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Fixed warnings in MLPPMultinomialNB.

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Relintai 2023-02-10 21:35:43 +01:00
parent e51a976a10
commit d467f1ccf1
2 changed files with 24 additions and 23 deletions
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44
mlpp/multinomial_nb/multinomial_nb.cpp
View File

@ -12,16 +12,18 @@
#include <iostream>
#include <random>
MLPPMultinomialNB::MLPPMultinomialNB(std::vector<std::vector<real_t>> pinputSet, std::vector<real_t> poutputSet, int pclass_num) {
inputSet = pinputSet;
outputSet = poutputSet;
class_num = pclass_num;
MLPPMultinomialNB::MLPPMultinomialNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int class_num) :
inputSet(inputSet), outputSet(outputSet), class_num(class_num) {
y_hat.resize(outputSet.size());
Evaluate();
}
std::vector<real_t> MLPPMultinomialNB::modelSetTest(std::vector<std::vector<real_t>> X) {
std::vector<real_t> y_hat;
for (int i = 0; i < X.size(); i++) {
for (uint32_t i = 0; i < X.size(); i++) {
y_hat.push_back(modelTest(X[i]));
}
return y_hat;
@ -31,8 +33,8 @@ real_t MLPPMultinomialNB::modelTest(std::vector<real_t> x) {
real_t score[class_num];
computeTheta();
for (int j = 0; j < x.size(); j++) {
for (int k = 0; k < vocab.size(); k++) {
for (uint32_t j = 0; j < x.size(); j++) {
for (uint32_t k = 0; k < vocab.size(); k++) {
if (x[j] == vocab[k]) {
for (int p = class_num - 1; p >= 0; p--) {
score[p] += std::log(theta[p][vocab[k]]);
@ -41,7 +43,7 @@ real_t MLPPMultinomialNB::modelTest(std::vector<real_t> x) {
}
}
for (int i = 0; i < priors.size(); i++) {
for (uint32_t i = 0; i < priors.size(); i++) {
score[i] += std::log(priors[i]);
}
@ -59,19 +61,19 @@ void MLPPMultinomialNB::computeTheta() {
// Setting all values in the hasmap by default to 0.
for (int i = class_num - 1; i >= 0; i--) {
for (int j = 0; j < vocab.size(); j++) {
for (uint32_t j = 0; j < vocab.size(); j++) {
theta[i][vocab[j]] = 0;
}
}
for (int i = 0; i < inputSet.size(); i++) {
for (int j = 0; j < inputSet[0].size(); j++) {
for (uint32_t i = 0; i < inputSet.size(); i++) {
for (uint32_t j = 0; j < inputSet[0].size(); j++) {
theta[outputSet[i]][inputSet[i][j]]++;
}
}
for (int i = 0; i < theta.size(); i++) {
for (int j = 0; j < theta[i].size(); j++) {
for (uint32_t i = 0; i < theta.size(); i++) {
for (uint32_t j = 0; j < theta[i].size(); j++) {
theta[i][j] /= priors[i] * y_hat.size();
}
}
@ -79,22 +81,22 @@ void MLPPMultinomialNB::computeTheta() {
void MLPPMultinomialNB::Evaluate() {
MLPPLinAlg alg;
for (int i = 0; i < outputSet.size(); i++) {
for (uint32_t i = 0; i < outputSet.size(); i++) {
// Pr(B | A) * Pr(A)
real_t score[class_num];
// Easy computation of priors, i.e. Pr(C_k)
priors.resize(class_num);
for (int i = 0; i < outputSet.size(); i++) {
priors[int(outputSet[i])]++;
for (uint32_t ii = 0; ii < outputSet.size(); ii++) {
priors[int(outputSet[ii])]++;
}
priors = alg.scalarMultiply(real_t(1) / real_t(outputSet.size()), priors);
// Evaluating Theta...
computeTheta();
for (int j = 0; j < inputSet.size(); j++) {
for (int k = 0; k < vocab.size(); k++) {
for (uint32_t j = 0; j < inputSet.size(); j++) {
for (uint32_t k = 0; k < vocab.size(); k++) {
if (inputSet[i][j] == vocab[k]) {
for (int p = class_num - 1; p >= 0; p--) {
score[p] += std::log(theta[i][vocab[k]]);
@ -103,13 +105,13 @@ void MLPPMultinomialNB::Evaluate() {
}
}
for (int i = 0; i < priors.size(); i++) {
score[i] += std::log(priors[i]);
score[i] = exp(score[i]);
for (uint32_t ii = 0; ii < priors.size(); ii++) {
score[ii] += std::log(priors[ii]);
score[ii] = exp(score[ii]);
}
for (int i = 0; i < 2; i++) {
std::cout << score[i] << std::endl;
for (int ii = 0; ii < 2; ii++) {
std::cout << score[ii] << std::endl;
}
// Assigning the traning example's y_hat to a class

1
mlpp/multinomial_nb/multinomial_nb.h
View File

@ -13,7 +13,6 @@
#include <map>
#include <vector>
class MLPPMultinomialNB {
public:
MLPPMultinomialNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int class_num);