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This commit is contained in:
novak_99 2022-01-21 17:08:31 -08:00
parent 1523eea7b8
commit c93400840a
8 changed files with 27 additions and 27 deletions
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14
MLPP/Activation/Activation.cpp
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@ -142,7 +142,7 @@ namespace MLPP{
double Activation::softplus(double z, bool deriv){
if(deriv){ return sigmoid(z); }
return log(1 + exp(z));
return std::log(1 + exp(z));
}
std::vector<double> Activation::softplus(std::vector<double> z, bool deriv){
@ -752,7 +752,7 @@ namespace MLPP{
double Activation::arsinh(double z, bool deriv){
if(deriv){ return 1 / sqrt(z * z + 1); }
return log(z + sqrt(z * z + 1));
return std::log(z + sqrt(z * z + 1));
}
std::vector<double> Activation::arsinh(std::vector<double> z, bool deriv){
@ -771,7 +771,7 @@ namespace MLPP{
if(deriv){
return 1/sqrt(z * z - 1);
}
return log(z + sqrt(z * z - 1));
return std::log(z + sqrt(z * z - 1));
}
std::vector<double> Activation::arcosh(std::vector<double> z, bool deriv){
@ -790,7 +790,7 @@ namespace MLPP{
if(deriv){
return 1/(1 - z * z);
}
return 0.5 * log((1 + z)/(1 - z));
return 0.5 * std::log((1 + z)/(1 - z));
}
std::vector<double> Activation::artanh(std::vector<double> z, bool deriv){
@ -809,7 +809,7 @@ namespace MLPP{
if(deriv){
return -1/((z * z) * sqrt(1 + (1/(z * z))));
}
return log(sqrt(1 + (1 / (z * z))) + (1/z));
return std::log(sqrt(1 + (1 / (z * z))) + (1/z));
}
std::vector<double> Activation::arcsch(std::vector<double> z, bool deriv){
@ -829,7 +829,7 @@ namespace MLPP{
if(deriv){
return -1/(z * sqrt(1 - z * z));
}
return log((1/z) + ((1/z) + 1) * ((1/z) - 1));
return std::log((1/z) + ((1/z) + 1) * ((1/z) - 1));
}
std::vector<double> Activation::arsech(std::vector<double> z, bool deriv){
@ -848,7 +848,7 @@ namespace MLPP{
if(deriv){
return 1/(1 - z * z);
}
return 0.5 * log((1 + z)/(z - 1));
return 0.5 * std::log((1 + z)/(z - 1));
}
std::vector<double> Activation::arcoth(std::vector<double> z, bool deriv){

12
MLPP/BernoulliNB/BernoulliNB.cpp
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@ -139,8 +139,8 @@ namespace MLPP{
for(int j = 0; j < inputSet.size(); j++){
for(int k = 0; k < vocab.size(); k++){
if(inputSet[i][j] == vocab[k]){
score_0 += log(theta[0][vocab[k]]);
score_1 += log(theta[1][vocab[k]]);
score_0 += std::log(theta[0][vocab[k]]);
score_1 += std::log(theta[1][vocab[k]]);
foundIndices.push_back(k);
}
@ -155,13 +155,13 @@ namespace MLPP{
}
}
if(!found){
score_0 += log(1 - theta[0][vocab[i]]);
score_1 += log(1 - theta[1][vocab[i]]);
score_0 += std::log(1 - theta[0][vocab[i]]);
score_1 += std::log(1 - theta[1][vocab[i]]);
}
}
score_0 += log(prior_0);
score_1 += log(prior_1);
score_0 += std::log(prior_0);
score_1 += std::log(prior_1);
score_0 = exp(score_0);
score_1 = exp(score_1);

8
MLPP/Cost/Cost.cpp
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@ -157,7 +157,7 @@ namespace MLPP{
double sum = 0;
double eps = 1e-8;
for(int i = 0; i < y_hat.size(); i++){
sum += -(y[i] * log(y_hat[i] + eps) + (1 - y[i]) * log(1 - y_hat[i] + eps));
sum += -(y[i] * std::log(y_hat[i] + eps) + (1 - y[i]) * std::log(1 - y_hat[i] + eps));
}
return sum / y_hat.size();
@ -168,7 +168,7 @@ namespace MLPP{
double eps = 1e-8;
for(int i = 0; i < y_hat.size(); i++){
for(int j = 0; j < y_hat[i].size(); j++){
sum += -(y[i][j] * log(y_hat[i][j] + eps) + (1 - y[i][j]) * log(1 - y_hat[i][j] + eps));
sum += -(y[i][j] * std::log(y_hat[i][j] + eps) + (1 - y[i][j]) * std::log(1 - y_hat[i][j] + eps));
}
}
@ -188,7 +188,7 @@ namespace MLPP{
double Cost::CrossEntropy(std::vector<double> y_hat, std::vector<double> y){
double sum = 0;
for(int i = 0; i < y_hat.size(); i++){
sum += y[i] * log(y_hat[i]);
sum += y[i] * std::log(y_hat[i]);
}
return -1 * sum;
@ -198,7 +198,7 @@ namespace MLPP{
double sum = 0;
for(int i = 0; i < y_hat.size(); i++){
for(int j = 0; j < y_hat[i].size(); j++){
sum += y[i][j] * log(y_hat[i][j]);
sum += y[i][j] * std::log(y_hat[i][j]);
}
}

2
MLPP/Data/Data.cpp
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@ -385,7 +385,7 @@ namespace MLPP{
IDF.resize(frequency.size());
for(int i = 0; i < IDF.size(); i++){
IDF[i] = log((double)segmented_sentences.size() / (double)frequency[i]);
IDF[i] = std::log((double)segmented_sentences.size() / (double)frequency[i]);
}
std::vector<std::vector<double>> TFIDF;

4
MLPP/GaussianNB/GaussianNB.cpp
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@ -36,7 +36,7 @@ namespace MLPP{
double score[class_num];
double y_hat_i = 1;
for(int i = class_num - 1; i >= 0; i--){
y_hat_i += log(priors[i] * (1 / sqrt(2 * M_PI * sigma[i] * sigma[i])) * exp(-(x[i] * mu[i]) * (x[i] * mu[i]) / (2 * sigma[i] * sigma[i])));
y_hat_i += std::log(priors[i] * (1 / sqrt(2 * M_PI * sigma[i] * sigma[i])) * exp(-(x[i] * mu[i]) * (x[i] * mu[i]) / (2 * sigma[i] * sigma[i])));
score[i] = exp(y_hat_i);
}
return std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(double)));
@ -79,7 +79,7 @@ namespace MLPP{
double y_hat_i = 1;
for(int j = class_num - 1; j >= 0; j--){
for(int k = 0; k < inputSet[i].size(); k++){
y_hat_i += log(priors[j] * (1 / sqrt(2 * M_PI * sigma[j] * sigma[j])) * exp(-(inputSet[i][k] * mu[j]) * (inputSet[i][k] * mu[j]) / (2 * sigma[j] * sigma[j])));
y_hat_i += std::log(priors[j] * (1 / sqrt(2 * M_PI * sigma[j] * sigma[j])) * exp(-(inputSet[i][k] * mu[j]) * (inputSet[i][k] * mu[j]) / (2 * sigma[j] * sigma[j])));
}
score[j] = exp(y_hat_i);
std::cout << score[j] << std::endl;

4
MLPP/LinAlg/LinAlg.hpp
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@ -34,7 +34,7 @@ namespace MLPP{
std::vector<std::vector<double>> scalarAdd(double scalar, std::vector<std::vector<double>> A);
std::vector<std::vector<double>> log(std::vector<std::vector<double>> A);
std::vector<std::vector<double>> std::log(std::vector<std::vector<double>> A);
std::vector<std::vector<double>> log10(std::vector<std::vector<double>> A);
@ -132,7 +132,7 @@ namespace MLPP{
std::vector<double> subtractMatrixRows(std::vector<double> a, std::vector<std::vector<double>> B);
std::vector<double> log(std::vector<double> a);
std::vector<double> std::log(std::vector<double> a);
std::vector<double> log10(std::vector<double> a);

8
MLPP/MultinomialNB/MultinomialNB.cpp
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@ -35,14 +35,14 @@ namespace MLPP{
for(int k = 0; k < vocab.size(); k++){
if(x[j] == vocab[k]){
for(int p = class_num - 1; p >= 0; p--){
score[p] += log(theta[p][vocab[k]]);
score[p] += std::log(theta[p][vocab[k]]);
}
}
}
}
for(int i = 0; i < priors.size(); i++){
score[i] += log(priors[i]);
score[i] += std::log(priors[i]);
}
return std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(double)));
@ -98,14 +98,14 @@ namespace MLPP{
for(int k = 0; k < vocab.size(); k++){
if(inputSet[i][j] == vocab[k]){
for(int p = class_num - 1; p >= 0; p--){
score[p] += log(theta[i][vocab[k]]);
score[p] += std::log(theta[i][vocab[k]]);
}
}
}
}
for(int i = 0; i < priors.size(); i++){
score[i] += log(priors[i]);
score[i] += std::log(priors[i]);
score[i] = exp(score[i]);
}

2
MLPP/Stat/Stat.cpp
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@ -214,6 +214,6 @@ namespace MLPP{
if(x == y){
return x;
}
return (y - x) / (log(y) - log(x));
return (y - x) / (log(y) - std::log(x));
}
}