From d4df76bc71e663183baeb287778eae4fa352825b Mon Sep 17 00:00:00 2001
From: Parikshit Ram <p.ram@gatech.edu>
Date: Fri, 25 Jan 2008 01:24:26 +0000
Subject: [PATCH] test class header file deleted

---
 fastlib/u/pram/nbc/test_class_simple_nbc.h | 84 ----------------------
 1 file changed, 84 deletions(-)
 delete mode 100644 fastlib/u/pram/nbc/test_class_simple_nbc.h

diff --git a/fastlib/u/pram/nbc/test_class_simple_nbc.h b/fastlib/u/pram/nbc/test_class_simple_nbc.h
deleted file mode 100644
index abebd7ed34..0000000000
--- a/fastlib/u/pram/nbc/test_class_simple_nbc.h
+++ /dev/null
@@ -1,84 +0,0 @@
-#include "simple_nbc.h"
-
-class TestClassSimpleNBC{
- private:
-  SimpleNaiveBayesClassifier *nbc_test_;
-  string filename_train_, filename_test_;
-  string train_result_, test_result_;
-  index_t number_of_classes_;
-
- public:
-
-  void Init() {
-    nbc_test_ = new SimpleNaiveBayesClassifier();
-    filename_train_ = filename_train;
-    filename_test_ = filename_test;
-    train_result_ = train_result;
-    test_result_ = test_result;
-    number_of_classes_ = number_of_classes;
-  }
-
-  void Destruct() {
-    delete nbc_test_;
-    delete filename_train_;
-    delete filename_test_;
-    delete number_of_classes_;
-    delete train_result_;
-    delete test_result_;
-  }
-
-  void TestInitTrain() {
-    Matrix train_data, train_res, calc_mat;
-    data::Load(filename_train_, &train_data);
-    data::Load(train_result_, &train_res); 
-    // training results will be like means followed by variances followed by class probabilities
-    nbc_test_->InitTrain(train_data, number_of_classes_);
-    index_t number_of_features = nbc_test_->means_.n_rows();
-    calc_mat.Init(2*number_of_features + 1, number_of_classes_);
-    for(index_t i = 0; i < number_of_features; i++) {
-      for(index_t j = 0; j < number_of_classes_; j++) {
-	calc_mat.set(i, j, nbc_test_->means_.get(i, j));
-	calc_mat.set(i + number_of_features, j, nbc_test_->variances_.get(i, j));
-      }
-    }
-    for(index_t i = 0; i < number_of_classes_; i++) {
-      calc_mat.set(2 * number_of_features, i, nbc_test_->class_probabilities_[i]);
-    }
-    for(index_t i = 0; i < calc_mat.n_rows(); i++) {
-      for(index_t j = 0; j < number_of_classes_; j++) {
-	TEST_DOUBLE_EXACT(train_res.get(i, j), calc_mat.get(i, j));
-      }
-    }
-    NONFATAL("Test InitTrain passed...\n");
-  }
-
-  void TestClassify() {
-    Matrix test_data, test_res;
-    Vector test_res_vec, calc_vec;
-    data::Load(filename_test_, &test_data);
-    data::Load(test_result_, &test_res); 
-    // training results will be like means followed by variances followed by class probabilities
-    nbc_test_->Classify(test_data, &calc_vec);
-    index_t number_of_datum = test_data.n_cols();
-    test_res.MakeColumnVector(0, &test_res_vec);
-    //for(index_t i = 0; i < number_of_datum; i++) {
-    //  for(index_t j = 0; j < number_of_classes_; j++) {
-    //calc_mat.set(i, j, nbc_test_->means_.get(i, j));
-    //calc_mat.set(i + number_of_features, j, nbc_test_->variances_.get(i, j));
-    //}
-    //}
-    //    for(index_t i = 0; i < number_of_datum; i++) {
-    //calc_mat.set(2 * number_of_features, i, nbc_test_->class_probabilities_[i]);
-    //}
-    for(index_t i = 0; i < number_of_datum; i++) {
-      TEST_DOUBLE_EXACT(test_res_vec.get(i), calc_vec.get(i));
-    }
-    NONFATAL("Test Classify passed...\n");
-  }
-
-  void TestAll() {
-    TestInitTrain();
-    TestClassify();
-  }
-};
-