├── .classpath
├── .project
├── .settings
└── org.eclipse.jdt.core.prefs
├── README.md
├── bin
└── com
│ └── machinelearning
│ ├── LogisticRegression.class
│ ├── MachineLearning.class
│ └── NaiveBayes.class
├── datasets
├── heart-test.txt
├── heart-train.txt
├── simple-test.txt
├── simple-train.txt
├── vote-test.txt
└── vote-train.txt
├── results
├── logisticregression.txt
├── naivebayes.txt
└── writeup.txt
└── src
└── com
└── machinelearning
├── LogisticRegression.java
├── MachineLearning.java
└── NaiveBayes.java
/.classpath:
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/.project:
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1 |
2 |
3 | naiveBayesLogisticRegression
4 |
5 |
6 |
7 |
8 |
9 | org.eclipse.jdt.core.javabuilder
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12 |
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14 |
15 | org.eclipse.jdt.core.javanature
16 |
17 |
18 |
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/.settings/org.eclipse.jdt.core.prefs:
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1 | #Sun Mar 07 15:29:59 PST 2010
2 | eclipse.preferences.version=1
3 | org.eclipse.jdt.core.compiler.codegen.inlineJsrBytecode=enabled
4 | org.eclipse.jdt.core.compiler.codegen.targetPlatform=1.5
5 | org.eclipse.jdt.core.compiler.codegen.unusedLocal=preserve
6 | org.eclipse.jdt.core.compiler.compliance=1.5
7 | org.eclipse.jdt.core.compiler.debug.lineNumber=generate
8 | org.eclipse.jdt.core.compiler.debug.localVariable=generate
9 | org.eclipse.jdt.core.compiler.debug.sourceFile=generate
10 | org.eclipse.jdt.core.compiler.problem.assertIdentifier=error
11 | org.eclipse.jdt.core.compiler.problem.enumIdentifier=error
12 | org.eclipse.jdt.core.compiler.source=1.5
13 |
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/README.md:
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1 | ## Machine Learning
2 | This project contains simple implementations of Naive Bayes and Logistic
3 | Regression.
4 |
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/bin/com/machinelearning/LogisticRegression.class:
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https://raw.githubusercontent.com/econner/MachineLearning/aa2115a8b1b49ad3427874488cfd80886635eddb/bin/com/machinelearning/LogisticRegression.class
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/bin/com/machinelearning/MachineLearning.class:
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https://raw.githubusercontent.com/econner/MachineLearning/aa2115a8b1b49ad3427874488cfd80886635eddb/bin/com/machinelearning/MachineLearning.class
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/bin/com/machinelearning/NaiveBayes.class:
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https://raw.githubusercontent.com/econner/MachineLearning/aa2115a8b1b49ad3427874488cfd80886635eddb/bin/com/machinelearning/NaiveBayes.class
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/datasets/heart-test.txt:
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1 | 22
2 | 187
3 | 1 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 0 0: 1
4 | 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0: 1
5 | 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1: 1
6 | 0 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 1 0: 1
7 | 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1: 1
8 | 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 1: 1
9 | 1 0 0 1 0 0 1 1 1 1 0 1 1 1 0 1 0 0 0 1 0 1: 1
10 | 1 0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0: 1
11 | 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0: 1
12 | 1 0 0 1 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1 0 0 0: 1
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14 | 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0: 1
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16 | 1 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0: 1
17 | 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0: 1
18 | 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0: 1
19 | 1 1 0 0 1 1 1 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1: 1
20 | 1 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1: 1
21 | 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0: 1
22 | 1 1 1 0 1 0 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0: 1
23 | 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0: 1
24 | 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1: 1
25 | 0 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 0 1 1 0: 1
26 | 1 1 0 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0: 1
27 | 1 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0: 1
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/datasets/heart-train.txt:
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1 | 22
2 | 80
3 | 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0: 1
4 | 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1: 1
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159 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0: 0
160 | 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0: 1
161 | 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0: 1
162 | 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
163 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
164 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
165 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
166 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1: 1
167 | 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
168 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0: 1
169 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1: 0
170 | 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1: 1
171 | 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1: 1
172 | 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
173 | 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1: 1
174 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
175 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1: 1
176 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0: 1
177 | 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
178 | 0 0 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1: 1
179 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
180 | 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
181 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
182 | 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0: 1
183 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
184 | 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 1: 1
185 | 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0: 1
186 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1: 1
187 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1: 0
188 | 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
189 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
190 | 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
191 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
192 | 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
193 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0: 0
194 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
195 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
196 | 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1: 1
197 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
198 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1: 1
199 | 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0: 0
200 | 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
201 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
202 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
203 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
204 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
205 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0: 1
206 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0: 1
207 | 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1: 1
208 | 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1: 1
209 | 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
210 | 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
211 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0: 1
212 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
213 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
214 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
215 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0: 1
216 | 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
217 | 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0: 1
218 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
219 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0: 0
220 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 1
221 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0: 1
222 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
223 | 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
224 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
225 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0: 1
226 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
227 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
228 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
229 | 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
230 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
231 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
232 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
233 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0: 1
234 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
235 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0: 1
236 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1: 0
237 | 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0: 1
238 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0: 1
239 | 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1: 1
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241 | 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
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248 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
249 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
250 | 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
251 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
252 | 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
253 | 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
254 | 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
255 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0: 1
256 | 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1: 0
257 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
258 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1: 0
259 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
260 | 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1: 1
261 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
262 | 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0: 1
263 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1: 0
264 | 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0: 1
265 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1: 1
266 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1: 0
267 | 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0: 1
268 | 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
269 | 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1: 1
270 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
271 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0: 0
272 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0: 1
273 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
274 | 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0: 0
275 | 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1: 1
276 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
277 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
278 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0: 1
279 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0: 1
280 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
281 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
282 | 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0: 0
283 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
284 | 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0: 1
285 | 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 1
286 | 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0: 1
287 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
288 | 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
289 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0: 1
290 | 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0: 1
291 | 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
292 | 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0: 1
293 | 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 1: 0
294 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
295 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1: 0
296 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0: 0
297 | 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
298 | 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0: 0
299 | 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0: 0
300 | 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0: 1
301 | 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1: 1
302 | 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0: 0
--------------------------------------------------------------------------------
/results/logisticregression.txt:
--------------------------------------------------------------------------------
1 | simple-test.txt
2 | ------------
3 | Class 0: tested 2, correctly classified 2
4 | Class 1: tested 2, correctly classified 2
5 | Overall: tested 4, correctly classified 4
6 | Accuracy = 1.0
7 |
8 | vote-test.txt
9 | ------------
10 | Class 0: tested 52, correctly classified 51
11 | Class 1: tested 83, correctly classified 83
12 | Overall: tested 135, correctly classified 134
13 | Accuracy = 0.9925925925925926
14 |
15 | heart-test.txt
16 | ------------
17 | Class 0: tested 15, correctly classified 12
18 | Class 1: tested 172, correctly classified 133
19 | Overall: tested 187, correctly classified 145
20 | Accuracy = 0.7754010695187166
--------------------------------------------------------------------------------
/results/naivebayes.txt:
--------------------------------------------------------------------------------
1 | simple-test.txt
2 | -----------------
3 | MLE:
4 | Class 0: tested 2, correctly classified 2
5 | Class 1: tested 2, correctly classified 2
6 | Overall: tested 4, correctly classified 4
7 | Accuracy = 1.0
8 |
9 | Laplace:
10 | Class 0: tested 2, correctly classified 2
11 | Class 1: tested 2, correctly classified 2
12 | Overall: tested 4, correctly classified 4
13 | Accuracy = 1.0
14 |
15 | vote-test.txt
16 | --------------
17 | MLE:
18 | Class 0: tested 52, correctly classified 48
19 | Class 1: tested 83, correctly classified 76
20 | Overall: tested 135, correctly classified 124
21 | Accuracy = 0.9185185185185185
22 |
23 | Laplace:
24 | Class 0: tested 52, correctly classified 48
25 | Class 1: tested 83, correctly classified 76
26 | Overall: tested 135, correctly classified 124
27 | Accuracy = 0.9185185185185185
28 |
29 | heart-test.txt
30 | --------------
31 | MLE:
32 | Class 0: tested 15, correctly classified 10
33 | Class 1: tested 172, correctly classified 135
34 | Overall: tested 187, correctly classified 145
35 | Accuracy = 0.7754010695187166
36 |
37 | Laplace:
38 | Class 0: tested 15, correctly classified 10
39 | Class 1: tested 172, correctly classified 130
40 | Overall: tested 187, correctly classified 140
41 | Accuracy = 0.7486631016042781
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/results/writeup.txt:
--------------------------------------------------------------------------------
1 | simple-test.txt
2 | -----------------
3 | Naive Bayes
4 |
5 | MLE:
6 | Class 0: tested 2, correctly classified 2
7 | Class 1: tested 2, correctly classified 2
8 | Overall: tested 4, correctly classified 4
9 | Accuracy = 1.0
10 |
11 | Laplace:
12 | Class 0: tested 2, correctly classified 2
13 | Class 1: tested 2, correctly classified 2
14 | Overall: tested 4, correctly classified 4
15 | Accuracy = 1.0
16 |
17 | Logistic Regression
18 |
19 | Class 0: tested 2, correctly classified 2
20 | Class 1: tested 2, correctly classified 2
21 | Overall: tested 4, correctly classified 4
22 | Accuracy = 1.0
23 |
24 |
25 | vote-test.txt
26 | -----------------
27 | Naive Bayes
28 |
29 | MLE:
30 | Class 0: tested 52, correctly classified 48
31 | Class 1: tested 83, correctly classified 76
32 | Overall: tested 135, correctly classified 124
33 | Accuracy = 0.9185185185185185
34 |
35 | Laplace:
36 | Class 0: tested 52, correctly classified 48
37 | Class 1: tested 83, correctly classified 76
38 | Overall: tested 135, correctly classified 124
39 | Accuracy = 0.9185185185185185
40 |
41 | Logistic Regression
42 |
43 | Class 0: tested 52, correctly classified 51
44 | Class 1: tested 83, correctly classified 83
45 | Overall: tested 135, correctly classified 134
46 | Accuracy = 0.9925925925925926
47 |
48 | b) For the vote data, logistic regression classified the test set almost perfectly, achieving 99% accuracy, which was about 7% better than the
49 | naive bayes model. This discrepancy most likely occurred because a particular member of Congress's voting record probably is highly dependent
50 | on the other votes he or she has made. Naive bayes does not do as well when the data points are not conditionally independent of each other.
51 | For logistic regression this dependency doesn't matter as much since we don't make such an assumption.
52 |
53 |
54 | heart-test.txt
55 | ------------------
56 | Naive Bayes
57 |
58 | MLE:
59 | Class 0: tested 15, correctly classified 10
60 | Class 1: tested 172, correctly classified 135
61 | Overall: tested 187, correctly classified 145
62 | Accuracy = 0.7754010695187166
63 |
64 | Laplace:
65 | Class 0: tested 15, correctly classified 10
66 | Class 1: tested 172, correctly classified 130
67 | Overall: tested 187, correctly classified 140
68 | Accuracy = 0.7486631016042781
69 |
70 | Logistic Regression
71 |
72 | Class 0: tested 15, correctly classified 12
73 | Class 1: tested 172, correctly classified 133
74 | Overall: tested 187, correctly classified 145
75 | Accuracy = 0.7754010695187166
76 |
77 | c) For the heart data, logistic regression had the same performance as normal naive bayes. The algorithm did not perform
78 | better than naive bayes because the tomography readings are probably pretty well independent of each other. The training
79 | set was also quite a bit smaller than the test set (less than half as large). Logistic regression needs more training
80 | data since it is trying to optimize the value of Y, rather than model the whole distribution, and did not perform
81 | great because of this. I would hypothesize that, given more data, logistic regression would outperform naive bayes. Another approach is to tune the learning rate (see d) or increase the number of learning epochs to improve the performance.
82 |
83 | d)
84 |
85 | Class 0: tested 15, correctly classified 7
86 | Class 1: tested 172, correctly classified 168
87 | Overall: tested 187, correctly classified 175
88 | Accuracy = 0.9358288770053476
89 |
90 | Using a learning rate of 0.000001 I was able to get an accuracy of 0.9358288770053476. Lowering the learning rate
91 | greatly increased the accuracy of logistic regression. This improved performance resulted from the accuracy with which
92 | the gradient descent algorithm was able to reach the global maximum of the log-conditional likelihood function. By lowering
93 | the learning rate we take a smaller step toward the global maximum at each epoch and this results in a better fit.
94 |
95 |
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/src/com/machinelearning/LogisticRegression.java:
--------------------------------------------------------------------------------
1 | package com.machinelearning;
2 |
3 | import java.io.BufferedReader;
4 | import java.io.FileReader;
5 | import java.io.IOException;
6 |
7 | public class LogisticRegression {
8 | private static final int NUM_EPOCHS = 10000;
9 | private static final double LEARNING_RATE = 0.000001;
10 |
11 | private int numTrainingExamples = 0;
12 | private int numFeatures = 0;
13 | private int[][] featureMatrix;
14 | private int[] LabelVector;
15 |
16 | // the learned weights
17 | private double[] weights;
18 |
19 | /*
20 | * Tests the logistic regression algorithm, assuming
21 | * the data has been correctly loaded into the instance variables
22 | * defined above.
23 | */
24 | public void testLogisticRegression()
25 | {
26 | int numNeg = 0;
27 | int numPos = 0;
28 | int numCorrectNeg = 0;
29 | int numCorrectPos = 0;
30 | for(int i = 0; i < featureMatrix.length; i++)
31 | {
32 | int classification = getClassification(featureMatrix[i]);
33 | if(LabelVector[i] == 0) {
34 | numNeg++;
35 | if(classification == LabelVector[i])
36 | numCorrectNeg++;
37 | } else {
38 | numPos++;
39 | if(classification == LabelVector[i])
40 | numCorrectPos++;
41 | }
42 | }
43 |
44 | System.out.println("Class 0: tested " + numNeg + ", correctly classified " + numCorrectNeg);
45 | System.out.println("Class 1: tested " + numPos + ", correctly classified " + numCorrectPos);
46 | System.out.println("Overall: tested " + (numNeg+numPos) + ", correctly classified " + (numCorrectPos+numCorrectNeg));
47 | System.out.println("Accuracy = " + (double)(numCorrectPos+numCorrectNeg) / (numNeg+numPos));
48 | }
49 |
50 | /*
51 | * Calculates the linear term in the sigmoid function ("z")
52 | */
53 | private double calculateLinearTerm(int[] featureVector)
54 | {
55 | double linearTerm = 0;
56 | for(int i = 0; i < featureVector.length; i++)
57 | {
58 | linearTerm += weights[i] * (double)featureVector[i];
59 | }
60 | return linearTerm;
61 | }
62 |
63 | /*
64 | * Gets our binary classification for a given feature vector
65 | */
66 | private int getClassification(int[] featureVector) {
67 | double logOdds = calculateLinearTerm(featureVector);
68 |
69 | if(logOdds > 0)
70 | return 1;
71 | return 0;
72 | }
73 |
74 | /*
75 | * Trains logistic regression using batch
76 | * gradient descent
77 | */
78 | public void trainLogisticRegression()
79 | {
80 | // Initialize: weights = 0 for all 0 �j�m
81 | // add 1 for bias term
82 | weights = new double[numFeatures+1];
83 |
84 | //weights initialization
85 | for(int i = 0; i < weights.length; i++)
86 | weights[i] = 0;
87 |
88 | for(int i = 0; i < NUM_EPOCHS; i++)
89 | {
90 | // add 1 for bias term
91 | double[] gradient = new double[numFeatures + 1];
92 |
93 | //gradients initialization
94 | for(int g = 0; g < gradient.length; g++)
95 | gradient[g] = 0;
96 |
97 | /*for(int row = 0; row < featureMatrix.length; row++)
98 | {
99 | for(int col = 0; col < featureMatrix[0].length; col++)
100 | {
101 | gradient[col] += (double)featureMatrix[row][col] * ((double)LabelVector[row] - sigmoid(featureMatrix[row]));
102 | }
103 | }
104 |
105 | for(int j = 0; j < weights.length; j++)
106 | weights[j] += LEARNING_RATE * gradient[j];*/
107 |
108 | for(int row = 0; row < featureMatrix.length; row++)
109 | {
110 | for(int col = 0; col < featureMatrix[0].length; col++)
111 | {
112 | weights[col] += LEARNING_RATE * ((double)featureMatrix[row][col] * ((double)LabelVector[row] - sigmoid(featureMatrix[row])));
113 | }
114 | }
115 | }
116 | }
117 |
118 |
119 | /*
120 | * Calculates the sigmoid function (of the form 1 / (1 + e^(-z))
121 | */
122 | private double sigmoid(int[] featureVector) {
123 |
124 | double linearTerm = calculateLinearTerm(featureVector);
125 | return 1.0 / (1.0 + Math.exp(-linearTerm));
126 | }
127 |
128 | /*
129 | * Input file are always of the format
130 | *
131 | *
132 | * < ... data ... >
133 | * This method reads those constants and sets up the appropriate instance variables.
134 | */
135 | private void readFileConstants(BufferedReader input) throws NumberFormatException, IOException
136 | {
137 | // Get num features and num training examples
138 | numFeatures = Integer.parseInt(input.readLine());
139 | numTrainingExamples = Integer.parseInt(input.readLine());
140 |
141 | // add 1 for bias term
142 | featureMatrix = new int[numTrainingExamples][numFeatures+1];
143 | LabelVector = new int[numTrainingExamples];
144 |
145 | }
146 |
147 | /*
148 | * Reads in the feature data and ground truth vector from
149 | * given input file.
150 | */
151 | public void readFeatureData(String fname)
152 | {
153 | try {
154 |
155 | BufferedReader input = new BufferedReader(new FileReader(fname));
156 | readFileConstants(input);
157 |
158 | String[] lineVector;
159 | int i = 0;
160 | for(String line = input.readLine(); line != null; line = input.readLine()) {
161 | // bias term
162 | featureMatrix[i][0] = 1;
163 |
164 | lineVector = line.split(" ");
165 | for(int j = 0; j < lineVector.length - 1; j++)
166 | {
167 | // semi-colon denotes the end of the feature data
168 | if(lineVector[j].indexOf(':') != -1) {
169 | lineVector[j] = lineVector[j].substring(0, 1);
170 | }
171 | featureMatrix[i][j+1] = Integer.parseInt(lineVector[j]);
172 | }
173 | LabelVector[i] = Integer.parseInt(lineVector[lineVector.length-1]);
174 |
175 | i++;
176 | }
177 | input.close();
178 |
179 | //printFeatureMatrix();
180 |
181 | } catch(IOException e) {
182 | e.printStackTrace();
183 | System.exit(1);
184 | }
185 | }
186 | }
187 |
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/src/com/machinelearning/MachineLearning.java:
--------------------------------------------------------------------------------
1 | package com.machinelearning;
2 |
3 | public class MachineLearning {
4 | // PARAMETERS
5 | private String dataDir = "datasets/";
6 | private String trainFile = "heart-train.txt";
7 | private String testFile = "heart-test.txt";
8 |
9 | public void doNaiveBayes() {
10 |
11 | String dataDir = "datasets/";
12 | String trainFile = "heart-train.txt";
13 | String testFile = "heart-test.txt";
14 | // PARAMETER
15 | // use laplace or not?
16 | boolean useLaplace = true;
17 |
18 | NaiveBayes nb = new NaiveBayes(useLaplace);
19 | nb.readFeatureData(dataDir + trainFile);
20 | nb.trainNaiveBayes();
21 |
22 | nb.readFeatureData(dataDir + testFile);
23 | nb.testNaiveBayes();
24 | }
25 |
26 | public void doLogisticRegression() {
27 | LogisticRegression lr = new LogisticRegression();
28 | lr.readFeatureData(dataDir + trainFile);
29 | lr.trainLogisticRegression();
30 |
31 | lr.readFeatureData(dataDir + testFile);
32 | lr.testLogisticRegression();
33 | }
34 |
35 | public static void main(String[] args) {
36 | MachineLearning ml = new MachineLearning();
37 | ml.doNaiveBayes();
38 | //ml.doLogisticRegression();
39 | }
40 | }
41 |
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/src/com/machinelearning/NaiveBayes.java:
--------------------------------------------------------------------------------
1 | package com.machinelearning;
2 |
3 | import java.io.BufferedReader;
4 | import java.io.FileReader;
5 | import java.io.IOException;
6 |
7 | public class NaiveBayes
8 | {
9 | private boolean useLaplace = false;
10 |
11 | private int numTrainingExamples = 0;
12 | private int numFeatures = 0;
13 | private int[][] featureMatrix;
14 | private int[] LabelVector;
15 |
16 | private int posCount = 0;
17 | private int negCount = 0;
18 | private int[] featureCountsPos;
19 | private int[] featureCountsNeg;
20 |
21 | public NaiveBayes(boolean useLap) {
22 | useLaplace = useLap;
23 | }
24 |
25 | private int getClassification(int[] featureVector)
26 | {
27 | double posDenom = posCount;
28 | double negDenom = negCount;
29 |
30 | if(useLaplace) {
31 | posDenom += featureCountsPos.length;
32 | negDenom += featureCountsNeg.length;
33 | }
34 | double logProbPos = Math.log((double) posCount / (posCount + negCount));
35 | double logProbNeg = Math.log((double) negCount / (posCount + negCount));
36 |
37 | double posClass = 0;
38 | double negClass = 0;
39 | for(int i = 0; i < featureVector.length; i++)
40 | {
41 | if(featureVector[i] == 1) {
42 | // has a "1" in position i and is of class pos or neg
43 | posClass = featureCountsPos[i];
44 | negClass = featureCountsNeg[i];
45 | } else {
46 | // has a "0" in position i and is of class positive or negative
47 | posClass = posCount - featureCountsPos[i];
48 | negClass = negCount - featureCountsNeg[i];
49 | }
50 |
51 | if(useLaplace) {
52 | posClass += 1;
53 | negClass += 1;
54 | }
55 |
56 | logProbPos += Math.log( posClass / posDenom );
57 | logProbNeg += Math.log( negClass / negDenom );
58 | }
59 |
60 | if(logProbPos > logProbNeg)
61 | return 1;
62 |
63 | return 0;
64 |
65 | }
66 |
67 |
68 | public void testNaiveBayes()
69 | {
70 | int numNeg = 0;
71 | int numPos = 0;
72 | int numCorrectNeg = 0;
73 | int numCorrectPos = 0;
74 | for(int i = 0; i < featureMatrix.length; i++)
75 | {
76 | int classification = getClassification(featureMatrix[i]);
77 | if(LabelVector[i] == 0) {
78 | numNeg++;
79 | if(classification == LabelVector[i])
80 | numCorrectNeg++;
81 | } else {
82 | numPos++;
83 | if(classification == LabelVector[i])
84 | numCorrectPos++;
85 | }
86 | }
87 |
88 | System.out.println("Class 0: tested " + numNeg + ", correctly classified " + numCorrectNeg);
89 | System.out.println("Class 1: tested " + numPos + ", correctly classified " + numCorrectPos);
90 | System.out.println("Overall: tested " + (numNeg+numPos) + ", correctly classified " + (numCorrectPos+numCorrectNeg));
91 | System.out.println("Accuracy = " + (double)(numCorrectPos+numCorrectNeg) / (numNeg+numPos));
92 |
93 | }
94 |
95 |
96 | /*
97 | * Trains the naive bayes classification model by initializing an array
98 | * for positive class counts and one for negative class counts. It then
99 | * iterates over the data and adds up the number of places where we see a
100 | * 1 with Y = 1 and the number of places we see a 1 with Y = 0.
101 | */
102 | public void trainNaiveBayes()
103 | {
104 | featureCountsPos = new int[numFeatures];
105 | featureCountsNeg = new int[numFeatures];
106 |
107 | for(int i = 0; i < featureMatrix.length; i++)
108 | {
109 | //Calculate the num of positive instance or negative instance
110 | if(LabelVector[i] == 1)
111 | posCount++;
112 | else
113 | negCount++;
114 |
115 | for(int j = 0; j < featureMatrix[0].length; j++)
116 | {
117 | if(LabelVector[i] == 1)
118 | featureCountsPos[j] += featureMatrix[i][j];
119 | else
120 | featureCountsNeg[j] += featureMatrix[i][j];
121 | }
122 | }
123 | }
124 |
125 | /*
126 | * Input file are always of the format
127 | *
128 | *
129 | * < ... data ... >
130 | * :