generic-library/opencv
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made everything compile and even run somehow

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This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
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72
modules/ml/include/opencv2/ml.hpp
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@@ -135,7 +135,7 @@ public:
virtual Mat getCatMap() const = 0;
virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
virtual void setTrainTestSplitRatio(float ratio, bool shuffle=true) = 0;
virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
virtual void shuffleTrainTest() = 0;
static Mat getSubVector(const Mat& vec, const Mat& idx);
@@ -156,7 +156,6 @@ class CV_EXPORTS_W StatModel : public Algorithm
{
public:
enum { UPDATE_MODEL = 1, RAW_OUTPUT=1, COMPRESSED_INPUT=2, PREPROCESSED_INPUT=4 };
virtual ~StatModel();
virtual void clear();
virtual int getVarCount() const = 0;
@@ -164,16 +163,30 @@ public:
virtual bool isTrained() const = 0;
virtual bool isClassifier() const = 0;
virtual bool train( const Ptr<TrainData>& trainData, int flags=0 ) = 0;
virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
virtual bool train( InputArray samples, int layout, InputArray responses );
virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
template<typename _Tp> static Ptr<_Tp> load(const String& filename)
{
FileStorage fs(filename, FileStorage::READ);
Ptr<_Tp> p = _Tp::create();
p->read(fs.getFirstTopLevelNode());
return p->isTrained() ? p : Ptr<_Tp>();
Ptr<_Tp> model = _Tp::create();
model->read(fs.getFirstTopLevelNode());
return model->isTrained() ? model : Ptr<_Tp>();
}
template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, const typename _Tp::Params& p, int flags=0)
{
Ptr<_Tp> model = _Tp::create(p);
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
}
template<typename _Tp> static Ptr<_Tp> train(InputArray samples, int layout, InputArray responses,
const typename _Tp::Params& p, int flags=0)
{
Ptr<_Tp> model = _Tp::create(p);
return !model.empty() && model->train(TrainData::create(samples, layout, responses), flags) ? model : Ptr<_Tp>();
}
virtual void save(const String& filename) const;
@@ -192,11 +205,17 @@ public:
class CV_EXPORTS_W NormalBayesClassifier : public StatModel
{
public:
virtual ~NormalBayesClassifier();
class CV_EXPORTS_W_MAP Params
{
public:
Params();
};
virtual float predictProb( InputArray inputs, OutputArray outputs,
OutputArray outputProbs, int flags=0 ) const = 0;
virtual void setParams(const Params& params) = 0;
virtual Params getParams() const = 0;
static Ptr<NormalBayesClassifier> create();
static Ptr<NormalBayesClassifier> create(const Params& params=Params());
};
/****************************************************************************************\
@@ -207,13 +226,21 @@ public:
class CV_EXPORTS_W KNearest : public StatModel
{
public:
virtual void setDefaultK(int k) = 0;
virtual int getDefaultK() const = 0;
class CV_EXPORTS_W_MAP Params
{
public:
Params(int defaultK=10, bool isclassifier=true);
int defaultK;
bool isclassifier;
};
virtual void setParams(const Params& p) = 0;
virtual Params getParams() const = 0;
virtual float findNearest( InputArray samples, int k,
OutputArray results,
OutputArray neighborResponses=noArray(),
OutputArray dist=noArray() ) const = 0;
static Ptr<KNearest> create(bool isclassifier=true);
static Ptr<KNearest> create(const Params& params=Params());
};
/****************************************************************************************\
@@ -247,7 +274,6 @@ public:
class CV_EXPORTS Kernel : public Algorithm
{
public:
virtual ~Kernel();
virtual int getType() const = 0;
virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
};
@@ -261,8 +287,6 @@ public:
// SVM params type
enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
virtual ~SVM();
virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
@@ -399,8 +423,6 @@ public:
int subsetOfs;
};
virtual ~DTrees();
virtual void setDParams(const Params& p);
virtual Params getDParams() const;
@@ -464,7 +486,6 @@ public:
// Boosting type
enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
virtual ~Boost();
virtual Params getBParams() const = 0;
virtual void setBParams(const Params& p) = 0;
@@ -491,7 +512,6 @@ public:
};
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
virtual ~GBTrees();
virtual void setK(int k) = 0;
@@ -513,10 +533,16 @@ public:
struct CV_EXPORTS_W_MAP Params
{
Params();
Params( TermCriteria termCrit, int trainMethod, double param1, double param2=0 );
Params( const Mat& layerSizes, int activateFunc, double fparam1, double fparam2,
TermCriteria termCrit, int trainMethod, double param1, double param2=0 );
enum { BACKPROP=0, RPROP=1 };
CV_PROP_RW Mat layerSizes;
CV_PROP_RW int activateFunc;
CV_PROP_RW double fparam1;
CV_PROP_RW double fparam2;
CV_PROP_RW TermCriteria termCrit;
CV_PROP_RW int trainMethod;
@@ -527,23 +553,17 @@ public:
CV_PROP_RW double rpDW0, rpDWPlus, rpDWMinus, rpDWMin, rpDWMax;
};
virtual ~ANN_MLP();
// possible activation functions
enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };
// available training flags
enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };
virtual Mat getLayerSizes() const = 0;
virtual Mat getWeights(int layerIdx) const = 0;
virtual void setParams(const Params& p) = 0;
virtual Params getParams() const = 0;
static Ptr<ANN_MLP> create(InputArray layerSizes=noArray(),
const Params& params=Params(),
int activateFunc=ANN_MLP::SIGMOID_SYM,
double fparam1=0, double fparam2=0);
static Ptr<ANN_MLP> create(const Params& params=Params());
};
/****************************************************************************************\