Author: tommaso
Date: Fri Oct 11 09:46:29 2013
New Revision: 1531245
URL: http://svn.apache.org/r1531245
Log:
HAMA-809 - applied draft patch to solve LR cost function problems
Modified:
hama/trunk/examples/src/test/resources/logistic_regression_sample.txt
hama/trunk/ml/src/main/java/org/apache/hama/ml/math/DenseDoubleVector.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/CostFunction.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/HypothesisFunction.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LinearRegressionModel.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/RegressionModel.java
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LinearRegressionModelTest.java
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LogisticRegressionModelTest.java
Modified: hama/trunk/examples/src/test/resources/logistic_regression_sample.txt
URL:
http://svn.apache.org/viewvc/hama/trunk/examples/src/test/resources/logistic_regression_sample.txt?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
--- hama/trunk/examples/src/test/resources/logistic_regression_sample.txt
(original)
+++ hama/trunk/examples/src/test/resources/logistic_regression_sample.txt Fri
Oct 11 09:46:29 2013
@@ -1,6 +1,8 @@
-2>1 9 2 4 5 6 7
+0>1 9 2 4 5 6 7
1>3 4 5 6 9 1 3
1>1 1 3 1 1 1 1
-2>2 4 1 1 4 1 8
+0>2 4 1 1 4 1 8
1>3 4 5 6 7 8 9
1>1 3 4 1 4 5 1
+0>1 10 3 2 1 6 1
+0>1 2 2 2 2 6 2
\ No newline at end of file
Modified:
hama/trunk/ml/src/main/java/org/apache/hama/ml/math/DenseDoubleVector.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/math/DenseDoubleVector.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/math/DenseDoubleVector.java
(original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/math/DenseDoubleVector.java
Fri Oct 11 09:46:29 2013
@@ -17,6 +17,7 @@
*/
package org.apache.hama.ml.math;
+import java.math.BigDecimal;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
@@ -357,11 +358,11 @@ public final class DenseDoubleVector imp
*/
@Override
public double dotUnsafe(DoubleVector vector) {
- double dotProduct = 0.0d;
+ BigDecimal dotProduct = BigDecimal.valueOf(0.0d);
for (int i = 0; i < getLength(); i++) {
- dotProduct += this.get(i) * vector.get(i);
+ dotProduct =
dotProduct.add(BigDecimal.valueOf(this.get(i)).multiply(BigDecimal.valueOf(vector.get(i))));
}
- return dotProduct;
+ return dotProduct.doubleValue();
}
/*
Modified:
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/CostFunction.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/CostFunction.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
--- hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/CostFunction.java
(original)
+++ hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/CostFunction.java
Fri Oct 11 09:46:29 2013
@@ -17,6 +17,8 @@
*/
package org.apache.hama.ml.regression;
+import java.math.BigDecimal;
+
import org.apache.hama.ml.math.DoubleVector;
/**
@@ -33,9 +35,9 @@ public interface CostFunction {
* @param m the number of existing items
* @param theta the parameters vector theta
* @param hypothesis the hypothesis function to model the problem
- * @return the calculated cost for input x and output y
+ * @return the calculated cost for input x and output y as a
<code>BigDecimal</code>
*/
- public double calculateCostForItem(DoubleVector x, double y, int m,
+ public BigDecimal calculateCostForItem(DoubleVector x, double y, int m,
DoubleVector theta, HypothesisFunction hypothesis);
}
Modified:
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
---
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java
(original)
+++
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java
Fri Oct 11 09:46:29 2013
@@ -17,6 +17,10 @@
*/
package org.apache.hama.ml.regression;
+import java.io.IOException;
+import java.math.BigDecimal;
+import java.util.Arrays;
+
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hama.bsp.BSP;
import org.apache.hama.bsp.BSPPeer;
@@ -28,9 +32,6 @@ import org.apache.hama.util.KeyValuePair
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
-import java.io.IOException;
-import java.util.Arrays;
-
/**
* A gradient descent (see
* <code>http://en.wikipedia.org/wiki/Gradient_descent</code>) BSP based
@@ -133,9 +134,7 @@ public class GradientDescentBSP
updateTheta(newTheta);
if (log.isDebugEnabled()) {
- log.debug(new StringBuilder(peer.getPeerName())
- .append(": new theta for cost ").append(cost).append(" is ")
- .append(theta.toString()).toString());
+ log.debug("{}: new theta for cost {} is {}", new
Object[]{peer.getPeerName(), cost, theta});
}
// master writes down the output
if (master) {
@@ -206,8 +205,7 @@ public class GradientDescentBSP
} else {
cost = totalCost;
if (log.isDebugEnabled()) {
- log.debug(new StringBuilder(peer.getPeerName())
- .append(": current cost is ").append(cost).toString());
+ log.debug("{}: current cost is {}", peer.getPeerName(), cost);
}
return false;
}
@@ -224,7 +222,7 @@ public class GradientDescentBSP
// calculate cost for given input
double y = kvp.getValue().get();
DoubleVector x = kvp.getKey().getVector();
- double costForX = regressionModel.calculateCostForItem(x, y, m, theta);
+ double costForX = regressionModel.calculateCostForItem(x, y, m,
theta).doubleValue();
// adds to local cost
localCost += costForX;
@@ -250,9 +248,9 @@ public class GradientDescentBSP
while ((kvp = peer.readNext()) != null) {
DoubleVector x = kvp.getKey().getVector();
double y = kvp.getValue().get();
- double difference = regressionModel.applyHypothesis(theta, x) - y;
+ BigDecimal difference = regressionModel.applyHypothesis(theta,
x).subtract(BigDecimal.valueOf(y));
for (int j = 0; j < theta.getLength(); j++) {
- thetaDelta[j] += difference * x.get(j);
+ thetaDelta[j] +=
difference.multiply(BigDecimal.valueOf(x.get(j))).doubleValue();
}
}
return thetaDelta;
@@ -266,9 +264,7 @@ public class GradientDescentBSP
if (master) {
peer.write(new VectorWritable(theta), new DoubleWritable(cost));
if (log.isInfoEnabled()) {
- log.info(new StringBuilder(peer.getPeerName())
- .append(":computation finished with cost ").append(cost)
- .append(" for theta ").append(theta).toString());
+ log.info("{}:computation finished with cost {} and theta {}", new
Object[]{peer.getPeerName(), cost, theta});
}
}
}
@@ -283,14 +279,12 @@ public class GradientDescentBSP
INITIAL_THETA_VALUES, 1));
broadcastVector(peer, theta.toArray());
if (log.isDebugEnabled()) {
- log.debug(new StringBuilder(peer.getPeerName()).append(
- ": sending theta").toString());
+ log.debug("{}: sending theta", peer.getPeerName());
}
peer.sync();
} else {
if (log.isDebugEnabled()) {
- log.debug(new StringBuilder(peer.getPeerName()).append(
- ": getting theta").toString());
+ log.debug("{}: getting theta", peer.getPeerName());
}
peer.sync();
VectorWritable vectorWritable = peer.getCurrentMessage();
Modified:
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/HypothesisFunction.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/HypothesisFunction.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
---
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/HypothesisFunction.java
(original)
+++
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/HypothesisFunction.java
Fri Oct 11 09:46:29 2013
@@ -17,6 +17,8 @@
*/
package org.apache.hama.ml.regression;
+import java.math.BigDecimal;
+
import org.apache.hama.ml.math.DoubleVector;
/**
@@ -25,13 +27,13 @@ import org.apache.hama.ml.math.DoubleVec
public interface HypothesisFunction {
/**
- * Applies the applyHypothesis given a set of parameters theta to a given
+ * Applies this <code>HypothesisFunction</code> to given a set of parameters
theta and
* input x
*
* @param theta the parameters vector
* @param x the input
- * @return a <code>double</code> number
+ * @return a <code>BigDecimal</code> representing the number
*/
- public double applyHypothesis(DoubleVector theta, DoubleVector x);
+ public BigDecimal applyHypothesis(DoubleVector theta, DoubleVector x);
}
Modified:
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LinearRegressionModel.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LinearRegressionModel.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
---
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LinearRegressionModel.java
(original)
+++
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LinearRegressionModel.java
Fri Oct 11 09:46:29 2013
@@ -17,6 +17,8 @@
*/
package org.apache.hama.ml.regression;
+import java.math.BigDecimal;
+
import org.apache.hama.ml.math.DoubleVector;
/**
@@ -29,20 +31,20 @@ public class LinearRegressionModel imple
public LinearRegressionModel() {
costFunction = new CostFunction() {
@Override
- public double calculateCostForItem(DoubleVector x, double y, int m,
+ public BigDecimal calculateCostForItem(DoubleVector x, double y, int m,
DoubleVector theta, HypothesisFunction hypothesis) {
- return y * Math.pow(applyHypothesis(theta, x) - y, 2) / (2 * m);
+ return BigDecimal.valueOf(y * Math.pow(applyHypothesis(theta,
x).doubleValue() - y, 2) / (2 * m));
}
};
}
@Override
- public double applyHypothesis(DoubleVector theta, DoubleVector x) {
- return theta.dotUnsafe(x);
+ public BigDecimal applyHypothesis(DoubleVector theta, DoubleVector x) {
+ return BigDecimal.valueOf(theta.dotUnsafe(x));
}
@Override
- public double calculateCostForItem(DoubleVector x, double y, int m,
+ public BigDecimal calculateCostForItem(DoubleVector x, double y, int m,
DoubleVector theta) {
return costFunction.calculateCostForItem(x, y, m, theta, this);
}
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=1531245&r1=1531244&r2=1531245&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 09:46:29 2013
@@ -27,59 +27,71 @@ import org.apache.hama.ml.math.DoubleVec
*/
public class LogisticRegressionModel implements RegressionModel {
+ private static final MathContext DEFAULT_PRECISION = MathContext.DECIMAL128;
+
private final CostFunction costFunction;
public LogisticRegressionModel() {
costFunction = new CostFunction() {
@Override
- public double calculateCostForItem(DoubleVector x, double y, int m,
- DoubleVector theta, HypothesisFunction hypothesis) {
- return (-1d * y
- * ln(applyHypothesisWithPrecision(theta, x)).doubleValue() + (1d -
y)
- * ln(
- applyHypothesisWithPrecision(theta, x).subtract(
- BigDecimal.valueOf(1))).doubleValue())
- / m;
+ public BigDecimal calculateCostForItem(DoubleVector x, double y, int m,
DoubleVector theta,
+ 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;
}
};
}
@Override
- public double applyHypothesis(DoubleVector theta, DoubleVector x) {
- return applyHypothesisWithPrecision(theta, x).doubleValue();
+ public BigDecimal applyHypothesis(DoubleVector theta, DoubleVector x) {
+ return applyHypothesisWithPrecision(theta, x);
}
- private BigDecimal applyHypothesisWithPrecision(DoubleVector theta,
- DoubleVector x) {
- return BigDecimal.valueOf(1).divide(
- BigDecimal.valueOf(1d).add(
- BigDecimal.valueOf(Math.exp(-1d * theta.dotUnsafe(x)))),
- MathContext.DECIMAL128);
+ 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 res = BigDecimal.valueOf(1).divide(den, DEFAULT_PRECISION);
+ if (res.doubleValue() == 1 && remainder.doubleValue() < 0) {
+ res = res.add(remainder);
+ }
+ return res;
}
private BigDecimal ln(BigDecimal x) {
- if (x.equals(BigDecimal.ONE)) {
- return BigDecimal.ZERO;
- }
- x = x.subtract(BigDecimal.ONE);
- int iterations = 1000;
- 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, MathContext.DECIMAL128);
- ret = N.divide(ret, MathContext.DECIMAL128);
-
- N = new BigDecimal(i + 1);
- ret = ret.add(N, MathContext.DECIMAL128);
-
- }
- ret = x.divide(ret, MathContext.DECIMAL128);
- return ret;
+// 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;
+ return BigDecimal.valueOf(Math.log(x.doubleValue()));
}
@Override
- public double calculateCostForItem(DoubleVector x, double y, int m,
- DoubleVector theta) {
+ public BigDecimal calculateCostForItem(DoubleVector x, double y, int m,
DoubleVector theta) {
return costFunction.calculateCostForItem(x, y, m, theta, this);
}
}
Modified:
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/RegressionModel.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/RegressionModel.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
---
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/RegressionModel.java
(original)
+++
hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/RegressionModel.java
Fri Oct 11 09:46:29 2013
@@ -17,6 +17,8 @@
*/
package org.apache.hama.ml.regression;
+import java.math.BigDecimal;
+
import org.apache.hama.ml.math.DoubleVector;
/**
@@ -32,9 +34,9 @@ public interface RegressionModel extends
* @param y the learned output for x
* @param m the total number of existing items
* @param theta the parameters vector theta
- * @return the calculated cost for input x and output y
+ * @return the calculated cost for input x and output y as a
<code>BigDecimal</code>
*/
- public double calculateCostForItem(DoubleVector x, double y, int m,
+ public BigDecimal calculateCostForItem(DoubleVector x, double y, int m,
DoubleVector theta);
}
Modified:
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LinearRegressionModelTest.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LinearRegressionModelTest.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
---
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LinearRegressionModelTest.java
(original)
+++
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LinearRegressionModelTest.java
Fri Oct 11 09:46:29 2013
@@ -17,12 +17,14 @@
*/
package org.apache.hama.ml.regression;
+import static org.junit.Assert.assertEquals;
+
+import java.math.BigDecimal;
+
import org.apache.hama.ml.math.DenseDoubleVector;
import org.apache.hama.ml.math.DoubleVector;
import org.junit.Test;
-import static org.junit.Assert.assertEquals;
-
/**
* Testcase for {@link LinearRegressionModel}
*/
@@ -34,15 +36,15 @@ public class LinearRegressionModelTest {
DoubleVector x = new DenseDoubleVector(new double[]{2, 3, 4});
double y = 1;
DoubleVector theta = new DenseDoubleVector(new double[]{1, 1, 1});
- Double cost = linearRegressionModel.calculateCostForItem(x, y, 2, theta);
- assertEquals("wrong cost calculation for linear regression",
Double.valueOf(16d), cost);
+ BigDecimal cost = linearRegressionModel.calculateCostForItem(x, y, 2,
theta);
+ assertEquals("wrong cost calculation for linear regression",
BigDecimal.valueOf(16d), cost);
}
@Test
public void testCorrectHypothesisCalculation() throws Exception {
LinearRegressionModel linearRegressionModel = new LinearRegressionModel();
- Double hypothesisValue = linearRegressionModel.applyHypothesis(new
DenseDoubleVector(new double[]{1, 1, 1}),
+ BigDecimal hypothesisValue = linearRegressionModel.applyHypothesis(new
DenseDoubleVector(new double[]{1, 1, 1}),
new DenseDoubleVector(new double[]{2, 3, 4}));
- assertEquals("wrong hypothesis value for linear regression",
Double.valueOf(9), hypothesisValue);
+ assertEquals("wrong hypothesis value for linear regression",
BigDecimal.valueOf(9d), hypothesisValue);
}
}
Modified:
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LogisticRegressionModelTest.java
URL:
http://svn.apache.org/viewvc/hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LogisticRegressionModelTest.java?rev=1531245&r1=1531244&r2=1531245&view=diff
==============================================================================
---
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LogisticRegressionModelTest.java
(original)
+++
hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LogisticRegressionModelTest.java
Fri Oct 11 09:46:29 2013
@@ -17,12 +17,15 @@
*/
package org.apache.hama.ml.regression;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+
+import java.math.BigDecimal;
+
import org.apache.hama.ml.math.DenseDoubleVector;
import org.apache.hama.ml.math.DoubleVector;
import org.junit.Test;
-import static org.junit.Assert.assertEquals;
-
/**
* Testcase for {@link LogisticRegressionModel}
*/
@@ -34,15 +37,40 @@ public class LogisticRegressionModelTest
DoubleVector x = new DenseDoubleVector(new double[]{2, 3, 4});
double y = 1;
DoubleVector theta = new DenseDoubleVector(new double[]{1, 1, 1});
- Double cost = logisticRegressionModel.calculateCostForItem(x, y, 2, theta);
- assertEquals("wrong cost calculation for logistic regression",
Double.valueOf(6.170109486162941E-5), cost);
+ BigDecimal cost = logisticRegressionModel.calculateCostForItem(x, y, 2,
theta);
+ assertEquals("wrong cost calculation for logistic regression",
6.170109486162941E-5d, cost.doubleValue(), 0.000001);
}
@Test
public void testCorrectHypothesisCalculation() throws Exception {
LogisticRegressionModel logisticRegressionModel = new
LogisticRegressionModel();
- Double hypothesisValue = logisticRegressionModel.applyHypothesis(new
DenseDoubleVector(new double[]{1, 1, 1}),
+ BigDecimal hypothesisValue = logisticRegressionModel.applyHypothesis(new
DenseDoubleVector(new double[]{1, 1, 1}),
new DenseDoubleVector(new double[]{2, 3, 4}));
- assertEquals("wrong hypothesis value for logistic regression",
Double.valueOf(0.9998766054240138), hypothesisValue);
+ assertEquals("wrong hypothesis value for logistic regression",
0.9998766054240137682597533152954043d, hypothesisValue.doubleValue(), 0.000001);
+ }
+
+ @Test
+ public void testMultipleCostCalculation() throws Exception {
+ LogisticRegressionModel logisticRegressionModel = new
LogisticRegressionModel();
+ double[] theta1 = new double[] { 10.010000000474975, 10.050000002374873,
10.01600000075996,
+ 10.018000000854954, 10.024000001139939, 10.038000001804903,
10.036000001709908 };
+ double[] theta2 = new double[] { 13.000000142492354, 25.00000071246177,
14.800000227987766,
+ 15.400000256486237, 17.20000034198165, 21.400000541470945,
20.800000512972474 };
+
+ DenseDoubleVector theta1Vector = new DenseDoubleVector(theta1);
+ DenseDoubleVector theta2Vector = new DenseDoubleVector(theta2);
+
+ DenseDoubleVector x = new DenseDoubleVector(new double[] { 1, 10, 3, 2, 1,
6, 1 });
+
+ BigDecimal res1 = logisticRegressionModel.applyHypothesis(theta1Vector, x);
+ BigDecimal res2 = logisticRegressionModel.applyHypothesis(theta2Vector, x);
+
+ assertFalse(res1 + " shouldn't be the same as " + res2, res1.equals(res2));
+
+ BigDecimal itemCost1 = logisticRegressionModel.calculateCostForItem(x, 2,
8, theta1Vector);
+ BigDecimal itemCost2 = logisticRegressionModel.calculateCostForItem(x, 2,
8, theta2Vector);
+
+ assertFalse(itemCost1 + " shouldn't be the same as " + itemCost2,
itemCost1.equals(itemCost2));
+
}
}
