<p>下面是一个使用注释中的数据的Python fitter示例,它适合于一个多形类型的等式。在这个例子中,不需要记录数据。这里X轴是按十年对数标度绘制的。请注意,示例代码中的数据是浮点数形式。在</p>
<p><a href="https://i.stack.imgur.com/jPl3B.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/jPl3B.png" alt="plot"/></a></p>
<pre><code>import numpy, scipy, matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
xData = numpy.array([7e-09, 9e-09, 1e-08, 2e-8, 1e-6])
yData = numpy.array([790.0, 870.0, 2400.0, 2450.0, 3100.0])
def func(x, a, b, offset): # polytrope equation from zunzun.com
return a / numpy.power(x, b) + offset
# these are the same as the scipy defaults
initialParameters = numpy.array([1.0, 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('Parameters:', fittedParameters)
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), 1000)
yModel = func(xModel, *fittedParameters)
# now the model as a line plot
axes.plot(xModel, yModel)
axes.set_xlabel('X Data') # X axis data label
axes.set_ylabel('Y Data') # Y axis data label
plt.xscale('log') # comment this out for default linear scaling
plt.show()
plt.close('all') # clean up after using pyplot
graphWidth = 800
graphHeight = 600
ModelAndScatterPlot(graphWidth, graphHeight)
</code></pre>