set default learning rate to 200

m-soltani · Aug 6, 2017 · fd9d6a8 · fd9d6a8
1 parent 3dc37a2
commit fd9d6a8
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Showing 3 changed files with 4 additions and 8 deletions.
diff --git a/core/src/main/java/smile/manifold/TSNE.java b/core/src/main/java/smile/manifold/TSNE.java
@@ -86,7 +86,7 @@ public class TSNE {
      * @param d the dimension of embedding space.
      */
     public TSNE(double[][] X, int d) {
-        this(X, d, 20, 500, 1000);
+        this(X, d, 20, 200, 1000);
     }
 
     /** Constructor.
@@ -264,7 +264,7 @@ private void compute(int i) {
             // Perform the update
             for (int k = 0; k < d; k++) {
                 // Update gains
-                g[k] = (signum(dC[k]) != signum(dYi[k])) ? (g[k] + .2) : (g[k] * .8);
+                g[k] = (Math.signum(dC[k]) != Math.signum(dYi[k])) ? (g[k] + .2) : (g[k] * .8);
                 if (g[k] < minGain) g[k] = minGain;
 
                 // gradient update with momentum and gains
@@ -274,10 +274,6 @@ private void compute(int i) {
         }
     }
 
-    private int signum(double x) {
-        return (x == .0 ? 0 : (x < .0 ? -1 : 1));
-    }
-
     /** Compute the Gaussian kernel (search the width for given perplexity. */
     private double[][] expd(double[][] D, double perplexity, double tol) {
         int n          = D.length;

diff --git a/demo/src/main/java/smile/demo/manifold/TSNEDemo.java b/demo/src/main/java/smile/demo/manifold/TSNEDemo.java
@@ -84,7 +84,7 @@ public JComponent learn() {
         pca.setProjection(50);
         double[][] X = pca.project(data);
         long clock = System.currentTimeMillis();
-        TSNE tsne = new TSNE(X, 2, perplexity, 500, 1000);
+        TSNE tsne = new TSNE(X, 2, perplexity, 200, 1000);
         System.out.format("Learn t-SNE from %d samples in %dms\n", data.length, System.currentTimeMillis() - clock);
 
         double[][] y = tsne.getCoordinates();

diff --git a/scala/src/main/scala/smile/manifold/Operators.scala b/scala/src/main/scala/smile/manifold/Operators.scala
@@ -140,7 +140,7 @@ trait Operators {
     * @param eta        the learning rate.
     * @param iterations the number of iterations.
     */
-  def tsne(X: Array[Array[Double]], d: Int = 2, perplexity: Double = 20.0, eta: Double = 500.0, iterations: Int = 1000): TSNE = {
+  def tsne(X: Array[Array[Double]], d: Int = 2, perplexity: Double = 20.0, eta: Double = 200.0, iterations: Int = 1000): TSNE = {
     time {
       new TSNE(X, d, perplexity, eta, iterations)
     }