Hermite_e 多项式和 x、y、z 采样点，使用d()Python Numpy 中的 hermite.hermevander3。该方法返回伪范德蒙矩阵。参数 x, y, z 是点坐标的数组，都具有相同的形状。dtypes 将转换为 float64 或 complex128，具体取决于任何元素是否复杂。标量被转换为一维数组。参数 deg 是 [x_deg, y_deg, z_deg] 形式的最大度数列表。
脚步
首先，导入所需的库 -
import numpy as np fromnumpy.polynomialimport hermite as H
使用以下方法创建点坐标数组，所有形状都相同-numpy.array()
x = np.array([1, 2]) y = np.array([3, 4]) z = np.array([5, 6])
显示数组 -
print("Array1...\n",x)
print("\nArray2...\n",y)
print("\nArray3...\n",z)显示数据类型 -
print("\nArray1 datatype...\n",x.dtype)
print("\nArray2 datatype...\n",y.dtype)
print("\nArray3 datatype...\n",z.dtype)检查两个阵列的尺寸 -
print("\nDimensions of Array1...\n",x.ndim)
print("\nDimensions of Array2...\n",y.ndim)
print("\nDimensions of Array3...\n",z.ndim)检查两个阵列的形状 -
print("\nShape of Array1...\n",x.shape)
print("\nShape of Array2...\n",y.shape)
print("\nShape of Array3...\n",z.shape)要生成 Hermite_e 多项式和 x、y、z 样本点的伪 Vandermonde 矩阵，请使用d()Python Numpy 中的 hermite.hermevander3 -
x_deg, y_deg, z_deg = 2, 3, 4
print("\nResult...\n",H.hermevander3d(x,y,z, [x_deg, y_deg, z_deg]))示例
import numpy as np
fromnumpy.polynomialimport hermite_e as H
#使用 numpy.array() 方法创建所有相同形状的点坐标数组
x = np.array([1, 2])
y = np.array([3, 4])
z = np.array([5, 6])
#显示数组
print("Array1...\n",x)
print("\nArray2...\n",y)
print("\nArray3...\n",z)
#显示数据类型
print("\nArray1 datatype...\n",x.dtype)
print("\nArray2 datatype...\n",y.dtype)
print("\nArray3 datatype...\n",z.dtype)
#检查两个数组的尺寸
print("\nDimensions of Array1...\n",x.ndim)
print("\nDimensions of Array2...\n",y.ndim)
print("\nDimensions of Array3...\n",z.ndim)
#检查两个数组的形状
print("\nShape of Array1...\n",x.shape)
print("\nShape of Array2...\n",y.shape)
print("\nShape of Array3...\n",z.shape)
#要生成 Hermite_e 多项式和 x、y、z 样本点的伪 Vandermonde 矩阵，请使用 Python Numpy 中的 hermite.hermevander3d()
x_deg, y_deg, z_deg = 2, 3, 4
print("\nResult...\n",H.hermevander3d(x,y,z, [x_deg, y_deg, z_deg]))输出结果Array1... [1 2] Array2... [3 4] Array3... [5 6] Array1 datatype... int64 Array2 datatype... int64 Array3 datatype... int64 Dimensions of Array1... 1 Dimensions of Array2... 1 Dimensions of Array3... 1 Shape of Array1... (2,) Shape of Array2... (2,) Shape of Array3... (2,) Result... [[1.00000e+00 5.00000e+00 2.40000e+01 1.10000e+02 4.78000e+02 3.00000e+00 1.50000e+01 7.20000e+01 3.30000e+02 1.43400e+03 8.00000e+00 4.00000e+01 1.92000e+02 8.80000e+02 3.82400e+03 1.80000e+01 9.00000e+01 4.32000e+02 1.98000e+03 8.60400e+03 1.00000e+00 5.00000e+00 2.40000e+01 1.10000e+02 4.78000e+02 3.00000e+00 1.50000e+01 7.20000e+01 3.30000e+02 1.43400e+03 8.00000e+00 4.00000e+01 1.92000e+02 8.80000e+02 3.82400e+03 1.80000e+01 9.00000e+01 4.32000e+02 1.98000e+03 8.60400e+03 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00 0.00000e+00] [1.00000e+00 6.00000e+00 3.50000e+01 1.98000e+02 1.08300e+03 4.00000e+00 2.40000e+01 1.40000e+02 7.92000e+02 4.33200e+03 1.50000e+01 9.00000e+01 5.25000e+02 2.97000e+03 1.62450e+04 5.20000e+01 3.12000e+02 1.82000e+03 1.02960e+04 5.63160e+04 2.00000e+00 1.20000e+01 7.00000e+01 3.96000e+02 2.16600e+03 8.00000e+00 4.80000e+01 2.80000e+02 1.58400e+03 8.66400e+03 3.00000e+01 1.80000e+02 1.05000e+03 5.94000e+03 3.24900e+04 1.04000e+02 6.24000e+02 3.64000e+03 2.05920e+04 1.12632e+05 3.00000e+00 1.80000e+01 1.05000e+02 5.94000e+02 3.24900e+03 1.20000e+01 7.20000e+01 4.20000e+02 2.37600e+03 1.29960e+04 4.50000e+01 2.70000e+02 1.57500e+03 8.91000e+03 4.87350e+04 1.56000e+02 9.36000e+02 5.46000e+03 3.08880e+04 1.68948e+05]]
