int main(int argc, char* argv[])
{
int topK;
if (argc != 5 || sscanf(argv[4], "%d", &topK) != 1) {
cerr << argc << endl;
for (int i = 1; i < argc; ++ i) {
cerr << argv[i] << endl;
}
fprintf(stderr, "[usage] <training/testing data, csv format> <selected rules> <new training/testing data, csv format> <top-K>\n");
return -1;
}
//cerr << "top-K = " << topK << endl;
int dimension = loadFeatureMatrix(argv[1]);
Rules rules;
rules.load(argv[2]);
rules.resize(topK);
FILE* out = tryOpen(argv[3], "w");
fprintf(out, "label");
for (int i = 0; i < rules.size(); ++ i) {
fprintf(out, ",rule_%s", rules[i].showup().c_str());
}
fprintf(out, "\n");
vector<int> sum(rules.size(), 0);
for (int i = 0; i < train.size(); ++ i) {
fprintf(out, "%d", (int)trainY[i]);
for (int j = 0; j < rules.size(); ++ j) {
int sat = rules[j].isSatisfiedBy(train[i]);
fprintf(out, ",%d", sat);
sum[j] += sat;
}
fprintf(out, "\n");
}
fclose(out);
string filename = argv[1];
if (filename.find("train") != -1) {
bool flag = false;
for (int i = 0; i < rules.size(); ++ i) {
if (rules[i].satisfiedTrainings.size() != sum[i]) {
fprintf(stderr, "[Warning] rule %d, %d v.s. %d\n", i, rules[i].satisfiedTrainings.size(), sum[i]);
flag = true;
}
}
if (!flag) {
//fprintf(stderr, "Self-check passed!\n");
;
} else {
fprintf(stderr, "Oooooooooooooooooops! You found some troubles!\n");
}
}
return 0;
}
TEST(Rules, reference) {
Rules r { {1, 2, 3}, {4, 5, 6}, {7, 8, 9}, { 358, 288, 2 } };
ASSERT_EQ(r[0], (Rule {1, 2, 3}));
ASSERT_EQ(r[1], (Rule {4, 5, 6}));
ASSERT_EQ(r[2], (Rule {7, 8, 9}));
ASSERT_EQ(r.size(), size_t(4));
swap(r[0], r[1]);
ASSERT_EQ(r[0], (Rule {4, 5, 6}));
ASSERT_EQ(r[1], (Rule {1, 2, 3}));
std::sort(r.begin(), r.end(), rule_compare {});
ASSERT_EQ(r[0], (Rule {1, 2, 3}));
ASSERT_EQ(r[1], (Rule {4, 5, 6}));
ASSERT_EQ(r[2], (Rule {7, 8, 9}));
ASSERT_EQ(r.size(), size_t(4));
}