在Python中生成厄米多项式的伪范德蒙德矩阵
要生成厄米多项式的伪范德蒙德矩阵,请在Python NumPy中使用hermite.hermvander2d()。此方法返回伪范德蒙德矩阵。
参数x,y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是最大次数列表,形式为[x_deg, y_deg]。
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()方法创建形状相同的点坐标数组:
x = np.array([1, 2]) y = np.array([3, 4])
显示数组:
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)
检查两个数组的维度:
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)
要生成厄米多项式的伪范德蒙德矩阵,请在Python NumPy中使用hermite.hermvander2d():
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([1, 2]) y = np.array([3, 4]) # 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 # The method returns the pseudo-Vandermonde matrix. x_deg, y_deg = 2, 3 print("\nResult...\n",H.hermvander2d(x,y, [x_deg, y_deg]))
输出
Array1... [1 2] Array2... [3 4] Array1 datatype... int64 Array2 datatype... int64 Dimensions of Array1... 1 Dimensions of Array2... 1 Shape of Array1... (2,) Shape of Array2... (2,) Result... [[1.000e+00 6.000e+00 3.400e+01 1.800e+02 2.000e+00 1.200e+01 6.800e+01 3.600e+02 2.000e+00 1.200e+01 6.800e+01 3.600e+02] [1.000e+00 8.000e+00 6.200e+01 4.640e+02 4.000e+00 3.200e+01 2.480e+02 1.856e+03 1.400e+01 1.120e+02 8.680e+02 6.496e+03]]
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