From 9f6baa02c62e8577b0768e2fdcd7925027d34e45 Mon Sep 17 00:00:00 2001
From: linlin <jajupmochi@gmail.com>
Date: Mon, 5 Oct 2020 16:36:01 +0200
Subject: [PATCH] New translations svm_model_matlab.c (French)

---
 .../lib/libsvm.3.22/matlab/svm_model_matlab.c      | 374 +++++++++++++++++++++
 1 file changed, 374 insertions(+)
 create mode 100644 lang/fr/gklearn/gedlib/lib/libsvm.3.22/matlab/svm_model_matlab.c

diff --git a/lang/fr/gklearn/gedlib/lib/libsvm.3.22/matlab/svm_model_matlab.c b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/matlab/svm_model_matlab.c
new file mode 100644
index 0000000..1fea1ba
--- /dev/null
+++ b/lang/fr/gklearn/gedlib/lib/libsvm.3.22/matlab/svm_model_matlab.c
@@ -0,0 +1,374 @@
+#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;
+}