def _transform(self, data, X):
logger.info("Transforming data")
loadings = self.model.loadings.T
L = DenseMatrix(numRows=loadings.shape[0], numCols=loadings.shape[1],
values=loadings.flatten(), isTransposed=True)
data = join(data, X.multiply(L), self.spark)
return data
def _transform(self, data, X):
logger.info("Transforming data")
loadings = self.model.loadings[:self.n_components]
loadings = DenseMatrix(X.numCols(), self.n_components,
loadings.flatten())
X = X.multiply(loadings)
data = join(data, X, self.spark)
del X
return data
def _transform(self, data):
logger.info("Transforming data")
W = self.model.loadings[:, :self.n_components]
W = DenseMatrix(numRows=W.shape[0],
numCols=W.shape[1],
isTransposed=True,
values=W.flatten())
X = self._row_matrix(data).multiply(W)
data = join(data, X, self.spark)
del X
return data
def _transform(self, data, X):
logger.info("Transforming data")
W = self.model.loadings
psi = self.model.error_vcov
Ih = numpy.eye(len(W))
Wpsi = W / psi
cov_z = numpy.linalg.inv(Ih + numpy.dot(Wpsi, W.T))
tmp = numpy.dot(Wpsi.T, cov_z)
tmp_dense = DenseMatrix(numRows=tmp.shape[0],
numCols=tmp.shape[1],
values=tmp.flatten(),
isTransposed=True)
X = X.multiply(tmp_dense)
data = join(data, X, self.spark)
del X
return data