logistic.train_LR(data,label,500,1.2)
#print(weights)
sg=float(shuru1.GetValue())
tz=float(shuru2.GetValue())
ceshi=(sg,tz)
logistic.huitu(data,label,weights,ceshi)
def ceshi2(even):
label,data=logistic.Loaddata()
weights=logistic.train_LR(data,label,500,1.2)
logistic.huitu2(data,label,weights)
app=wx.App()
win=wx.Frame(None,title="logistic分类器",size=(600,600))
panel=wx.Panel(win)
win.Show()
huitu1Button=wx.Button(panel,label="测试",pos=(400,180))
huituButton=wx.Button(panel,label="查看样本分布",pos=(400,140))
shuchu1=wx.StaticText(panel,pos=(10,50),label=("错误率"))
shuchu2=wx.TextCtrl(panel,pos=(50,50),style=wx.TE_READONLY,size=(240,30))
shuru1=wx.TextCtrl(panel,pos=(50,100),style=wx.TE_RICH)
shuru2=wx.TextCtrl(panel,pos=(50,130),style=wx.TE_RICH)
sgshuru=wx.StaticText(panel,pos=(10,100),label="身高")
tzshuru=wx.StaticText(panel,pos=(10,130),label="体重")
tishi0=wx.StaticText(panel,pos=(10,230),label="提示:")
tishi1=wx.StaticText(panel,pos=(10,260),label="1,红色类,代表女生")
tishi1.SetForegroundColour("red")
tishi1.SetFont(wx.Font(13,wx.SWISS,wx.NORMAL,wx.NORMAL))
tishi2=wx.StaticText(panel,pos=(10,290),label="2,绿色类，代表男生")
tishi2.SetForegroundColour("green")
tishi2.SetFont(wx.Font(13,wx.SWISS,wx.NORMAL,wx.NORMAL))
tishi3=wx.StaticText(panel,pos=(10,320),label="3,黑色类，代表输入个人数据")
tishi3.SetFont(wx.Font(13,wx.SWISS,wx.NORMAL,wx.NORMAL))
tishi3.SetForegroundColour("black")
huitu1Button.Bind(wx.EVT_BUTTON,ceshi1)
huituButton.Bind(wx.EVT_BUTTON,ceshi2)
app.MainLoop()
