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yola/zaoYun/master/clf/clf_svm_linear_pro.c
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coco 85d885e008 a
2026-07-03 16:29:47 +08:00

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#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#define N_FEATURES 6
#define N_CLASSES 5
#define N_VECTORS 62
#define N_ROWS 5
#define N_COEFFICIENTS 4
#define N_INTERCEPTS 10
#define KERNEL_TYPE 'l'
#define KERNEL_GAMMA 0.02
#define KERNEL_COEF 0.0
#define KERNEL_DEGREE 3
double vectors[62][6] = {{22.0, 141.0, 215.0, 193.0, 128.0, 176.0}, {25.0, 158.0, 224.0, 206.0, 122.0, 187.0}, {23.0, 156.0, 218.0, 196.0, 126.0, 183.0}, {22.0, 165.0, 219.0, 193.0, 129.0, 185.0}, {25.0, 94.0, 226.0, 216.0, 122.0, 164.0}, {26.0, 130.0, 178.0, 172.0, 121.0, 170.0}, {26.0, 131.0, 180.0, 174.0, 121.0, 171.0}, {25.0, 159.0, 220.0, 203.0, 122.0, 187.0}, {26.0, 161.0, 204.0, 192.0, 120.0, 185.0}, {26.0, 111.0, 185.0, 181.0, 121.0, 165.0}, {26.0, 163.0, 180.0, 172.0, 121.0, 180.0}, {26.0, 131.0, 190.0, 184.0, 121.0, 173.0}, {27.0, 162.0, 175.0, 169.0, 120.0, 179.0}, {26.0, 128.0, 183.0, 177.0, 121.0, 170.0}, {26.0, 166.0, 174.0, 165.0, 122.0, 180.0}, {26.0, 98.0, 186.0, 183.0, 122.0, 161.0}, {26.0, 163.0, 178.0, 169.0, 121.0, 179.0}, {26.0, 125.0, 179.0, 175.0, 120.0, 169.0}, {26.0, 116.0, 176.0, 171.0, 122.0, 165.0}, {26.0, 130.0, 182.0, 176.0, 120.0, 171.0}, {26.0, 127.0, 189.0, 183.0, 121.0, 171.0}, {26.0, 159.0, 179.0, 172.0, 120.0, 179.0}, {26.0, 130.0, 183.0, 177.0, 121.0, 171.0}, {26.0, 135.0, 175.0, 170.0, 121.0, 171.0}, {26.0, 133.0, 177.0, 171.0, 121.0, 171.0}, {28.0, 127.0, 205.0, 202.0, 116.0, 176.0}, {26.0, 126.0, 177.0, 173.0, 121.0, 169.0}, {26.0, 126.0, 173.0, 169.0, 121.0, 168.0}, {27.0, 127.0, 177.0, 173.0, 121.0, 169.0}, {27.0, 168.0, 202.0, 192.0, 119.0, 187.0}, {26.0, 132.0, 176.0, 170.0, 121.0, 170.0}, {26.0, 168.0, 201.0, 190.0, 119.0, 187.0}, {26.0, 124.0, 177.0, 172.0, 121.0, 168.0}, {26.0, 133.0, 177.0, 171.0, 121.0, 170.0}, {26.0, 128.0, 174.0, 168.0, 122.0, 168.0}, {29.0, 100.0, 165.0, 168.0, 119.0, 160.0}, {26.0, 128.0, 179.0, 174.0, 120.0, 169.0}, {26.0, 156.0, 192.0, 184.0, 120.0, 181.0}, {29.0, 117.0, 178.0, 179.0, 117.0, 169.0}, {28.0, 125.0, 172.0, 173.0, 117.0, 170.0}, {26.0, 170.0, 198.0, 187.0, 120.0, 186.0}, {26.0, 128.0, 176.0, 170.0, 122.0, 168.0}, {34.0, 96.0, 172.0, 174.0, 113.0, 160.0}, {30.0, 136.0, 175.0, 178.0, 115.0, 175.0}, {35.0, 91.0, 165.0, 168.0, 114.0, 157.0}, {35.0, 102.0, 160.0, 163.0, 113.0, 160.0}, {34.0, 77.0, 165.0, 168.0, 117.0, 153.0}, {32.0, 105.0, 184.0, 186.0, 114.0, 166.0}, {36.0, 88.0, 149.0, 152.0, 115.0, 154.0}, {35.0, 103.0, 160.0, 162.0, 113.0, 160.0}, {30.0, 136.0, 175.0, 178.0, 115.0, 175.0}, {31.0, 137.0, 183.0, 185.0, 113.0, 177.0}, {35.0, 104.0, 155.0, 158.0, 113.0, 160.0}, {35.0, 102.0, 167.0, 169.0, 112.0, 161.0}, {34.0, 106.0, 168.0, 170.0, 113.0, 162.0}, {35.0, 100.0, 176.0, 177.0, 112.0, 162.0}, {36.0, 70.0, 166.0, 169.0, 116.0, 150.0}, {36.0, 90.0, 152.0, 155.0, 114.0, 155.0}, {36.0, 90.0, 152.0, 155.0, 114.0, 155.0}, {36.0, 90.0, 157.0, 160.0, 114.0, 156.0}, {35.0, 101.0, 166.0, 168.0, 113.0, 161.0}, {36.0, 93.0, 152.0, 155.0, 114.0, 155.0}};
double coefficients[4][62] = {{0.0, 0.14840231726321196, 0.039261948690546444, 0.0209412395721969, 0.0, -0.0, -0.0, -0.20860550552595525, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0031344318169009215, -0.0, -0.0, -0.0, -0.003655675139530277, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.001417434443656989, -0.0, -0.0, -0.0, -0.0005871521377968213, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0}, {0.0030248956801895086, 0.0, 0.0037652112762416898, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.34801591318003544, 0.0, 0.9470322783124342, 1.0, 1.0, 1.0, 0.1190647944888346, 1.0, 1.0, -1.0, -1.0, -0.0, -1.0, -0.9191873143094769, -0.5853053513640583, -0.9096203203077597, -1.0, -1.0, -1.0, -1.0, -1.0, -0.0, -1.0, -1.0, -0.0, -0.0, -1.0, -1.0, -0.0, -0.0, -0.0, -0.0, -0.00214452397132578, -0.0, -0.0, -0.0, -0.024885645069570812, -0.0, -0.0, -0.0, -0.0, -0.008162929283275827, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0}, {0.001417434443656989, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.005090581338098007, 0.0, 0.0, 0.0, 0.0, 0.021939587702798583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15549955595462564, 0.028604873673860467, 0.0, 0.0, -0.0, -0.025227153827495952, -0.0, -0.0, -0.02725948479373349, -0.05283188423037765, -0.0, -0.0, -0.07878590677687888, -0.0, -0.0, -0.0, -0.03368062815080514, -0.0, -0.0021054672852750564, -0.0, -0.0, -0.0, -0.0, -0.0}, {0.0003111058735373569, 0.0, 0.0, 0.0, 0.00027604626425946436, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.005198480088866544, 0.0, 0.0, 0.002964449194409284, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.027802518595050722, 0.0, 0.0, 0.007983576841029474, 0.0, 0.0, 0.0, 0.8407891498034413, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.7460798959789274, -0.8280132821307199, -1.0, -0.0, -0.0, -0.27542132331031716, -1.0, -0.6321885096378135, -1.0, -0.8512459307035196}};
double intercepts[10] = {-75.75078006378519, -20.95003189340926, -13.931254429482717, -10.88428749656037, -60.3146600706958, -6.927566866842887, -23.395247507217405, -33.671889415817894, -23.54485078615138, -108.26857343348372};
int weights[5] = {5, 18, 19, 11, 9};
int predict (double features[]) {
int i, j, k, d, l;
double kernels[N_VECTORS];
double kernel;
switch (KERNEL_TYPE) {
case 'l':
// <x,x'>
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += vectors[i][j] * features[j];
}
kernels[i] = kernel;
}
break;
case 'p':
// (y<x,x'>+r)^d
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += vectors[i][j] * features[j];
}
kernels[i] = pow((KERNEL_GAMMA * kernel) + KERNEL_COEF, KERNEL_DEGREE);
}
break;
case 'r':
// exp(-y|x-x'|^2)
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += pow(vectors[i][j] - features[j], 2);
}
kernels[i] = exp(-KERNEL_GAMMA * kernel);
}
break;
case 's':
// tanh(y<x,x'>+r)
for (i = 0; i < N_VECTORS; i++) {
kernel = 0.;
for (j = 0; j < N_FEATURES; j++) {
kernel += vectors[i][j] * features[j];
}
kernels[i] = tanh((KERNEL_GAMMA * kernel) + KERNEL_COEF);
}
break;
}
int starts[N_ROWS];
int start;
for (i = 0; i < N_ROWS; i++) {
if (i != 0) {
start = 0;
for (j = 0; j < i; j++) {
start += weights[j];
}
starts[i] = start;
} else {
starts[0] = 0;
}
}
int ends[N_ROWS];
for (i = 0; i < N_ROWS; i++) {
ends[i] = weights[i] + starts[i];
}
if (N_CLASSES == 2) {
for (i = 0; i < N_VECTORS; i++) {
kernels[i] = -kernels[i];
}
double decision = 0.;
for (k = starts[1]; k < ends[1]; k++) {
decision += kernels[k] * coefficients[0][k];
}
for (k = starts[0]; k < ends[0]; k++) {
decision += kernels[k] * coefficients[0][k];
}
decision += intercepts[0];
if (decision > 0) {
return 0;
}
return 1;
}
double decisions[N_INTERCEPTS];
double tmp;
for (i = 0, d = 0, l = N_ROWS; i < l; i++) {
for (j = i + 1; j < l; j++) {
tmp = 0.;
for (k = starts[j]; k < ends[j]; k++) {
tmp += kernels[k] * coefficients[i][k];
}
for (k = starts[i]; k < ends[i]; k++) {
tmp += kernels[k] * coefficients[j - 1][k];
}
decisions[d] = tmp + intercepts[d];
d = d + 1;
}
}
int votes[N_INTERCEPTS];
for (i = 0, d = 0, l = N_ROWS; i < l; i++) {
for (j = i + 1; j < l; j++) {
votes[d] = decisions[d] > 0 ? i : j;
d = d + 1;
}
}
int amounts[N_CLASSES];
for (i = 0, l = N_CLASSES; i < l; i++) {
amounts[i] = 0;
}
for (i = 0; i < N_INTERCEPTS; i++) {
amounts[votes[i]] += 1;
}
int classVal = -1;
int classIdx = -1;
for (i = 0; i < N_CLASSES; i++) {
if (amounts[i] > classVal) {
classVal = amounts[i];
classIdx= i;
}
}
return classIdx;
}
int main(int argc, const char * argv[]) {
/* Features: */
double features[argc-1];
int i;
for (i = 1; i < argc; i++) {
features[i-1] = atof(argv[i]);
}
/* Prediction: */
printf("%d", predict(features));
return 0;
}
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