R语言 wavethresh包 GenW()函数中文帮助文档(中英文对照)

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GenW(wavethresh)
GenW()所属R语言包：wavethresh

                                        Generate (inverse) discrete wavelet transform matrix.
                                         生成（反向）离散小波变换矩阵。

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function generates a matrix that can perform the discrete wavelet transform (useful for understanding the DWT but use the fast algorithm coded in wd for general use). The function returns the matrix for the inverse transform. Since the matrix is orthogonal transpose the matrix to obtain the forward transform matrix.
这个函数生成一个矩阵，可以进行离散小波变换（了解DWT有用，但使用的快速算法编码wd一般用）。该函数返回矩阵的逆变换。由于矩阵是正交的转置矩阵，得到的正向变换矩阵。


用法----------Usage----------


GenW(n=8, filter.number=10, family="DaubLeAsymm", bc="periodic")



参数----------Arguments----------

参数：n
The order of the DWT matrix will be n times n. n should be a power of two.
的DWT矩阵的顺序将是n的次数n。 n应为二的幂的。


参数：filter.number
This selects the smoothness of wavelet that you want to use in the decomposition. By default this is 10, the Daubechies least-asymmetric orthonormal compactly supported wavelet with 10 vanishing moments.
选择要使用的分解小波的平滑度。默认情况下，这是10，至少不对称的Daubechies正交的紧支撑小波与10个消失矩。


参数：family
specifies the family of wavelets that you want to use. The options are "DaubExPhase" and "DaubLeAsymm".
指定要使用的小波家庭。的选项“DaubExPhase”和“DaubLeAsymm”。


参数：bc
boundary conditions to use. This can be periodic or symmetric depending on whether you want the returned matrix to assume periodic or symmetric end-reflection boundary conditions.
边界条件下使用。这可能是periodic或symmetric取决于是否要返回的矩阵承担定期或对称的最终反射边界条件。


Details

详细信息----------Details----------

The discrete wavelet transform is usually computed using the fast pyramid algorithm of Mallat. However, the transform can be written in a matrix form and this is useful for understanding what the fast transform does. One wouldn't normally use the matrix for performing the transform but use the fast transform function wd instead.
通常使用快速的Mallat金字塔算法计算离散小波变换。然而，变换可以以矩阵的形式被写入，这是非常有用的，用于理解快速变换。将无法正常使用该矩阵进行转换，但使用快速变换功能wd。

The matrix returned by this function represents the inverse DWT. Since the matrix (and transform) is orthogonal one can obtain the matrix representation of the forward transform simply by transposing the matrix using the t function in S-Plus.
这个函数返回的矩阵逆DWT。由于是正交矩阵（变换），可以得到正变换只需通过使用t功能在S-PLUS转置矩阵的矩阵表示。

The returned matrix is organised as follows. The first column always corresponds to the linear combination corresponding to the scaling function coefficient (so the column is constant. The next n/2 columns correspond to the finest scale wavelet coefficients; the next n/4 columns to the next finest scale and so on until the last column which corresponds to the coarsest scale wavelet coefficients.
返回的矩阵组织结构如下。第一列始终对应于相应的尺度函数系数（所以列是恒定的线性组合。接下来n/2列对应于最好的规模的小波系数;下一n/4列的下一个最好的规模，依此类推，直到对应于粗尺度小波系数的最后一列。

The matrix is computed by performing successive fast DWTs on unit vectors.
矩阵计算进行连续快速载重吨的单位向量。


值----------Value----------

A matrix of order n that contains the inverse discrete wavelet transform.
阶矩阵的n包含的逆离散小波变换。


RELEASE----------RELEASE----------

Version 3.2 Copyright Guy Nason 1998
版本3.2版权所有1998年盖利晨


（作者）----------Author(s)----------


G P Nason



参见----------See Also----------

wd, wr.
wd，wr。


实例----------Examples----------


#[]
# Generate the wavelet transform matrix corresponding to the Haar wavelet[生成小波变换矩阵对应的Haar小波]
# transform of order 8[改造8阶]
#[]
haarmat <- GenW(8, filter.number=1, family="DaubExPhase")
#[]
# Let's look at this matrix[让我们来看看这个矩阵]
#[]
#haarmat[haarmat]
#          [,1]       [,2]       [,3]       [,4]       [,5] [,6] [,7]       [,8] [[1] [2] [3] [4] [5] [6] [7] [8]]
#[1,] 0.3535534  0.7071068  0.0000000  0.0000000  0.0000000  0.5  0.0  0.3535534[[1] 0.3535534 0.7071068 0.0000000 0.0000000 0.0000000 0.5 0.0 0.3535534]
#[2,] 0.3535534 -0.7071068  0.0000000  0.0000000  0.0000000  0.5  0.0  0.3535534[[2] 0.3535534 -0.7071068 0.0000000 0.0000000 0.0000000 0.5 0.0 0.3535534]
#[3,] 0.3535534  0.0000000  0.7071068  0.0000000  0.0000000 -0.5  0.0  0.3535534[[3] 0.3535534 0.0000000 0.7071068 0.0000000 0.0000000 -0.5 0.0 0.3535534]
#[4,] 0.3535534  0.0000000 -0.7071068  0.0000000  0.0000000 -0.5  0.0  0.3535534[[4] 0.3535534 0.0000000 -0.7071068 0.0000000 0.0000000 -0.5 0.0 0.3535534]
#[5,] 0.3535534  0.0000000  0.0000000  0.7071068  0.0000000  0.0  0.5 -0.3535534[[5] 0.3535534 0.0000000 0.0000000 0.7071068 0.0000000 0.0 0.5 -0.3535534]
#[6,] 0.3535534  0.0000000  0.0000000 -0.7071068  0.0000000  0.0  0.5 -0.3535534[[6] 0.3535534 0.0000000 0.0000000 -0.7071068 0.0000000 0.0 0.5 -0.3535534]
#[7,] 0.3535534  0.0000000  0.0000000  0.0000000  0.7071068  0.0 -0.5 -0.3535534[[7] 0.3535534 0.0000000 0.0000000 0.0000000 0.7071068 0.0 -0.5 -0.3535534]
#[8,] 0.3535534  0.0000000  0.0000000  0.0000000 -0.7071068  0.0 -0.5 -0.3535534[[8] 0.3535534 0.0000000 0.0000000 0.0000000 -0.7071068 0.0 -0.5 -0.3535534]
#[]
# As noted above the first column is the l.c. corresponding to the scaling[正如上面指出的第一列是L.C.对应于缩放]
# function coefficient and then the l.c.s corresponding to the wavelet[函数系数，然后对应于小波LCS]
# coefficients from the finest to the coarsest.[从最优秀的最粗糙系数。]
#[]
# The above matrix represented the inverse DWT. Let's compute the forward[上述矩阵的逆DWT。让我们计算的前进]
# transform matrix representation:[变换矩阵表示：]
#[]
#t(haarmat)[T（haarmat）]
#          [,1]       [,2]       [,3]       [,4]       [,5]       [,6]       [,7]       [,8] [[1] [2] [3] [4] [5] [6] [7] [8]]
#[1,] 0.3535534  0.3535534  0.3535534  0.3535534  0.3535534  0.3535534  0.3535534  0.3535534[[1] 0.3535534 0.3535534 0.3535534 0.3535534 0.3535534 0.3535534 0.3535534 0.3535534]
#[2,] 0.7071068 -0.7071068  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000[[2] 0.7071068 -0.7071068 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000]
#[3,] 0.0000000  0.0000000  0.7071068 -0.7071068  0.0000000  0.0000000  0.0000000  0.0000000[[3] 0.0000000 0.0000000 0.7071068 -0.7071068 0.0000000 0.0000000 0.0000000 0.0000000]
#[4,] 0.0000000  0.0000000  0.0000000  0.0000000  0.7071068 -0.7071068  0.0000000  0.0000000[[4] 0.0000000 0.0000000 0.0000000 0.0000000 0.7071068 -0.7071068 0.0000000 0.0000000]
#[5,] 0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.7071068 -0.7071068[[5] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.7071068 -0.7071068]
#[6,] 0.5000000  0.5000000 -0.5000000 -0.5000000  0.0000000  0.0000000  0.0000000  0.0000000[[6] 0.5000000 0.5000000 -0.5000000 -0.5000000 0.0000000 0.0000000 0.0000000 0.0000000]
#[7,] 0.0000000  0.0000000  0.0000000  0.0000000  0.5000000  0.5000000 -0.5000000 -0.5000000[[7] 0.0000000 0.0000000 0.0000000 0.0000000 0.5000000 0.5000000 -0.5000000 -0.5000000]
#[8,] 0.3535534  0.3535534  0.3535534  0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534[[8] 0.3535534 0.3535534 0.3535534 0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534]
#[]
#[]

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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