在Python中生成带有浮点数组点坐标的Hermite多项式的伪Vandermonde矩阵
要生成Hermite多项式的伪Vandermonde矩阵,请使用Python Numpy中的hermite.hermvander2d()函数。该方法返回伪Vandermonde矩阵。 参数x和y是一个具有相同形状的点坐标数组。dtypes将根据元素是否为复数而转换为float64或complex128。标量将被转换为1-D数组。参数deg是形式为[x_deg, y_deg]的最大度列表。
步骤
首先,导入所需的库−
import numpy as np
from numpy.polynomial import hermite as H
使用numpy.array()方法创建具有相同形状的点坐标数组 –
x = np.array([0.1, 1.4])
y = np.array([1.7, 2.8])
展示数组 –
print("Array1...\n",x)
print("\nArray2...\n",y)
显示数据类型 –
print("\nArray1 datatype...\n",x.dtype)
print("\nArray2 datatype...\n",y.dtype)
检查两个数组的尺寸 –
print("\nDimensions of Array1...\n",x.ndim)
print("\nDimensions of Array2...\n",y.ndim)
检查两个数组的形状 –
print("\nShape of Array1...\n",x.shape)
print("\nShape of Array2...\n",y.shape)
要生成一个赫米特多项式的伪范德蒙德矩阵,请使用Python Numpy中的hermite.hermvander2d()函数-
x_deg, y_deg = 2, 3
print("\nResult...\n",H.hermvander2d(x,y, [x_deg, y_deg]))
示例
import numpy as np
from numpy.polynomial import hermite as H
# Create arrays of point coordinates, all of the same shape using the numpy.array() method
x = np.array([0.1, 1.4])
y = np.array([1.7, 2.8])
# Display the arrays
print("Array1...\n",x)
print("\nArray2...\n",y)
# Display the datatype
print("\nArray1 datatype...\n",x.dtype)
print("\nArray2 datatype...\n",y.dtype)
# Check the Dimensions of both the arrays
print("\nDimensions of Array1...\n",x.ndim)
print("\nDimensions of Array2...\n",y.ndim)
# Check the Shape of both the arrays
print("\nShape of Array1...\n",x.shape)
print("\nShape of Array2...\n",y.shape)
# To generate a pseudo Vandermonde matrix of the Hermite polynomial, use the hermite.hermvander2d() in Python Numpy
x_deg, y_deg = 2, 3
print("\nResult...\n",H.hermvander2d(x,y, [x_deg, y_deg]))
输出
Array1...
[0.1 1.4]
Array2...
[1.7 2.8]
Array1 datatype...
float64
Array2 datatype...
float64
Dimensions of Array1...
1
Dimensions of Array2...
1
Shape of Array1...
(2,)
Shape of Array2...
(2,)
Result...
[[ 1.0000000e+00 3.4000000e+00 9.5600000e+00 1.8904000e+01
2.0000000e-01 6.8000000e-01 1.9120000e+00 3.7808000e+00
-1.9600000e+00 -6.6640000e+00 -1.8737600e+01 -3.7051840e+01]
[ 1.0000000e+00 5.6000000e+00 2.9360000e+01 1.4201600e+02
2.8000000e+00 1.5680000e+01 8.2208000e+01 3.9764480e+02
5.8400000e+00 3.2704000e+01 1.7146240e+02 8.2937344e+02]]