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Reworked more methods.
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parent
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@ -15,6 +15,7 @@
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#include "../lin_alg/lin_alg_old.h"
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#include "../softmax_net/softmax_net.h"
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#include "../stat/stat_old.h"
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#include "data_old.h"
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#include <algorithm>
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#include <cmath>
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@ -430,9 +431,10 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> M
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const int ONE_HOT_NUM = 3;
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> tempOutputSet;
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MLPPDataOld d;
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setData(IRIS_SIZE, "/Users/marcmelikyan/Desktop/Data/Iris.csv", inputSet, tempOutputSet);
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std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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std::vector<std::vector<real_t>> outputSet = d.oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return { inputSet, outputSet };
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}
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@ -441,9 +443,10 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> M
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const int ONE_HOT_NUM = 3;
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> tempOutputSet;
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MLPPDataOld d;
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setData(WINE_SIZE, "MLPP/Data/Datasets/Iris.csv", inputSet, tempOutputSet);
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std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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std::vector<std::vector<real_t>> outputSet = d.oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return { inputSet, outputSet };
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}
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@ -452,9 +455,10 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> M
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const int ONE_HOT_NUM = 10;
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> tempOutputSet;
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MLPPDataOld d;
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setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTrain.csv", inputSet, tempOutputSet);
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std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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std::vector<std::vector<real_t>> outputSet = d.oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return { inputSet, outputSet };
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}
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@ -463,9 +467,10 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> M
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const int ONE_HOT_NUM = 10;
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> tempOutputSet;
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MLPPDataOld d;
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setData(MNIST_SIZE, "MLPP/Data/Datasets/MnistTest.csv", inputSet, tempOutputSet);
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std::vector<std::vector<real_t>> outputSet = oneHotRep(tempOutputSet, ONE_HOT_NUM);
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std::vector<std::vector<real_t>> outputSet = d.oneHotRep(tempOutputSet, ONE_HOT_NUM);
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return { inputSet, outputSet };
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}
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@ -1117,80 +1122,40 @@ void MLPPData::setInputNames(std::string fileName, std::vector<std::string> &inp
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dataFile.close();
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}
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std::vector<std::vector<real_t>> MLPPData::featureScaling(std::vector<std::vector<real_t>> X) {
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MLPPLinAlgOld alg;
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X = alg.transpose(X);
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std::vector<real_t> max_elements, min_elements;
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max_elements.resize(X.size());
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min_elements.resize(X.size());
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Ref<MLPPMatrix> MLPPData::feature_scaling(const Ref<MLPPMatrix> &p_X) {
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Ref<MLPPMatrix> X = p_X->transposen();
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for (uint32_t i = 0; i < X.size(); i++) {
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max_elements[i] = alg.max(X[i]);
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min_elements[i] = alg.min(X[i]);
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Size2i x_size = X->size();
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LocalVector<real_t> max_elements;
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LocalVector<real_t> min_elements;
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max_elements.resize(x_size.y);
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min_elements.resize(x_size.y);
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Ref<MLPPVector> row_tmp;
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row_tmp.instance();
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row_tmp->resize(x_size.x);
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for (int i = 0; i < x_size.y; ++i) {
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X->row_get_into_mlpp_vector(i, row_tmp);
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max_elements[i] = row_tmp->max_element();
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min_elements[i] = row_tmp->min_element();
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}
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for (uint32_t i = 0; i < X.size(); i++) {
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for (uint32_t j = 0; j < X[i].size(); j++) {
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X[i][j] = (X[i][j] - min_elements[i]) / (max_elements[i] - min_elements[i]);
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for (int i = 0; i < x_size.y; i++) {
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real_t maxe = max_elements[i];
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real_t mine = min_elements[i];
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for (int j = 0; j < x_size.x; j++) {
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real_t xij = X->element_get(i, j);
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X->element_set(i, j, (xij - mine) / (maxe - mine));
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}
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}
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return alg.transpose(X);
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}
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std::vector<std::vector<real_t>> MLPPData::meanNormalization(std::vector<std::vector<real_t>> X) {
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MLPPLinAlgOld alg;
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MLPPStatOld stat;
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// (X_j - mu_j) / std_j, for every j
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X = meanCentering(X);
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for (uint32_t i = 0; i < X.size(); i++) {
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X[i] = alg.scalarMultiply(1 / stat.standardDeviation(X[i]), X[i]);
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}
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return X;
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}
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std::vector<std::vector<real_t>> MLPPData::meanCentering(std::vector<std::vector<real_t>> X) {
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MLPPStatOld stat;
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for (uint32_t i = 0; i < X.size(); i++) {
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real_t mean_i = stat.mean(X[i]);
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for (uint32_t j = 0; j < X[i].size(); j++) {
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X[i][j] -= mean_i;
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}
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}
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return X;
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}
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std::vector<std::vector<real_t>> MLPPData::oneHotRep(std::vector<real_t> tempOutputSet, int n_class) {
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std::vector<std::vector<real_t>> outputSet;
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outputSet.resize(tempOutputSet.size());
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for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
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for (int j = 0; j <= n_class - 1; j++) {
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if (tempOutputSet[i] == j) {
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outputSet[i].push_back(1);
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} else {
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outputSet[i].push_back(0);
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}
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}
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}
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return outputSet;
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}
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std::vector<real_t> MLPPData::reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet) {
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std::vector<real_t> outputSet;
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//uint32_t n_class = tempOutputSet[0].size();
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for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
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int current_class = 1;
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for (uint32_t j = 0; j < tempOutputSet[i].size(); j++) {
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if (tempOutputSet[i][j] == 1) {
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break;
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} else {
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current_class++;
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}
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}
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outputSet.push_back(current_class);
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}
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return outputSet;
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return X->transposen();
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}
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Ref<MLPPMatrix> MLPPData::mean_centering(const Ref<MLPPMatrix> &p_X) {
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@ -1207,7 +1172,7 @@ Ref<MLPPMatrix> MLPPData::mean_centering(const Ref<MLPPMatrix> &p_X) {
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x_row_tmp->resize(x_size.x);
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for (int i = 0; i < x_size.y; ++i) {
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X->row_get_into_mlpp_vector(i, x_row_tmp);
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p_X->row_get_into_mlpp_vector(i, x_row_tmp);
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real_t mean_i = stat.meanv(x_row_tmp);
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@ -1219,6 +1184,30 @@ Ref<MLPPMatrix> MLPPData::mean_centering(const Ref<MLPPMatrix> &p_X) {
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return X;
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}
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Ref<MLPPMatrix> MLPPData::mean_normalization(const Ref<MLPPMatrix> &p_X) {
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MLPPLinAlg alg;
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MLPPStat stat;
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// (X_j - mu_j) / std_j, for every j
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Ref<MLPPMatrix> X = mean_centering(p_X);
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Size2i x_size = X->size();
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Ref<MLPPVector> x_row_tmp;
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x_row_tmp.instance();
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x_row_tmp->resize(x_size.x);
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for (int i = 0; i < x_size.y; i++) {
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X->row_get_into_mlpp_vector(i, x_row_tmp);
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x_row_tmp->scalar_multiply((real_t)1 / stat.standard_deviationv(x_row_tmp));
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X->row_set_mlpp_vector(i, x_row_tmp);
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}
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return X;
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}
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Ref<MLPPMatrix> MLPPData::one_hot_rep(const Ref<MLPPVector> &temp_output_set, int n_class) {
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ERR_FAIL_COND_V(!temp_output_set.is_valid(), Ref<MLPPMatrix>());
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@ -1243,6 +1232,24 @@ Ref<MLPPMatrix> MLPPData::one_hot_rep(const Ref<MLPPVector> &temp_output_set, in
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return output_set;
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}
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std::vector<real_t> MLPPData::reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet) {
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std::vector<real_t> outputSet;
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//uint32_t n_class = tempOutputSet[0].size();
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for (uint32_t i = 0; i < tempOutputSet.size(); i++) {
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int current_class = 1;
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for (uint32_t j = 0; j < tempOutputSet[i].size(); j++) {
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if (tempOutputSet[i][j] == 1) {
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break;
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} else {
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current_class++;
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}
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}
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outputSet.push_back(current_class);
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}
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return outputSet;
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}
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void MLPPData::load_default_suffixes() {
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// Our list of suffixes which we use to compare against
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suffixes = String("eer er ion ity ment ness or sion ship th able ible al ant ary ful ic ious ous ive less y ed en ing ize ise ly ward wise").split_spaces();
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@ -176,14 +176,11 @@ public:
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// Extra
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void setInputNames(std::string fileName, std::vector<std::string> &inputNames);
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std::vector<std::vector<real_t>> featureScaling(std::vector<std::vector<real_t>> X);
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std::vector<std::vector<real_t>> meanNormalization(std::vector<std::vector<real_t>> X);
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std::vector<std::vector<real_t>> meanCentering(std::vector<std::vector<real_t>> X);
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std::vector<std::vector<real_t>> oneHotRep(std::vector<real_t> tempOutputSet, int n_class);
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std::vector<real_t> reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet);
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Ref<MLPPMatrix> feature_scaling(const Ref<MLPPMatrix> &X);
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Ref<MLPPMatrix> mean_centering(const Ref<MLPPMatrix> &X);
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Ref<MLPPMatrix> mean_normalization(const Ref<MLPPMatrix> &X);
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Ref<MLPPMatrix> one_hot_rep(const Ref<MLPPVector> &temp_output_set, int n_class);
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std::vector<real_t> reverseOneHot(std::vector<std::vector<real_t>> tempOutputSet);
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template <class T>
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std::vector<T> vecToSet(std::vector<T> inputSet) {
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@ -5,7 +5,7 @@
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//
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#include "pca_old.h"
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#include "../data/data.h"
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#include "../data/data_old.h"
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#include "../lin_alg/lin_alg_old.h"
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#include <iostream>
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@ -19,7 +19,7 @@ MLPPPCAOld::MLPPPCAOld(std::vector<std::vector<real_t>> inputSet, int k) :
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std::vector<std::vector<real_t>> MLPPPCAOld::principalComponents() {
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MLPPLinAlgOld alg;
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MLPPData data;
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MLPPDataOld data;
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MLPPLinAlgOld::SVDResultOld svr_res = alg.SVD(alg.cov(inputSet));
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X_normalized = data.meanCentering(inputSet);
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@ -978,20 +978,18 @@ void MLPPTests::test_nlp_and_data(bool ui) {
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PLOG_MSG("LSA:");
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PLOG_MSG(data.lsa(text_archive2, 2)->to_string());
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/*
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std::vector<std::vector<real_t>> inputSet = { { 1, 2 }, { 2, 3 }, { 3, 4 }, { 4, 5 }, { 5, 6 } };
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std::cout << "Feature Scaling Example:" << std::endl;
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alg.printMatrix(data.featureScaling(inputSet));
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std::cout << std::endl;
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std::vector<std::vector<real_t>> input_set_vec = { { 1, 2 }, { 2, 3 }, { 3, 4 }, { 4, 5 }, { 5, 6 } };
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std::cout << "Mean Centering Example:" << std::endl;
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alg.printMatrix(data.meanCentering(inputSet));
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std::cout << std::endl;
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Ref<MLPPMatrix> input_set = Ref<MLPPMatrix>(memnew(MLPPMatrix(input_set_vec)));
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std::cout << "Mean Normalization Example:" << std::endl;
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alg.printMatrix(data.meanNormalization(inputSet));
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std::cout << std::endl;
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*/
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PLOG_MSG("Feature Scaling Example:");
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PLOG_MSG(data.feature_scaling(input_set)->to_string());
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PLOG_MSG("Mean Centering Example:");
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PLOG_MSG(data.mean_centering(input_set)->to_string());
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PLOG_MSG("Mean Normalization Example:");
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PLOG_MSG(data.mean_normalization(input_set)->to_string());
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}
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void MLPPTests::test_outlier_finder(bool ui) {
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MLPPLinAlg alg;
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@ -400,7 +400,6 @@ void MLPPTestsOld::test_outlier_finder(bool ui) {
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MLPPLinAlgOld alg;
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// Outlier Finder
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//std::vector<real_t> inputSet = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 23554332523523 };
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std::vector<real_t> inputSet = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 23554332 };
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MLPPOutlierFinderOld outlierFinderOld(2); // Any datapoint outside of 2 stds from the mean is marked as an outlier.
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alg.printVector(outlierFinderOld.modelTest(inputSet));
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