def execute(self, rowmatrix, objective, return_N=False):
"""
matrix: a RowMatrix object storing the matrix A
objective: either 'x' or 'N'
'x': the function returns the solution to the problem min_x || PA[:,:-1]x - PA[:,-1] ||_2
'N': the function returns a square matrix N such that PA[:,:-1]*inv(N) is a matrix with orthonormal columns
return_N: when the objective is 'x', whether to return the matrix N which makes PA[:,:-1]*inv(N) have orthonormal columns
"""
logger.info('In projections, computing {0}!'.format(objective))
PA = self.__project(rowmatrix)
if objective == 'x':
return PA.map(lambda (key,pa): get_x(pa,return_N)).collect()
elif objective == 'N':
return PA.map(lambda (key,pa): get_N(pa)).collect()
else:
raise ValueError('Please enter a valid objective!')
def execute(self, matrix, objective, s, return_N=False):
"""
matrix: a RowMatrix object storing the matrix A
objective: either 'x' or 'N'
'x': the function returns the solution to the problem min_x || SA[:,:-1]x - SA[:,-1] ||_2
'N': the function returns a square matrix N such that SA[:,:-1]*inv(N) is a matrix with orthonormal columns
s: samping size
return_N: when the objective is 'x', whether to return the matrix N which makes SA[:,:-1]*inv(N) has orthonormal columns
"""
logger.info('In sampling, computing {0}!'.format(objective))
lev_sum = self.__get_lev_sum(matrix)
SA = self.__sample(matrix, s, lev_sum)
if objective == 'x':
return SA.map(lambda (key,sa): get_x(sa,return_N)).collect()
elif objective == 'N':
return SA.map(lambda (key,sa): get_N(sa)).collect()
else:
raise ValueError('Please enter a valid objective!')