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https://github.com/Relintai/pmlpp.git
synced 2024-12-22 15:06:47 +01:00
Fixed warnings in MLPPBernoulliNB.
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c37237aef8
commit
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@ -12,15 +12,18 @@
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#include <iostream>
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#include <iostream>
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#include <random>
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#include <random>
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MLPPBernoulliNB::MLPPBernoulliNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet) :
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MLPPBernoulliNB::MLPPBernoulliNB(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet) {
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inputSet(inputSet), outputSet(outputSet), class_num(2) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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class_num = 2;
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y_hat.resize(outputSet.size());
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y_hat.resize(outputSet.size());
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Evaluate();
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Evaluate();
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}
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}
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std::vector<real_t> MLPPBernoulliNB::modelSetTest(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> MLPPBernoulliNB::modelSetTest(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> y_hat;
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std::vector<real_t> y_hat;
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for (int i = 0; i < X.size(); i++) {
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for (uint32_t i = 0; i < X.size(); i++) {
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y_hat.push_back(modelTest(X[i]));
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y_hat.push_back(modelTest(X[i]));
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}
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}
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return y_hat;
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return y_hat;
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@ -32,8 +35,8 @@ real_t MLPPBernoulliNB::modelTest(std::vector<real_t> x) {
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std::vector<int> foundIndices;
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std::vector<int> foundIndices;
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for (int j = 0; j < x.size(); j++) {
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for (uint32_t j = 0; j < x.size(); j++) {
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for (int k = 0; k < vocab.size(); k++) {
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for (uint32_t k = 0; k < vocab.size(); k++) {
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if (x[j] == vocab[k]) {
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if (x[j] == vocab[k]) {
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score_0 *= theta[0][vocab[k]];
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score_0 *= theta[0][vocab[k]];
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score_1 *= theta[1][vocab[k]];
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score_1 *= theta[1][vocab[k]];
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@ -43,9 +46,9 @@ real_t MLPPBernoulliNB::modelTest(std::vector<real_t> x) {
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}
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}
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}
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}
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for (int i = 0; i < vocab.size(); i++) {
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for (uint32_t i = 0; i < vocab.size(); i++) {
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bool found = false;
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bool found = false;
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for (int j = 0; j < foundIndices.size(); j++) {
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for (uint32_t j = 0; j < foundIndices.size(); j++) {
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if (vocab[i] == vocab[foundIndices[j]]) {
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if (vocab[i] == vocab[foundIndices[j]]) {
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found = true;
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found = true;
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}
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}
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@ -85,19 +88,19 @@ void MLPPBernoulliNB::computeTheta() {
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// Setting all values in the hasmap by default to 0.
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// Setting all values in the hasmap by default to 0.
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for (int i = class_num - 1; i >= 0; i--) {
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for (int i = class_num - 1; i >= 0; i--) {
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for (int j = 0; j < vocab.size(); j++) {
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for (uint32_t j = 0; j < vocab.size(); j++) {
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theta[i][vocab[j]] = 0;
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theta[i][vocab[j]] = 0;
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}
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}
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}
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}
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for (int i = 0; i < inputSet.size(); i++) {
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for (uint32_t i = 0; i < inputSet.size(); i++) {
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for (int j = 0; j < inputSet[0].size(); j++) {
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for (uint32_t j = 0; j < inputSet[0].size(); j++) {
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theta[outputSet[i]][inputSet[i][j]]++;
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theta[outputSet[i]][inputSet[i][j]]++;
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}
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}
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}
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}
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for (int i = 0; i < theta.size(); i++) {
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for (uint32_t i = 0; i < theta.size(); i++) {
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for (int j = 0; j < theta[i].size(); j++) {
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for (uint32_t j = 0; j < theta[i].size(); j++) {
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if (i == 0) {
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if (i == 0) {
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theta[i][j] /= prior_0 * y_hat.size();
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theta[i][j] /= prior_0 * y_hat.size();
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} else {
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} else {
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@ -108,15 +111,15 @@ void MLPPBernoulliNB::computeTheta() {
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}
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}
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void MLPPBernoulliNB::Evaluate() {
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void MLPPBernoulliNB::Evaluate() {
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for (int i = 0; i < outputSet.size(); i++) {
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for (uint32_t i = 0; i < outputSet.size(); i++) {
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// Pr(B | A) * Pr(A)
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// Pr(B | A) * Pr(A)
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real_t score_0 = 1;
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real_t score_0 = 1;
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real_t score_1 = 1;
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real_t score_1 = 1;
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real_t sum = 0;
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real_t sum = 0;
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for (int i = 0; i < outputSet.size(); i++) {
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for (uint32_t ii = 0; ii < outputSet.size(); ii++) {
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if (outputSet[i] == 1) {
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if (outputSet[ii] == 1) {
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sum += outputSet[i];
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sum += outputSet[ii];
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}
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}
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}
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}
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@ -132,8 +135,8 @@ void MLPPBernoulliNB::Evaluate() {
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std::vector<int> foundIndices;
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std::vector<int> foundIndices;
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for (int j = 0; j < inputSet.size(); j++) {
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for (uint32_t j = 0; j < inputSet.size(); j++) {
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for (int k = 0; k < vocab.size(); k++) {
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for (uint32_t k = 0; k < vocab.size(); k++) {
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if (inputSet[i][j] == vocab[k]) {
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if (inputSet[i][j] == vocab[k]) {
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score_0 += std::log(theta[0][vocab[k]]);
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score_0 += std::log(theta[0][vocab[k]]);
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score_1 += std::log(theta[1][vocab[k]]);
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score_1 += std::log(theta[1][vocab[k]]);
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@ -143,16 +146,16 @@ void MLPPBernoulliNB::Evaluate() {
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}
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}
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}
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}
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for (int i = 0; i < vocab.size(); i++) {
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for (uint32_t ii = 0; ii < vocab.size(); ii++) {
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bool found = false;
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bool found = false;
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for (int j = 0; j < foundIndices.size(); j++) {
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for (uint32_t j = 0; j < foundIndices.size(); j++) {
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if (vocab[i] == vocab[foundIndices[j]]) {
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if (vocab[ii] == vocab[foundIndices[j]]) {
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found = true;
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found = true;
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}
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}
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}
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}
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if (!found) {
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if (!found) {
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score_0 += std::log(1 - theta[0][vocab[i]]);
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score_0 += std::log(1 - theta[0][vocab[ii]]);
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score_1 += std::log(1 - theta[1][vocab[i]]);
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score_1 += std::log(1 - theta[1][vocab[ii]]);
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}
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}
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}
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}
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