本文整理汇总了Java中happy.coding.io.FileIO.deserialize方法的典型用法代码示例。如果您正苦于以下问题：Java FileIO.deserialize方法的具体用法？Java FileIO.deserialize怎么用？Java FileIO.deserialize使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类happy.coding.io.FileIO的用法示例。
在下文中一共展示了FileIO.deserialize方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: testSerialization
import happy.coding.io.FileIO; //导入方法依赖的package包/类
@Test
public void testSerialization() throws Exception {
String filePath = Systems.getDesktop() + "vec.dat";
DenseVector vec = new DenseVector(11);
for (int i = 10, j = 0; i >= 0; i--, j++)
vec.set(j, i);
FileIO.serialize(vec, filePath);
DenseVector v2 = (DenseVector) FileIO.deserialize(filePath);
Logs.debug(v2.toString());
DenseMatrix mat = new DenseMatrix(3, 4);
for (int i = 0; i < 3; i++)
for (int j = 0; j < 4; j++)
mat.set(i, j, i + j);
Logs.debug(mat);
String matPath = Systems.getDesktop() + "mat.dat";
FileIO.serialize(mat, matPath);
DenseMatrix mat2 = (DenseMatrix) FileIO.deserialize(matPath);
Logs.debug(mat2);
}
示例2: initModel
import happy.coding.io.FileIO; //导入方法依赖的package包/类
@Override
protected void initModel() throws Exception {
P = new DenseMatrix(numUsers, numFactors);
Q = new DenseMatrix(numUsers, numFactors);
P.init(0.01);
Q.init(0.01);
itemBias = new DenseVector(numItems);
itemBias.init(0.01);
rho = cf.getInt("FISM.rho");
alpha = cf.getDouble("FISM.alpha");
regBeta = cf.getDouble("FISM.reg.beta");
regGamma = cf.getDouble("FISM.reg.gamma");
tau = cf.getDouble("FUST.trust.tau");
flag = Debug.ON;
numUsers = trainMatrix.numRows();
userCorr = flag ? buildCorrs(true) : null;
users = numUsers;
numUsers = socialMatrix.numRows();
String dirPath = "Results\\TrustPredictor\\";
uFactors = (DenseMatrix) FileIO.deserialize(dirPath + "userFactors.bin");
vFactors = (DenseMatrix) FileIO.deserialize(dirPath + "itemFactors.bin");
}
示例3: testSparseMatrix
import happy.coding.io.FileIO; //导入方法依赖的package包/类
@Test
public void testSparseMatrix() throws Exception {
Table<Integer, Integer, Double> vals = HashBasedTable.create();
vals.put(0, 0, 10.0);
vals.put(0, 4, -2.0);
vals.put(1, 0, 3.0);
vals.put(1, 1, 9.0);
vals.put(1, 5, 3.0);
vals.put(2, 1, 7.0);
vals.put(2, 2, 8.0);
vals.put(2, 3, 7.0);
vals.put(3, 0, 3.0);
vals.put(3, 2, 8.0);
vals.put(3, 3, 7.0);
vals.put(3, 4, 5.0);
vals.put(4, 1, 8.0);
vals.put(4, 3, 9.0);
vals.put(4, 4, 9.0);
vals.put(4, 5, 13.0);
vals.put(5, 1, 4.0);
vals.put(5, 4, 2.0);
vals.put(5, 5, -1.0);
SparseMatrix A = new SparseMatrix(6, 6, vals);
Logs.debug(A);
String dirPath = FileIO.desktop;
FileIO.serialize(A, dirPath + "A.mat");
SparseMatrix A2 = (SparseMatrix) FileIO.deserialize(dirPath + "A.mat");
Logs.debug(A2);
SparseVector v = new SparseVector(10);
v.set(2, 5);
v.set(9, 10);
Logs.debug(v);
FileIO.serialize(v, dirPath + "v.vec");
SparseVector v2 = (SparseVector) FileIO.deserialize(dirPath + "v.vec");
Logs.debug(v2);
SymmMatrix mm = new SymmMatrix(5);
mm.set(0, 1, 0.5);
mm.set(2, 3, 0.3);
mm.set(4, 2, 0.8);
Logs.debug(mm);
FileIO.serialize(mm, dirPath + "mm.mat");
SymmMatrix mm2 = (SymmMatrix) FileIO.deserialize(dirPath + "mm.mat");
Logs.debug(mm2);
}