int LogisticRegression::gradientAscent() {
long double* deviations = new long double[sampleNum];
int iterations = 0;
do {
++iterations;
/*
* How to handle the case that 'z' is either too big or too small???
*/
// calculate deviations for all samples, one per sample.
for(int i = 0; i < sampleNum; ++i)
deviations[i] = categories[i] - rf(trainingSamples.getSample(i),
parameters, featureNum);
// adjust parameters
for(int j = 0; j < featureNum; ++j) {
long double factor = 0.0;
for(int i = 0; i < sampleNum; ++i)
factor += trainingSamples.getSample(i)[j] * deviations[i];
parameters[j] += alpha * factor;
}
} while(convergent(deviations, categoryRange) == false);
delete[] deviations;
return iterations;
}
int main(void) {
uint64_t count = 0;
for(uint64_t n = 1; n < 10000000; ++n) {
if(convergent(n) == 89) count++;
}
printf("%lu\n", count);
}
