Changeset 1271

Timestamp:

Apr 9, 2008, 6:11:07 PM (15 years ago)

Author:

Peter

Message:

replaced u_int with unsigned int or size_t

Location:

trunk/yat

Files:

• classifier/IGP.h (modified) (2 diffs)
• classifier/KNN.h (modified) (5 diffs)
• classifier/Kernel.cc (modified) (4 diffs)
• classifier/Kernel.h (modified) (3 diffs)
• classifier/SupervisedClassifier.h (modified) (1 diff)
• classifier/utility.cc (modified) (2 diffs)
• random/random.cc (modified) (12 diffs)
• random/random.h (modified) (14 diffs)
• regression/OneDimensional.cc (modified) (1 diff)
• regression/OneDimensional.h (modified) (1 diff)
• regression/Polynomial.cc (modified) (1 diff)
• regression/PolynomialWeighted.cc (modified) (1 diff)
• statistics/Averager.cc (modified) (3 diffs)
• statistics/Averager.h (modified) (5 diffs)
• statistics/Fisher.cc (modified) (3 diffs)
• statistics/Fisher.h (modified) (5 diffs)
• statistics/Histogram.cc (modified) (2 diffs)
• statistics/PearsonCorrelation.cc (modified) (1 diff)
• statistics/PearsonCorrelation.h (modified) (1 diff)
• statistics/ROC.cc (modified) (2 diffs)
• statistics/ROC.h (modified) (4 diffs)
• statistics/utility.cc (modified) (2 diffs)
• statistics/utility.h (modified) (2 diffs)
• utility/Matrix.cc (modified) (3 diffs)
• utility/NNI.cc (modified) (3 diffs)
• utility/NNI.h (modified) (5 diffs)
• utility/SmartPtr.h (modified) (3 diffs)
• utility/Vector.cc (modified) (2 diffs)
• utility/WeNNI.cc (modified) (2 diffs)
• utility/WeNNI.h (modified) (2 diffs)
• utility/kNNI.cc (modified) (2 diffs)
• utility/kNNI.h (modified) (2 diffs)

trunk/yat/classifier/IGP.h

@@ -116 +116 @@
     igp_ = utility::Vector(target_.nof_classes());
     
-    for(u_int i=0; i<target_.size(); i++) {
-      u_int neighbor=i;
+    for(size_t i=0; i<target_.size(); i++) {
+      size_t neighbor=i;
       double mindist=std::numeric_limits<double>::max();
       const DataLookup1D a(matrix_,i,false);
-      for(u_int j=0; j<target_.size(); j++) {           
+      for(size_t j=0; j<target_.size(); j++) {            
         DataLookup1D b(matrix_,j,false);
         double dist=distance_(a.begin, a.end(), b.begin());
@@ -132 +132 @@
       
     }
-    for(u_int i=0; i<target_.nof_classes(); i++) {
+    for(size_t i=0; i<target_.nof_classes(); i++) {
       igp_(i)/=static_cast<double>(target_.size(i));
     }

trunk/yat/classifier/KNN.h

@@ -96 +96 @@
        \return The number of neighbors.
     */
-    u_int k() const;
+    unsigned int k() const;
 
     /**
@@ -103 +103 @@
        Sets the number of neighbors to \a k_in. 
     */
-    void k(u_int k_in);
+    void k(unsigned int k_in);
 
 
@@ -166 +166 @@
 
     // The number of neighbors
-    u_int k_;
+    unsigned int k_;
 
     Distance distance_;
@@ -240 +240 @@
   
   template <typename Distance, typename NeighborWeighting>
-  u_int KNN<Distance, NeighborWeighting>::k() const
+  unsigned int KNN<Distance, NeighborWeighting>::k() const
   {
     return k_;
@@ -246 +246 @@
 
   template <typename Distance, typename NeighborWeighting>
-  void KNN<Distance, NeighborWeighting>::k(u_int k)
+  void KNN<Distance, NeighborWeighting>::k(unsigned k)
   {
     k_=k;

trunk/yat/classifier/Kernel.cc

@@ -42 +42 @@
   {
     if (own)
-      ref_count_ = new u_int(1);
+      ref_count_ = new unsigned int(1);
     else
       ref_count_ = NULL;
@@ -53 +53 @@
   {
     if (own){
-      ref_count_w_ = new u_int(1);
+      ref_count_w_ = new unsigned int(1);
     }
     else {
@@ -68 +68 @@
     if (other.weighted()){
       mlw_ = new MatrixLookupWeighted(*other.mlw_, utility::Index(index),true);
-      ref_count_w_ = new u_int(1);
+      ref_count_w_ = new unsigned int(1);
       ml_=NULL;
       ref_count_ = NULL;
@@ -74 +74 @@
     else{
       ml_ = new MatrixLookup(*other.ml_, utility::Index(index),true);
-      ref_count_ = new u_int(1);
+      ref_count_ = new unsigned int(1);
       mlw_=NULL;
       ref_count_w_ = NULL;

trunk/yat/classifier/Kernel.h

@@ -30 +30 @@
 #include "KernelFunction.h"
 
-#include <cctype>
+#include <cstddef>
 #include <vector>
-
-#include <sys/types.h>
 
 namespace theplu {
@@ -183 +181 @@
     /// (data_). NULL if this is not an owner.
     ///
-    u_int* ref_count_;
+    unsigned int* ref_count_;
 
     ///
@@ -189 +187 @@
     /// (data_w_). NULL if this is not an owner.
     ///
-    u_int* ref_count_w_;
+    unsigned int* ref_count_w_;
 
   private:

trunk/yat/classifier/SupervisedClassifier.h

@@ -27 +27 @@
 */
 
-#include <stddef.h>
+#include <cstddef>
 
 namespace theplu {

trunk/yat/classifier/utility.cc

@@ -36 +36 @@
   {
     vector = utility::Vector(lookup.size());
-    for(u_int i=0; i<lookup.size(); i++)
+    for(size_t i=0; i<lookup.size(); i++)
       vector(i)=lookup(i);
   }
@@ -46 +46 @@
     value = utility::Vector(lookup.size());
     weight = utility::Vector(lookup.size());
-    for(u_int i=0; i<lookup.size(); i++){
+    for(size_t i=0; i<lookup.size(); i++){
       value(i)=lookup.data(i);
       weight(i)=lookup.weight(i);

trunk/yat/random/random.cc

@@ -61 +61 @@
 
 
-  u_long RNG::max(void) const
+  unsigned long RNG::max(void) const
   {
     return gsl_rng_max(rng_);
@@ -67 +67 @@
 
 
-  u_long RNG::min(void) const
+  unsigned long RNG::min(void) const
   {
     return gsl_rng_min(rng_);
@@ -85 +85 @@
 
 
-  void RNG::seed(u_long s) const
+  void RNG::seed(unsigned long s) const
   {
     gsl_rng_set(rng_,s);
@@ -91 +91 @@
 
 
-  u_long RNG::seed_from_devurandom(void)
-  {
-    u_char ulongsize=sizeof(u_long);
+  unsigned long RNG::seed_from_devurandom(void)
+  {
+    unsigned char ulongsize=sizeof(unsigned long);
     char* buffer=new char[ulongsize];
     std::ifstream is("/dev/urandom");
     is.read(buffer,ulongsize);
     is.close();
-    u_long s=0;
-    for (u_int i=0; i<ulongsize; i++) {
-      u_char ch=buffer[i];
+    unsigned long s=0;
+    for (unsigned int i=0; i<ulongsize; i++) {
+      unsigned char ch=buffer[i];
       s+=ch<<((ulongsize-1-i)*8);
     }
@@ -146 +146 @@
 
 
-  void Discrete::seed(u_long s) const
+  void Discrete::seed(unsigned long s) const
   {
     rng_->seed(s);
+  }
+
+
+  unsigned long Discrete::seed_from_devurandom(void) 
+  { 
+    return rng_->seed_from_devurandom(); 
   }
 
@@ -172 +178 @@
 
 
-  u_long DiscreteGeneral::operator()(void) const
+  unsigned long DiscreteGeneral::operator()(void) const
   {
     return gsl_ran_discrete(rng_->rng(), gen_);
@@ -178 +184 @@
 
 
-  DiscreteUniform::DiscreteUniform(u_long n)
+  DiscreteUniform::DiscreteUniform(unsigned long n)
     : range_(n)
   {
@@ -189 +195 @@
 
 
-  u_long DiscreteUniform::operator()(void) const
+  unsigned long DiscreteUniform::operator()(void) const
   {
     return (range_ ?
@@ -196 +202 @@
 
 
-  u_long DiscreteUniform::operator()(u_long n) const
+  unsigned long DiscreteUniform::operator()(unsigned long n) const
   {
     // making sure that n is not larger than the range of the
     // underlying RNG
     if (n>rng_->max()) {
-      std::stringstream ss("DiscreteUniform::operator(u_long): ");
+      std::stringstream ss("DiscreteUniform::operator(unsigned long): ");
       ss << n << " is too large for RNG " << rng_->name();
       throw utility::GSL_error(ss.str());
@@ -214 +220 @@
   }
 
-  u_long Poisson::operator()(void) const
+  unsigned long Poisson::operator()(void) const
   {
     return gsl_ran_poisson(rng_->rng(), m_);
@@ -220 +226 @@
 
 
-  u_long Poisson::operator()(const double m) const
+  unsigned long Poisson::operator()(const double m) const
   {
     return gsl_ran_poisson(rng_->rng(), m);
@@ -238 +244 @@
 
 
-  void Continuous::seed(u_long s) const
+  void Continuous::seed(unsigned long s) const
   {
     rng_->seed(s);

trunk/yat/random/random.h

@@ -98 +98 @@
     /// generator can return.
     ///
-    u_long max(void) const;
+    unsigned long max(void) const;
 
     ///
@@ -104 +104 @@
     /// generator can return.
     ///
-    u_long min(void) const;
+    unsigned long min(void) const;
 
     ///
@@ -125 +125 @@
     /// @see seed_from_devurandom
     ///
-    void seed(u_long s) const;
+    void seed(unsigned long s) const;
 
     ///
@@ -132 +132 @@
     /// @return The seed acquired from /dev/urandom.
     ///
-    u_long seed_from_devurandom(void);
+    unsigned long seed_from_devurandom(void);
 
     /**
@@ -221 +221 @@
     /// @see seed_from_devurandom, RNG::seed_from_devurandom, RNG::seed
     ///
-    void seed(u_long s) const;
+    void seed(unsigned long s) const;
 
     ///
@@ -230 +230 @@
     /// @see seed, RNG::seed_from_devurandom, RNG::seed
     ///
-    u_long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); }
+    unsigned long seed_from_devurandom(void);
 
     ///
     /// @return A random number.
     ///
-    virtual u_long operator()(void) const = 0;
+    virtual unsigned long operator()(void) const = 0;
     
   protected:
@@ -268 +268 @@
     /// @return A random number.
     ///
-    u_long operator()(void) const;
+    unsigned long operator()(void) const;
 
   private:
@@ -302 +302 @@
        is thrown.
     */
-    DiscreteUniform(u_long n=0);
+    DiscreteUniform(unsigned long n=0);
 
     /**
@@ -311 +311 @@
        created.
 
-       \see DiscreteUniform(const u_long n=0)
+       \see DiscreteUniform(const unsigned long n=0)
     */
-    u_long operator()(void) const;
+    unsigned long operator()(void) const;
 
     /**
@@ -325 +325 @@
        underlying generator.
     */
-    u_long operator()(u_long n) const;
-
-  private:
-    u_long range_;
+    unsigned long operator()(unsigned long n) const;
+
+  private:
+    unsigned long range_;
   };
 
@@ -359 +359 @@
     /// @return A Poisson distributed number.
     ///
-    u_long operator()(void) const;
+    unsigned long operator()(void) const;
 
     ///
@@ -367 +367 @@
     /// @note this operator ignores parameters set in Constructor
     ///
-    u_long operator()(const double m) const;
+    unsigned long operator()(const double m) const;
 
   private:
@@ -403 +403 @@
     /// @see seed_from_devurandom, RNG::seed_from_devurandom, RNG::seed
     ///
-    void seed(u_long s) const;
+    void seed(unsigned long s) const;
 
     ///
@@ -412 +412 @@
     /// @see seed, RNG::seed_from_devurandom, RNG::seed
     ///
-    u_long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); }
+    unsigned long seed_from_devurandom(void) { return rng_->seed_from_devurandom(); }
 
     ///

trunk/yat/regression/OneDimensional.cc

@@ -53 +53 @@
 
   std::ostream& OneDimensional::print(std::ostream& os, const double min, 
-                                      double max, const u_int n) const
+                                      double max, const unsigned int n) const
   {
     double dx;

trunk/yat/regression/OneDimensional.h

@@ -106 +106 @@
     ///
     std::ostream& print(std::ostream& os,const double min, 
-                        double max, const u_int n) const;
+                        double max, const unsigned int n) const;
 
     /**

trunk/yat/regression/Polynomial.cc

@@ -55 +55 @@
     utility::Matrix X=utility::Matrix(x.size(),power_+1,1);
     for (size_t i=0; i<X.rows(); ++i)
-      for (u_int j=1; j<X.columns(); j++)
+      for (size_t j=1; j<X.columns(); ++j)
         X(i,j)=X(i,j-1)*x(i);
     md_.fit(X,y);

trunk/yat/regression/PolynomialWeighted.cc

@@ -56 +56 @@
     utility::Matrix X=utility::Matrix(x.size(),power_+1,1);
     for (size_t i=0; i<X.rows(); ++i)
-      for (u_int j=1; j<X.columns(); j++)
+      for (size_t j=1; j<X.columns(); ++j)
         X(i,j)=X(i,j-1)*x(i);
     md_.fit(X,y,w);

trunk/yat/statistics/Averager.cc

@@ -37 +37 @@
   }
 
-  Averager::Averager(double x, double xx, u_long n)
+  Averager::Averager(double x, double xx, unsigned long n)
     : n_(n), x_(x), xx_(xx) 
   {
@@ -47 +47 @@
   }
 
-  void Averager::add(double d, u_long n)
+  void Averager::add(double d, unsigned long n)
   {
     assert(!std::isnan(d));
@@ -65 +65 @@
   }
 
-  u_long Averager::n(void) const
+  unsigned long Averager::n(void) const
   {
     return n_;

trunk/yat/statistics/Averager.h

@@ -30 +30 @@
 
 #include <cmath>
-#include <sys/types.h>
 
 namespace theplu{
@@ -56 +55 @@
     /// number of samples \a n.
     ///
-    Averager(double x, double xx, u_long n);
+    Averager(double x, double xx, unsigned long n);
 
     ///
@@ -66 +65 @@
     /// Adding \a n (default=1) number of data point(s) with value \a d.
     ///
-    void add(double d, u_long n=1);
+    void add(double d, unsigned long n=1);
 
     /**
@@ -86 +85 @@
     /// @return Number of data points
     ///
-    u_long n(void) const;
+    unsigned long n(void) const;
 
     ///
@@ -179 +178 @@
 
   private:
-    u_long  n_;
+    unsigned long n_;
     double  x_, xx_;
   };

trunk/yat/statistics/Fisher.cc

@@ -72 +72 @@
 
 
-  u_int& Fisher::minimum_size(void)
+  unsigned int& Fisher::minimum_size(void)
   {
     return minimum_size_;
@@ -78 +78 @@
 
 
-  const u_int& Fisher::minimum_size(void) const
+  const unsigned int& Fisher::minimum_size(void) const
   {
     return minimum_size_;
@@ -84 +84 @@
 
 
-  double Fisher::oddsratio(const u_int a,
-                           const u_int b,
-                           const u_int c,
-                           const u_int d) 
+  double Fisher::oddsratio(const unsigned int a,
+                           const unsigned int b,
+                           const unsigned int c,
+                           const unsigned int d) 
   {
     // If a column sum or a row sum is zero, the table is nonsense

trunk/yat/statistics/Fisher.h

@@ -28 +28 @@
 
 #include "Score.h"
-
-#include <sys/types.h>
 
 #include <cmath>
@@ -106 +104 @@
     /// @return reference to minimum_size
     ///
-    u_int& minimum_size(void);
+    unsigned int& minimum_size(void);
 
     ///
@@ -114 +112 @@
     /// @return const reference to minimum_size
     ///
-    const u_int& minimum_size(void) const;
+    const unsigned int& minimum_size(void) const;
 
     ///
@@ -150 +148 @@
        is invalid if a row or column sum is zero.
     */
-    double oddsratio(const u_int a, const u_int b, 
-                     const u_int c, const u_int d);
+    double oddsratio(const unsigned a, const unsigned int b, 
+                     const unsigned int c, const unsigned int d);
 
   private:
@@ -161 +159 @@
     double p_value_exact(void) const;
 
-    u_int a_;
-    u_int b_;
-    u_int c_;
-    u_int d_;
-    u_int minimum_size_;
+    unsigned int a_;
+    unsigned int b_;
+    unsigned int c_;
+    unsigned int d_;
+    unsigned int minimum_size_;
     double oddsratio_;
   };

trunk/yat/statistics/Histogram.cc

@@ -118 +118 @@
   void Histogram::reset(void)
   {
-    for (u_int i=0; i<histogram_.size(); i++)
+    for (size_t i=0; i<histogram_.size(); i++)
       histogram_[i]=0;
     sum_all_.reset();
@@ -174 +174 @@
       << "# column 2: frequency\n";
 
-    for (u_int i=0; i<histogram.nof_bins(); i++) {
+    for (size_t i=0; i<histogram.nof_bins(); i++) {
       s.width(12);
       s << histogram.observation_value(i);

trunk/yat/statistics/PearsonCorrelation.cc

@@ -42 +42 @@
   {
     return pearson_p_value(ap_.correlation(), 
-                           static_cast<u_int>(ap_.n())); 
+                           static_cast<unsigned int>(ap_.n())); 
   }
 

trunk/yat/statistics/PearsonCorrelation.h

@@ -70 +70 @@
        
        P-value is calculated using function pearson_p_value(double,
-       u_int) where degrees of freedom is calculated using n(void) in
+       unsigned int) where degrees of freedom is calculated using n(void) in
        AveragerPairWeighted.
 

trunk/yat/statistics/ROC.cc

@@ -103 +103 @@
   }
 
-  u_int& ROC::minimum_size(void)
+  unsigned int& ROC::minimum_size(void)
   {
     return minimum_size_;
@@ -109 +109 @@
 
 
-  const u_int& ROC::minimum_size(void) const
+  const unsigned int& ROC::minimum_size(void) const
   {
     return minimum_size_;

trunk/yat/statistics/ROC.h

@@ -32 +32 @@
 #include <map>
 #include <utility>
-
-#include <sys/types.h>
 
 namespace theplu {
@@ -80 +78 @@
     /// @return reference to minimum_size
     ///
-    u_int& minimum_size(void);
+    unsigned int& minimum_size(void);
 
     /**
@@ -88 +86 @@
        @return const reference to minimum_size
     */
-    const u_int& minimum_size(void) const;
+    const unsigned int& minimum_size(void) const;
 
     ///
@@ -144 +142 @@
 
     double area_;
-    u_int minimum_size_;
+    unsigned int minimum_size_;
     double w_neg_;
     double w_pos_;

trunk/yat/statistics/utility.cc

@@ -37 +37 @@
 namespace statistics {  
 
-  double cdf_hypergeometric_P(u_int k, u_int n1, u_int n2, u_int t)
+  double cdf_hypergeometric_P(unsigned int k, unsigned int n1, 
+                              unsigned int n2, unsigned int t)
   {
     double p=0;
-    for (u_int i=0; i<=k; i++)
+    for (size_t i=0; i<=k; i++)
       p+= gsl_ran_hypergeometric_pdf(i, n1, n2, t);
     return p;
@@ -46 +47 @@
 
 
-  double pearson_p_value(double r, u_int n)
+  double pearson_p_value(double r, unsigned int n)
   {
     assert(n>=2);
-    if (n<2)
+    if (n<=2)
       return std::numeric_limits<double>::quiet_NaN();
     return gsl_cdf_tdist_Q(r*sqrt((n-2)/(1-r*r)), n-2);

trunk/yat/statistics/utility.h

@@ -77 +77 @@
   /// @return cumulative hypergeomtric distribution functions P(k).
   ///
-  double cdf_hypergeometric_P(u_int k, u_int n1, u_int n2, u_int t);
+  double cdf_hypergeometric_P(unsigned int k, unsigned int n1, 
+                              unsigned int n2, unsigned int t);
 
 
@@ -93 +94 @@
      chance when having \a n samples.
    */
-  double pearson_p_value(double r, u_int n);
+  double pearson_p_value(double r, unsigned int n);
 
   ///

trunk/yat/utility/Matrix.cc

@@ -37 +37 @@
 #include <vector>
 
-#include <sys/types.h>
-
 #include <gsl/gsl_blas.h>
 
@@ -78 +76 @@
     // expandable)
     std::vector<std::vector<double> > data_matrix;
-    u_int nof_columns=0;
-    u_int nof_rows = 0;
+    unsigned int nof_columns=0;
+    unsigned int nof_rows = 0;
     std::string line;
     while(getline(is, line, '\n')){
@@ -140 +138 @@
     // if gsl error handler disabled, out of bounds index will not
     // abort the program.
-    for(u_int i=0;i<nof_rows;i++)
-      for(u_int j=0;j<nof_columns;j++)
+    for(size_t i=0;i<nof_rows;i++)
+      for(size_t j=0;j<nof_columns;j++)
         gsl_matrix_set( m_, i, j, data_matrix[i][j] );
   }

trunk/yat/utility/NNI.cc

@@ -39 +39 @@
   // documentation.
   NNI::NNI(const utility::Matrix& matrix,const utility::Matrix& weight,
-           const u_int neighbours)
+           const unsigned int neighbours)
     : data_(matrix), imputed_data_(matrix), neighbours_(neighbours),
       weight_(weight)
@@ -49 +49 @@
   //                {\sum_{k=l,C} w_{ik} w_{jk} }
   // where C is the number of columns
-  std::vector<std::pair<u_int,double> >
-  NNI::calculate_distances(const u_int row) const
+  std::vector<std::pair<size_t,double> >
+  NNI::calculate_distances(const size_t row) const
   {
-    std::vector<std::pair<u_int,double> > distance;
+    std::vector<std::pair<size_t,double> > distance;
     for (size_t i=0; i<data_.rows(); i++)
       if (i!=row) {
         double contribs=0;
-        std::pair<u_int,double> this_distance(i,0.0);
+        std::pair<size_t,double> this_distance(i,0.0);
         for (size_t j=0; j<data_.columns(); j++)
           // 0 contribution for missing values
@@ -90 +90 @@
   // number, and neighbours are disqualified if their element (column)
   // weight is zero
-  std::vector<u_int> 
-  NNI::nearest_neighbours(const u_int column,
-                          const std::vector<std::pair<u_int,double> >& d) const
+  std::vector<size_t> 
+  NNI::nearest_neighbours(const size_t column,
+                          const std::vector<std::pair<size_t,double> >& d) const
   {
-    std::vector<u_int> index;
+    std::vector<size_t> index;
     double contribs=0;
-    for (u_int i=0; ((i<d.size()) &&
+    for (size_t i=0; ((i<d.size()) &&
                      (contribs+=weight_(d[i].first,column))<=neighbours_); i++)
       if (weight_(d[i].first,column))

trunk/yat/utility/NNI.h

@@ -33 +33 @@
 #include <utility>
 #include <vector>
-
-#include <sys/types.h>
 
 namespace theplu {
@@ -92 +90 @@
     ///
     NNI(const utility::Matrix& matrix,const utility::Matrix& weight,
-        const u_int neighbours);
+        const unsigned int neighbours);
 
     virtual ~NNI(void) {};
@@ -101 +99 @@
     /// @return number of rows not imputed
     ///
-    virtual u_int estimate(void)=0;
+    virtual unsigned int estimate(void)=0;
 
     ///
@@ -118 +116 @@
        }{\sum_{k=l}^C w_{ik} w_{jk} } \f$ where C is the number of columns
     */
-    std::vector<std::pair<u_int,double> > calculate_distances(const u_int) const;
+    std::vector<std::pair<size_t,double> > 
+    calculate_distances(const size_t) const;
     /// Contributing nearest neighbours are added up to the user set
     /// number, and neighbours are disqualified if their element
     /// (column) weight is zero
-    std::vector<u_int> nearest_neighbours(const u_int,
-                             const std::vector<std::pair<u_int,double> >&) const;
+    std::vector<size_t> 
+    nearest_neighbours(const size_t,
+                       const std::vector<std::pair<size_t,double> >&) const;
     ///
     /// original data matrix
@@ -137 +137 @@
     /// number of neighbor to use
     ///
-    u_int neighbours_;
+    unsigned int neighbours_;
 
     ///

trunk/yat/utility/SmartPtr.h

@@ -26 +26 @@
 
 #include "yat_assert.h"
-
-// debug
-#include <iostream>
-
-#include <sys/types.h>
 
 namespace theplu {
@@ -74 +69 @@
     {
       if (owner)
-        ref_count_ = new u_int(1);
+        ref_count_ = new unsigned int(1);
       else
         ref_count_ = NULL;
@@ -134 +129 @@
   private:
     T* pointee_;
-    u_int* ref_count_;
+    unsigned int* ref_count_;
 
     void detach(void)

trunk/yat/utility/Vector.cc

@@ -27 +27 @@
 #include "Vector.h"
 #include "utility.h"
-#include "yat/random/random.h"
 
 #include <algorithm>
@@ -80 +79 @@
     // expandable)
     std::vector<std::vector<double> > data_matrix;
-    u_int nof_columns=0;
-    u_int nof_rows=0;
+    unsigned int nof_columns=0;
+    unsigned int nof_rows=0;
     std::string line;
     while(getline(is, line, '\n')) {

trunk/yat/utility/WeNNI.cc

@@ -38 +38 @@
 
   WeNNI::WeNNI(const utility::Matrix& matrix,const utility::Matrix& flag,
-               const u_int neighbours)
+               const unsigned int neighbours)
     : NNI(matrix,flag,neighbours), imputed_data_raw_(matrix)
   {
@@ -50 +50 @@
   // where N is defined in the paper cited in the NNI class definition
   // documentation.
-  u_int WeNNI::estimate(void)
+  unsigned int WeNNI::estimate(void)
   {
-    for (unsigned int i=0; i<data_.rows(); i++) {
-      std::vector<std::pair<u_int,double> > distance(calculate_distances(i));
+    for (size_t i=0; i<data_.rows(); i++) {
+      std::vector<std::pair<size_t,double> > distance(calculate_distances(i));
       std::sort(distance.begin(),distance.end(),
-                pair_value_compare<u_int,double>());
+                pair_value_compare<size_t,double>());
       bool row_imputed=true;
-      for (unsigned int j=0; j<data_.columns(); j++) {
-        std::vector<u_int> knn=nearest_neighbours(j,distance);
+      for (size_t j=0; j<data_.columns(); j++) {
+        std::vector<size_t> knn=nearest_neighbours(j,distance);
         double new_value=0.0;
         double norm=0.0;
-        for (std::vector<u_int>::const_iterator k=knn.begin(); k!=knn.end();
+        for (std::vector<size_t>::const_iterator k=knn.begin(); k!=knn.end();
              ++k) {
           // Avoid division with zero (perfect match vectors)

trunk/yat/utility/WeNNI.h

@@ -55 +55 @@
     ///
     WeNNI(const utility::Matrix& matrix,const utility::Matrix& weight,
-          const u_int neighbours);
+          const unsigned int neighbours);
 
     ///
@@ -62 +62 @@
     /// value.
     ///
-    u_int estimate(void);
+    unsigned int estimate(void);
 
     ///

trunk/yat/utility/kNNI.cc

@@ -37 +37 @@
 
   kNNI::kNNI(const utility::Matrix& matrix,const utility::Matrix& flag,
-             const u_int neighbours)
+             const unsigned int neighbours)
     : NNI(matrix,flag,neighbours)
   {
@@ -55 +55 @@
   // where N is defined in the paper cited in the NNI class definition
   // documentation.
-  u_int kNNI::estimate(void)
+  unsigned int kNNI::estimate(void)
   {
-    for (unsigned int i=0; i<mv_rows_.size(); i++) {
-      std::vector<std::pair<u_int,double> >
+    for (size_t i=0; i<mv_rows_.size(); i++) {
+      std::vector<std::pair<size_t,double> >
         distance(calculate_distances(mv_rows_[i]));
       std::sort(distance.begin(),distance.end(),
-                pair_value_compare<u_int,double>());
-      for (unsigned int j=0; j<data_.columns(); j++)
+                pair_value_compare<size_t,double>());
+      for (size_t j=0; j<data_.columns(); j++)
         if (!weight_(mv_rows_[i],j)) {
-          std::vector<u_int> knn=nearest_neighbours(j,distance);
+          std::vector<size_t> knn=nearest_neighbours(j,distance);
           double new_value=0.0;
           double norm=0.0;
-          for (std::vector<u_int>::const_iterator k=knn.begin(); k!=knn.end();
+          for (std::vector<size_t>::const_iterator k=knn.begin(); k!=knn.end();
                ++k) {
             // Avoid division with zero (perfect match vectors)

trunk/yat/utility/kNNI.h

@@ -56 +56 @@
     ///
     kNNI(const utility::Matrix& matrix,const utility::Matrix& weight,
-         const u_int neighbours);
+         const unsigned int neighbours);
 
     ///
@@ -63 +63 @@
     /// value.
     ///
-    u_int estimate(void);
+    unsigned int estimate(void);
 
   private:
-    std::vector<u_int> mv_rows_;  // index to rows that have values to estimate
+    std::vector<size_t> mv_rows_; // index to rows that have values to estimate
   };