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#include "_ml.h"
#if 0
ML_IMPL int
icvCmpIntegers (const void* a, const void* b) {return *(const int*)a - *(const int*)b;}
/****************************************************************************************\
* Cross-validation algorithms realizations *
\****************************************************************************************/
// Return pointer to trainIdx. Function DOES NOT FILL this matrix!
ML_IMPL
const CvMat* cvCrossValGetTrainIdxMatrix (const CvStatModel* estimateModel)
{
CvMat* result = NULL;
CV_FUNCNAME ("cvCrossValGetTrainIdxMatrix");
__BEGIN__
if (!CV_IS_CROSSVAL(estimateModel))
{
CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
}
result = ((CvCrossValidationModel*)estimateModel)->sampleIdxTrain;
__END__
return result;
} // End of cvCrossValGetTrainIdxMatrix
/****************************************************************************************/
// Return pointer to checkIdx. Function DOES NOT FILL this matrix!
ML_IMPL
const CvMat* cvCrossValGetCheckIdxMatrix (const CvStatModel* estimateModel)
{
CvMat* result = NULL;
CV_FUNCNAME ("cvCrossValGetCheckIdxMatrix");
__BEGIN__
if (!CV_IS_CROSSVAL (estimateModel))
{
CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
}
result = ((CvCrossValidationModel*)estimateModel)->sampleIdxEval;
__END__
return result;
} // End of cvCrossValGetCheckIdxMatrix
/****************************************************************************************/
// Create new Idx-matrix for next classifiers training and return code of result.
// Result is 0 if function can't make next step (error input or folds are finished),
// it is 1 if all was correct, and it is 2 if current fold wasn't' checked.
ML_IMPL
int cvCrossValNextStep (CvStatModel* estimateModel)
{
int result = 0;
CV_FUNCNAME ("cvCrossValGetNextTrainIdx");
__BEGIN__
CvCrossValidationModel* crVal = (CvCrossValidationModel*) estimateModel;
int k, fold;
if (!CV_IS_CROSSVAL (estimateModel))
{
CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
}
fold = ++crVal->current_fold;
if (fold >= crVal->folds_all)
{
if (fold == crVal->folds_all)
EXIT;
else
{
CV_ERROR (CV_StsInternal, "All iterations has end long ago");
}
}
k = crVal->folds[fold + 1] - crVal->folds[fold];
crVal->sampleIdxTrain->data.i = crVal->sampleIdxAll + crVal->folds[fold + 1];
crVal->sampleIdxTrain->cols = crVal->samples_all - k;
crVal->sampleIdxEval->data.i = crVal->sampleIdxAll + crVal->folds[fold];
crVal->sampleIdxEval->cols = k;
if (crVal->is_checked)
{
crVal->is_checked = 0;
result = 1;
}
else
{
result = 2;
}
__END__
return result;
}
/****************************************************************************************/
// Do checking part of loop of cross-validations metod.
ML_IMPL
void cvCrossValCheckClassifier (CvStatModel* estimateModel,
const CvStatModel* model,
const CvMat* trainData,
int sample_t_flag,
const CvMat* trainClasses)
{
CV_FUNCNAME ("cvCrossValCheckClassifier ");
__BEGIN__
CvCrossValidationModel* crVal = (CvCrossValidationModel*) estimateModel;
int i, j, k;
int* data;
float* responses_fl;
int step;
float* responses_result;
int* responses_i;
double te, te1;
double sum_c, sum_p, sum_pp, sum_cp, sum_cc, sq_err;
// Check input data to correct values.
if (!CV_IS_CROSSVAL (estimateModel))
{
CV_ERROR (CV_StsBadArg,"First parameter point to not CvCrossValidationModel");
}
if (!CV_IS_STAT_MODEL (model))
{
CV_ERROR (CV_StsBadArg, "Second parameter point to not CvStatModel");
}
if (!CV_IS_MAT (trainData))
{
CV_ERROR (CV_StsBadArg, "Third parameter point to not CvMat");
}
if (!CV_IS_MAT (trainClasses))
{
CV_ERROR (CV_StsBadArg, "Fifth parameter point to not CvMat");
}
if (crVal->is_checked)
{
CV_ERROR (CV_StsInternal, "This iterations already was checked");
}
// Initialize.
k = crVal->sampleIdxEval->cols;
data = crVal->sampleIdxEval->data.i;
// Eval tested feature vectors.
CV_CALL (cvStatModelMultiPredict (model, trainData, sample_t_flag,
crVal->predict_results, NULL, crVal->sampleIdxEval));
// Count number if correct results.
responses_result = crVal->predict_results->data.fl;
if (crVal->is_regression)
{
sum_c = sum_p = sum_pp = sum_cp = sum_cc = sq_err = 0;
if (CV_MAT_TYPE (trainClasses->type) == CV_32FC1)
{
responses_fl = trainClasses->data.fl;
step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(float);
for (i = 0; i < k; i++)
{
te = responses_result[*data];
te1 = responses_fl[*data * step];
sum_c += te1;
sum_p += te;
sum_cc += te1 * te1;
sum_pp += te * te;
sum_cp += te1 * te;
te -= te1;
sq_err += te * te;
data++;
}
}
else
{
responses_i = trainClasses->data.i;
step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(int);
for (i = 0; i < k; i++)
{
te = responses_result[*data];
te1 = responses_i[*data * step];
sum_c += te1;
sum_p += te;
sum_cc += te1 * te1;
sum_pp += te * te;
sum_cp += te1 * te;
te -= te1;
sq_err += te * te;
data++;
}
}
// Fixing new internal values of accuracy.
crVal->sum_correct += sum_c;
crVal->sum_predict += sum_p;
crVal->sum_cc += sum_cc;
crVal->sum_pp += sum_pp;
crVal->sum_cp += sum_cp;
crVal->sq_error += sq_err;
}
else
{
if (CV_MAT_TYPE (trainClasses->type) == CV_32FC1)
{
responses_fl = trainClasses->data.fl;
step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(float);
for (i = 0, j = 0; i < k; i++)
{
if (cvRound (responses_result[*data]) == cvRound (responses_fl[*data * step]))
j++;
data++;
}
}
else
{
responses_i = trainClasses->data.i;
step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(int);
for (i = 0, j = 0; i < k; i++)
{
if (cvRound (responses_result[*data]) == responses_i[*data * step])
j++;
data++;
}
}
// Fixing new internal values of accuracy.
crVal->correct_results += j;
}
// Fixing that this fold already checked.
crVal->all_results += k;
crVal->is_checked = 1;
__END__
} // End of cvCrossValCheckClassifier
/****************************************************************************************/
// Return current accuracy.
ML_IMPL
float cvCrossValGetResult (const CvStatModel* estimateModel,
float* correlation)
{
float result = 0;
CV_FUNCNAME ("cvCrossValGetResult");
__BEGIN__
double te, te1;
CvCrossValidationModel* crVal = (CvCrossValidationModel*)estimateModel;
if (!CV_IS_CROSSVAL (estimateModel))
{
CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
}
if (crVal->all_results)
{
if (crVal->is_regression)
{
result = ((float)crVal->sq_error) / crVal->all_results;
if (correlation)
{
te = crVal->all_results * crVal->sum_cp -
crVal->sum_correct * crVal->sum_predict;
te *= te;
te1 = (crVal->all_results * crVal->sum_cc -
crVal->sum_correct * crVal->sum_correct) *
(crVal->all_results * crVal->sum_pp -
crVal->sum_predict * crVal->sum_predict);
*correlation = (float)(te / te1);
}
}
else
{
result = ((float)crVal->correct_results) / crVal->all_results;
}
}
__END__
return result;
}
/****************************************************************************************/
// Reset cross-validation EstimateModel to state the same as it was immidiatly after
// its creating.
ML_IMPL
void cvCrossValReset (CvStatModel* estimateModel)
{
CV_FUNCNAME ("cvCrossValReset");
__BEGIN__
CvCrossValidationModel* crVal = (CvCrossValidationModel*)estimateModel;
if (!CV_IS_CROSSVAL (estimateModel))
{
CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
}
crVal->current_fold = -1;
crVal->is_checked = 1;
crVal->all_results = 0;
crVal->correct_results = 0;
crVal->sq_error = 0;
crVal->sum_correct = 0;
crVal->sum_predict = 0;
crVal->sum_cc = 0;
crVal->sum_pp = 0;
crVal->sum_cp = 0;
__END__
}
/****************************************************************************************/
// This function is standart CvStatModel field to release cross-validation EstimateModel.
ML_IMPL
void cvReleaseCrossValidationModel (CvStatModel** model)
{
CvCrossValidationModel* pModel;
CV_FUNCNAME ("cvReleaseCrossValidationModel");
__BEGIN__
if (!model)
{
CV_ERROR (CV_StsNullPtr, "");
}
pModel = (CvCrossValidationModel*)*model;
if (!pModel)
{
return;
}
if (!CV_IS_CROSSVAL (pModel))
{
CV_ERROR (CV_StsBadArg, "");
}
cvFree (&pModel->sampleIdxAll);
cvFree (&pModel->folds);
cvReleaseMat (&pModel->sampleIdxEval);
cvReleaseMat (&pModel->sampleIdxTrain);
cvReleaseMat (&pModel->predict_results);
cvFree (model);
__END__
} // End of cvReleaseCrossValidationModel.
/****************************************************************************************/
// This function create cross-validation EstimateModel.
ML_IMPL CvStatModel*
cvCreateCrossValidationEstimateModel(
int samples_all,
const CvStatModelParams* estimateParams,
const CvMat* sampleIdx)
{
CvStatModel* model = NULL;
CvCrossValidationModel* crVal = NULL;
CV_FUNCNAME ("cvCreateCrossValidationEstimateModel");
__BEGIN__
int k_fold = 10;
int i, j, k, s_len;
int samples_selected;
CvRNG rng;
CvRNG* prng;
int* res_s_data;
int* te_s_data;
int* folds;
rng = cvRNG(cvGetTickCount());
cvRandInt (&rng); cvRandInt (&rng); cvRandInt (&rng); cvRandInt (&rng);
// Check input parameters.
if (estimateParams)
k_fold = ((CvCrossValidationParams*)estimateParams)->k_fold;
if (!k_fold)
{
CV_ERROR (CV_StsBadArg, "Error in parameters of cross-validation (k_fold == 0)!");
}
if (samples_all <= 0)
{
CV_ERROR (CV_StsBadArg, "<samples_all> should be positive!");
}
// Alloc memory and fill standart StatModel's fields.
CV_CALL (crVal = (CvCrossValidationModel*)cvCreateStatModel (
CV_STAT_MODEL_MAGIC_VAL | CV_CROSSVAL_MAGIC_VAL,
sizeof(CvCrossValidationModel),
cvReleaseCrossValidationModel,
NULL, NULL));
crVal->current_fold = -1;
crVal->folds_all = k_fold;
if (estimateParams && ((CvCrossValidationParams*)estimateParams)->is_regression)
crVal->is_regression = 1;
else
crVal->is_regression = 0;
if (estimateParams && ((CvCrossValidationParams*)estimateParams)->rng)
prng = ((CvCrossValidationParams*)estimateParams)->rng;
else
prng = &rng;
// Check and preprocess sample indices.
if (sampleIdx)
{
int s_step;
int s_type = 0;
if (!CV_IS_MAT (sampleIdx))
CV_ERROR (CV_StsBadArg, "Invalid sampleIdx array");
if (sampleIdx->rows != 1 && sampleIdx->cols != 1)
CV_ERROR (CV_StsBadSize, "sampleIdx array must be 1-dimensional");
s_len = sampleIdx->rows + sampleIdx->cols - 1;
s_step = sampleIdx->rows == 1 ?
1 : sampleIdx->step / CV_ELEM_SIZE(sampleIdx->type);
s_type = CV_MAT_TYPE (sampleIdx->type);
switch (s_type)
{
case CV_8UC1:
case CV_8SC1:
{
uchar* s_data = sampleIdx->data.ptr;
// sampleIdx is array of 1's and 0's -
// i.e. it is a mask of the selected samples
if( s_len != samples_all )
CV_ERROR (CV_StsUnmatchedSizes,
"Sample mask should contain as many elements as the total number of samples");
samples_selected = 0;
for (i = 0; i < s_len; i++)
samples_selected += s_data[i * s_step] != 0;
if (samples_selected == 0)
CV_ERROR (CV_StsOutOfRange, "No samples is selected!");
}
s_len = samples_selected;
break;
case CV_32SC1:
if (s_len > samples_all)
CV_ERROR (CV_StsOutOfRange,
"sampleIdx array may not contain more elements than the total number of samples");
samples_selected = s_len;
break;
default:
CV_ERROR (CV_StsUnsupportedFormat, "Unsupported sampleIdx array data type "
"(it should be 8uC1, 8sC1 or 32sC1)");
}
// Alloc additional memory for internal Idx and fill it.
/*!!*/ CV_CALL (res_s_data = crVal->sampleIdxAll =
(int*)cvAlloc (2 * s_len * sizeof(int)));
if (s_type < CV_32SC1)
{
uchar* s_data = sampleIdx->data.ptr;
for (i = 0; i < s_len; i++)
if (s_data[i * s_step])
{
*res_s_data++ = i;
}
res_s_data = crVal->sampleIdxAll;
}
else
{
int* s_data = sampleIdx->data.i;
int out_of_order = 0;
for (i = 0; i < s_len; i++)
{
res_s_data[i] = s_data[i * s_step];
if (i > 0 && res_s_data[i] < res_s_data[i - 1])
out_of_order = 1;
}
if (out_of_order)
qsort (res_s_data, s_len, sizeof(res_s_data[0]), icvCmpIntegers);
if (res_s_data[0] < 0 ||
res_s_data[s_len - 1] >= samples_all)
CV_ERROR (CV_StsBadArg, "There are out-of-range sample indices");
for (i = 1; i < s_len; i++)
if (res_s_data[i] <= res_s_data[i - 1])
CV_ERROR (CV_StsBadArg, "There are duplicated");
}
}
else // if (sampleIdx)
{
// Alloc additional memory for internal Idx and fill it.
s_len = samples_all;
CV_CALL (res_s_data = crVal->sampleIdxAll = (int*)cvAlloc (2 * s_len * sizeof(int)));
for (i = 0; i < s_len; i++)
{
*res_s_data++ = i;
}
res_s_data = crVal->sampleIdxAll;
} // if (sampleIdx) ... else
// Resort internal Idx.
te_s_data = res_s_data + s_len;
for (i = s_len; i > 1; i--)
{
j = cvRandInt (prng) % i;
k = *(--te_s_data);
*te_s_data = res_s_data[j];
res_s_data[j] = k;
}
// Duplicate resorted internal Idx.
// It will be used to simplify operation of getting trainIdx.
te_s_data = res_s_data + s_len;
for (i = 0; i < s_len; i++)
{
*te_s_data++ = *res_s_data++;
}
// Cut sampleIdxAll to parts.
if (k_fold > 0)
{
if (k_fold > s_len)
{
CV_ERROR (CV_StsBadArg,
"Error in parameters of cross-validation ('k_fold' > #samples)!");
}
folds = crVal->folds = (int*) cvAlloc ((k_fold + 1) * sizeof (int));
*folds++ = 0;
for (i = 1; i < k_fold; i++)
{
*folds++ = cvRound (i * s_len * 1. / k_fold);
}
*folds = s_len;
folds = crVal->folds;
crVal->max_fold_size = (s_len - 1) / k_fold + 1;
}
else
{
k = -k_fold;
crVal->max_fold_size = k;
if (k >= s_len)
{
CV_ERROR (CV_StsBadArg,
"Error in parameters of cross-validation (-'k_fold' > #samples)!");
}
crVal->folds_all = k = (s_len - 1) / k + 1;
folds = crVal->folds = (int*) cvAlloc ((k + 1) * sizeof (int));
for (i = 0; i < k; i++)
{
*folds++ = -i * k_fold;
}
*folds = s_len;
folds = crVal->folds;
}
// Prepare other internal fields to working.
CV_CALL (crVal->predict_results = cvCreateMat (1, samples_all, CV_32FC1));
CV_CALL (crVal->sampleIdxEval = cvCreateMatHeader (1, 1, CV_32SC1));
CV_CALL (crVal->sampleIdxTrain = cvCreateMatHeader (1, 1, CV_32SC1));
crVal->sampleIdxEval->cols = 0;
crVal->sampleIdxTrain->cols = 0;
crVal->samples_all = s_len;
crVal->is_checked = 1;
crVal->getTrainIdxMat = cvCrossValGetTrainIdxMatrix;
crVal->getCheckIdxMat = cvCrossValGetCheckIdxMatrix;
crVal->nextStep = cvCrossValNextStep;
crVal->check = cvCrossValCheckClassifier;
crVal->getResult = cvCrossValGetResult;
crVal->reset = cvCrossValReset;
model = (CvStatModel*)crVal;
__END__
if (!model)
{
cvReleaseCrossValidationModel ((CvStatModel**)&crVal);
}
return model;
} // End of cvCreateCrossValidationEstimateModel
/****************************************************************************************\
* Extended interface with backcalls for models *
\****************************************************************************************/
ML_IMPL float
cvCrossValidation (const CvMat* trueData,
int tflag,
const CvMat* trueClasses,
CvStatModel* (*createClassifier) (const CvMat*,
int,
const CvMat*,
const CvClassifierTrainParams*,
const CvMat*,
const CvMat*,
const CvMat*,
const CvMat*),
const CvClassifierTrainParams* estimateParams,
const CvClassifierTrainParams* trainParams,
const CvMat* compIdx,
const CvMat* sampleIdx,
CvStatModel** pCrValModel,
const CvMat* typeMask,
const CvMat* missedMeasurementMask)
{
CvCrossValidationModel* crVal = NULL;
float result = 0;
CvStatModel* pClassifier = NULL;
CV_FUNCNAME ("cvCrossValidation");
__BEGIN__
const CvMat* trainDataIdx;
int samples_all;
// checking input data
if ((createClassifier) == NULL)
{
CV_ERROR (CV_StsNullPtr, "Null pointer to functiion which create classifier");
}
if (pCrValModel && *pCrValModel && !CV_IS_CROSSVAL(*pCrValModel))
{
CV_ERROR (CV_StsBadArg,
"<pCrValModel> point to not cross-validation model");
}
// initialization
if (pCrValModel && *pCrValModel)
{
crVal = (CvCrossValidationModel*)*pCrValModel;
crVal->reset ((CvStatModel*)crVal);
}
else
{
samples_all = ((tflag) ? trueData->rows : trueData->cols);
CV_CALL (crVal = (CvCrossValidationModel*)
cvCreateCrossValidationEstimateModel (samples_all, estimateParams, sampleIdx));
}
CV_CALL (trainDataIdx = crVal->getTrainIdxMat ((CvStatModel*)crVal));
// operation loop
for (; crVal->nextStep((CvStatModel*)crVal) != 0; )
{
CV_CALL (pClassifier = createClassifier (trueData, tflag, trueClasses,
trainParams, compIdx, trainDataIdx, typeMask, missedMeasurementMask));
CV_CALL (crVal->check ((CvStatModel*)crVal, pClassifier,
trueData, tflag, trueClasses));
pClassifier->release (&pClassifier);
}
// Get result and fill output field.
CV_CALL (result = crVal->getResult ((CvStatModel*)crVal, 0));
if (pCrValModel && !*pCrValModel)
*pCrValModel = (CvStatModel*)crVal;
__END__
// Free all memory that should be freed.
if (pClassifier)
pClassifier->release (&pClassifier);
if (crVal && (!pCrValModel || !*pCrValModel))
crVal->release ((CvStatModel**)&crVal);
return result;
} // End of cvCrossValidation
#endif
/* End of file */