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New translations svm_model_matlab.c (French)

l10n_v0.2.x
linlin 4 years ago
parent
bc0ea41c44
commit
9f6baa02c6
1 changed files with 374 additions and 0 deletions
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      lang/fr/gklearn/gedlib/lib/libsvm.3.22/matlab/svm_model_matlab.c

+ 374
- 0
lang/fr/gklearn/gedlib/lib/libsvm.3.22/matlab/svm_model_matlab.c View File

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#include <stdlib.h>
#include <string.h>
#include "svm.h"

#include "mex.h"

#ifdef MX_API_VER
#if MX_API_VER < 0x07030000
typedef int mwIndex;
#endif
#endif

#define NUM_OF_RETURN_FIELD 11

#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

static const char *field_names[] = {
"Parameters",
"nr_class",
"totalSV",
"rho",
"Label",
"sv_indices",
"ProbA",
"ProbB",
"nSV",
"sv_coef",
"SVs"
};

const char *model_to_matlab_structure(mxArray *plhs[], int num_of_feature, struct svm_model *model)
{
int i, j, n;
double *ptr;
mxArray *return_model, **rhs;
int out_id = 0;

rhs = (mxArray **)mxMalloc(sizeof(mxArray *)*NUM_OF_RETURN_FIELD);

// Parameters
rhs[out_id] = mxCreateDoubleMatrix(5, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
ptr[0] = model->param.svm_type;
ptr[1] = model->param.kernel_type;
ptr[2] = model->param.degree;
ptr[3] = model->param.gamma;
ptr[4] = model->param.coef0;
out_id++;

// nr_class
rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
ptr[0] = model->nr_class;
out_id++;

// total SV
rhs[out_id] = mxCreateDoubleMatrix(1, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
ptr[0] = model->l;
out_id++;

// rho
n = model->nr_class*(model->nr_class-1)/2;
rhs[out_id] = mxCreateDoubleMatrix(n, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < n; i++)
ptr[i] = model->rho[i];
out_id++;

// Label
if(model->label)
{
rhs[out_id] = mxCreateDoubleMatrix(model->nr_class, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < model->nr_class; i++)
ptr[i] = model->label[i];
}
else
rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
out_id++;

// sv_indices
if(model->sv_indices)
{
rhs[out_id] = mxCreateDoubleMatrix(model->l, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < model->l; i++)
ptr[i] = model->sv_indices[i];
}
else
rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
out_id++;

// probA
if(model->probA != NULL)
{
rhs[out_id] = mxCreateDoubleMatrix(n, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < n; i++)
ptr[i] = model->probA[i];
}
else
rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
out_id ++;

// probB
if(model->probB != NULL)
{
rhs[out_id] = mxCreateDoubleMatrix(n, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < n; i++)
ptr[i] = model->probB[i];
}
else
rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
out_id++;

// nSV
if(model->nSV)
{
rhs[out_id] = mxCreateDoubleMatrix(model->nr_class, 1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < model->nr_class; i++)
ptr[i] = model->nSV[i];
}
else
rhs[out_id] = mxCreateDoubleMatrix(0, 0, mxREAL);
out_id++;

// sv_coef
rhs[out_id] = mxCreateDoubleMatrix(model->l, model->nr_class-1, mxREAL);
ptr = mxGetPr(rhs[out_id]);
for(i = 0; i < model->nr_class-1; i++)
for(j = 0; j < model->l; j++)
ptr[(i*(model->l))+j] = model->sv_coef[i][j];
out_id++;

// SVs
{
int ir_index, nonzero_element;
mwIndex *ir, *jc;
mxArray *pprhs[1], *pplhs[1];

if(model->param.kernel_type == PRECOMPUTED)
{
nonzero_element = model->l;
num_of_feature = 1;
}
else
{
nonzero_element = 0;
for(i = 0; i < model->l; i++) {
j = 0;
while(model->SV[i][j].index != -1)
{
nonzero_element++;
j++;
}
}
}

// SV in column, easier accessing
rhs[out_id] = mxCreateSparse(num_of_feature, model->l, nonzero_element, mxREAL);
ir = mxGetIr(rhs[out_id]);
jc = mxGetJc(rhs[out_id]);
ptr = mxGetPr(rhs[out_id]);
jc[0] = ir_index = 0;
for(i = 0;i < model->l; i++)
{
if(model->param.kernel_type == PRECOMPUTED)
{
// make a (1 x model->l) matrix
ir[ir_index] = 0;
ptr[ir_index] = model->SV[i][0].value;
ir_index++;
jc[i+1] = jc[i] + 1;
}
else
{
int x_index = 0;
while (model->SV[i][x_index].index != -1)
{
ir[ir_index] = model->SV[i][x_index].index - 1;
ptr[ir_index] = model->SV[i][x_index].value;
ir_index++, x_index++;
}
jc[i+1] = jc[i] + x_index;
}
}
// transpose back to SV in row
pprhs[0] = rhs[out_id];
if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
return "cannot transpose SV matrix";
rhs[out_id] = pplhs[0];
out_id++;
}

/* Create a struct matrix contains NUM_OF_RETURN_FIELD fields */
return_model = mxCreateStructMatrix(1, 1, NUM_OF_RETURN_FIELD, field_names);

/* Fill struct matrix with input arguments */
for(i = 0; i < NUM_OF_RETURN_FIELD; i++)
mxSetField(return_model,0,field_names[i],mxDuplicateArray(rhs[i]));
/* return */
plhs[0] = return_model;
mxFree(rhs);

return NULL;
}

struct svm_model *matlab_matrix_to_model(const mxArray *matlab_struct, const char **msg)
{
int i, j, n, num_of_fields;
double *ptr;
int id = 0;
struct svm_node *x_space;
struct svm_model *model;
mxArray **rhs;

num_of_fields = mxGetNumberOfFields(matlab_struct);
if(num_of_fields != NUM_OF_RETURN_FIELD)
{
*msg = "number of return field is not correct";
return NULL;
}
rhs = (mxArray **) mxMalloc(sizeof(mxArray *)*num_of_fields);

for(i=0;i<num_of_fields;i++)
rhs[i] = mxGetFieldByNumber(matlab_struct, 0, i);

model = Malloc(struct svm_model, 1);
model->rho = NULL;
model->probA = NULL;
model->probB = NULL;
model->label = NULL;
model->sv_indices = NULL;
model->nSV = NULL;
model->free_sv = 1; // XXX

ptr = mxGetPr(rhs[id]);
model->param.svm_type = (int)ptr[0];
model->param.kernel_type = (int)ptr[1];
model->param.degree = (int)ptr[2];
model->param.gamma = ptr[3];
model->param.coef0 = ptr[4];
id++;

ptr = mxGetPr(rhs[id]);
model->nr_class = (int)ptr[0];
id++;

ptr = mxGetPr(rhs[id]);
model->l = (int)ptr[0];
id++;

// rho
n = model->nr_class * (model->nr_class-1)/2;
model->rho = (double*) malloc(n*sizeof(double));
ptr = mxGetPr(rhs[id]);
for(i=0;i<n;i++)
model->rho[i] = ptr[i];
id++;

// label
if(mxIsEmpty(rhs[id]) == 0)
{
model->label = (int*) malloc(model->nr_class*sizeof(int));
ptr = mxGetPr(rhs[id]);
for(i=0;i<model->nr_class;i++)
model->label[i] = (int)ptr[i];
}
id++;

// sv_indices
if(mxIsEmpty(rhs[id]) == 0)
{
model->sv_indices = (int*) malloc(model->l*sizeof(int));
ptr = mxGetPr(rhs[id]);
for(i=0;i<model->l;i++)
model->sv_indices[i] = (int)ptr[i];
}
id++;

// probA
if(mxIsEmpty(rhs[id]) == 0)
{
model->probA = (double*) malloc(n*sizeof(double));
ptr = mxGetPr(rhs[id]);
for(i=0;i<n;i++)
model->probA[i] = ptr[i];
}
id++;

// probB
if(mxIsEmpty(rhs[id]) == 0)
{
model->probB = (double*) malloc(n*sizeof(double));
ptr = mxGetPr(rhs[id]);
for(i=0;i<n;i++)
model->probB[i] = ptr[i];
}
id++;

// nSV
if(mxIsEmpty(rhs[id]) == 0)
{
model->nSV = (int*) malloc(model->nr_class*sizeof(int));
ptr = mxGetPr(rhs[id]);
for(i=0;i<model->nr_class;i++)
model->nSV[i] = (int)ptr[i];
}
id++;

// sv_coef
ptr = mxGetPr(rhs[id]);
model->sv_coef = (double**) malloc((model->nr_class-1)*sizeof(double));
for( i=0 ; i< model->nr_class -1 ; i++ )
model->sv_coef[i] = (double*) malloc((model->l)*sizeof(double));
for(i = 0; i < model->nr_class - 1; i++)
for(j = 0; j < model->l; j++)
model->sv_coef[i][j] = ptr[i*(model->l)+j];
id++;

// SV
{
int sr, elements;
int num_samples;
mwIndex *ir, *jc;
mxArray *pprhs[1], *pplhs[1];

// transpose SV
pprhs[0] = rhs[id];
if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
{
svm_free_and_destroy_model(&model);
*msg = "cannot transpose SV matrix";
return NULL;
}
rhs[id] = pplhs[0];

sr = (int)mxGetN(rhs[id]);

ptr = mxGetPr(rhs[id]);
ir = mxGetIr(rhs[id]);
jc = mxGetJc(rhs[id]);

num_samples = (int)mxGetNzmax(rhs[id]);

elements = num_samples + sr;

model->SV = (struct svm_node **) malloc(sr * sizeof(struct svm_node *));
x_space = (struct svm_node *)malloc(elements * sizeof(struct svm_node));

// SV is in column
for(i=0;i<sr;i++)
{
int low = (int)jc[i], high = (int)jc[i+1];
int x_index = 0;
model->SV[i] = &x_space[low+i];
for(j=low;j<high;j++)
{
model->SV[i][x_index].index = (int)ir[j] + 1;
model->SV[i][x_index].value = ptr[j];
x_index++;
}
model->SV[i][x_index].index = -1;
}

id++;
}
mxFree(rhs);

return model;
}

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