置信椭圆长短轴计算
def confidence_oval(self,factor, ppf_rate):
pca1_std = np.std(factor.iloc[:, 0])
pca2_std = np.std(factor.iloc[:, 1])
f_value = scipy.stats.f.ppf(ppf_rate, dfn=2, dfd=factor.iloc[:, 0].shape[0] - 2)
x_axis = np.sqrt(
pca1_std ** 2 * f_value * 2 * ((factor.iloc[:, 0].shape[0] - 1) / (factor.iloc[:, 0].shape[0] - 2)))
y_axis = np.sqrt(
pca2_std ** 2 * f_value * 2 * ((factor.iloc[:, 0].shape[0] - 1) / (factor.iloc[:, 0].shape[0] - 2)))
x_axis = '%.2f' % x_axis
y_axis = '%.2f' % y_axis
return x_axis, y_axis
Python图形绘制
def elli_plot(self,full_data, ellipse, y):
'''
:param full_data: pls后的点
:param ellipse: [椭圆长轴，椭圆短轴]
:param y:
:return:
'''
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(111)
elli = Ellipse(xy=(0, 0), width=float(ellipse[0]) * 2, height=float(ellipse[1]) * 2)
ax.add_patch(elli)
# 偏厚
outlier_data = y.loc[y[y.columns[0]] == 3, :]
# 偏薄
outlier_data_less = y.loc[y[y.columns[0]] == 1, :]
inner_data = full_data['pls']['pls'].loc[full_data['pls']['pls'].index.isin(outlier_data.index.tolist()+outlier_data_less.index.tolist()) == False, :]
ax.plot(outlier_data.iloc[:, 0], outlier_data.iloc[:, 1], 'ro')
ax.plot(outlier_data_less.iloc[:, 0], outlier_data_less.iloc[:, 1], 'bo')
ax.plot(inner_data.iloc[:, 0], inner_data.iloc[:, 1], 'yo')
name = str(self.picture_id)
plt.savefig("E:\\shhl\\1118_两次PLS\\偏厚\\图\\"+name+".png")
self.picture_id = self.picture_id +1
plt.show()
from matplotlib.patches import Ellipse, Circle
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ell1 = Ellipse(xy = (0.0, 0.0), width = 4, height = 8, angle = 30.0, facecolor= 'yellow', alpha=0.3)
cir1 = Circle(xy = (0.0, 0.0), radius=2, alpha=0.5)
ax.add_patch(ell1)
ax.add_patch(cir1)
x, y = 0, 0
ax.scatter([0,1], [0,1],color='red')
ax.scatter([2,1], [1,1],color='green')
plt.axis('scaled')
plt.axis('equal') #changes limits of x or y axis so that equal increments of x and y have the same length
plt.show()
