使用Matplotlib库来可视化原始数据点和插值结果。 # 绘制原始数据点plt.scatter(x,y,label='Original Data',color='red')# 绘制原始数据点# 绘制插值结果plt.plot(x_new,y_new,label='Cubic Interpolation',color='blue')# 绘制插值结果# 添加图例和标签plt.title('Cubic Spline Interpolation')plt.xlabel('X-...
kind='linear(默认)':使用一次函数插值 kind='cubic':使用三次函数插值 interpolate.interp2d 官方文档 :scipy.interpolate.interp2d Spline interpolation 样条插值 样条插值需要两个基本步骤: 计算曲线的样条表示 在所需的点对样条曲线求值 为了找到样条曲线表示,有两种不同的方法来表示曲线并获得(平滑)样条系数:直...
import matplotlib.pyplot as plt # 创建新的x值用于绘图 x_new = np.linspace(min(x), max(x), 400) y_new = cs(x_new) # 绘制原始数据点 plt.plot(x, y, 'o', label='Data points') # 绘制插值曲线 plt.plot(x_new, y_new, '-', label='Cubic Spline Interpolation') plt.legend() ...
The reason is exactly what you mention: currently CubicSpline has a simple and easy to understand invariant that interpolation knots are exactly at the data points, and adding more to it is probably too much. Your trick --- and it's a nice one! --- can probably be a nice example in...
ravel(), smoothing=0, kernel='cubic') # explicit default smoothing=0 for interpolation z_dense_evil_rbf = zfun_evil_rbf(dense_points).reshape(x_dense.shape) #visual grid_x = x_dense grid_y = y_dense grid_z0 = z_dense_evil_griddata triang = tri.Triangulation(grid_x.ravel(), grid...
【视频】什么是非线性模型与R语言多项式回归、局部平滑样条、 广义相加GAM分析工资数据|数据分享|附代码...
cm.jet, interpolation='nearest', origin="lower") ax.set_xlabel(method) ax.scatter(x, y, c=z) 8. 径向基函数插值 # %fig=一维RBF插值 from scipy.interpolate import Rbf x1 = np.array([-1, 0, 2.0, 1.0]) y1 = np.array([1.0, 0.3, -0.5, 0.8]) funcs = ['multiquadric', '...
How about adding a very simple cubic spline interpolator without concepts of B-splines, knots and so on? Specifically I'm talking about piecewise cubic polynomials between given x[i] as described here https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation and what provided in MATLAB http:/...
f_spline = interp1d(x, y, kind='cubic') #打印插值结果 print("Linearinterpolation: ", f_linear(2.5)) print("Polynomial interpolation: ", f_poly(2.5)) print("Spline interpolation: ", f_spline(2.5)) 在比较插值结果时,我们可以根据拟合的平滑度、过拟合现象等来选择合适的插值方法。 第五步:...
(x, y, fvals, kind='cubic')#x y z#计算100*100的网格上的插值xnew = np.linspace(-1,1,100)#xynew = np.linspace(-1,1,100)#yfnew = newfunc(xnew, ynew)#仅仅是y值 100*100的值### 绘图#为了更明显地比较插值前后的区别,使用关键字参数interpolation='nearest'#关闭 imshow()内置的插值...