samples/cpp/sample_logistic_regression.cpp

   1 ///////////////////////////////////////////////////////////////////////////////////////
   2 // sample_logistic_regression.cpp
   3 // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
   4
   5 //  By downloading, copying, installing or using the software you agree to this license.
   6 //  If you do not agree to this license, do not download, install,
   7 //  copy or use the software.
   8
   9 // This is a sample program demostrating classification of digits 0 and 1 using Logistic Regression
  10
  11 // AUTHOR:
  12 // Rahul Kavi rahulkavi[at]live[at]com
  13 //
  14
  15 #include <iostream>
  16
  17 #include <opencv2/core/core.hpp>
  18 #include <opencv2/ml/ml.hpp>
  19
  20 using namespace std;
  21 using namespace cv;
  22
  23
  24 int main()
  25 {
  26     Mat data_temp, labels_temp;
  27     Mat data, labels;
  28     Mat responses, result;
  29
  30     FileStorage f;
  31
  32     cout<<"*****************************************************************************************"<<endl;
  33     cout<<"\"data01.xml\" contains digits 0 and 1 of 20 samples each, collected on an Android device"<<endl;
  34     cout<<"Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix"<<endl;
  35     cout<<"*****************************************************************************************\n\n"<<endl;
  36
  37     cout<<"loading the dataset\n"<<endl;
  38
  39     f.open("data01.xml", FileStorage::READ);
  40
  41     f["datamat"] >> data_temp;
  42     f["labelsmat"] >> labels_temp;
  43
  44     data_temp.convertTo(data, CV_32F);
  45     labels_temp.convertTo(labels, CV_32F);
  46
  47     cout<<"initializing Logisitc Regression Parameters\n"<<endl;
  48
  49     CvLR_TrainParams params = CvLR_TrainParams();
  50
  51     params.alpha = 0.001;
  52     params.num_iters = 10;
  53     params.norm = CvLR::REG_L2;
  54     params.regularized = 1;
  55     params.train_method = CvLR::BATCH;
  56
  57     cout<<"training Logisitc Regression classifier\n"<<endl;
  58
  59     CvLR lr_(data, labels, params);
  60
  61     cout<<"predicting the trained dataset\n"<<endl;
  62
  63     lr_.predict(data, responses);
  64
  65     labels.convertTo(labels, CV_32S);
  66
  67     cout<<"Original Label ::  Predicted Label"<<endl;
  68     result = (labels == responses)/255;
  69     for(int i=0;i<labels.rows;i++)
  70     {
  71         cout<<labels.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
  72     }
  73     // calculate accuracy
  74     cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
  75
  76     // save the classfier
  77     lr_.save("NewLR_Trained.xml");
  78
  79     // load the classifier onto new object
  80     CvLR lr2;
  81     cout<<"loading a new classifier"<<endl;
  82
  83     lr2.load("NewLR_Trained.xml");
  84
  85     Mat responses2;
  86
  87     // predict using loaded classifier
  88     cout<<"predicting the dataset using the loaded classfier\n"<<endl;
  89
  90     lr2.predict(data, responses2);
  91
  92     // calculate accuracy
  93     result = (labels == responses2)/255;
  94     cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
  95
  96     return 0;
  97 }