private void getnegphase() {
/*
* It does the negative phase of unsupervised RBM training algorithm
*
* For details, please refer to Dr. Hinton's paper:
* Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
*/
// start calculate the negative phase
// calculate the curved value of v1,h1
// find the vector of v1
Matrix negdata = poshidstates.times(vishid.transpose());
// (1 * numhid) * (numhid * numdims) = (1 * numdims)
negdata.plusEquals(visbiases);
// poshidstates*vishid' + visbiases
double[][] tmp1 = negdata.getArray();
int i1 = 0;
while (i1 < numdims) {
tmp1[0][i1] = 1 / (1 + Math.exp(-tmp1[0][i1]));
i1++;
}
// find the vector of h1
neghidprobs = negdata.times(vishid);
// (1 * numdims) * (numdims * numhid) = (1 * numhid)
neghidprobs.plusEquals(hidbiases);
double[][] tmp2 = neghidprobs.getArray();
int i2 = 0;
while (i2 < numhid) {
tmp2[0][i2] = 1 / (1 + Math.exp(-tmp2[0][i2]));
i2++;
}
negprods = negdata.transpose().times(neghidprobs);
// (numdims * 1) *(1 * numhid) = (numdims * numhid)
}
private void prop2nextLayer() {
/*
* It computes the forward propagation algorithm.
*/
poshidprobs = data.times(vishid);
// (1 * numdims) * (numdims * numhid)
poshidprobs.plusEquals(hidbiases);
// data*vishid + hidbiases
double[][] product_tmp2 = poshidprobs.getArray();
for (int i2 = 0; i2 < numhid; i2++) {
/*
* compute the updated input, and write them to newinput
*/
product_tmp2[0][i2] = 1 / (1 + Math.exp(-product_tmp2[0][i2]));
newinput[i2] = (int) (product_tmp2[0][i2] * 255.0);
}
}
/** toy example */
public static void test2() {
int N = 500;
double[][] m1 = new double[N][N];
double[][] m2 = new double[N][N];
double[][] m3 = new double[N][N];
// init
Random rand = new Random();
for (int i = 0; i < N; i++)
for (int j = 0; j < N; j++) {
m1[i][j] = 10 * (rand.nextDouble() - 0.2);
m2[i][j] = 20 * (rand.nextDouble() - 0.8);
}
// inverse
System.out.println("Start");
Matrix mat1 = new Matrix(m1);
Matrix mat2 = mat1.inverse();
Matrix mat3 = mat1.times(mat2);
double[][] m4 = mat3.getArray();
/*
for (int i = 0; i < m4.length; i++) {
int ss = 10;
for (int j = 0; j < ss; j++) {
System.out.printf("%f ", m4[i][j]);
}
System.out.print("\n");
}
*/
System.out.println("Done");
/*
// matrix *
System.out.println("Start");
for (int i = 0; i < N; i++) for (int j = 0; j < N; j++) {
double cell = 0;
for (int k = 0; k < N; k++)
cell += m1[i][k] * m2[k][j];
// System.out.printf("%f ", cell);
m3[i][j] = cell;
}
System.out.println("Done");
*/
}
private void getposphase() {
/*
* It does the positive phase of unsupervised RBM training algorithm
*
* For details, please refer to Dr. Hinton's paper:
* Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
*/
// Start calculate the positive phase
// calculate the cured value of h0
poshidprobs = data.times(vishid);
// (1 * numdims) * (numdims * numhid)
poshidprobs.plusEquals(hidbiases);
// data*vishid + hidbiases
double[][] product_tmp2 = poshidprobs.getArray();
int i2 = 0;
while (i2 < numhid) {
product_tmp2[0][i2] = 1 / (1 + Math.exp(-product_tmp2[0][i2]));
i2++;
}
posprods = data.transpose().times(poshidprobs);
// (numdims * 1) * (1 * numhid)
// end of the positive phase calculation, find the binary presentation of h0
int i3 = 0;
double[][] tmp1 = poshidprobs.getArray();
double[][] tmp2 = new double[1][numhid];
Random randomgenerator = new Random();
while (i3 < numhid) {
/*
* a sampling according to possiblity given by poshidprobs
*/
if (tmp1[0][i3] > randomgenerator.nextDouble()) tmp2[0][i3] = 1;
else tmp2[0][i3] = 0;
i3++;
}
// poshidstates is a binary sampling according to possiblity given by poshidprobs
poshidstates = new Matrix(tmp2);
}
// update the weights and biases
// This serves as a reducer
private void update() {
/*
* It computes the update of weights using previous results and parameters
*
* For details, please refer to Dr. Hinton's paper:
* Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
*/
double momentum;
// if (epoch > 5)
// momentum = finalmomentum;
// else
// momentum = initialmomentum;
// vishidinc = momentum*vishidinc + epsilonw*( (posprods-negprods)/numcases -
// weightcost*vishid);
// vishidinc.timesEquals(momentum);
Matrix temp1 = posprods.minus(negprods);
Matrix temp2 = vishid.times(weightcost);
temp1.minusEquals(temp2);
temp1.timesEquals(epsilonw);
// the final updates of weights are written in vishidinc
vishidinc.plusEquals(temp1);
}
/*
* backward pass 1: update
* (1) \hat{mu} (mu_hat_s)
* (2) \hat{grad_mu} (grad_mu_hat_s)
* (3) \hat{V} (v_hat_s)
*/
public static void backward1(boolean update_grad) {
for (int t1 = T - 1; t1 > t0; t1--) {
int t = t1 - t0;
// System.out.println("backward 1;\tt = " + t1);
if (t != T - 1 - t0) {
double V_pre_t = v_s.get(t - 1); // V^{t-1}
double V_hat_t = v_hat_s.get(t); // \hat{V}^{t}
double[][] mu_pre_t = mu_s.get(t - 1); // \mu^{t-1}
double[][] mu_hat_t = mu_hat_s.get(t); // \hat{\mu}^{t} [t-1]
Matrix A_pre_t = new Matrix(AS.get(t - 1)); // A^{t-1}
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1)); // h'^{t-1}
Matrix ave_neighbors = A_pre_t.times(hprime_pre_t); // n * 1
/* calculate \hat{\mu} at time t-1 */
double factor_1 =
(1 - lambda) * V_pre_t / (sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
double factor_2 = (sigma * sigma) / (sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
double[][] mu_hat_pre_t = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
mu_hat_pre_t[i][k] =
factor_1 * (mu_hat_t[i][k] - lambda * ave_neighbors.get(i, k))
+ factor_2 * mu_pre_t[i][k];
}
/* calculate \hat{V} at time t-1 */
double V_hat_pre_t =
V_pre_t
+ factor_1
* factor_1
* (V_hat_t - (1 - lambda) * (1 - lambda) * V_pre_t - (sigma * sigma));
/* update \mu and V */
mu_hat_s.set(t - 1, mu_hat_pre_t);
v_hat_s.set(t - 1, V_hat_pre_t);
/* calculate and update grad_mu_hat at time t-1 */
if (update_grad)
for (int s = 0; s < T - t0; s++) {
double[][] grad_hat_t_s = grad_mu_hat_s.get(t * (T - t0) + s);
double[][] grad_pre_t_s = grad_mu_s.get((t - 1) * (T - t0) + s);
double[][] grad_hat_pre_t_s = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad_hat_pre_t_s[i][k] =
factor_1 * grad_hat_t_s[i][k] + factor_2 * grad_pre_t_s[i][k];
}
grad_mu_hat_s.set((t - 1) * (T - t0) + s, grad_hat_pre_t_s);
}
} else {
/*
* initial condition for backward pass:
* (1) \hat{mu}^{T} = mu^{T}
* (2) \hat{V}^{T} = V^{T}
* (3) \hat{grad_mu}^{T/s} = grad_mu^{T/s}, \forall s
*/
mu_hat_s.set(t, mu_s.get(t));
v_hat_s.set(t, v_s.get(t));
if (update_grad)
for (int s = 0; s < T - t0; s++) {
grad_mu_hat_s.set(t * (T - t0) + s, grad_mu_s.get(t * (T - t0) + s));
}
}
// Scanner sc = new Scanner(System.in);
// int gu; gu = sc.nextInt();
/* end for each t */
}
}
/**
* forward pass 1: update intrinsic features (1) mu (mu_s) (2) grad_mu (grad_mu_s) (3) variance V
* (v_s)
*/
public static void forward1(boolean update_grad, int iter) {
/*
if (iter == 4) {
int t = 15;
double[][] h_t = new double[n][K];
double[][] h_hat_t = new double[n][K];
double[][] h_prime_t = new double[n][K];
double[][] h_hat_prime_t = new double[n][K];
for (int i = 0; i < n; i++) for (int k = 0; k < K; k++) {
h_t[i][k] = h_s.get(t-1)[i][k];
h_hat_t[i][k] = h_hat_s.get(t-1)[i][k];
h_prime_t[i][k] = h_prime_s.get(t-1)[i][k];
h_hat_prime_t[i][k] = h_hat_prime_s.get(t-1)[i][k];
}
h_s.set(t, h_t); h_hat_s.set(t, h_hat_t);
h_prime_s.set(t, h_prime_t); h_hat_prime_s.set(t, h_hat_prime_t);
}
*/
for (int t = 0; t < T - t0; t++) {
// System.out.println("forward 1;\tt = " + t1);
if (t != 0) {
double delta_t = delta_s.get(t); // delta_t
double[][] h_hat_t = h_hat_s.get(t); // \hat{h}^t [t]
double[][] mu_pre_t = mu_s.get(t - 1); // mu^{t-1} (N*1)
double V_pre_t = v_s.get(t - 1); // V^{t-1}
Matrix a = new Matrix(AS.get(t - 1)); // A^{t-1}
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1)); // h'^{t-1}
Matrix ave_neighbors = a.times(hprime_pre_t);
/* calculate \mu */
double[][] mu_t = new double[n][K];
double factor_1 =
(delta_t * delta_t)
/ (delta_t * delta_t + sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
double factor_2 =
(sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t)
/ (delta_t * delta_t + sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
mu_t[i][k] =
factor_1 * ((1 - lambda) * mu_pre_t[i][k] + lambda * ave_neighbors.get(i, k))
+ factor_2 * h_hat_t[i][k];
}
/* calculate V */
double V_t = factor_2 * delta_t * delta_t;
/* update \mu and V */
mu_s.set(t, mu_t);
v_s.set(t, V_t);
/* calculate and update grad_mu */
if (update_grad)
for (int s = 0; s < T - t0; s++) {
double[][] grad_pre_t_s = grad_mu_s.get((t - 1) * (T - t0) + s);
double[][] grad_t_s = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad_t_s[i][k] = factor_1 * (1 - lambda) * grad_pre_t_s[i][k];
if (t == s) {
grad_t_s[i][k] += factor_2;
}
}
grad_mu_s.set(t * (T - t0) + s, grad_t_s);
}
} else {
/* mu, V: random init (keep unchanged) */
/* grad_mu: set to 0 (keep unchanged) */
}
// Scanner sc = new Scanner(System.in);
// int gu; gu = sc.nextInt();
/* end for each t */
}
}
public static void compute_gradient2(int iteration) {
double[][][] tmp_grad_h_hat_prime_s = new double[T - t0][n][K];
/*
* compute
* nti[t][i] = \sum_{j} { n_{ij} }
* and
* nti_h[t][j][k] = \sum_{i} { n_{ij}^{t} h_{ik}^{t} }
*/
double[][] nti = new double[T - t0][n];
double[][][] nti_h = new double[T - t0][n][K];
for (int t = 0; t < T - t0; t++) {
double[][] G_t = GS.get(t);
double[][] h_t = h_s.get(t); // h^{t}
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++) {
nti[t][i] += G_t[i][j];
for (int k = 0; k < K; k++) {
nti_h[t][j][k] += G_t[i][j] * h_t[i][k];
}
}
}
for (int t = 0; t < T - t0; t++) {
double delta_t = delta_prime_s.get(t);
double[][] h_t = h_s.get(t); // h^{t}
double[][] h_hat_prime_t = h_hat_prime_s.get(t); // \hat{h}^{t}
double[][] mu_hat_t = mu_hat_s.get(t); // \hat{\mu}^{t}
double[][] mu_hat_prime_t = mu_hat_prime_s.get(t); // \hat{\mu}'^{t}
double[][] h_prime_t = h_prime_s.get(t);
if (t != 0) {
Matrix a = new Matrix(AS.get(t - 1));
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1));
Matrix ave_neighbors = a.times(hprime_pre_t);
double[][] G_pre_t = GS.get(t - 1); // G^{t-1}
double[][] A_pre_t = AS.get(t - 1); // A^{t-1}
double[][] h_pre_t = h_s.get(t - 1); // h^{t-1}
double[][] mu_hat_prime_pre_t = mu_hat_prime_s.get(t - 1); // \hat{\mu}'^{t-1} [t]
for (int s = 0; s < T - t0; s++) {
double[][] grad_mu_hat_prime_t = grad_mu_hat_prime_s.get(t * (T - t0) + s);
double[][] grad_mu_hat_prime_pre_t = grad_mu_hat_prime_s.get((t - 1) * (T - t0) + s);
double[] h2delta2 = new double[n];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
h2delta2[i] += 0.5 * h_t[i][k] * h_t[i][k] * delta_t * delta_t;
}
/* compute weighted_exp for later use */
double[][][] weighted_exp_num = new double[K][n][n];
double[][] weighted_exp_den = new double[K][n];
double[][][] weighted_exp = new double[K][n][n];
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++) {
double h_muhp = Operations.inner_product(h_t[j], mu_hat_prime_t[i], K);
for (int k = 0; k < K; k++) {
weighted_exp_num[k][i][j] = h_t[j][k] * Math.exp(h_muhp + h2delta2[j]);
weighted_exp_den[k][j] += Math.exp(h_muhp + h2delta2[j]);
}
}
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
for (int k = 0; k < K; k++) {
weighted_exp[k][i][j] = weighted_exp_num[k][i][j] / weighted_exp_den[k][j];
}
/* compute sum_mu_hat_prime for later use */
double[] sum_mu_hat_prime = new double[K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
sum_mu_hat_prime[k] += mu_hat_prime_pre_t[i][k];
}
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
/* first term */
double g1 = nti_h[t][i][k] * grad_mu_hat_prime_t[i][k];
tmp_grad_h_hat_prime_s[s][i][k] += g1;
/* second term */
double g2 = 0;
for (int j = 0; j < n; j++) {
g2 -= nti[t][j] * weighted_exp[k][i][j] * grad_mu_hat_prime_t[i][k];
}
tmp_grad_h_hat_prime_s[s][i][k] += g2;
/* third term */
for (int j = 0; j < n; j++)
if (G_pre_t[j][i] != 0) {
// double g3 = ( h_t[j][k] - (1-lambda) * h_pre_t[j][k] - lambda *
// A_pre_t[j][i] * sum_mu_hat_prime[k] )
double g3 =
(h_t[j][k]
- (1 - lambda) * h_pre_t[j][k]
- lambda * A_pre_t[j][i] * mu_hat_prime_pre_t[i][k])
* lambda
* A_pre_t[j][i]
* grad_mu_hat_prime_pre_t[i][k]
/ (sigma * sigma);
tmp_grad_h_hat_prime_s[s][j][k] += g3; // j instead of i!
}
}
/* fourth term */
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
double g4 =
-(mu_hat_prime_t[i][k] - mu_hat_prime_pre_t[i][k])
* (grad_mu_hat_prime_t[i][k] - grad_mu_hat_prime_pre_t[i][k])
/ (sigma * sigma);
tmp_grad_h_hat_prime_s[s][i][k] += g4;
}
}
} else {
/*
for (int s = 0; s < T-t0; s++) {
double[] grad_mu_hat_prime_t = grad_mu_hat_prime_s.get(t * (T-t0) + s);
for (int i = 0; i < n; i++) {
// first term
double g1 = nti_hp[t][i] * grad_mu_hat_prime_t[i];
tmp_grad_h_hat_prime_s[s][i] += g1;
// second term
double g2 = 0;
for (int _j = 0; _j < NEG; _j++) {
double weighted_exp_num = 0, weighted_exp_den = 0;
int j = neg_samples.get(t)[i][_j];
double htj = h_t[j][0]; double muhti = mu_hat_t[i];
weighted_exp_num += htj * Math.exp(htj * muhti + 0.5 * htj * htj * delta_t * delta_t);
for (int _k = 0; _k < NEG; _k++) {
int k = neg_samples.get(t)[i][_k];
double muhtk = mu_hat_t[k];
weighted_exp_den += Math.exp(htj * muhtk + 0.5 * htj * htj * delta_t * delta_t);
}
g2 -= nti[t][j] * weighted_exp_num / weighted_exp_den * grad_mu_hat_prime_t[i];
}
tmp_grad_h_hat_prime_s[s][i] += g2;
}
// fourth term (if any)
if (s == t) for (int i = 0; i < n; i++) {
double g4 = -h_hat_prime_t[i][0] / (sigma*sigma);
tmp_grad_h_hat_prime_s[s][i] += g4;
}
}
*/
}
}
/* update global gradient */
for (int t = 0; t < T - t0; t++) {
double[][] grad = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad[i][k] = tmp_grad_h_hat_prime_s[t][i][k];
}
grad_h_hat_prime_s.set(t, grad);
}
FileParser.output_2d(grad_h_hat_prime_s, "./grad/grad_prime_" + iteration + ".txt");
return;
}
public static void compute_gradient1(int iteration) {
double[][][] tmp_grad_h_hat_s = new double[T - t0][n][K];
for (int t = 0; t < T - t0; t++) {
// System.out.println("compute gradient 1, t = " + t);
double delta_t = delta_s.get(t);
double[][] G_t = GS.get(t);
double[][] h_prime_t = h_prime_s.get(t);
double[][] mu_hat_t = mu_hat_s.get(t);
if (t != 0) {
double[][] mu_hat_pre_t = mu_hat_s.get(t - 1);
Matrix a = new Matrix(AS.get(t - 1));
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1));
Matrix ave_neighbors = a.times(hprime_pre_t);
/* TODO: check whether we can save computation by comparing s and t */
for (int s = 0; s < T - t0; s++) {
double[][] grad_hat_t = grad_mu_hat_s.get(t * (T - t0) + s);
double[][] grad_hat_pre_t = grad_mu_hat_s.get((t - 1) * (T - t0) + s);
double[] hp2delta2 = new double[n];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
hp2delta2[i] += 0.5 * h_prime_t[i][k] * h_prime_t[i][k] * delta_t * delta_t;
}
for (int i = 0; i < n; i++) {
/* first term */
double[] weighted_exp_num = new double[K];
double weighted_exp_den = 0;
for (int l = 0; l < n; l++) {
double hp_muh = Operations.inner_product(h_prime_t[l], mu_hat_t[i], K);
double e = Math.exp(hp_muh + hp2delta2[l]);
if (Double.isNaN(e)) {
/* check if e explodes */
System.out.println("ERROR2");
Scanner sc = new Scanner(System.in);
int gu;
gu = sc.nextInt();
}
for (int k = 0; k < K; k++) {
weighted_exp_num[k] += h_prime_t[l][k] * e;
weighted_exp_den += e;
}
}
for (int j = 0; j < n; j++)
for (int k = 0; k < K; k++) {
double weighted_exp = weighted_exp_num[k] / weighted_exp_den;
double gi1 = G_t[i][j] * grad_hat_t[i][k] * (h_prime_t[j][k] - weighted_exp);
tmp_grad_h_hat_s[s][i][k] += gi1;
}
/* second term */
for (int k = 0; k < K; k++) {
double gi2 =
-(mu_hat_t[i][k]
- (1 - lambda) * mu_hat_pre_t[i][k]
- lambda * ave_neighbors.get(i, k))
* (grad_hat_t[i][k] - (1 - lambda) * grad_hat_pre_t[i][k])
/ (sigma * sigma);
tmp_grad_h_hat_s[s][i][k] += gi2;
}
}
}
} else {
/* no such term (t=0) in ELBO */
/*
for (int s = 0; s < T-t0; s++) {
double[] grad_hat_t = grad_mu_hat_s.get(t * (T-t0) + s);
for (int i = 0; i < n; i++) {
double n_it = 0;
for (int j = 0; j < n; j++) n_it += G_t[i][j];
// first term
double gi1 = -mu_hat_t[i] * grad_hat_t[i] / (sigma * sigma);
tmp_grad_h_hat_s[s][i] += gi1;
// second term
double gi2 = 0;
double weighted_exp_num = 0, weighted_exp_den = 0;
for (int j = 0; j < NEG; j++) {
int l = neg_samples.get(t)[i][j];
double hpl = h_prime_t[l][0];
double muit = mu_hat_t[i];
double e = Math.exp(hpl * muit + 0.5 * hpl * hpl * delta_t * delta_t);
// TODO: check if e explodes
if (Double.isNaN(e)) {
System.out.println("ERROR3");
Scanner sc = new Scanner(System.in);
int gu; gu = sc.nextInt();
}
weighted_exp_num += hpl * e;
weighted_exp_den += e;
}
double weighted_exp = weighted_exp_num / weighted_exp_den;
for (int j = 0; j < n; j++) {
gi2 += G_t[i][j] * grad_hat_t[i] * (h_prime_t[j][0] - weighted_exp);
}
tmp_grad_h_hat_s[s][i] += gi2;
}
}
*/
}
/* end if-else */
}
/* update global gradient */
for (int t = 0; t < T - t0; t++) {
double[][] grad = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad[i][k] = tmp_grad_h_hat_s[t][i][k];
}
grad_h_hat_s.set(t, grad);
}
FileParser.output_2d(grad_h_hat_s, "./grad/grad_" + iteration + ".txt");
return;
}
/** compute_objective1: return the lower bound when h' is fixed */
public static double compute_objective1() {
double res = 0;
for (int t = 0; t < T - t0; t++) {
if (t != 0) {
double[][] G_t = GS.get(t);
double[][] h_prime_t = h_prime_s.get(t);
double[][] h_prime_pre_t = h_prime_s.get(t - 1);
double[][] mu_hat_t = mu_hat_s.get(t);
double[][] mu_hat_pre_t = mu_hat_s.get(t - 1);
double delta_t = delta_s.get(t);
Matrix a = new Matrix(AS.get(t - 1));
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1));
Matrix ave_neighbors = a.times(hprime_pre_t);
double[] hp2delta2 = new double[n];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
hp2delta2[i] += 0.5 * h_prime_t[i][k] * h_prime_t[i][k] * delta_t * delta_t;
}
for (int i = 0; i < n; i++) {
/* first term */
List<Double> powers = new ArrayList<Double>();
for (int l = 0; l < n; l++) {
double hp_muh = Operations.inner_product(h_prime_t[l], mu_hat_t[i], K);
powers.add(hp_muh + hp2delta2[l]);
}
double lse = log_sum_exp(powers);
for (int j = 0; j < n; j++)
if (G_t[i][j] != 0) {
double hp_muh = Operations.inner_product(h_prime_t[j], mu_hat_t[i], K);
res += G_t[i][j] * (hp_muh - lse);
}
/* second term */
for (int k = 0; k < K; k++) {
double diff =
mu_hat_t[i][k]
- (1 - lambda) * mu_hat_pre_t[i][k]
- lambda * ave_neighbors.get(i, k);
res -= 0.5 * diff * diff / (sigma * sigma);
}
}
} else {
/*
double[][] G_t = GS.get(t);
double[][] h_prime_t = h_prime_s.get(t);
double[] mu_hat_t = mu_hat_s.get(t);
double delta_t = delta_s.get(t);
int[][] neg_sam_t = neg_samples.get(t);
for (int i = 0; i < n; i++) {
// first term
for (int j = 0; j < n; j++) if (G_t[i][j] != 0) {
List<Double> powers = new ArrayList<Double>();
for (int _l = 0; _l < NEG; _l++) {
int l = neg_sam_t[i][_l];
powers.add(h_prime_t[l][0] * mu_hat_t[i]
+ 0.5 * h_prime_t[l][0] * h_prime_t[l][0] * delta_t * delta_t);
}
double lse = log_sum_exp(powers);
res += G_t[i][j] * (h_prime_t[j][0] * mu_hat_t[i] - lse);
}
}
*/
}
}
return res;
}
