慕的地6264312
这是使用 scipy 的 curve_fit() 的图形拟合器示例：import numpy, scipy, matplotlibimport matplotlib.pyplot as pltfrom scipy.optimize import curve_fitxData = 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): return (a * numpy.square(x))# same as the scipy defaultinitialParameters = numpy.array([1.0])# curve fit the test datafittedParameters, pcov = curve_fit(func, xData, yData, initialParameters)modelPredictions = func(xData, *fittedParameters) absError = modelPredictions - yDataSE = numpy.square(absError) # squared errorsMSE = numpy.mean(SE) # mean squared errorsRMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSERsquared = 1.0 - (numpy.var(absError) / numpy.var(yData))print('Parameters:', fittedParameters)print('RMSE:', RMSE)print('R-squared:', Rsquared)print()########################################################### graphics output sectiondef 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) 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 pyplotgraphWidth = 800graphHeight = 600ModelAndScatterPlot(graphWidth, graphHeight)