python - Python numpy 矩阵的矩阵
问题描述
我有这个代码,它可以工作。似乎有更好的方法可以做到这一点。有谁知道更清洁的解决方案?
def Matrix2toMatrix(Matrix2):
scaleSize = len(Matrix2[0, 0])
FinalMatrix = np.empty([len(Matrix2)*scaleSize, len(Matrix2[0])*scaleSize])
for x in range(0, len(Matrix2)):
for y in range(0, len(Matrix2[0])):
for xFinal in range(0, scaleSize):
for yFinal in range(0, scaleSize):
FinalMatrix[(x*scaleSize)+xFinal, (y*scaleSize)+yFinal] = Matrix2[x, y][xFinal, yFinal]
return FinalMatrix
这里 Matrix2 是一个 4x4 矩阵,每个单元格包含一个 2x2 矩阵
完整的代码以防有人想知道:
import matplotlib.pyplot as plt
import numpy as np
def Matrix2toMatrix(Matrix2):
scaleSize = len(Matrix2[0, 0])
FinalMatrix = np.empty([len(Matrix2)*scaleSize, len(Matrix2[0])*scaleSize])
for x in range(0, len(Matrix2)):
for y in range(0, len(Matrix2[0])):
for xFinal in range(0, scaleSize):
for yFinal in range(0, scaleSize):
FinalMatrix[(x*scaleSize)+xFinal, (y*scaleSize)+yFinal] = Matrix2[x, y][xFinal, yFinal]
return FinalMatrix
XSize = 4
Xtest = np.array([[255, 255, 255, 255]
,[255, 255, 255, 255]
,[127, 127, 127, 127]
,[0, 0, 0, 0]
])
scaleFactor = 2
XMarixOfMatrix = np.empty([XSize, XSize], dtype=object)
Xexpanded = np.empty([XSize*scaleFactor, XSize*scaleFactor], dtype=int) # careful, will contain garbage data
for xOrg in range(0, XSize):
for yOrg in range(0, XSize):
newMatrix = np.empty([scaleFactor, scaleFactor], dtype=int) # careful, will contain garbage data
# grab org point equivalent
pointValue = Xtest[xOrg, yOrg]
newMatrix.fill(pointValue)
# now write the data
XMarixOfMatrix[xOrg, yOrg] = newMatrix
# need to concat all matrix together to form a larger singular matrix
Xexpanded = Matrix2toMatrix(XMarixOfMatrix)
img = plt.imshow(Xexpanded)
img.set_cmap('gray')
plt.axis('off')
plt.show()
解决方案
置换轴并重塑 -
m,n = Matrix2.shape[0], Matrix2.shape[2]
out = Matrix2.swapaxes(1,2).reshape(m*n,-1)
对于置换轴,我们也可以使用np.transpose
or np.rollaxis
,因为在功能上都是相同的。
通过样品运行验证 -
In [17]: Matrix2 = np.random.rand(3,3,3,3)
# With given solution
In [18]: out1 = Matrix2toMatrix(Matrix2)
In [19]: m,n = Matrix2.shape[0], Matrix2.shape[2]
...: out2 = Matrix2.swapaxes(1,2).reshape(m*n,-1)
In [20]: np.allclose(out1, out2)
Out[20]: True