make_interp_spline(x, y, k=3, t=None, bc_type=None, axis=0, check_finite=True)# 计算插值 B-spline 的(系数)。 参数 :: x: 数组, 形状 (n,) 横坐标。 y: 数组,形状(n,...) 纵坐标。 k: 整数,可选 B-spline学位。默认为立方体,k = 3。 t: 数组,形状(nt + k + 1,),可...
2,3,4,5,6]withassert_raises(ValueError):make_interp_spline(x, y, bc_type=([(1,0.)],None))withassert_raises(ValueError):make_interp_spline(x, y, bc_type=(1,0.))withassert_raises(ValueError):make_interp_spline(x, y, bc_type=[(1,0.)])with...
reduceByKey(_+_)是reduceByKey((x,y) => x+y)的一个 简洁的形式 */ val rdd08 = sc...
from scipy.interpolate import make_interp_spline x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) y_smooth = make_interp_spline(x, y)(x_smooth) plt.plot(x,y,'r') plt.plot(x_smooth, y_smooth, 'b',linesty...
The make_interp_spline function takes two arrays, x and y, that represent the x and y coordinates of the data points. It also optionally takes an integer k that specifies the degree of the spline fit. The default value of k is 3, which corresponds to cubic spline interpolation. import ...
x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) y_smooth = make_interp_spline(x, y)(x_smooth) plt.plot(x,y,'r') plt.plot(x_smooth, y_smooth, 'b',linestyle=':') ...
x = np.array([6, 8, 10, 12]) y = np.array([35, 7, 2, 1]) x_smooth = np.linspace(x.min(), x.max(), 100) y_smooth = make_interp_spline(x, y)(x_smooth) plt.plot(x,y,'r') plt.plot(x_smooth, y_smooth, 'b',linestyle=':') ...