svn commit: r1531265 - /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java

[tommaso]

Author: tommaso
Date: Fri Oct 11 12:12:55 2013
New Revision: 1531265

URL: http://svn.apache.org/r1531265
Log:
HAMA-809 - small refactoring of LRM

Modified:
    
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java

Modified: 
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java
URL: 
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java?rev=1531265&r1=1531264&r2=1531265&view=diff
==============================================================================
--- 
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java
 (original)
+++ 
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java
 Fri Oct 11 12:12:55 2013
@@ -35,17 +35,14 @@ public class LogisticRegressionModel imp
     costFunction = new CostFunction() {
       @Override
       public BigDecimal calculateCostForItem(DoubleVector x, double y, int m, 
DoubleVector theta,
-              HypothesisFunction hypothesis) {
+                                             HypothesisFunction hypothesis) {
         // -1/m*(y*ln(hx) + (1-y)*ln(1-hx))
         BigDecimal hx = applyHypothesisWithPrecision(theta, x);
-        BigDecimal first = BigDecimal.valueOf(y).multiply(ln(hx));
-        BigDecimal logarg = BigDecimal.valueOf(1).subtract(hx, 
DEFAULT_PRECISION);
-        BigDecimal ln = ln(logarg);
-        BigDecimal second = BigDecimal.valueOf(1d - y).multiply(ln);
-        BigDecimal num = first.add(second);
-        BigDecimal den = BigDecimal.valueOf(-1*m);
-        BigDecimal res = num.divide(den, DEFAULT_PRECISION);
-        return res;
+        BigDecimal firstTerm = BigDecimal.valueOf(y).multiply(ln(hx));
+        BigDecimal secondTerm = BigDecimal.valueOf(1d - 
y).multiply(ln(BigDecimal.valueOf(1).subtract(hx, DEFAULT_PRECISION)));
+        BigDecimal num = firstTerm.add(secondTerm);
+        BigDecimal den = BigDecimal.valueOf(-1 * m);
+        return num.divide(den, DEFAULT_PRECISION);
       }
     };
   }
@@ -58,11 +55,9 @@ public class LogisticRegressionModel imp
   private BigDecimal applyHypothesisWithPrecision(DoubleVector theta, 
DoubleVector x) {
     // 1 / (1 + (e^(-theta'x)))
     double dotUnsafe = theta.multiply(-1d).dotUnsafe(x);
-    double d = Math.exp(dotUnsafe);
-    BigDecimal exp = BigDecimal.valueOf(d);
-    BigDecimal den = BigDecimal.valueOf(1d).add(exp);
-    BigDecimal remainder = BigDecimal.valueOf(1).subtract(den, 
DEFAULT_PRECISION);
+    BigDecimal den = 
BigDecimal.valueOf(1d).add(BigDecimal.valueOf(Math.exp(dotUnsafe)));
     BigDecimal res = BigDecimal.valueOf(1).divide(den, DEFAULT_PRECISION);
+    BigDecimal remainder = BigDecimal.valueOf(1).subtract(den, 
DEFAULT_PRECISION);
     if (res.doubleValue() == 1 && remainder.doubleValue() < 0) {
       res = res.add(remainder);
     }
@@ -70,23 +65,7 @@ public class LogisticRegressionModel imp
   }
 
   private BigDecimal ln(BigDecimal x) {
-//    if (x.equals(BigDecimal.ONE)) {
-//      return BigDecimal.ZERO;
-//    }
-//    x = x.subtract(BigDecimal.ONE);
-//    int iterations = 10000000;
-//    BigDecimal ret = new BigDecimal(iterations + 1);
-//    for (long i = iterations; i >= 0; i--) {
-//      BigDecimal N = new BigDecimal(i / 2 + 1).pow(2);
-//      N = N.multiply(x, DEFAULT_PRECISION);
-//      ret = N.divide(ret, DEFAULT_PRECISION);
-//
-//      N = new BigDecimal(i + 1);
-//      ret = ret.add(N, DEFAULT_PRECISION);
-//
-//    }
-//    ret = x.divide(ret, DEFAULT_PRECISION);
-//    return ret;
+    // TODO : implement this using proper logarithm for BigDecimals
     return BigDecimal.valueOf(Math.log(x.doubleValue()));
   }