From 64c02b32edbff124979975a1d8dd21129d2c8a9d Mon Sep 17 00:00:00 2001
From: liyanghua <time.clifford@gmail.com>
Date: Fri, 6 Jan 2012 14:39:40 +0800
Subject: [PATCH] the init version

---
 Makefile        |  21 ++++
 README          |  14 +++
 data_loader.hpp | 119 +++++++++++++++++++++
 heart_scale     | 270 ++++++++++++++++++++++++++++++++++++++++++++++++
 lr.cpp          | 138 +++++++++++++++++++++++++
 util.hpp        |  45 ++++++++
 6 files changed, 607 insertions(+)
 create mode 100644 Makefile
 create mode 100644 README
 create mode 100644 data_loader.hpp
 create mode 100644 heart_scale
 create mode 100644 lr.cpp
 create mode 100644 util.hpp

diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..74f6d5a
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,21 @@
+CPPFLAGS=-g -Wall 
+
+BOOST_HOME=/home/yichen.lyh/boost_home
+
+LIBS= -L /usr/include/
+
+CPLUS_INCLUDE_PATH=${BOOST_HOME}/include
+export CPLUS_INCLUDE_PATH
+
+.PHONY : clean all
+
+all: $(subst .cpp,.o,$(SOURCES))  lr
+
+
+%.O: %.cpp
+	$(CXX) $(CPPFLAGS) ${LIBS} $^ $@
+lr: lr.cpp 
+	$(CXX) $(CPPFLAGS) $^  ${LIBS} -o $@
+
+clean:
+	rm -rf  *.o  lr
diff --git a/README b/README
new file mode 100644
index 0000000..53eb10e
--- /dev/null
+++ b/README
@@ -0,0 +1,14 @@
+This is a simple implementation of logistic regresion in c++.
+Please refer to the paper:
+
+http://people.csail.mit.edu/jrennie/writing/lr.pdf
+
+for more details.
+
+How to run:
+
+cd your_path_to_lr
+./make
+./lr heart_scale
+
+heart_scale is the testing data
diff --git a/data_loader.hpp b/data_loader.hpp
new file mode 100644
index 0000000..2d6db59
--- /dev/null
+++ b/data_loader.hpp
@@ -0,0 +1,119 @@
+#ifndef _CPP_DATA_LOADER_
+#define _CPP_DATA_LOADER_
+
+#include <iostream>
+#include <fstream>
+#include <string>
+#include <sstream>
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include <boost/numeric/ublas/vector.hpp>
+#include <boost/numeric/ublas/matrix.hpp>
+#include <boost/numeric/ublas/io.hpp>
+
+#include <boost/random/mersenne_twister.hpp>
+#include <boost/random/uniform_int.hpp>
+
+// refer to matrix row
+#include <boost/numeric/ublas/matrix_proxy.hpp>
+
+
+#include "util.hpp"
+
+class SimpleDataLoader {
+    private:
+        int record_num;
+        int dim_num;
+
+        bool debug;
+
+    private:
+
+
+        int get_cat(const string& data) {
+            int c;
+            convert_from_string(c, data);
+
+            return c;
+        }
+
+        bool get_features(const string& data, int& index, double& value) {
+            int pos = data.find(":");
+            if (pos == -1) return false;
+            convert_from_string(index, data.substr(0, pos));
+            convert_from_string(value, data.substr(pos + 1));
+
+            return true;
+        }
+
+        // please note we need to add a default feature to each instance and set the feature weight to 1
+        bool parse_line(const string& line, int& cat, const int line_num, boost::numeric::ublas::matrix<double>& x) {
+            if (line.empty()) return false;
+            size_t start_pos = 0;
+            char space = ' ';
+            
+            // the dummy feature 
+            x(line_num, 0) = 1;
+
+            while (true) {
+                size_t pos = line.find(space, start_pos);
+                string data = line.substr(start_pos, pos - start_pos);
+                if (!data.empty()) {
+                    if (start_pos == 0) {
+                        cat = get_cat(data);
+                    }
+                    else {
+                        int index = -1;
+                        double v = 0;
+                        get_features(data, index, v);
+                        if (debug)
+                            cout << "index: " << index << "," << "value: " << v << endl;
+                        if (index != -1) {
+                            x(line_num, index) = v;
+                        }
+                    }
+                }
+                if ((int)pos != -1) {
+                    start_pos = pos + 1;
+                }
+                else {
+                    break;
+                }
+            }
+
+            return true;
+
+        }
+
+
+    public:
+        SimpleDataLoader(const int r, const int c) : record_num(r), dim_num(c), debug(false) {}
+
+        void load_file(char*& file_path, boost::numeric::ublas::vector<double>& y, boost::numeric::ublas::matrix<double>& x) {
+            ifstream in(file_path);
+            string line;
+            int line_num = 0;
+            if (in.is_open()) {
+                while (in.good()) {
+                    getline(in, line);
+                    if (line.empty()) continue;
+                    int cat = 0;
+                    if (!parse_line(line, cat, line_num, x)) {
+                        cout << "parse line: " << line << ", failed.." << endl;
+                        continue;
+                    }
+
+                    y(line_num) = cat;
+
+                    line_num += 1;
+                }
+                in.close();
+            }
+
+        }
+};
+
+
+#endif
diff --git a/heart_scale b/heart_scale
new file mode 100644
index 0000000..23bac94
--- /dev/null
+++ b/heart_scale
@@ -0,0 +1,270 @@
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+-1 1:-0.0416667 2:1 3:-0.333333 4:-0.509434 5:-0.0913242 6:-1 7:-1 8:0.541985 9:-1 10:-0.935484 11:-1 12:-1 13:-1 
++1 1:0.625 2:1 3:0.333333 4:0.622642 5:-0.324201 6:1 7:1 8:0.206107 9:1 10:-0.483871 12:-1 13:1 
+-1 1:-0.583333 2:1 3:0.333333 4:-0.132075 5:-0.109589 6:-1 7:1 8:0.694656 9:-1 10:-1 11:-1 12:-1 13:-1 
+-1 2:-1 3:1 4:-0.320755 5:-0.369863 6:-1 7:1 8:0.0992366 9:-1 10:-0.870968 12:-1 13:-1 
++1 1:0.375 2:-1 3:1 4:-0.132075 5:-0.351598 6:-1 7:1 8:0.358779 9:-1 10:0.16129 11:1 12:0.333333 13:-1 
+-1 1:-0.0833333 2:-1 3:0.333333 4:-0.132075 5:-0.16895 6:-1 7:1 8:0.0839695 9:-1 10:-0.516129 11:-1 12:-0.333333 13:-1 
++1 1:0.291667 2:1 3:1 4:-0.320755 5:-0.420091 6:-1 7:-1 8:0.114504 9:1 10:-0.548387 11:-1 12:-0.333333 13:1 
++1 1:0.5 2:1 3:1 4:-0.698113 5:-0.442922 6:-1 7:1 8:0.328244 9:-1 10:-0.806452 11:-1 12:0.333333 13:0.5 
+-1 1:0.5 2:-1 3:0.333333 4:0.150943 5:-0.347032 6:-1 7:-1 8:0.175573 9:-1 10:-0.741935 11:-1 12:-1 13:-1 
++1 1:0.291667 2:1 3:0.333333 4:-0.132075 5:-0.730594 6:-1 7:1 8:0.282443 9:-1 10:-0.0322581 12:-1 13:-1 
++1 1:0.291667 2:1 3:1 4:-0.0377358 5:-0.287671 6:-1 7:1 8:0.0839695 9:1 10:-0.0967742 12:0.333333 13:1 
++1 1:0.0416667 2:1 3:1 4:-0.509434 5:-0.716895 6:-1 7:-1 8:-0.358779 9:-1 10:-0.548387 12:-0.333333 13:1 
+-1 1:-0.375 2:1 3:-0.333333 4:-0.320755 5:-0.575342 6:-1 7:1 8:0.78626 9:-1 10:-1 11:-1 12:-1 13:-1 
++1 1:-0.375 2:1 3:1 4:-0.660377 5:-0.251142 6:-1 7:1 8:0.251908 9:-1 10:-1 11:-1 12:-0.333333 13:-1 
+-1 1:-0.0833333 2:1 3:0.333333 4:-0.698113 5:-0.776256 6:-1 7:-1 8:-0.206107 9:-1 10:-0.806452 11:-1 12:-1 13:-1 
+-1 1:0.25 2:1 3:0.333333 4:0.0566038 5:-0.607306 6:1 7:-1 8:0.312977 9:-1 10:-0.483871 11:-1 12:-1 13:-1 
+-1 1:0.75 2:-1 3:-0.333333 4:0.245283 5:-0.196347 6:-1 7:-1 8:0.389313 9:-1 10:-0.870968 11:-1 12:0.333333 13:-1 
+-1 1:0.333333 2:1 3:0.333333 4:0.0566038 5:-0.465753 6:1 7:-1 8:0.00763359 9:1 10:-0.677419 12:-1 13:-1 
++1 1:0.0833333 2:1 3:1 4:-0.283019 5:0.0365297 6:-1 7:-1 8:-0.0687023 9:1 10:-0.612903 12:-0.333333 13:1 
++1 1:0.458333 2:1 3:0.333333 4:-0.132075 5:-0.0456621 6:-1 7:-1 8:0.328244 9:-1 10:-1 11:-1 12:-1 13:-1 
+-1 1:-0.416667 2:1 3:1 4:0.0566038 5:-0.447489 6:-1 7:-1 8:0.526718 9:-1 10:-0.516129 11:-1 12:-1 13:-1 
+-1 1:0.208333 2:-1 3:0.333333 4:-0.509434 5:-0.0228311 6:-1 7:-1 8:0.541985 9:-1 10:-1 11:-1 12:-1 13:-1 
++1 1:0.291667 2:1 3:1 4:-0.320755 5:-0.634703 6:-1 7:1 8:-0.0687023 9:1 10:-0.225806 12:0.333333 13:1 
++1 1:0.208333 2:1 3:-0.333333 4:-0.509434 5:-0.278539 6:-1 7:1 8:0.358779 9:-1 10:-0.419355 12:-1 13:-1 
+-1 1:-0.166667 2:1 3:-0.333333 4:-0.320755 5:-0.360731 6:-1 7:-1 8:0.526718 9:-1 10:-0.806452 11:-1 12:-1 13:-1 
++1 1:-0.208333 2:1 3:-0.333333 4:-0.698113 5:-0.52968 6:-1 7:-1 8:0.480916 9:-1 10:-0.677419 11:1 12:-1 13:1 
+-1 1:-0.0416667 2:1 3:0.333333 4:0.471698 5:-0.666667 6:1 7:-1 8:0.389313 9:-1 10:-0.83871 11:-1 12:-1 13:1 
+-1 1:-0.375 2:1 3:-0.333333 4:-0.509434 5:-0.374429 6:-1 7:-1 8:0.557252 9:-1 10:-1 11:-1 12:-1 13:1 
+-1 1:0.125 2:-1 3:-0.333333 4:-0.132075 5:-0.232877 6:-1 7:1 8:0.251908 9:-1 10:-0.580645 12:-1 13:-1 
+-1 1:0.166667 2:1 3:1 4:-0.132075 5:-0.69863 6:-1 7:-1 8:0.175573 9:-1 10:-0.870968 12:-1 13:0.5 
++1 1:0.583333 2:1 3:1 4:0.245283 5:-0.269406 6:-1 7:1 8:-0.435115 9:1 10:-0.516129 12:1 13:-1 
diff --git a/lr.cpp b/lr.cpp
new file mode 100644
index 0000000..11fa038
--- /dev/null
+++ b/lr.cpp
@@ -0,0 +1,138 @@
+#include <iostream>
+#include <fstream>
+#include <string>
+#include <sstream>
+#include <set>
+#include <map>
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#include <boost/numeric/ublas/vector.hpp>
+#include <boost/numeric/ublas/matrix.hpp>
+#include <boost/numeric/ublas/io.hpp>
+
+#include <boost/random/mersenne_twister.hpp>
+#include <boost/random/uniform_int.hpp>
+
+// refer to matrix row
+#include <boost/numeric/ublas/matrix_proxy.hpp>
+
+#include "util.hpp"
+#include "data_loader.hpp"
+
+
+using namespace std;
+using namespace boost::numeric::ublas;
+
+
+bool debug = true;
+
+//
+double sigmoid(double x) {
+    double e = 2.718281828;
+
+    return 1.0 / (1.0 + pow(e, -x));
+}
+
+
+// target: max { sum {log f(y(i)z(i)}} for i in (1, n) where f(x) = 1/1+e**(-x)
+// and z(i) = sum(w(k) * x(i)(k)) for k in (1, l) where i denotes the ith training instance
+// and k denotes the kth feature. 
+// The gradient of the log-likehood with respect to the kth weight is:
+// gra = sum{y(i)x(i)(k)f(-y(i)z(i))}, then we know how to update the weight in each iteration:
+// w(k)(t+1) = w(k)(t) + e * gra
+void lr_without_regularization(boost::numeric::ublas::matrix<double>& x,
+        boost::numeric::ublas::vector<double>& y
+        ) {
+
+    // the convergence rate
+    double epsilon = 0.0001;
+    // the learning rate
+    double gamma = 0.00005;
+    int max_iters = 2000;
+    int iter = 0;
+
+    // init
+    boost::numeric::ublas::vector<double> weight_old(x.size2());
+    for (size_t i=0; i<weight_old.size(); ++i) {
+        weight_old(i) = 0; 
+    } 
+
+    cout << "old weight: " << weight_old << endl;
+
+    boost::numeric::ublas::vector<double> weight_new(x.size2());
+    for (size_t i=0; i<weight_new.size(); ++i) {
+        weight_new(i) = 0; 
+    } 
+    cout << "new weight: " << weight_new << endl;
+    
+    while (true) {
+        // update each weight
+        for (size_t k=0; k<weight_new.size(); ++k) {
+            double gradient = 0;
+            for (size_t i=0; i<x.size1(); ++i) {
+                double z_i = 0;
+                for (size_t j=0; j<weight_old.size(); ++j) {
+#if 0
+                    cout << "x(i,j):" << x(i,j) << endl;
+                    cout << "weight_old(j):" << weight_old(j) << endl;
+#endif
+                    z_i += weight_old(j) * x(i,j);
+                }
+#if 0
+                cout << "z_i:" << z_i << endl;
+                cout << "y(i):" << y(i) << endl;
+                cout << "x(i,k)" << x(i, k) << endl;
+                cout << "sigmoid(-y(i) * z_i)" << sigmoid(-y(i) * z_i) << endl; 
+#endif
+                gradient = y(i) * x(i,k) * sigmoid(-y(i) * z_i);
+            }
+            weight_new(k) = weight_old(k) + gamma * gradient;
+        }
+
+        double dist = norm(weight_new, weight_old);
+        if (dist < epsilon) {
+            cout << "the best weight: " << weight_new << endl;
+            break;
+        }
+        else {
+            weight_old.swap(weight_new);
+           // weight_old = weight_new;
+        }
+
+        iter += 1;
+        if (iter >= max_iters) {
+            cout << "Reach max_iters=" << max_iters << endl;
+            break;
+        }
+        
+        cout << "================================================" << endl;
+        cout << "The " << iter << " th iteration, weight:" << endl;
+        cout << weight_new << endl << endl;
+        cout << "the diff between the old weight and the new weight: " << dist << endl << endl;
+    }
+
+    cout << "The best weight:" << endl;
+    cout << weight_new << endl;
+}
+
+int main(int argc, char* argv[]) {
+    if (argc != 2) {
+        cout << "Usage: " << argv[0] << " data_file" << endl;
+        return -1;
+    }
+
+    const int record_num = 270;
+    const int dim_num = 13 + 1;
+
+    boost::numeric::ublas::vector<double> y(record_num);
+    boost::numeric::ublas::matrix<double> x(record_num, dim_num);
+    SimpleDataLoader loader(record_num, dim_num);
+    loader.load_file(argv[1], y, x);
+
+    // lr_method
+    lr_without_regularization(x, y);
+
+    return 0;
+}
diff --git a/util.hpp b/util.hpp
new file mode 100644
index 0000000..e510f56
--- /dev/null
+++ b/util.hpp
@@ -0,0 +1,45 @@
+#ifndef _CPP_UTIL_
+#define _CPP_UTIL_
+
+#include<iostream>
+#include<string>
+#include<sstream>
+#include<boost/numeric/ublas/vector.hpp>
+
+using namespace std;
+
+//////////////////// utils/////////////////////////////////
+template < class T> 
+void convert_from_string(T& value, const string& s)
+{
+    stringstream ss(s);
+    ss >> value;
+}
+
+double norm(const boost::numeric::ublas::vector<double>& v1, const boost::numeric::ublas::vector<double>& v2) {
+    assert (v1.size() == v2.size());
+    double sum = 0;
+    for (size_t i=0; i<v1.size(); ++i)
+    {
+        double minus = v1(i) - v2(i);
+        double r = minus * minus;
+        sum += r;
+    }
+
+    return sqrt(sum);
+}
+
+
+double norm_1(const boost::numeric::ublas::vector<double>& v1, const boost::numeric::ublas::vector<double>& v2) {
+    assert (v1.size() == v2.size());
+    double sum = 0;
+    for (size_t i=0; i<v1.size(); ++i)
+    {
+        double minus = abs(v1(i) - v2(i));
+        sum += minus;
+    }
+
+    return sum;
+}
+
+#endif