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package hex.genmodel.algos.glrm;
import hex.genmodel.utils.ArrayUtils;
import hex.genmodel.utils.MathUtils;
import java.util.Random;
/**
* Regularization method for matrices X and Y in the GLRM algorithm.
*
* Examples:
* + Non-negative matrix factorization (NNMF): r_x = r_y = NonNegative
* + Orthogonal NNMF: r_x = OneSparse, r_y = NonNegative
* + K-means clustering: r_x = UnitOneSparse, r_y = 0 (\gamma_y = 0)
* + Quadratic mixture: r_x = Simplex, r_y = 0 (\gamma_y = 0)
*/
public enum GlrmRegularizer {
None {
@Override public double regularize(double[] u) {
return 0;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
return u;
}
@Override public double[] project(double[] u, Random rand) {
return u;
}
},
Quadratic {
@Override public double regularize(double[] u) {
if (u == null) return 0;
double ureg = 0;
for (double ui : u) ureg += ui * ui;
return ureg;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
double[] v = new double[u.length];
double f = 1/(1 + 2*delta);
for (int i = 0; i < u.length; i++)
v[i] = u[i] * f;
return v;
}
@Override public double[] project(double[] u, Random rand) {
return u;
}
},
L2 {
@Override public double regularize(double[] u) {
if (u == null) return 0;
double ureg = 0;
for (double ui : u) ureg += ui * ui;
return Math.sqrt(ureg);
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
double[] v = new double[u.length];
// Proof uses Moreau decomposition;
// see section 6.5.1 of Parikh and Boyd https://web.stanford.edu/~boyd/papers/pdf/prox_algs.pdf
double weight = 1 - delta/ArrayUtils.l2norm(u);
if (weight < 0) return v; // Zero vector
for (int i = 0; i < u.length; i++)
v[i] = weight * u[i];
return v;
}
@Override public double[] project(double[] u, Random rand) {
return u;
}
},
L1 {
@Override public double regularize(double[] u) {
if (u == null) return 0;
double ureg = 0;
for (double ui : u) ureg += Math.abs(ui);
return ureg;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
double[] v = new double[u.length];
for (int i = 0; i < u.length; i++)
v[i] = Math.max(u[i] - delta, 0) + Math.min(u[i] + delta, 0);
return v;
}
@Override public double[] project(double[] u, Random rand) {
return u;
}
},
NonNegative {
@Override public double regularize(double[] u) {
if (u == null) return 0;
for (double ui : u)
if (ui < 0)
return Double.POSITIVE_INFINITY;
return 0;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
double[] v = new double[u.length];
for (int i = 0; i < u.length; i++)
v[i] = Math.max(u[i], 0);
return v;
}
// Proximal operator of indicator function for a set C is (Euclidean) projection onto C
@Override public double[] project(double[] u, Random rand) {
return u == null? null : rproxgrad(u, 1, rand);
}
},
OneSparse {
@Override public double regularize(double[] u) {
if (u == null) return 0;
int card = 0;
for (double ui : u) {
if (ui < 0) return Double.POSITIVE_INFINITY;
else if (ui > 0) card++;
}
return card == 1 ? 0 : Double.POSITIVE_INFINITY;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
double[] v = new double[u.length];
int idx = ArrayUtils.maxIndex(u, rand);
v[idx] = u[idx] > 0 ? u[idx] : 1e-6;
return v;
}
@Override public double[] project(double[] u, Random rand) {
return u == null? null : rproxgrad(u, 1, rand);
}
},
UnitOneSparse {
@Override public double regularize(double[] u) {
if (u == null) return 0;
int ones = 0, zeros = 0;
for (double ui : u) {
if (ui == 1) ones++;
else if (ui == 0) zeros++;
else return Double.POSITIVE_INFINITY;
}
return ones == 1 && zeros == u.length-1 ? 0 : Double.POSITIVE_INFINITY;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
double[] v = new double[u.length];
int idx = ArrayUtils.maxIndex(u, rand);
v[idx] = 1;
return v;
}
@Override public double[] project(double[] u, Random rand) {
return u == null? null : rproxgrad(u, 1, rand);
}
},
Simplex {
@Override public double regularize(double[] u) {
if (u == null) return 0;
double sum = 0, absum = 0;
for (double ui : u) {
if (ui < 0) return Double.POSITIVE_INFINITY;
else {
sum += ui;
absum += Math.abs(ui);
}
}
return MathUtils.equalsWithinRecSumErr(sum, 1.0, u.length, absum) ? 0 : Double.POSITIVE_INFINITY;
}
@Override public double[] rproxgrad(double[] u, double delta, Random rand) {
if (u == null || delta == 0) return u;
// Proximal gradient algorithm by Chen and Ye in http://arxiv.org/pdf/1101.6081v2.pdf
// 1) Sort input vector u in ascending order: u[1] <= ... <= u[n]
int n = u.length;
int[] idxs = new int[n];
for (int i = 0; i < n; i++) idxs[i] = i;
ArrayUtils.sort(idxs, u);
// 2) Calculate cumulative sum of u in descending order
// cumsum(u) = (..., u[n-2]+u[n-1]+u[n], u[n-1]+u[n], u[n])
double[] ucsum = new double[n];
ucsum[n-1] = u[idxs[n-1]];
for (int i = n-2; i >= 0; i--)
ucsum[i] = ucsum[i+1] + u[idxs[i]];
// 3) Let t_i = (\sum_{j=i+1}^n u[j] - 1)/(n - i)
// For i = n-1,...,1, set optimal t* to first t_i >= u[i]
double t = (ucsum[0] - 1)/n; // Default t* = (\sum_{j=1}^n u[j] - 1)/n
for (int i = n-1; i >= 1; i--) {
double tmp = (ucsum[i] - 1)/(n - i);
if (tmp >= u[idxs[i-1]]) {
t = tmp;
break;
}
}
// 4) Return max(u - t*, 0) as projection of u onto simplex
double[] x = new double[u.length];
for (int i = 0; i < u.length; i++)
x[i] = Math.max(u[i] - t, 0);
return x;
}
@Override public double[] project(double[] u, Random rand) {
double reg = regularize(u); // Check if inside simplex before projecting since algo is complicated
if (reg == 0) return u;
return rproxgrad(u, 1, rand);
}
};
/** Regularization function applied to a single row x_i or column y_j */
public abstract double regularize(double[] u);
/** Regularization applied to an entire matrix (sum over rows) */
public final double regularize(double[][] u) {
if (u == null || this == None) return 0;
double ureg = 0;
for (double[] uarr : u) {
ureg += regularize(uarr);
if (Double.isInfinite(ureg)) break;
}
return ureg;
}
/** \prox_{\alpha_k*r}(u): Proximal gradient of (step size) * (regularization function) evaluated at vector u */
public abstract double[] rproxgrad(double[] u, double delta, Random rand);
/** Project X,Y matrices into appropriate subspace so regularizer is finite. Used during initialization. */
public abstract double[] project(double[] u, Random rand);
}
