curve_brute_force = nppolyfitN times_brute_force 20curve_backtrack = nppolyfitN times_backtrack 20curve_branch_bound = nppolyfitN times_branch_bound 20 #使用polyfit最小二乘法进行多项式拟合smooth_N = nplinsp

日期: 2027-09-06

标签: 教育

The code is fitting a polynomial curve to the data points using the numpy function polyfit.
curve_brute_force, curve_backtrack, and curve_branch_bound are the coefficients of the polynomial curve that best fits the data points for the respective algorithms.
smooth_N is an array of 100 evenly spaced values between the minimum and maximum values of N.
smooth_times_brute_force, smooth_times_backtrack, and smooth_times_branch_bound are the values of the polynomial curve evaluated at the points in smooth_N.
The code then plots the polynomial curves for each algorithm using plt.plot.
The x-axis is labeled as 'N' and the y-axis is labeled as 'Time (s)' using plt.xlabel and plt.ylabel.
The legend is displayed using plt.legend.
The plot is displayed using plt.show.

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