我已经完成了线性回归和最佳拟合线,但还希望有一条线将表示预测误差的实点(蓝色的点)与预测点(红色的点x)连接起来,或者所谓的残差该图应以类似的方式显示:
到目前为止,我所拥有的是:
# draw the plot
xx=X[:,np.newaxis]
yy=y[:,np.newaxis]
slr=LinearRegression()
slr.fit(xx,yy)
y_pred=slr.predict(xx)
plt.scatter(xx,yy)
plt.plot(xx,y_pred,'r')
plt.plot(X,y_pred,'rx') #add the prediction points
plt.show()
非常感谢您!
答案 0 :(得分:0)
下面是带有垂直线的示例代码
import numpy, scipy, matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
xData = numpy.array([1.1, 2.2, 3.3, 4.4, 5.0, 6.6, 7.7])
yData = numpy.array([1.1, 20.2, 30.3, 60.4, 50.0, 60.6, 70.7])
def func(x, a, b): # simple linear example
return a * x + b
initialParameters = numpy.array([1.0, 1.0])
# curve fit the test data
fittedParameters, pcov = curve_fit(func, xData, yData, initialParameters)
modelPredictions = func(xData, *fittedParameters)
absError = modelPredictions - yData
SE = numpy.square(absError) # squared errors
MSE = numpy.mean(SE) # mean squared errors
RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (numpy.var(absError) / numpy.var(yData))
print('RMSE:', RMSE)
print('R-squared:', Rsquared)
print()
##########################################################
# graphics output section
def ModelAndScatterPlot(graphWidth, graphHeight):
f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
axes = f.add_subplot(111)
# first the raw data as a scatter plot
axes.plot(xData, yData, 'D')
# create data for the fitted equation plot
xModel = numpy.linspace(min(xData), max(xData))
yModel = func(xModel, *fittedParameters)
# now the model as a line plot
axes.plot(xModel, yModel)
# now add individual line for each point
for i in range(len(xData)):
lineXdata = (xData[i], xData[i]) # same X
lineYdata = (yData[i], modelPredictions[i]) # different Y
plt.plot(lineXdata, lineYdata)
axes.set_xlabel('X Data') # X axis data label
axes.set_ylabel('Y Data') # Y axis data label
plt.show()
plt.close('all') # clean up after using pyplot
graphWidth = 800
graphHeight = 600
ModelAndScatterPlot(graphWidth, graphHeight)