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This commit is contained in:
OpenCV Buildbot
2012-10-17 03:18:30 +04:00
committed by Andrey Kamaev
parent 69020da607
commit 04384a71e4
1516 changed files with 258846 additions and 258162 deletions
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62
modules/ml/src/estimate.cpp
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@@ -149,8 +149,8 @@ int cvCrossValNextStep (CvStatModel* estimateModel)
// Do checking part of loop of cross-validations metod.
ML_IMPL
void cvCrossValCheckClassifier (CvStatModel* estimateModel,
const CvStatModel* model,
const CvMat* trainData,
const CvStatModel* model,
const CvMat* trainData,
int sample_t_flag,
const CvMat* trainClasses)
{
@@ -194,7 +194,7 @@ void cvCrossValCheckClassifier (CvStatModel* estimateModel,
data = crVal->sampleIdxEval->data.i;
// Eval tested feature vectors.
CV_CALL (cvStatModelMultiPredict (model, trainData, sample_t_flag,
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;
@@ -307,12 +307,12 @@ float cvCrossValGetResult (const CvStatModel* estimateModel,
result = ((float)crVal->sq_error) / crVal->all_results;
if (correlation)
{
te = crVal->all_results * crVal->sum_cp -
te = crVal->all_results * crVal->sum_cp -
crVal->sum_correct * crVal->sum_predict;
te *= te;
te1 = (crVal->all_results * crVal->sum_cc -
te1 = (crVal->all_results * crVal->sum_cc -
crVal->sum_correct * crVal->sum_correct) *
(crVal->all_results * crVal->sum_pp -
(crVal->all_results * crVal->sum_pp -
crVal->sum_predict * crVal->sum_predict);
*correlation = (float)(te / te1);
@@ -330,7 +330,7 @@ float cvCrossValGetResult (const CvStatModel* estimateModel,
}
/****************************************************************************************/
// Reset cross-validation EstimateModel to state the same as it was immidiatly after
// Reset cross-validation EstimateModel to state the same as it was immidiatly after
// its creating.
ML_IMPL
void cvCrossValReset (CvStatModel* estimateModel)
@@ -368,7 +368,7 @@ void cvReleaseCrossValidationModel (CvStatModel** model)
CV_FUNCNAME ("cvReleaseCrossValidationModel");
__BEGIN__
if (!model)
{
CV_ERROR (CV_StsNullPtr, "");
@@ -397,7 +397,7 @@ void cvReleaseCrossValidationModel (CvStatModel** model)
/****************************************************************************************/
// This function create cross-validation EstimateModel.
ML_IMPL CvStatModel*
ML_IMPL CvStatModel*
cvCreateCrossValidationEstimateModel(
int samples_all,
const CvStatModelParams* estimateParams,
@@ -413,7 +413,7 @@ cvCreateCrossValidationEstimateModel(
int i, j, k, s_len;
int samples_selected;
CvRNG rng;
CvRNG rng;
CvRNG* prng;
int* res_s_data;
int* te_s_data;
@@ -435,7 +435,7 @@ cvCreateCrossValidationEstimateModel(
// Alloc memory and fill standart StatModel's fields.
CV_CALL (crVal = (CvCrossValidationModel*)cvCreateStatModel (
CV_STAT_MODEL_MAGIC_VAL | CV_CROSSVAL_MAGIC_VAL,
CV_STAT_MODEL_MAGIC_VAL | CV_CROSSVAL_MAGIC_VAL,
sizeof(CvCrossValidationModel),
cvReleaseCrossValidationModel,
NULL, NULL));
@@ -443,7 +443,7 @@ cvCreateCrossValidationEstimateModel(
crVal->folds_all = k_fold;
if (estimateParams && ((CvCrossValidationParams*)estimateParams)->is_regression)
crVal->is_regression = 1;
else
else
crVal->is_regression = 0;
if (estimateParams && ((CvCrossValidationParams*)estimateParams)->rng)
prng = ((CvCrossValidationParams*)estimateParams)->rng;
@@ -455,7 +455,7 @@ cvCreateCrossValidationEstimateModel(
{
int s_step;
int s_type = 0;
if (!CV_IS_MAT (sampleIdx))
CV_ERROR (CV_StsBadArg, "Invalid sampleIdx array");
@@ -463,7 +463,7 @@ cvCreateCrossValidationEstimateModel(
CV_ERROR (CV_StsBadSize, "sampleIdx array must be 1-dimensional");
s_len = sampleIdx->rows + sampleIdx->cols - 1;
s_step = sampleIdx->rows == 1 ?
s_step = sampleIdx->rows == 1 ?
1 : sampleIdx->step / CV_ELEM_SIZE(sampleIdx->type);
s_type = CV_MAT_TYPE (sampleIdx->type);
@@ -474,13 +474,13 @@ cvCreateCrossValidationEstimateModel(
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;
@@ -502,7 +502,7 @@ cvCreateCrossValidationEstimateModel(
}
// Alloc additional memory for internal Idx and fill it.
/*!!*/ CV_CALL (res_s_data = crVal->sampleIdxAll =
/*!!*/ CV_CALL (res_s_data = crVal->sampleIdxAll =
(int*)cvAlloc (2 * s_len * sizeof(int)));
if (s_type < CV_32SC1)
@@ -529,7 +529,7 @@ cvCreateCrossValidationEstimateModel(
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");
@@ -548,7 +548,7 @@ cvCreateCrossValidationEstimateModel(
*res_s_data++ = i;
}
res_s_data = crVal->sampleIdxAll;
} // if (sampleIdx) ... else
} // if (sampleIdx) ... else
// Resort internal Idx.
te_s_data = res_s_data + s_len;
@@ -560,7 +560,7 @@ cvCreateCrossValidationEstimateModel(
res_s_data[j] = k;
}
// Duplicate resorted internal Idx.
// 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++)
@@ -573,7 +573,7 @@ cvCreateCrossValidationEstimateModel(
{
if (k_fold > s_len)
{
CV_ERROR (CV_StsBadArg,
CV_ERROR (CV_StsBadArg,
"Error in parameters of cross-validation ('k_fold' > #samples)!");
}
folds = crVal->folds = (int*) cvAlloc ((k_fold + 1) * sizeof (int));
@@ -593,7 +593,7 @@ cvCreateCrossValidationEstimateModel(
crVal->max_fold_size = k;
if (k >= s_len)
{
CV_ERROR (CV_StsBadArg,
CV_ERROR (CV_StsBadArg,
"Error in parameters of cross-validation (-'k_fold' > #samples)!");
}
crVal->folds_all = k = (s_len - 1) / k + 1;
@@ -634,7 +634,7 @@ cvCreateCrossValidationEstimateModel(
return model;
} // End of cvCreateCrossValidationEstimateModel
/****************************************************************************************\
* Extended interface with backcalls for models *
@@ -643,13 +643,13 @@ ML_IMPL float
cvCrossValidation (const CvMat* trueData,
int tflag,
const CvMat* trueClasses,
CvStatModel* (*createClassifier) (const CvMat*,
int,
CvStatModel* (*createClassifier) (const CvMat*,
int,
const CvMat*,
const CvClassifierTrainParams*,
const CvMat*,
const CvMat*,
const CvMat*,
const CvMat*,
const CvMat*,
const CvMat*,
const CvMat*),
const CvClassifierTrainParams* estimateParams,
const CvClassifierTrainParams* trainParams,
@@ -676,7 +676,7 @@ cvCrossValidation (const CvMat* trueData,
}
if (pCrValModel && *pCrValModel && !CV_IS_CROSSVAL(*pCrValModel))
{
CV_ERROR (CV_StsBadArg,
CV_ERROR (CV_StsBadArg,
"<pCrValModel> point to not cross-validation model");
}
@@ -698,9 +698,9 @@ cvCrossValidation (const CvMat* trueData,
// operation loop
for (; crVal->nextStep((CvStatModel*)crVal) != 0; )
{
CV_CALL (pClassifier = createClassifier (trueData, tflag, trueClasses,
CV_CALL (pClassifier = createClassifier (trueData, tflag, trueClasses,
trainParams, compIdx, trainDataIdx, typeMask, missedMeasurementMask));
CV_CALL (crVal->check ((CvStatModel*)crVal, pClassifier,
CV_CALL (crVal->check ((CvStatModel*)crVal, pClassifier,
trueData, tflag, trueClasses));
pClassifier->release (&pClassifier);