def process(self,matrix):
lMatrix = matrix
matrix = tn.matrix2numpy(matrix.getMatrix())
# hungarian_pairs = self._context["pairs"]
# minDim = min(lMatrix._width, lMatrix._height)
# m = [array('f',[1.0 for _ in xrange(lMatrix._min)]) for _ in xrange(lMatrix._min)]
# clean_matrix = tn.matrix2numpy(m)
lClean_matrix = LinedMatrix(lMatrix._min,lMatrix._min, 1.0)
# lClean_matrix.convertMatrix(m)
# pairs = copy.copy(self._context["pairs"])
pairs = self._context['pairs']
l = pairs[0:lMatrix._min]
l.sort()
new_pairs = []
for i,j in enumerate(self._context["pairs"]) :
if i >= lMatrix._min :
break
new_j = l.index(j)
# clean_matrix[i][new_j] = matrix[i][j]
lClean_matrix.set(new_j,i,matrix[i][j])
new_pairs.append(new_j)
self._context["pairs"] = new_pairs
# lClean_matrix = LinedMatrix(0,0)
# lClean_matrix.convertDistMatrix(clean_matrix)
return lClean_matrix
def process(self, matrix):
lMatrix = matrix
matrix = tn.matrix2numpy(matrix.getMatrix())
# hungarian_pairs = self._context["pairs"]
# minDim = min(lMatrix._width, lMatrix._height)
# m = [array('f',[1.0 for _ in xrange(lMatrix._min)]) for _ in xrange(lMatrix._min)]
# clean_matrix = tn.matrix2numpy(m)
lClean_matrix = LinedMatrix(lMatrix._min, lMatrix._min, 1.0)
# lClean_matrix.convertMatrix(m)
# pairs = copy.copy(self._context["pairs"])
pairs = self._context['pairs']
l = pairs[0:lMatrix._min]
l.sort()
new_pairs = []
for i, j in enumerate(self._context["pairs"]):
if i >= lMatrix._min:
break
new_j = l.index(j)
# clean_matrix[i][new_j] = matrix[i][j]
lClean_matrix.set(new_j, i, matrix[i][j])
new_pairs.append(new_j)
self._context["pairs"] = new_pairs
# lClean_matrix = LinedMatrix(0,0)
# lClean_matrix.convertDistMatrix(clean_matrix)
return lClean_matrix
def process(self, matrixOrig):
lDim = sorted([matrixOrig._height, matrixOrig._width])
matrixOrig = tds.squarify(matrixOrig,1.)
matrix = matrixOrig.getMatrix()
nMatrix = [[1.-val for val in line] for line in matrix]
pairs = hungarian.lap(matrix)[0]
# res = munk.maxWeightMatching(nMatrix)
# pairs = [res[0][i] for i in res[0].keys()]
lA_void = LinedMatrix(lDim[1],lDim[0],1.)
for i in xrange(lDim[0]) :
# j = pairs[i]
ni = pairs[i]+i*lA_void._width
lA_void.data[ni] = matrix[i][pairs[i]]
# lA_void.set(j,i,matrix[i][j])
self._context["pairs"] = pairs
return lA_void
def process(self, matrixOrig):
lDim = sorted([matrixOrig._height, matrixOrig._width])
matrixOrig = tds.squarify(matrixOrig, 1.)
matrix = matrixOrig.getMatrix()
nMatrix = [[1. - val for val in line] for line in matrix]
pairs = hungarian.lap(matrix)[0]
# res = munk.maxWeightMatching(nMatrix)
# pairs = [res[0][i] for i in res[0].keys()]
lA_void = LinedMatrix(lDim[1], lDim[0], 1.)
for i in xrange(lDim[0]):
# j = pairs[i]
ni = pairs[i] + i * lA_void._width
lA_void.data[ni] = matrix[i][pairs[i]]
# lA_void.set(j,i,matrix[i][j])
self._context["pairs"] = pairs
return lA_void
