svm-predict.c
Go to the documentation of this file.
1 #include <stdio.h>
2 #include <ctype.h>
3 #include <stdlib.h>
4 #include <string.h>
5 #include <errno.h>
6 #include "svm.h"
7 
8 int print_null(const char *s, ...) {}
9 
10 static int (*info)(const char *fmt, ...) = &printf;
11 
12 struct svm_node *x;
13 int max_nr_attr = 64;
14 
15 struct svm_model* model;
17 
18 static char *line = NULL;
19 static int max_line_len;
20 
21 static char* readline(FILE *input)
22 {
23  int len;
24 
25  if (fgets(line, max_line_len, input) == NULL)
26  return NULL;
27 
28  while (strrchr(line, '\n') == NULL)
29  {
30  max_line_len *= 2;
31  line = (char *) realloc(line, max_line_len);
32  len = (int) strlen(line);
33  if (fgets(line + len, max_line_len - len, input) == NULL)
34  break;
35  }
36  return line;
37 }
38 
39 void exit_input_error(int line_num)
40 {
41  fprintf(stderr, "Wrong input format at line %d\n", line_num);
42  exit(1);
43 }
44 
45 void predict(FILE *input, FILE *output)
46 {
47  int correct = 0;
48  int total = 0;
49  double error = 0;
50  double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
51 
52  int svm_type = svm_get_svm_type(model);
53  int nr_class = svm_get_nr_class(model);
54  double *prob_estimates = NULL;
55  int j;
56 
58  {
59  if (svm_type == NU_SVR || svm_type == EPSILON_SVR)
60  info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n", svm_get_svr_probability(model));
61  else
62  {
63  int *labels = (int *) malloc(nr_class * sizeof(int));
64  svm_get_labels(model, labels);
65  prob_estimates = (double *) malloc(nr_class * sizeof(double));
66  fprintf(output, "labels");
67  for (j = 0; j < nr_class; j++)
68  fprintf(output, " %d", labels[j]);
69  fprintf(output, "\n");
70  free(labels);
71  }
72  }
73 
74  max_line_len = 1024;
75  line = (char *)malloc(max_line_len * sizeof(char));
76  while (readline(input) != NULL)
77  {
78  int i = 0;
79  double target_label, predict_label;
80  char *idx, *val, *label, *endptr;
81  int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
82 
83  label = strtok(line, " \t\n");
84  if (label == NULL) // empty line
85  exit_input_error(total + 1);
86 
87  target_label = strtod(label, &endptr);
88  if (endptr == label || *endptr != '\0')
89  exit_input_error(total + 1);
90 
91  while (1)
92  {
93  if (i >= max_nr_attr - 1) // need one more for index = -1
94  {
95  max_nr_attr *= 2;
96  x = (struct svm_node *) realloc(x, max_nr_attr * sizeof(struct svm_node));
97  }
98 
99  idx = strtok(NULL, ":");
100  val = strtok(NULL, " \t");
101 
102  if (val == NULL)
103  break;
104  errno = 0;
105  x[i].index = (int) strtol(idx, &endptr, 10);
106  if (endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
107  exit_input_error(total + 1);
108  else
109  inst_max_index = x[i].index;
110 
111  errno = 0;
112  x[i].value = strtod(val, &endptr);
113  if (endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
114  exit_input_error(total + 1);
115 
116  ++i;
117  }
118  x[i].index = -1;
119 
120  if (predict_probability && (svm_type == C_SVC || svm_type == NU_SVC))
121  {
122  predict_label = svm_predict_probability(model, x, prob_estimates);
123  fprintf(output, "%g", predict_label);
124  for (j = 0; j < nr_class; j++)
125  fprintf(output, " %g", prob_estimates[j]);
126  fprintf(output, "\n");
127  }
128  else
129  {
130  predict_label = svm_predict(model, x);
131  fprintf(output, "%g\n", predict_label);
132  }
133 
134  if (predict_label == target_label)
135  ++correct;
136  error += (predict_label - target_label) * (predict_label - target_label);
137  sump += predict_label;
138  sumt += target_label;
139  sumpp += predict_label * predict_label;
140  sumtt += target_label * target_label;
141  sumpt += predict_label * target_label;
142  ++total;
143  }
144  if (svm_type == NU_SVR || svm_type == EPSILON_SVR)
145  {
146  info("Mean squared error = %g (regression)\n", error / total);
147  info("Squared correlation coefficient = %g (regression)\n",
148  ((total * sumpt - sump * sumt) * (total * sumpt - sump * sumt)) /
149  ((total * sumpp - sump * sump) * (total * sumtt - sumt * sumt))
150  );
151  }
152  else
153  info("Accuracy = %g%% (%d/%d) (classification)\n",
154  (double)correct / total * 100, correct, total);
156  free(prob_estimates);
157 }
158 
160 {
161  printf(
162  "Usage: svm-predict [options] test_file model_file output_file\n"
163  "options:\n"
164  "-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported\n"
165  "-q : quiet mode (no outputs)\n"
166  );
167  exit(1);
168 }
169 
170 int main(int argc, char **argv)
171 {
172  FILE *input, *output;
173  int i;
174  // parse options
175  for (i = 1; i < argc; i++)
176  {
177  if (argv[i][0] != '-') break;
178  ++i;
179  switch (argv[i - 1][1])
180  {
181  case 'b':
182  predict_probability = atoi(argv[i]);
183  break;
184  case 'q':
185  info = &print_null;
186  i--;
187  break;
188  default:
189  fprintf(stderr, "Unknown option: -%c\n", argv[i - 1][1]);
190  exit_with_help();
191  }
192  }
193 
194  if (i >= argc - 2)
195  exit_with_help();
196 
197  input = fopen(argv[i], "r");
198  if (input == NULL)
199  {
200  fprintf(stderr, "can't open input file %s\n", argv[i]);
201  exit(1);
202  }
203 
204  output = fopen(argv[i + 2], "w");
205  if (output == NULL)
206  {
207  fprintf(stderr, "can't open output file %s\n", argv[i + 2]);
208  exit(1);
209  }
210 
211  if ((model = svm_load_model(argv[i + 1])) == 0)
212  {
213  fprintf(stderr, "can't open model file %s\n", argv[i + 1]);
214  exit(1);
215  }
216 
217  x = (struct svm_node *) malloc(max_nr_attr * sizeof(struct svm_node));
219  {
220  if (svm_check_probability_model(model) == 0)
221  {
222  fprintf(stderr, "Model does not support probabiliy estimates\n");
223  exit(1);
224  }
225  }
226  else
227  {
228  if (svm_check_probability_model(model) != 0)
229  info("Model supports probability estimates, but disabled in prediction.\n");
230  }
231 
232  predict(input, output);
234  free(x);
235  free(line);
236  fclose(input);
237  fclose(output);
238  return 0;
239 }
static int max_line_len
Definition: svm-predict.c:19
void exit_with_help()
Definition: svm-predict.c:159
static char * readline(FILE *input)
Definition: svm-predict.c:21
double value
Definition: svm.h:15
def svm_load_model(model_file_name)
Definition: svmutil.py:27
void exit_input_error(int line_num)
Definition: svm-predict.c:39
Definition: svm.h:25
double svm_get_svr_probability(const svm_model *model)
Definition: svm.cpp:2499
int nr_class
Definition: svm.h:55
Definition: svm.h:52
def svm_predict(y, x, m, options="")
Definition: svmutil.py:164
struct svm_node * x
Definition: svm-predict.c:12
Definition: svm.h:25
int svm_get_nr_class(const svm_model *model)
Definition: svm.cpp:2475
int svm_check_probability_model(const svm_model *model)
Definition: svm.cpp:3150
void output(int index, double value)
Definition: svm-scale.c:367
int main(int argc, char **argv)
Definition: svm-predict.c:170
static char * line
Definition: svm-predict.c:18
int predict_probability
Definition: svm-predict.c:16
struct svm_model * model
Definition: svm-predict.c:15
int * label
Definition: svm.h:66
void svm_free_and_destroy_model(svm_model **model_ptr_ptr)
Definition: svm.cpp:3013
double svm_predict_probability(const svm_model *model, const svm_node *x, double *prob_estimates)
Definition: svm.cpp:2602
static int(* info)(const char *fmt,...)
Definition: svm-predict.c:10
int index
Definition: svm.h:14
int max_nr_attr
Definition: svm-predict.c:13
int svm_get_svm_type(const svm_model *model)
Definition: svm.cpp:2470
Definition: svm.h:25
Definition: svm.h:12
void predict(FILE *input, FILE *output)
Definition: svm-predict.c:45
void svm_get_labels(const svm_model *model, int *label)
Definition: svm.cpp:2480
int print_null(const char *s,...)
Definition: svm-predict.c:8


ml_classifiers
Author(s): Scott Niekum , Joshua Whitley
autogenerated on Tue May 14 2019 02:28:35