python 求kl离散度 python离散点插值 |
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python 求kl离散度 python离散点插值
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python 求kl离散度 python离散点插值
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mob6454cc6acccd 2023-06-07 20:03:56 文章标签 python 求kl离散度 python matplotlib 开发语言 Powered by 金山文档 文章分类 Python 后端开发 离散点拟合闭合曲线scipy.interpolate import numpy as np from scipy.interpolate import interp1d, splprep, splev, CubicHermiteSpline import matplotlib.pyplot as plt pts = np.array( [ [-846724, 0], [-423362, 10029], [0, 13942], [289000, 14733], [547558, 13942], [730000, 11948], [846746, 9015], [846746, 0], [423373, -8311], [0, -12759], [-486000, -14733], [-846724, -12759], [-846724, 0], ] ) tck, u = splprep(pts.T, u=None, s=0, k=1, per=1) # 0.45 is chosen to get roughly the part not covered by the piecewise spline demo u_new = np.linspace(u.min() + 0.45, u.max(), 1000) x_new, y_new = splev(u_new, tck, der=0) fix, axs = plt.subplots() axs.plot(x_new, y_new, "b-") def derivative_via_neighbors(index) -> float: delta = pts[index + 1] - pts[index - 1] return delta[1] / delta[0] for i in range(5): spline = CubicHermiteSpline( pts[i : i + 2, 0], pts[i : i + 2, 1], [derivative_via_neighbors(i), derivative_via_neighbors(i + 1)], ) spline_x = np.linspace(*spline.x) spline_y = spline(spline_x) axs.plot(spline_x, spline_y, "r--") axs.plot(pts[:, 0], pts[:, 1], "ro") # axs.set_aspect("equal", "box") plt.show()
import numpy as np from scipy.interpolate import interp1d, splprep, splev import matplotlib.pyplot as plt pts = np.array([ [-846724, 0], [-423362, 10029], [0, 13942], [289000, 14733], [547558, 13942], [730000, 11948], [846746, 9015], [846746, 0], [423373, -8311], [0, -12759], [-486000, -14733], [-846724, -12759], [-846724, 0]]) tck, u = splprep(pts.T, u=None, s=0, k=1, per=1) u_new = np.linspace(u.min(), u.max(), 1000) x_new, y_new = splev(u_new, tck, der=0) plt.plot(pts[:,0], pts[:,1], 'ro') plt.plot(x_new, y_new, 'b--') plt.show()
Scipy插值函数 导入插值模块 一维插值函数interp1d 二维插值函数 样条插值函数 #导入插值模块from scipy import interpolate #导入插值模块 from scipy import interpolate #导入插值模块 #__all__ = ['interp1d', 'interp2d', 'spline', 'spleval', 'splmake', 'spltopp', # 'ppform', 'lagrange', 'PPoly', 'BPoly', 'RegularGridInterpolator', # 'interpn'] import numpy as np import matplotlib.pyplot as plt #导入插值模块 from scipy.interpolate import interp1d #生成数据 x = np.linspace(0, 1, 30) y = np.sin(5*x) + np.cos(10*x) #**一维插值函数***# #零次插值 y0 = interp1d(x, y, kind='zero') #一次插值 y1 = interp1d(x, y, kind='linear') #二次插值 y2 = interp1d(x, y, kind='quadratic') #三次插值 y3 = interp1d(x, y, kind='cubic') #新变量 new_x = np.linspace(0, 1, 100) #绘图 plt.figure() plt.plot(x, y, 'o', label='data') plt.plot(new_x, y0(new_x), label='zero') plt.plot(new_x, y1(new_x), label='linear') plt.plot(new_x, y2(new_x), label='quadratic') plt.plot(new_x, y3(new_x), label='cubic') plt.title("interp1d") plt.xlabel("x") plt.ylabel("f(x)") plt.legend() plt.show()
二维插值函数import numpy as np import matplotlib.pyplot as plt #导入插值模块 from scipy.interpolate import interp2d #生成数据 x = np.linspace(0, 1, 20) y = np.linspace(0, 1, 30) xx, yy = np.meshgrid(x, y) rand = np.random.rand(600).reshape([30, 20]) z = np.sin(xx**2) + np.cos(yy**2) + rand new_x = np.linspace(0, 1, 100) new_y = np.linspace(0, 1, 100) #**样条插值函数插值函数***# #一次插值 z1 = interp2d(x, y, z, kind='linear') new_z1 = z1(new_x, new_y) #三次插值 z3 = interp2d(x, y, z, kind='cubic') new_z3 = z3(new_x, new_y) #绘图 plt.figure() plt.plot(x, z[0, :], 'o', label='data') plt.plot(new_x, new_z1[0, :], label='linear') plt.plot(new_x, new_z3[0, :], label='cubic') plt.title("interp2d") plt.xlabel("x") plt.ylabel("f") plt.legend() plt.show() #用矩阵显示z plt.matshow(z) plt.title("data") plt.xlabel("x") plt.ylabel("y") plt.show() #用矩阵显示z plt.matshow(new_z1) plt.title("linear") plt.xlabel("x") plt.ylabel("y") plt.show() #用矩阵显示z plt.matshow(new_z3) plt.title("cubic") plt.xlabel("x") plt.ylabel("y") plt.show()
二维插值显示：原始
线性插值效果
三次插值图
样条插值函数import numpy as np import matplotlib.pyplot as plt #导入插值模块 from scipy.interpolate import spline #生成数据 x = np.linspace(0, 1, 30) y = np.sin(5*x) + np.cos(10*x) + np.random.rand(30) new_x = np.linspace(0, 1, 100) #**样条插值函数插值函数***# #零次插值 y0 = spline(x, y, new_x, order=0, kind='smoothest') #一次插值 y1 = spline(x, y, new_x, order=1, kind='smoothest') #二次插值 y2 = spline(x, y, new_x, order=2, kind='smoothest') #三次插值 y3 = spline(x, y, new_x, order=3, kind='smoothest') #绘图 plt.figure() plt.plot(x, y, 'o', label='data') plt.plot(new_x, y0, label='zero') plt.plot(new_x, y1, label='linear') plt.plot(new_x, y2, label='quadratic') plt.plot(new_x, y3, label='cubic') plt.title("spline") plt.xlabel("x") plt.ylabel("f") plt.legend() plt.show()
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