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classification_using_twinsvm.py
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classification_using_twinsvm.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Mar 24 10:08:46 2020
@author: Harini
"""
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from twinsvm import twinsvmclassifier
from sklearn.datasets import make_moons, make_circles
X,y = make_moons(n_samples = 10,noise=0.3, random_state=0)
# X,y = make_circles(n_samples = 10,noise=0.2, factor=0.5, random_state=1)
# df = pd.read_csv("nonlin_points1.csv")
# df = pd.read_csv("nonparallel.csv")
# df = pd.read_csv("xor1.csv")
# X=df.values[:,:2]
# y=df.values[:,-1]
print(X)
print(y)
N=len(X)
M=len(X[0])
print("M:"+str(M))
print("N:"+str(N))
#linear kernel
params1 = {'Epsilon1': 0, 'Epsilon2': 0, 'C1': 1, 'C2': 1,'kernel_type':0,'kernel_param': 1}
#rbf kernel
params2 = {'Epsilon1': 0.1, 'Epsilon2': 0.1, 'C1': 1, 'C2': 1,'kernel_type':2,'kernel_param': 2}
def get_C(y1,ypred1,index_list):
C1=list()
C2=list()
C3=list()
C4=list()
for i in range(len(y1)):
if(ypred1[i]==1): #Actual=1
# print("Actual:1")
if(y1[i]==1): #Desired=1
# print("***C2***")
C2.append(index_list[i])
if(y1[i]==0): #Desired=0
# print("***C4***")
C4.append(index_list[i])
if(ypred1[i]==0): #Actual=0
# print("Actual:0")
if(y1[i]==1): #Desired=1
# print("***C3***")
C3.append(index_list[i])
if(y1[i]==0): #Desired=0
# print("***C1***")
C1.append(index_list[i])
return C1,C2,C3,C4
def get_y(C1,C2,C3,C4):
y_a=list('d'*N)
y_b=list('d'*N)
# print("****************")
# print("ya:"+str(y_a))
# print("yb:"+str(y_b))
# print("****************")
for i in range(len(X)):
if i in C1:
y_a[i]=0
y_b[i]='d'
if i in C2:
y_a[i]='d'
y_b[i]=0
if i in C3:
y_a[i]=1
y_b[i]=0
if i in C4:
y_a[i]=0
y_b[i]=1
# if i in Cd:
# y_a[i]="dontcare"
# y_b[i]="dontcare"
return y_a,y_b
y_tree=list()
sol_tree=list()
w_plane1 = list()
b_plane1 = list()
w_plane2 = list()
b_plane2 = list()
def loop(X,y1,dontcare,AX,BX):
# combine X features with AX and BX
classifier = twinsvmclassifier(**params2)
X1 = np.hstack((X,AX,BX))
# X1 = X
X_list = list()
y_list = list()
index_list = list()
for i in range(N):
if i not in dontcare:
X_list.append(X1[i])
y_list.append(y1[i])
index_list.append(i)
X1 = np.asarray(X_list)
y1 = np.asarray(y_list)
# scaling input features
X1 = StandardScaler().fit_transform(X1)
# print("::::::::data::::::::")
# print(X1)
# if all 1's are satisfied return
if(type(y1)==np.ndarray):
y11=y1.tolist()
else:
y11=y1
if(y11.count(1)==0):
return
# else solve
else:
classifier.fit(X1, y1)
ypred1 = classifier.predict(X1)
print("y:"+str(y1))
print("ypred:"+str(ypred1))
print("index:"+str(index_list))
print("dc:"+str(dontcare))
C1,C2,C3,C4=get_C(y1,ypred1,index_list)
print("C1:"+str(C1))
print("C2:"+str(C2))
print("C3:"+str(C3))
print("C4:"+str(C4))
y1_new,y2_new=get_y(C1,C2,C3,C4)
dontcare1=list()
dontcare2=list()
for i in range(len(X)):
if y1_new[i]=='d':
dontcare1.append(i)
if y2_new[i]=='d':
dontcare2.append(i)
print("y1_new:"+str(y1_new))
print("y2_new:"+str(y2_new))
print("dontcare1:"+str(dontcare1))
print("dontcare2:"+str(dontcare2))
q=input("c?")
# y_tree.append(y1)
# sol_tree.append(sol1)
print("#########")
loop(X,y1_new,dontcare1,AX,BX)
print("#########")
print("@@@@@@@@@")
loop(X,y2_new,dontcare2,AX,BX)
print("@@@@@@@@@")
y1=y
#y2=0
dontcare=list()
AX=list(N*[0])
BX=list(N*[0])
AX = np.asarray(AX).reshape(N,1)
BX = np.asarray(BX).reshape(N,1)
print("AX:"+str(AX))
print("BX:"+str(BX))
loop(X,y1,dontcare,AX,BX)