线性不可分

logistic/train_noseparability.py

@@ -0,0 +1,65 @@
+# -*- encoding:utf-8 -*-
+from sklearn import datasets
+from sklearn.model_selection import train_test_split 
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import cross_val_predict
+from numpy import shape
+from sklearn import metrics
+import numpy as np
+import random
+'''
+线性不可分
+'''
+def curve(x_train,w,w0):
+	results=x_train.tolist()
+	results=[x[0:2] for x in results]
+	step=0.0001
+	for i in np.arange(-0.2,1.2,step):
+		x1=i+step
+		x2=-1*(w[0]*x1+w0)/(w[1]+w[2]*x1) # 计算mse
+		if abs(x2)>5.0:
+			continue
+		results.append([x1,x2])
+	results=["{},{}".format(x1,x2) for [x1,x2] in results]
+	return results
+
+
+def get_data(center_label,num=100):
+	X_train=[]
+	y_train=[]
+	sigma=0.01
+	for point,label in center_label:
+		c1,c2=point
+		for _ in range(0,num):
+			x1=c1+random.uniform(-sigma,sigma)
+			x2=c2+random.uniform(-sigma,sigma)
+			X_train.append([x1,x2])
+			y_train.append([label])
+	return X_train,y_train
+
+
+center_label=[[[0,0],1],[[1,1],1],[[0,1],0],[[1,0],0]]
+X_train,y_train=get_data(center_label)
+#X_train=10*[[0,0],[1,1],[1,0],[0,1]]
+X_train=[ x+[x[0]*x[1]] for x in X_train]
+X_train=np.array(X_train)
+ 
+#model = LogisticRegression(penalty="l2")
+model = LogisticRegression()
+model.fit(X_train, y_train)
+ 
+print (model.coef_)
+print (model.intercept_)
+curve_results=curve(X_train,model.coef_.tolist()[0],model.intercept_.tolist()[0])
+
+'''
+with open("no_separa_traindata.csv","w") as f :
+	f.writelines("\n".join(curve_results[0:400]))
+with open("no_separa_train_with_splitline.csv","w") as f :
+	f.writelines("\n".join(curve_results))
+	'''
+
+
+
+ 
+