Adding the Decision tree and the mushroom database

CMakeLists.txt

@@ -64,6 +64,7 @@ add_executable( example_17-01 example_17-01.cpp )
 add_executable( example_18-01 example_18-01.cpp )
 add_executable( example_20-01 example_20-01.cpp )
 add_executable( example_20-02 example_20-02.cpp )
+add_executable( example_21-01 example_21-01.cpp )
 #...
 
 target_link_libraries( example_02-01 ${OpenCV_LIBS} )
@@ -120,4 +121,5 @@ target_link_libraries( example_17-01 ${OpenCV_LIBS} )
 target_link_libraries( example_18-01 ${OpenCV_LIBS} )
 target_link_libraries( example_20-01 ${OpenCV_LIBS} )
 target_link_libraries( example_20-02 ${OpenCV_LIBS} )
+target_link_libraries( example_21-01 ${OpenCV_LIBS} )
 #...

example_21-01.cpp

@@ -0,0 +1,106 @@
+#include <opencv2/opencv.hpp>
+#include <stdio.h>
+#include <iostream>
+using namespace std;
+using namespace cv;
+int main(int argc, char *argv[]) {
+  // If the caller gave a filename, great. Otherwise, use a default.
+  //
+  const char *csv_file_name = argc >= 2 ? argv[1] : "agaricus-lepiota.data";
+  cout << "OpenCV Version: " << CV_VERSION << endl;
+  // Read in the CSV file that we were given.
+  //
+  cv::Ptr<cv::ml::TrainData> data_set =
+      cv::ml::TrainData::loadFromCSV(csv_file_name,
+                                     // Input file name
+                                     0,
+                                     // Header lines (ignore this many)
+                                     0,
+                                     // Responses are (start) at thie column
+                                     1,
+                                     // Inputs start at this column
+                                     "cat[0-22]"
+                                     // All 23 columns are categorical
+                                     );
+  // Use defaults for delimeter (',') and missch ('?')
+  // Verify that we read in what we think.
+  //
+  int n_samples = data_set->getNSamples();
+  if (n_samples == 0) {
+    cerr << "Could not read file: " << csv_file_name << endl;
+    exit(-1);
+  } else {
+    cout << "Read " << n_samples << " samples from " << csv_file_name << endl;
+  }
+  // Split the data, so that 90% is train data
+  //
+  data_set->setTrainTestSplitRatio(0.90, false);
+  int n_train_samples = data_set->getNTrainSamples();
+  int n_test_samples = data_set->getNTestSamples();
+  cout << "Found " << n_train_samples << " Train Samples, and "
+       << n_test_samples << " Test Samples" << endl;
+  // Create a DTrees classifier.
+  //
+  cv::Ptr<cv::ml::RTrees> dtree = cv::ml::RTrees::create();
+  // set parameters
+  //
+  // These are the parameters from the old mushrooms.cpp code
+  // Set up priors to penalize "poisonous" 10x as much as "edible"
+  //
+  float _priors[] = {1.0, 10.0};
+  cv::Mat priors(1, 2, CV_32F, _priors);
+  dtree->setMaxDepth(8);
+  dtree->setMinSampleCount(10);
+  dtree->setRegressionAccuracy(0.01f);
+  dtree->setUseSurrogates(false /* true */);
+  dtree->setMaxCategories(15);
+  dtree->setCVFolds(0 /*10*/); // nonzero causes core dump
+  dtree->setUse1SERule(true);
+  dtree->setTruncatePrunedTree(true);
+  // dtree->setPriors( priors );
+  dtree->setPriors(cv::Mat()); // ignore priors for now...
+  // Now train the model
+  // NB: we are only using the "train" part of the data set
+  //
+  dtree->train(data_set);
+  // Having successfully trained the data, we should be able
+  // to calculate the error on both the training data, as well
+  // as the test data that we held out.
+  //
+  cv::Mat results;
+  float train_performance = dtree->calcError(data_set, false,
+                                             // use train data
+                                             results // cv::noArray()
+                                             );
+  std::vector<cv::String> names;
+  data_set->getNames(names);
+  Mat flags = data_set->getVarSymbolFlags();
+  // Compute some statistics on our own:
+  //
+  {
+    cv::Mat expected_responses = data_set->getResponses();
+    int good = 0, bad = 0, total = 0;
+    for (int i = 0; i < data_set->getNTrainSamples(); ++i) {
+      float received = results.at<float>(i, 0);
+      float expected = expected_responses.at<float>(i, 0);
+      cv::String r_str = names[(int)received];
+      cv::String e_str = names[(int)expected];
+      cout << "Expected: " << e_str << ", got: " << r_str << endl;
+      if (received == expected)
+        good++;
+      else
+        bad++;
+      total++;
+    }
+    cout << "Correct answers: " <<(float(good)/total) <<" % " << endl;
+                cout << "Incorrect answers: " << (float(bad) / total) << "%"
+         << endl;
+  }
+  float test_performance = dtree->calcError(data_set, true,
+                                            // use test data
+                                            results // cv::noArray()
+                                            );
+  cout << "Performance on training data: " << train_performance << "%" << endl;
+  cout << "Performance on test data: " <<test_performance <<" % " <<endl;
+  return 0;
+}

mushroom/Index

@@ -0,0 +1,7 @@
+Index of mushroom
+
+02 Dec 1996      193 Index
+25 Jun 1990   111577 expanded.Z
+26 Feb 1990     4167 agaricus-lepiota.names
+30 May 1989      853 README
+30 May 1989   373704 agaricus-lepiota.data