Exemplo n.º 1
0
Arquivo: Main.java Projeto: YpGu/gcoev
  public static void test1(String seed) {
    /* read, init data & parameters */
    for (int t = t0; t < T; t++) {
      //      String fileDir = "../../data/graph/" + Integer.toString(t) + ".csv";  // original
      // co-voting dataset
      //      String fileDir = "./data/" + Integer.toString(t) + ".csv";  // artificial toy dataset
      String fileDir =
          "../../data_sm/nips_17/out/"
              + seed
              + "/"
              + Integer.toString(t)
              + ".train.csv"; // nips dataset (smaller)
      Map<Integer, Double> freq = FileParser.readCSVDict(fileDir);

      double[][] G = new double[n][n];
      double[][] A = new double[n][n];
      double[][] mu = new double[n][K];
      double[][] mu_hat = new double[n][K];
      double[][] mu_prime = new double[n][K];
      double[][] mu_hat_prime = new double[n][K];
      double[][] h = new double[n][K];
      double[][] h_hat = new double[n][K];
      FileParser.readCSVGraph(fileDir, freq, G, A);
      for (int i = 0; i < n; i++)
        for (int k = 0; k < K; k++) {
          mu[i][k] = scale_0 * (rand.nextDouble() - 0.5);
          mu_hat[i][k] = scale_0 * (rand.nextDouble() - 0.5);
          mu_prime[i][k] = mu[i][k];
          mu_hat_prime[i][k] = mu_hat[i][k];
          h[i][k] = scale * (rand.nextDouble() - 0.5);
          h_hat[i][k] = scale * (rand.nextDouble() - 0.5);
        }

      GS.add(G);
      AS.add(A);
      mu_s.add(mu);
      mu_hat_s.add(mu_hat);
      mu_prime_s.add(mu_prime);
      mu_hat_prime_s.add(mu_hat_prime);
      h_s.add(h);
      h_prime_s.add(h);
      h_hat_s.add(h_hat);
      h_hat_prime_s.add(h);

      /* for test */
      delta_s.add(delta);
      delta_prime_s.add(delta); // TODO previous: 0.1

      v_s.add(0.1);
      v_hat_s.add(0.1);
      v_prime_s.add(0.1);
      v_hat_prime_s.add(0.1);

      System.out.println("done! t = " + t);
    }

    for (int t = t0; t < T; t++) {
      for (int s = t0; s < T; s++) {
        grad_mu_s.add(new double[n][K]);
        grad_mu_hat_s.add(new double[n][K]);
        grad_mu_prime_s.add(new double[n][K]);
        grad_mu_hat_prime_s.add(new double[n][K]);
      }
      grad_h_hat_s.add(new double[n][K]);
      grad_h_hat_prime_s.add(new double[n][K]);
    }
    /* end initialization */

    /* outer for-loop */
    double old_obj_1 = -1, old_obj_2 = -1;
    for (int iter = 0; iter < MAX_ITER; iter++) {
      //      Scanner sc = new Scanner(System.in); int gu; gu = sc.nextInt();
      System.out.println("====== iter = " + iter + " ======");
      /** intrinsic feature * */
      forward1(true, iter);
      backward1(true);
      compute_gradient1(iter);
      double new_obj_1 = 0;
      /* gradient descent: inner for-loop here */
      int inner_iter_1 = 0;
      while (inner_iter_1 < INNER_ITER) {
        /* update variational parameters \hat{h} using gradient descent */
        for (int t = 0; t < T - t0; t++) {
          double[][] h_hat_t = h_hat_s.get(t);
          double[][] grad_h_hat_t = grad_h_hat_s.get(t);
          for (int i = 0; i < n; i++)
            for (int k = 0; k < K; k++) {
              h_hat_t[i][k] += lr_1 * grad_h_hat_t[i][k];
            }
          h_hat_s.set(t, h_hat_t);
        }
        /* update \hat{\mu} and \hat{V}, since both are function of \hat{h} */
        forward1(false, iter);
        backward1(false);
        double obj1 = compute_objective1();
        if (inner_iter_1 % 10 == 0)
          System.out.println("(1) iter = " + inner_iter_1 + ", obj 1 = " + obj1);
        if (inner_iter_1 != 0 && obj1 < new_obj_1) {
          lr_1 *= 0.8;
          break;
        }
        new_obj_1 = obj1;
        inner_iter_1 += 1;
      }
      if (inner_iter_1 == INNER_ITER) lr_1 *= 2;
      /* sample */
      for (int t = 0; t < T - t0; t++) {
        double[][] samples =
            Operations.sample_multivariate_normal(mu_hat_s.get(t), v_hat_s.get(t), N_SAMPLES);
        double[][] h_t = new double[n][K];
        for (int i = 0; i < n; i++)
          for (int k = 0; k < K; k++) {
            h_t[i][k] = samples[i][k];
          }
        h_s.set(t, h_t);
      }

      /** impression feature * */
      forward2(true);
      backward2(true);
      compute_gradient2(iter);
      double new_obj_2 = 0;
      /* gradient descent: inner for-loop here */
      int inner_iter_2 = 0;
      while (inner_iter_2 < INNER_ITER) {
        /* update \hat{h}' using gradient descent */
        for (int t = 0; t < T - t0; t++) {
          double[][] h_hat_prime_t = h_hat_prime_s.get(t);
          double[][] grad_h_hat_prime_t = grad_h_hat_prime_s.get(t);
          for (int i = 0; i < n; i++)
            for (int k = 0; k < K; k++) {
              h_hat_prime_t[i][k] += lr_2 * grad_h_hat_prime_t[i][k];
            }
          h_hat_prime_s.set(t, h_hat_prime_t);
        }
        /* update \hat{\mu}' and \hat{V}', since both are function of \hat{h}' */
        forward2(false);
        backward2(false);
        double obj2 = compute_objective2();
        if (inner_iter_2 % 10 == 0)
          System.out.println("(2) iter = " + inner_iter_2 + ", obj 2 = " + obj2);
        if (inner_iter_2 != 0 && obj2 < new_obj_2) {
          lr_2 *= 0.8;
          break;
        }
        new_obj_2 = obj2;
        inner_iter_2 += 1;
      }
      if (inner_iter_2 == INNER_ITER) lr_2 *= 2;
      /* sample */
      for (int t = 0; t < T - t0; t++) {
        double[][] samples =
            Operations.sample_multivariate_normal(
                mu_hat_prime_s.get(t), v_hat_prime_s.get(t), N_SAMPLES);
        double[][] h_prime_t = new double[n][K];
        for (int i = 0; i < n; i++)
          for (int k = 0; k < K; k++) {
            h_prime_t[i][k] = samples[i][k];
          }
        h_prime_s.set(t, h_prime_t);
      }

      /** output * */
      for (int t = 0; t < T - t0; t++) {
        double[][] h_t = h_s.get(t);
        double[][] h_prime_t = h_prime_s.get(t);
        /* output filename:
         *    ./res/<seed>_<sigma>/h_<time>_<iter>.txt
         */
        FileParser.output(
            h_t, "./res/" + seed + "_" + delta_str + "/h_" + (t + t0) + "_" + iter + ".txt");
        FileParser.output(
            h_prime_t,
            "./res/" + seed + "_" + delta_str + "/h_p_" + (t + t0) + "_" + iter + ".txt");
      }

      /* check convergence */
      double diff_1 = -(new_obj_1 - old_obj_1) / old_obj_1;
      double diff_2 = -(new_obj_2 - old_obj_2) / old_obj_2;
      if (iter != 0 && diff_1 < 1e-6 && diff_2 < 1e-6) {
        System.out.println("diff_1 = " + diff_1);
        System.out.println("diff_2 = " + diff_2);
        break;
      }
      old_obj_1 = new_obj_1;
      old_obj_2 = new_obj_2;
    }
  }
Exemplo n.º 2
0
Arquivo: Main.java Projeto: YpGu/gcoev
  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;
  }
Exemplo n.º 3
0
Arquivo: Main.java Projeto: YpGu/gcoev
  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;
  }
Exemplo n.º 4
0
Arquivo: Main.java Projeto: YpGu/gcoev
  /** compute_objective2: return the lower bound when h is fixed */
  public static double compute_objective2() {
    double res = 0;
    for (int t = 0; t < T - t0; t++) {
      if (t != 0) {
        double[][] G_t = GS.get(t);
        double[][] G_t_pre = GS.get(t - 1);
        double[][] h_t = h_s.get(t);
        double[][] h_pre_t = h_s.get(t - 1);
        double[][] mu_hat_prime_t = mu_hat_prime_s.get(t);
        double[][] mu_hat_prime_pre_t = mu_hat_prime_s.get(t - 1);
        double delta_t = delta_s.get(t);

        double[][] a_pre = AS.get(t - 1);
        double[][] ave_neighbors = new double[n][K];
        for (int i = 0; i < n; i++)
          for (int j = 0; j < n; j++)
            if (G_t_pre[i][j] != 0) {
              for (int k = 0; k < K; k++) {
                ave_neighbors[i][k] += a_pre[i][j] * mu_hat_prime_pre_t[j][k];
              }
            }

        for (int i = 0; i < n; i++) {
          /* first term */
          double h2delta2 = 0;
          for (int k = 0; k < K; k++) {
            h2delta2 += 0.5 * h_t[i][k] * h_t[i][k] * delta_t * delta_t;
          }
          List<Double> powers = new ArrayList<Double>();
          for (int l = 0; l < n; l++) {
            double h_muhp = Operations.inner_product(h_t[i], mu_hat_prime_t[l], K);
            powers.add(h_muhp + h2delta2);
          }
          double lse = log_sum_exp(powers);

          for (int j = 0; j < n; j++)
            if (G_t[i][j] != 0) {
              double h_muhp = Operations.inner_product(h_t[i], mu_hat_prime_t[j], K);
              res += G_t[i][j] * (h_muhp - lse);
            }

          /* second term */
          for (int k = 0; k < K; k++) {
            double diff = h_t[i][k] - (1 - lambda) * h_pre_t[i][k] - lambda * ave_neighbors[i][k];
            res -= 0.5 * diff * diff / (sigma * sigma);
          }

          /* third term */
          for (int k = 0; k < K; k++) {
            double diff_3 = mu_hat_prime_t[i][k] - mu_hat_prime_pre_t[i][k];
            res -= 0.5 * diff_3 * diff_3 / (sigma * sigma);
          }
        }
      } else {
        /*
        double[][] G_t = GS.get(t);
        double[][] h_t = h_s.get(t);
        double[] mu_hat_prime_t = mu_hat_prime_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_t[i][0] * mu_hat_prime_t[l]
        	  + 0.5 * h_t[i][0] * h_t[i][0] * delta_t * delta_t);
            }
            double lse = log_sum_exp(powers);
            res += G_t[i][j] * (h_t[i][0] * mu_hat_prime_t[j] - lse);
          }
        }
        */
      }
    }
    return res;
  }
Exemplo n.º 5
0
Arquivo: Main.java Projeto: YpGu/gcoev
  /** 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;
  }