R = args.rank
seed = args.seed
iter = args.iterations
innerIter = 10
tol = 1e-2
zeroThr = 1e-5
# input file and output file
inputFile = args.inputFile.format("data")
yaxis = decompTools.loadAxisInfo(args.inputFile.format("info"))
print "Starting Tensor Factorization with ID:{0}".format(exptID)
X = sptensor.loadTensor(inputFile)
np.random.seed(seed)
Y, ls = CP_ALS.cp_als(X, R, tol=tol, maxiters=iter)
ll = CP_APR.loglikelihood(X, Y)
# normalize the factors using the 1 norm and then sort in descending order
Y.normalize_sort(1)
Y = decompTools.zeroSmallFactors(Y, zeroThr=zeroThr)
Y.writeRawFile("results/als-raw-{0}.dat".format(exptID))
Youtfile = "results/als-db-{0}-{1}.csv".format(exptID, iter)
Ysqlfile = "results/als-sql-{0}.sql".format(exptID)
# save the decomposition into the format
Yout = decompTools.getDBOutput(Y, yaxis)
Yout = np.column_stack((np.repeat(exptID, Yout.shape[0]), Yout))
np.savetxt(Youtfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
sqlOut.write("load client from /home/joyceho/workspace/tensor/{0} of del modified by coldel| insert into joyceho.tensor_factors;\n".format(Youtfile))
R = args.rank
seed = args.seed
iter = args.iterations
innerIter = 10
tol = 1e-2
zeroThr = 1e-5
# input file and output file
inputFile = args.inputFile.format("data")
yaxis = decompTools.loadAxisInfo(args.inputFile.format("info"))
print "Starting Tensor Factorization with ID:{0}".format(exptID)
X = sptensor.loadTensor(inputFile)
np.random.seed(seed)
Y, ls = CP_ALS.cp_als(X, R, tol=tol, maxiters=iter)
ll = CP_APR.loglikelihood(X, Y)
# normalize the factors using the 1 norm and then sort in descending order
Y.normalize_sort(1)
Y = decompTools.zeroSmallFactors(Y, zeroThr=zeroThr)
Y.writeRawFile("results/als-raw-{0}.dat".format(exptID))
Youtfile = "results/als-db-{0}-{1}.csv".format(exptID, iter)
Ysqlfile = "results/als-sql-{0}.sql".format(exptID)
# save the decomposition into the format
Yout = decompTools.getDBOutput(Y, yaxis)
Yout = np.column_stack((np.repeat(exptID, Yout.shape[0]), Yout))
np.savetxt(Youtfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
sqlOut.write(
def logLikelihood(self):
## calculate the log likelihood for each tensor
ll = [CP_APR.loglikelihood(self.X[i], self.M[i]) for i in range(len(self.X))]
return np.sum(np.array(ll))
