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法律研究在Python中生成给定阶数和x、y、z样本点的伪范德蒙德矩阵
要生成给定阶数和x、y、z样本点的伪范德蒙德矩阵，可以使用Python NumPy中的`polynomial.polyvander3d()`。该方法返回度数为deg且样本点为(x, y, z)的伪范德蒙德矩阵。参数x、y、z是点坐标数组，形状相同。数据类型将根据元素是否为复数转换为float64或complex128。标量将转换为一维数组。参数deg是最大度数列表，格式为[x_deg, y_deg, z_deg]。
步骤
首先，导入所需的库：
import numpy as np from numpy.polynomial.polynomial import polyvander3d
使用numpy.array()方法创建形状相同的点坐标数组：
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)检查维度：
print("\nDimensions of Array1...\n",x.ndim)
print("\nDimensions of Array2...\n",y.ndim)
print("\nDimensions of Array3...\n",z.ndim)检查形状：
print("\nShape of Array1...\n",x.shape)
print("\nShape of Array2...\n",y.shape)
print("\nShape of Array3...\n",z.shape)要生成给定阶数和x、y、z样本点的伪范德蒙德矩阵，可以使用`polynomial.polyvander3d()`：
x_deg, y_deg, z_deg = 2, 3, 4
print("\nResult...\n",polyvander3d(x,y, z, [x_deg, y_deg, z_deg]))示例
import numpy as np
from numpy.polynomial.polynomial import polyvander3d
# Create arrays of point coordinates, all of the same shape using the numpy.array() method
x = np.array([1, 2])
y = np.array([3, 4])
z = np.array([5, 6])
# Display the arrays
print("Array1...\n",x)
print("\nArray2...\n",y)
print("\nArray3...\n",z)
# Display the datatype
print("\nArray1 datatype...\n",x.dtype)
print("\nArray2 datatype...\n",y.dtype)
print("\nArray3 datatype...\n",z.dtype)
# Check the Dimensions
print("\nDimensions of Array1...\n",x.ndim)
print("\nDimensions of Array2...\n",y.ndim)
print("\nDimensions of Array3...\n",z.ndim)
# Check the Shape
print("\nShape of Array1...\n",x.shape)
print("\nShape of Array2...\n",y.shape)
print("\nShape of Array3...\n",z.shape)
# To generate a pseudo Vandermonde matrix of given degree and x, y, z sample points, use the polynomial.polyvander3d() in Python Numpy
x_deg, y_deg, z_deg = 2, 3, 4
print("\nResult...\n",polyvander3d(x,y, z, [x_deg, y_deg, z_deg]))输出
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 2.50000e+01 1.25000e+02 6.25000e+02 3.00000e+00 1.50000e+01 7.50000e+01 3.75000e+02 1.87500e+03 9.00000e+00 4.50000e+01 2.25000e+02 1.12500e+03 5.62500e+03 2.70000e+01 1.35000e+02 6.75000e+02 3.37500e+03 1.68750e+04 1.00000e+00 5.00000e+00 2.50000e+01 1.25000e+02 6.25000e+02 3.00000e+00 1.50000e+01 7.50000e+01 3.75000e+02 1.87500e+03 9.00000e+00 4.50000e+01 2.25000e+02 1.12500e+03 5.62500e+03 2.70000e+01 1.35000e+02 6.75000e+02 3.37500e+03 1.68750e+04 1.00000e+00 5.00000e+00 2.50000e+01 1.25000e+02 6.25000e+02 3.00000e+00 1.50000e+01 7.50000e+01 3.75000e+02 1.87500e+03 9.00000e+00 4.50000e+01 2.25000e+02 1.12500e+03 5.62500e+03 2.70000e+01 1.35000e+02 6.75000e+02 3.37500e+03 1.68750e+04] [1.00000e+00 6.00000e+00 3.60000e+01 2.16000e+02 1.29600e+03 4.00000e+00 2.40000e+01 1.44000e+02 8.64000e+02 5.18400e+03 1.60000e+01 9.60000e+01 5.76000e+02 3.45600e+03 2.07360e+04 6.40000e+01 3.84000e+02 2.30400e+03 1.38240e+04 8.29440e+04 2.00000e+00 1.20000e+01 7.20000e+01 4.32000e+02 2.59200e+03 8.00000e+00 4.80000e+01 2.88000e+02 1.72800e+03 1.03680e+04 3.20000e+01 1.92000e+02 1.15200e+03 6.91200e+03 4.14720e+04 1.28000e+02 7.68000e+02 4.60800e+03 2.76480e+04 1.65888e+05 4.00000e+00 2.40000e+01 1.44000e+02 8.64000e+02 5.18400e+03 1.60000e+01 9.60000e+01 5.76000e+02 3.45600e+03 2.07360e+04 6.40000e+01 3.84000e+02 2.30400e+03 1.38240e+04 8.29440e+04 2.56000e+02 1.53600e+03 9.21600e+03 5.52960e+04 3.31776e+05]]
更新于：2022年2月28日
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