def centerRows(X, mu=None, inds=None):
"""
Simply subtract the mean value of a row from each non-zero element.
"""
if inds == None:
rowInds, colInds = X.nonzero()
else:
rowInds, colInds = inds
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
if mu == None:
#This is the mean of the nonzero values in each row
nonZeroCounts = numpy.bincount(rowInds, minlength=X.shape[0])
inds = nonZeroCounts == 0
nonZeroCounts += inds
#This is required because when we do X.sum(1) for centering it uses the same
#dtype as X to store the sum, and this can result in overflow for e.g. uint8
if X.dtype == numpy.uint8:
sumCol = SparseUtilsCython.sumCols(
rowInds,
numpy.array(X[rowInds, colInds]).flatten(), X.shape[0])
else:
sumCol = numpy.array(X.sum(1)).flatten()
mu = sumCol / nonZeroCounts
mu[inds] = 0
vals = SparseUtilsCython.partialOuterProduct(
rowInds, colInds, numpy.array(mu, numpy.float),
numpy.ones(X.shape[1]))
X[X.nonzero()] = numpy.array(X[X.nonzero()] - vals, numpy.float)
return X, mu
def centerRows(X, mu=None, inds=None):
"""
Simply subtract the mean value of a row from each non-zero element.
"""
if inds == None:
rowInds, colInds = X.nonzero()
else:
rowInds, colInds = inds
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
if mu == None:
#This is the mean of the nonzero values in each row
nonZeroCounts = numpy.bincount(rowInds, minlength=X.shape[0])
inds = nonZeroCounts==0
nonZeroCounts += inds
#This is required because when we do X.sum(1) for centering it uses the same
#dtype as X to store the sum, and this can result in overflow for e.g. uint8
if X.dtype == numpy.uint8:
sumCol = SparseUtilsCython.sumCols(rowInds, numpy.array(X[rowInds, colInds]).flatten(), X.shape[0])
else:
sumCol = numpy.array(X.sum(1)).flatten()
mu = sumCol/nonZeroCounts
mu[inds] = 0
vals = SparseUtilsCython.partialOuterProduct(rowInds, colInds, numpy.array(mu, numpy.float), numpy.ones(X.shape[1]))
X[X.nonzero()] = numpy.array(X[X.nonzero()] - vals, numpy.float)
return X, mu
def testSumCols(self):
A = scipy.sparse.rand(10, 15, 0.5)*10
A = scipy.sparse.csc_matrix(A, dtype=numpy.uint8)
rowInds, colInds = A.nonzero()
rowInds = numpy.array(rowInds, numpy.int32)
colInds = numpy.array(colInds, numpy.int32)
sumCol = SparseUtilsCython.sumCols(rowInds, numpy.array(A[rowInds, colInds]).flatten(), A.shape[0])
nptst.assert_array_equal(numpy.array(A.sum(1)).flatten(), sumCol)
