import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
def f(x):
a = theta4
b = theta5 * X1_new + theta2
c = theta0 + theta1 * x + theta3 * x * x
X2_new_boundary1 = (-b + np.sqrt(b * b - 4 * a * c)) / (2 * a)
X2_new_boundary2 = (-b - np.sqrt(b * b - 4 * a * c)) / (2 * a)
return X2_new_boundary1, X2_new_boundary2
if __name__ == '__main__':
'''
逻辑回归
'''
# load the data
data = pd.read_csv('')
data.head()
'''
第一次查看所有数据
'''
# visualize the data
fig1 = plt.figure()
plt.scatter(data.loc[:, 'example1'], data.loc[:, 'example2']) # .......导入数据
plt.title('example1-example2') # 设置表名
plt.xlabel('example1') # 设置X坐标轴
plt.ylabel('example2') # 设置Y坐标轴
plt.show() # 查看图像
'''
第二次查看带有正确错误标识的数据
'''
# add label mask
mask = data.loc[:, 'pass'] == 1
fig2 = plt.figure()
passed = plt.scatter(data.loc[:, 'example1'][mask], data.loc[:, 'example2'][mask]) # .......导入数据
failed = plt.scatter(data.loc[:, 'example1'][~mask], data.loc[:, 'example2'][~mask]) # .......导入数据
plt.title('example1-example2') # 设置表名
plt.xlabel('example1') # 设置X坐标轴
plt.ylabel('example2') # 设置Y坐标轴
plt.legend((passed, failed), ('passed', 'failed'))
plt.show() # 查看图像
# define X,Y
X = data.drop(['pass'], axis=1)
y = data.loc[:, 'pass']
y.head # 查看数据
X1 = data.loc[:, 'example1']
X2 = data.loc[:, 'example2']
X1_2 = X1 * X1
X2_2 = X2 * X2
X1_X2 = X1 * X2
X_new = {'X1': X1, 'X2': X2, 'X1_2': X1_2, 'X2_2': X2_2, 'X1_X2': X1_X2}
X_new = pd.DataFrame(X_new)
print(X_new)
# 创建新的训练
LR2 = LogisticRegression()
LR2.fit(X_new, y)
y2_predict = LR2.predict(X_new) # 预测
accuracy2 = accuracy_score(y, y2_predict)
print(accuracy2)
X1_new = X1.sort_values() # 从小到大排序
theta0 = LR2.intercept_
theta1, theta2, theta3, theta4, theta5 = LR2.coef_[0][0], LR2.coef_[0][1], LR2.coef_[0][2], LR2.coef_[0][3], \
LR2.coef_[0][4]
# 制作曲线参数
a = theta4
b = theta5 * X1_new + theta2
c = theta0 + theta1 * X1_new + theta3 * X1_new * X1_new
X2_new_boundary = (-b + np.sqrt(b * b - 4 * a * c)) / (2 * a)
fig4 = plt.figure()
passed = plt.scatter(data.loc[:, 'example1'][mask], data.loc[:, 'example2'][mask]) # .......导入数据
failed = plt.scatter(data.loc[:, 'example1'][~mask], data.loc[:, 'example2'][~mask]) # .......导入数据
plt.plot(X1_new, X2_new_boundary)
plt.title('example1-example2') # 设置表名
plt.xlabel('example1') # 设置X坐标轴
plt.ylabel('example2') # 设置Y坐标轴
plt.legend((passed, failed), ('passed', 'failed'))
plt.show() # 查看图像
'''
如果在这里使用二阶线性回归，那么只有一半的数据能被隔离开
也就是说忽略了X的第二种结果
接下来就是加上这种结果的第二条曲线
'''
'''
正确方法
'''
# define f(x) --> 8
X2_new_boundary1 = []
X2_new_boundary2 = []
for x in X1_new:
X2_new_boundary1.append(f(x)[0])
X2_new_boundary2.append(f(x)[1])
print(X2_new_boundary1, X2_new_boundary2)
fig5 = plt.figure()
passed = plt.scatter(data.loc[:, 'example1'][mask], data.loc[:, 'example2'][mask]) # .......导入数据
failed = plt.scatter(data.loc[:, 'example1'][~mask], data.loc[:, 'example2'][~mask]) # .......导入数据
plt.plot(X1_new, X2_new_boundary1)
plt.plot(X1_new, X2_new_boundary2)
plt.title('example1-example2') # 设置表名
plt.xlabel('example1') # 设置X坐标轴
plt.ylabel('example2') # 设置Y坐标轴
plt.legend((passed, failed), ('passed', 'failed'))
plt.show() # 查看图像
'''
你会发现虽然你补上了另外一个X值，但是两个X对应的曲线并没有连在一起
这是因为数之间本来就是有间隔的，不全，所以连不上，这时候需要我们把他补全
'''
X1_range = [-0.9 + x / 10000 for x in range(0, 19000)]
X1_range = np.array(X1_range)
X2_new_boundary1 = []
X2_new_boundary2 = []
for x in X1_new:
X2_new_boundary1.append(f(x)[0])
X2_new_boundary2.append(f(x)[1])
fig5 = plt.figure()
passed = plt.scatter(data.loc[:, 'example1'][mask], data.loc[:, 'example2'][mask]) # .......导入数据
failed = plt.scatter(data.loc[:, 'example1'][~mask], data.loc[:, 'example2'][~mask]) # .......导入数据
plt.plot(X1_range, X2_new_boundary1)
plt.plot(X1_range, X2_new_boundary2)
plt.title('example1-example2') # 设置表名
plt.xlabel('example1') # 设置X坐标轴
plt.ylabel('example2') # 设置Y坐标轴
plt.legend((passed, failed), ('passed', 'failed'))
plt.show() # 查看图像
