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fixed build issue (MSVC2010, x64)

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Vladimir Dudnik
2011-04-15 19:46:45 +00:00
parent a811a08d0d
commit 26d348a5b7
1 changed files with 17 additions and 13 deletions
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30
samples/cpp/letter_recog.cpp
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@@ -519,21 +519,21 @@ int build_knearest_classifier( char* data_filename, int K )
CvKNearest knearest(&train_data, train_resp); CvKNearest knearest(&train_data, train_resp);
CvMat* nearests = cvCreateMat( (nsamples_all - ntrain_samples), K, CV_32FC1); CvMat* nearests = cvCreateMat( (nsamples_all - ntrain_samples), K, CV_32FC1);
float _sample[var_count * (nsamples_all - ntrain_samples)]; float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample ); CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
float true_results[nsamples_all - ntrain_samples]; float* true_results = new float[nsamples_all - ntrain_samples];
for (int j = ntrain_samples; j < nsamples_all; j++) for (int j = ntrain_samples; j < nsamples_all; j++)
{ {
float *s = data->data.fl + j * var_count; float *s = data->data.fl + j * var_count;
for (int i = 0; i < var_count; i++) for (int i = 0; i < var_count; i++)
{ {
sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i]; sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
} }
true_results[j - ntrain_samples] = responses->data.fl[j]; true_results[j - ntrain_samples] = responses->data.fl[j];
} }
CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1); CvMat *result = cvCreateMat(1, nsamples_all - ntrain_samples, CV_32FC1);
knearest.find_nearest(&sample, K, result, 0, nearests, 0); knearest.find_nearest(&sample, K, result, 0, nearests, 0);
int true_resp = 0; int true_resp = 0;
int accuracy = 0; int accuracy = 0;
for (int i = 0; i < nsamples_all - ntrain_samples; i++) for (int i = 0; i < nsamples_all - ntrain_samples; i++)
@@ -546,10 +546,12 @@ int build_knearest_classifier( char* data_filename, int K )
accuracy++; accuracy++;
} }
} }
printf("true_resp = %f%%\tavg accuracy = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100, printf("true_resp = %f%%\tavg accuracy = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100,
(float)accuracy / (nsamples_all - ntrain_samples) / K * 100); (float)accuracy / (nsamples_all - ntrain_samples) / K * 100);
delete[] true_results;
delete[] _sample;
cvReleaseMat( &train_resp ); cvReleaseMat( &train_resp );
cvReleaseMat( &nearests ); cvReleaseMat( &nearests );
cvReleaseMat( &result ); cvReleaseMat( &result );
@@ -593,15 +595,15 @@ int build_nbayes_classifier( char* data_filename )
train_resp->data.fl[i] = responses->data.fl[i]; train_resp->data.fl[i] = responses->data.fl[i];
CvNormalBayesClassifier nbayes(&train_data, train_resp); CvNormalBayesClassifier nbayes(&train_data, train_resp);
float _sample[var_count * (nsamples_all - ntrain_samples)]; float* _sample = new float[var_count * (nsamples_all - ntrain_samples)];
CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample ); CvMat sample = cvMat( nsamples_all - ntrain_samples, 16, CV_32FC1, _sample );
float true_results[nsamples_all - ntrain_samples]; float* true_results = new float[nsamples_all - ntrain_samples];
for (int j = ntrain_samples; j < nsamples_all; j++) for (int j = ntrain_samples; j < nsamples_all; j++)
{ {
float *s = data->data.fl + j * var_count; float *s = data->data.fl + j * var_count;
for (int i = 0; i < var_count; i++) for (int i = 0; i < var_count; i++)
{ {
sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i]; sample.data.fl[(j - ntrain_samples) * var_count + i] = s[i];
} }
true_results[j - ntrain_samples] = responses->data.fl[j]; true_results[j - ntrain_samples] = responses->data.fl[j];
@@ -615,9 +617,11 @@ int build_nbayes_classifier( char* data_filename )
if (result->data.fl[i] == true_results[i]) if (result->data.fl[i] == true_results[i])
true_resp++; true_resp++;
} }
printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100); printf("true_resp = %f%%\n", (float)true_resp / (nsamples_all - ntrain_samples) * 100);
delete[] true_results;
delete[] _sample;
cvReleaseMat( &train_resp ); cvReleaseMat( &train_resp );
cvReleaseMat( &result ); cvReleaseMat( &result );
cvReleaseMat( &data ); cvReleaseMat( &data );