First commit

liblinear-2.49/matlab/predict.c

@@ -0,0 +1,341 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include "linear.h"
+
+#include "mex.h"
+#include "linear_model_matlab.h"
+
+#ifdef MX_API_VER
+#if MX_API_VER < 0x07030000
+typedef int mwIndex;
+#endif
+#endif
+
+#define CMD_LEN 2048
+
+#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
+
+int print_null(const char *s,...) {return 0;}
+int (*info)(const char *fmt,...);
+
+int col_format_flag;
+
+void read_sparse_instance(const mxArray *prhs, int index, struct feature_node *x, int feature_number, double bias)
+{
+	int j;
+	mwIndex *ir, *jc, low, high, i;
+	double *samples;
+
+	ir = mxGetIr(prhs);
+	jc = mxGetJc(prhs);
+	samples = mxGetPr(prhs);
+
+	// each column is one instance
+	j = 0;
+	low = jc[index], high = jc[index+1];
+	for(i=low; i<high && (int) (ir[i])<feature_number; i++)
+	{
+		x[j].index = (int) ir[i]+1;
+		x[j].value = samples[i];
+		j++;
+	}
+	if(bias>=0)
+	{
+		x[j].index = feature_number+1;
+		x[j].value = bias;
+		j++;
+	}
+	x[j].index = -1;
+}
+
+static void fake_answer(int nlhs, mxArray *plhs[])
+{
+	int i;
+	for(i=0;i<nlhs;i++)
+		plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
+}
+
+void do_predict(int nlhs, mxArray *plhs[], const mxArray *prhs[], struct model *model_, const int predict_probability_flag)
+{
+	int label_vector_row_num, label_vector_col_num;
+	int feature_number, testing_instance_number;
+	int instance_index;
+	double *ptr_label, *ptr_predict_label;
+	double *ptr_prob_estimates, *ptr_dec_values, *ptr;
+	struct feature_node *x;
+	mxArray *pplhs[1]; // instance sparse matrix in row format
+	mxArray *tplhs[3]; // temporary storage for plhs[]
+
+	int correct = 0;
+	int total = 0;
+	double error = 0;
+	double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
+
+	int nr_class=get_nr_class(model_);
+	int nr_w;
+	double *prob_estimates=NULL;
+
+	if(nr_class==2 && model_->param.solver_type!=MCSVM_CS)
+		nr_w=1;
+	else
+		nr_w=nr_class;
+
+	// prhs[1] = testing instance matrix
+	feature_number = get_nr_feature(model_);
+	testing_instance_number = (int) mxGetM(prhs[1]);
+	if(col_format_flag)
+	{
+		feature_number = (int) mxGetM(prhs[1]);
+		testing_instance_number = (int) mxGetN(prhs[1]);
+	}
+
+	label_vector_row_num = (int) mxGetM(prhs[0]);
+	label_vector_col_num = (int) mxGetN(prhs[0]);
+
+	if(label_vector_row_num!=testing_instance_number)
+	{
+		mexPrintf("Length of label vector does not match # of instances.\n");
+		fake_answer(nlhs, plhs);
+		return;
+	}
+	if(label_vector_col_num!=1)
+	{
+		mexPrintf("label (1st argument) should be a vector (# of column is 1).\n");
+		fake_answer(nlhs, plhs);
+		return;
+	}
+
+	ptr_label    = mxGetPr(prhs[0]);
+
+	// transpose instance matrix
+	if(col_format_flag)
+		pplhs[0] = (mxArray *)prhs[1];
+	else
+	{
+		mxArray *pprhs[1];
+		pprhs[0] = mxDuplicateArray(prhs[1]);
+		if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
+		{
+			mexPrintf("Error: cannot transpose testing instance matrix\n");
+			fake_answer(nlhs, plhs);
+			return;
+		}
+	}
+
+
+	prob_estimates = Malloc(double, nr_class);
+
+	tplhs[0] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
+	if(predict_probability_flag)
+		tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class, mxREAL);
+	else
+		tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_w, mxREAL);
+
+	ptr_predict_label = mxGetPr(tplhs[0]);
+	ptr_prob_estimates = mxGetPr(tplhs[2]);
+	ptr_dec_values = mxGetPr(tplhs[2]);
+	x = Malloc(struct feature_node, feature_number+2);
+	for(instance_index=0;instance_index<testing_instance_number;instance_index++)
+	{
+		int i;
+		double target_label, predict_label;
+
+		target_label = ptr_label[instance_index];
+
+		// prhs[1] and prhs[1]^T are sparse
+		read_sparse_instance(pplhs[0], instance_index, x, feature_number, model_->bias);
+
+		if(predict_probability_flag)
+		{
+			predict_label = predict_probability(model_, x, prob_estimates);
+			ptr_predict_label[instance_index] = predict_label;
+			for(i=0;i<nr_class;i++)
+				ptr_prob_estimates[instance_index + i * testing_instance_number] = prob_estimates[i];
+		}
+		else
+		{
+			double *dec_values = Malloc(double, nr_class);
+			predict_label = predict_values(model_, x, dec_values);
+			ptr_predict_label[instance_index] = predict_label;
+
+			for(i=0;i<nr_w;i++)
+				ptr_dec_values[instance_index + i * testing_instance_number] = dec_values[i];
+			free(dec_values);
+		}
+
+		if(predict_label == target_label)
+			++correct;
+		error += (predict_label-target_label)*(predict_label-target_label);
+		sump += predict_label;
+		sumt += target_label;
+		sumpp += predict_label*predict_label;
+		sumtt += target_label*target_label;
+		sumpt += predict_label*target_label;
+
+		++total;
+	}
+
+	if(check_regression_model(model_))
+	{
+		info("Mean squared error = %g (regression)\n",error/total);
+		info("Squared correlation coefficient = %g (regression)\n",
+			((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
+			((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
+			);
+	}
+	else
+		info("Accuracy = %g%% (%d/%d)\n", (double) correct/total*100,correct,total);
+
+	// return accuracy, mean squared error, squared correlation coefficient
+	tplhs[1] = mxCreateDoubleMatrix(3, 1, mxREAL);
+	ptr = mxGetPr(tplhs[1]);
+	ptr[0] = (double)correct/total*100;
+	ptr[1] = error/total;
+	ptr[2] = ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
+				((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt));
+
+	free(x);
+	if(prob_estimates != NULL)
+		free(prob_estimates);
+
+	switch(nlhs)
+	{
+		case 3:
+			plhs[2] = tplhs[2];
+			plhs[1] = tplhs[1];
+		case 1:
+		case 0:
+			plhs[0] = tplhs[0];
+	}
+}
+
+void exit_with_help()
+{
+	mexPrintf(
+			"Usage: [predicted_label, accuracy, decision_values/prob_estimates] = predict(testing_label_vector, testing_instance_matrix, model, 'liblinear_options','col')\n"
+			"       [predicted_label] = predict(testing_label_vector, testing_instance_matrix, model, 'liblinear_options','col')\n"
+			"liblinear_options:\n"
+			"-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only\n"
+			"-q quiet mode (no outputs)\n"
+			"col: if 'col' is setted testing_instance_matrix is parsed in column format, otherwise is in row format\n"
+			"Returns:\n"
+			"  predicted_label: prediction output vector.\n"
+			"  accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.\n"
+			"  prob_estimates: If selected, probability estimate vector.\n"
+			);
+}
+
+void mexFunction( int nlhs, mxArray *plhs[],
+		int nrhs, const mxArray *prhs[] )
+{
+	int prob_estimate_flag = 0;
+	struct model *model_;
+	char cmd[CMD_LEN];
+	info = &mexPrintf;
+	col_format_flag = 0;
+
+	if(nlhs == 2 || nlhs > 3 || nrhs > 5 || nrhs < 3)
+	{
+		exit_with_help();
+		fake_answer(nlhs, plhs);
+		return;
+	}
+	if(nrhs == 5)
+	{
+		mxGetString(prhs[4], cmd, mxGetN(prhs[4])+1);
+		if(strcmp(cmd, "col") == 0)
+		{
+			col_format_flag = 1;
+		}
+	}
+
+	if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) {
+		mexPrintf("Error: label vector and instance matrix must be double\n");
+		fake_answer(nlhs, plhs);
+		return;
+	}
+
+	if(mxIsStruct(prhs[2]))
+	{
+		const char *error_msg;
+
+		// parse options
+		if(nrhs>=4)
+		{
+			int i, argc = 1;
+			char *argv[CMD_LEN/2];
+
+			// put options in argv[]
+			mxGetString(prhs[3], cmd,  mxGetN(prhs[3]) + 1);
+			if((argv[argc] = strtok(cmd, " ")) != NULL)
+				while((argv[++argc] = strtok(NULL, " ")) != NULL)
+					;
+
+			for(i=1;i<argc;i++)
+			{
+				if(argv[i][0] != '-') break;
+				++i;
+				if(i>=argc && argv[i-1][1] != 'q')
+				{
+					exit_with_help();
+					fake_answer(nlhs, plhs);
+					return;
+				}
+				switch(argv[i-1][1])
+				{
+					case 'b':
+						prob_estimate_flag = atoi(argv[i]);
+						break;
+					case 'q':
+						info = &print_null;
+						i--;
+						break;
+					default:
+						mexPrintf("unknown option\n");
+						exit_with_help();
+						fake_answer(nlhs, plhs);
+						return;
+				}
+			}
+		}
+
+		model_ = Malloc(struct model, 1);
+		error_msg = matlab_matrix_to_model(model_, prhs[2]);
+		if(error_msg)
+		{
+			mexPrintf("Error: can't read model: %s\n", error_msg);
+			free_and_destroy_model(&model_);
+			fake_answer(nlhs, plhs);
+			return;
+		}
+
+		if(prob_estimate_flag)
+		{
+			if(!check_probability_model(model_))
+			{
+				mexPrintf("probability output is only supported for logistic regression\n");
+				prob_estimate_flag=0;
+			}
+		}
+
+		if(mxIsSparse(prhs[1]))
+			do_predict(nlhs, plhs, prhs, model_, prob_estimate_flag);
+		else
+		{
+			mexPrintf("Testing_instance_matrix must be sparse; "
+				"use sparse(Testing_instance_matrix) first\n");
+			fake_answer(nlhs, plhs);
+		}
+
+		// destroy model_
+		free_and_destroy_model(&model_);
+	}
+	else
+	{
+		mexPrintf("model file should be a struct array\n");
+		fake_answer(nlhs, plhs);
+	}
+
+	return;
+}