绘制非线性决策边界

Question

• 数据可以在这里找到：ex2data2.txt
• 我不确定应该使用什么调用plt.contour（）来重现此内容。

• 相关的Matlab函数调用为：

  contour(u, v, z, [0, 0], 'LineWidth', 2)
  

• 我正在尝试绘制非线性逻辑回归的决策边界，如下图所示

  import scikitplot.plotters as skplt
  import matplotlib.pyplot as plt
  import numpy as np 
  import pandas as pd
  from sklearn.linear_model import LogisticRegression
  from sklearn.datasets import make_classification
  from sklearn import metrics
  from ggplot import *
  import time
  
  def mapFeature(X1, X2, df=True):
      """
          X1, X2: dtype = pd.DataFrame, float, int 
              either a single value or a vector of values
          df    : dtype = boolean
              whether it's a single scalar value or a vector of values
          ----------
          Return: dtype = m row vector or m x n vector of feature values
              Calculates each feature and returns its value
      """ 
      # add a column of ones for intercept parameter
      out = pd.DataFrame({'1':np.ones(X1.size)})
      # max 6th degree polynomial
      for i in range(1,7):
          for j in range(i+1):
              # all the combinations of polynomials up to 7th degree 
              value = (X1**(i-j))*(X2**j)
              col_name = 'X1**{} * X2**{}'.format(i-j, j)
              # When we give a vector with only one dimension, we need to specify 
              # whether to add it as a column or a row. 0 denotes adding a row, 
              # and 1 would be a column. 
              if df:
                  out = out.join(pd.DataFrame({col_name: value}))
              else:
                  out = out.join(pd.DataFrame({col_name: value}, index=[0]))
      return out
  if __name__ == '__main__':
  
      data = pd.read_csv('ex2data2.txt', header=None,
                          names=['Test1', 'Test2', 'Pass'])
  
      X = data.iloc[:, :2]
      y = data.iloc[:,2]
      X = mapFeature(X.iloc[:,0], X.iloc[:,1])
      clf = LogisticRegression().fit(X, y)
      theta = clf.coef_
  
      u = np.linspace(start, end, 30)
      v = np.linspace(start, end, 30)
      uu, vv = np.meshgrid(u, v)
      z = np.zeros((30, 30))
      for i in range(30):
          for j in range(30):
              z[i,j] = mapFeature(u[i], v[i], df=False).values.dot(theta.T)
  
      plt.contour(uu, vv, z, [0])
      plt.show()