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Merged the trunk r8589:8653 - all changes related to build warnings

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This commit is contained in:
Andrey Kamaev
2012-06-15 13:04:17 +00:00
parent 73c152abc4
commit bd0e0b5800
438 changed files with 20374 additions and 19674 deletions
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54
modules/ml/src/ann_mlp.cpp
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@@ -504,7 +504,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df,
n *= cols;
xf -= n; df -= n;
for( i = 0; i < n; i++ )
df[i] *= xf[i];
}
@@ -517,7 +517,7 @@ void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df,
xf[j] = (xf[j] + bias[j])*scale;
df[j] = -fabs(xf[j]);
}
cvExp( _df, _df );
n *= cols;
@@ -1023,9 +1023,9 @@ int CvANN_MLP::train_backprop( CvVectors x0, CvVectors u, const double* sw )
}
struct rprop_loop {
rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0,
rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0,
int& _l_count, CvMat*& _layer_sizes, int& _ovcount, int& _max_count,
CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz)
CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz)
{
point = _point;
weights = _weights;
@@ -1044,7 +1044,7 @@ struct rprop_loop {
E = _E;
buf_sz = _buf_sz;
}
const CvANN_MLP* point;
double** weights;
int count;
@@ -1062,14 +1062,14 @@ struct rprop_loop {
double* E;
int buf_sz;
void operator()( const cv::BlockedRange& range ) const
{
double* buf_ptr;
double** x = 0;
double **df = 0;
double **df = 0;
int total = 0;
for(int i = 0; i < l_count; i++ )
total += layer_sizes->data.i[i];
CvMat* buf;
@@ -1087,7 +1087,7 @@ struct rprop_loop {
for(int si = range.begin(); si < range.end(); si++ )
{
if (si % dcount0 != 0) continue;
int n1, n2, j, k;
int n1, n2, k;
double* w;
CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df;
CvMat *x1, *x2, *grad1, *grad2, *temp;
@@ -1100,23 +1100,23 @@ struct rprop_loop {
// grab and preprocess input data
if( x0->type == CV_32F )
{
{
for(int i = 0; i < dcount; i++ )
{
const float* x0data = x0->data.fl[si+i];
double* xdata = x[0]+i*ivcount;
for( j = 0; j < ivcount; j++ )
for(int j = 0; j < ivcount; j++ )
xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
}
}
}
else
for(int i = 0; i < dcount; i++ )
{
const double* x0data = x0->data.db[si+i];
double* xdata = x[0]+i*ivcount;
for( j = 0; j < ivcount; j++ )
for(int j = 0; j < ivcount; j++ )
xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
}
}
cvInitMatHeader( x1, dcount, ivcount, CV_64F, x[0] );
// forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i])
@@ -1144,7 +1144,7 @@ struct rprop_loop {
double* gdata = grad1->data.db + i*ovcount;
double sweight = sw ? sw[si+i] : inv_count, E1 = 0;
for( j = 0; j < ovcount; j++ )
for(int j = 0; j < ovcount; j++ )
{
double t = udata[j]*w[j*2] + w[j*2+1] - xdata[j];
gdata[j] = t*sweight;
@@ -1168,7 +1168,7 @@ struct rprop_loop {
}
*E += sweight*E1;
}
// backward pass, update dEdw
#ifdef HAVE_TBB
static tbb::spin_mutex mutex;
@@ -1191,10 +1191,10 @@ struct rprop_loop {
{
double* dst = _dEdw.data.db + n1*n2;
const double* src = grad1->data.db + k*n2;
for( j = 0; j < n2; j++ )
for(int j = 0; j < n2; j++ )
dst[j] += src[j];
}
if (i > 1)
cvInitMatHeader( &_w, n1, n2, CV_64F, weights[i] );
#ifdef HAVE_TBB
@@ -1215,7 +1215,7 @@ struct rprop_loop {
int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
{
const int max_buf_sz = 1 << 16;
const int max_buf_size = 1 << 16;
CvMat* dw = 0;
CvMat* dEdw = 0;
CvMat* prev_dEdw_sign = 0;
@@ -1256,7 +1256,7 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
cvZero( prev_dEdw_sign );
inv_count = 1./count;
dcount0 = max_buf_sz/(2*total);
dcount0 = max_buf_size/(2*total);
dcount0 = MAX( dcount0, 1 );
dcount0 = MIN( dcount0, count );
buf_sz = dcount0*(total + max_count)*2;
@@ -1297,8 +1297,8 @@ int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
double E = 0;
// first, iterate through all the samples and compute dEdw
cv::parallel_for(cv::BlockedRange(0, count),
rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes,
cv::parallel_for(cv::BlockedRange(0, count),
rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes,
ovcount, max_count, &u, sw, inv_count, dEdw, dcount0, &E, buf_sz)
);
@@ -1600,8 +1600,8 @@ CvANN_MLP::CvANN_MLP( const Mat& _layer_sizes, int _activ_func,
void CvANN_MLP::create( const Mat& _layer_sizes, int _activ_func,
double _f_param1, double _f_param2 )
{
CvMat layer_sizes = _layer_sizes;
create( &layer_sizes, _activ_func, _f_param1, _f_param2 );
CvMat cvlayer_sizes = _layer_sizes;
create( &cvlayer_sizes, _activ_func, _f_param1, _f_param2 );
}
int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs,
@@ -1610,7 +1610,7 @@ int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs,
{
CvMat inputs = _inputs, outputs = _outputs, sweights = _sample_weights, sidx = _sample_idx;
return train(&inputs, &outputs, sweights.data.ptr ? &sweights : 0,
sidx.data.ptr ? &sidx : 0, _params, flags);
sidx.data.ptr ? &sidx : 0, _params, flags);
}
float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const
@@ -1618,8 +1618,8 @@ float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const
CV_Assert(layer_sizes != 0);
_outputs.create(_inputs.rows, layer_sizes->data.i[layer_sizes->cols-1], _inputs.type());
CvMat inputs = _inputs, outputs = _outputs;
return predict(&inputs, &outputs);
return predict(&inputs, &outputs);
}
/* End of file. */