MBDyn-1.7.3
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Groups
Functions | Variables
ann_sf.c File Reference
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <getopt.h>
#include <string.h>
#include "ann.h"
Include dependency graph for ann_sf.c:

Go to the source code of this file.

Functions

void print_usage (void)
 
int main (int argc, char *argv[])
 

Variables

static int TRAINING_MODE = 1
 
static int MODE = 1
 
static float MEAN = 0.
 
static float VARIANCE = 1.
 
static float MIN = -1.
 
static float MAX = 1.
 
static char * filename = "data.dat"
 

Function Documentation

int main ( int  argc,
char *  argv[] 
)

Definition at line 74 of file ann_sf.c.

References ANN_DataRead(), filename, getopt(), matrix::mat, matrix_init(), matrix_write(), MAX, maximum(), MEAN, mean_value(), MIN, minimum(), matrix::Ncolumn, optarg, print_usage(), grad::sqrt(), VARIANCE, variance(), and W_M_TEXT.

75 {
76  int opt;
77  extern char *optarg;
78  matrix MAT, SF;
79  int N_sample, i;
80  double mean, var, min, max;
81 
82  /* 0. Training options */
83  do {
84 #ifdef HAVE_GETOPT_LONG
85  static struct option options[] = {
86  { "usage", 0, 0, 'u' },
87  { "mode", 1, 0, 'm' },
88  { "mean", 1, 0, 'M' },
89  { "variance", 1, 0, 'V' },
90  { "min", 1, 0, 'N' },
91  { "max", 1, 0, 'X' },
92  { "file", 1, 0, 'f' }
93  };
94  opt = getopt_long(argc, argv, "uf:m:M:V:N:X:", options, NULL);
95 #else /* ! HAVE_GETOPT_LONG */
96  opt = getopt(argc, argv, "uf:m:M:V:N:X:");
97 #endif /* ! HAVE_GETOPT_LONG */
98 
99  switch (opt) {
100  case 'u': print_usage();
101  break;
102  case 'f': filename = optarg;
103  break;
104  case 'm': MODE = atoi(optarg);
105  break;
106  case 'M': MEAN = atof(optarg);
107  break;
108  case 'V': VARIANCE = atof(optarg);
109  break;
110  case 'N': MIN = atof(optarg);
111  break;
112  case 'X': MAX = atof(optarg);
113  break;
114  default: break;
115  }
116  } while (opt >= 0);
117 
118  N_sample = 0;
119 
120  if (ANN_DataRead(&MAT, &N_sample, filename)) {
121  fprintf(stderr, "Error in Input data acquisition\n");
122  return 1;
123  }
124 
125  matrix_init(&SF, MAT.Ncolumn, 2);
126 
127  switch (MODE) {
128  case 1:
129  for (i = 0; i < MAT.Ncolumn; i++) {
130  mean = mean_value(&MAT, i);
131  var = variance(&MAT, i);
132  SF.mat[i][0] = sqrt(VARIANCE/var);
133  SF.mat[i][1] = MEAN - SF.mat[i][0]*mean;
134  }
135  break;
136 
137  case 2:
138  for (i = 0; i < MAT.Ncolumn; i++) {
139  min = minimum(&MAT, i);
140  max = maximum(&MAT, i);
141  printf("AAAA %lf %lf\n", min, max);
142  SF.mat[i][0] = (MAX - MIN)/(max - min);
143  SF.mat[i][1] = (max*MIN-MAX*min)/(max-min);
144  }
145  break;
146  }
147 
148  matrix_write(&SF, stdout, W_M_TEXT);
149 
150  return 0;
151 }
matrix_write
mat_res_t matrix_write(matrix *MAT, FILE *fh, unsigned flags)
Definition: matrix.c:467
MAX
static float MAX
Definition: ann_sf.c:70
variance
double variance(matrix *MAT, int column)
Definition: matrix.c:598
W_M_TEXT
#define W_M_TEXT
Definition: matrix.h:47
filename
static char * filename
Definition: ann_sf.c:71
matrix::Ncolumn
unsigned Ncolumn
Definition: matrix.h:64
ANN_DataRead
ann_res_t ANN_DataRead(matrix *MAT, int *N_sample, char *FileName)
Definition: ann.c:475
MODE
static int MODE
Definition: ann_sf.c:66
matrix
Definition: matrix.h:61
VARIANCE
static float VARIANCE
Definition: ann_sf.c:68
mean_value
double mean_value(matrix *MAT, int column)
Definition: matrix.c:586
print_usage
void print_usage(void)
Definition: ann_sf.c:43
MEAN
static float MEAN
Definition: ann_sf.c:67
grad::sqrt
GradientExpression< UnaryExpr< FuncSqrt, Expr > > sqrt(const GradientExpression< Expr > &u)
Definition: gradient.h:2974
maximum
double maximum(matrix *MAT, int column)
Definition: matrix.c:612
options
struct option options[]
Definition: ann_in.c:46
matrix::mat
double ** mat
Definition: matrix.h:62
MIN
static float MIN
Definition: ann_sf.c:69
getopt
int getopt(int argc, char *const argv[], const char *opts)
Definition: getopt.c:93
optarg
char * optarg
Definition: getopt.c:74
matrix_init
mat_res_t matrix_init(matrix *MAT, unsigned Nrow, unsigned Ncolumn)
Definition: matrix.c:43
minimum
double minimum(matrix *MAT, int column)
Definition: matrix.c:628

Here is the call graph for this function:

void print_usage ( void  )

Definition at line 43 of file ann_sf.c.

Referenced by main().

44 {
45  fprintf(stdout, "\nUSAGE OPTIONS:\n"
46  " -u, --usage\n"
47  " print usage\n"
48  " -f, --file\n"
49  " input file name\n"
50  " -m, --mode\n"
51  " scaling mode: 1 mean-variance (default)\n"
52  " 2 min-max\n"
53  " -M, --mean\n"
54  " mean value (defaul 0.)\n"
55  " -V, --variance"
56  " variance value (defaul 1.)\n"
57  " -N, --min\n"
58  " minimum value (defaul -1.)\n"
59  " -X, --max\n"
60  " maximum value (defaul 1.)\n"
61  );
62  exit(0);
63 }

Variable Documentation

char* filename = "data.dat"
static

Definition at line 71 of file ann_sf.c.

Referenced by MBDynParser::C81Data_int(), main(), VariableStepDR::Read(), FixedStepDR::Read(), GRAALLDamperCLR::Read(), and resolve_filename().

float MAX = 1.
static

Definition at line 70 of file ann_sf.c.

Referenced by main().

float MEAN = 0.
static

Definition at line 67 of file ann_sf.c.

Referenced by main().

float MIN = -1.
static

Definition at line 69 of file ann_sf.c.

Referenced by main().

int MODE = 1
static

Definition at line 66 of file ann_sf.c.

int TRAINING_MODE = 1
static

Definition at line 40 of file ann_sf.c.

float VARIANCE = 1.
static

Definition at line 68 of file ann_sf.c.

Referenced by main().