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Commit 8379effb authored by Stéphane NGNEPIEPAYE WEMBE's avatar Stéphane NGNEPIEPAYE WEMBE
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[EVALUATION OF OUR CODE] : Now we have the confusion matrix of each approch... 

[EVALUATION OF OUR CODE] : Now we have the confusion matrix of each approch and we can compare with SKLearn confusion matrix
parent cdb487fa
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ANN/python/start_ANN.ipynb
View file @ 8379effb
This diff is collapsed.
ANN/src/ann_cpp_music_style.cpp
View file @ 8379effb
......@@ -4,6 +4,37 @@
#include <cmath>
#include <fstream>
int M = 10;
std::istream& operator>>(std::istream& str, CSVRow& data)
{
data.readNextRow(str);
return str;
}
void get_confusion_matrix_in_csv_file(std::ifstream &file, std::vector<std::vector<int>> &confusion_matrix_sklearn)
{
CSVRow line;
for (int i = 0; i < M; ++i) {
file >> line;
for (int j = 0; j < M; ++j) {
confusion_matrix_sklearn[i][j] = (std::stoi(line[j]));
}
}
}
void print_confusion_matrix(std::vector<std::vector<int>> &confusion_matrix)
{
std::cout<<"["<<std::endl;
for (int i = 0; i < M; ++i) {
std::cout<<"[ ";
for (int j = 0; j < M; ++j) {
std::cout<<confusion_matrix[i][j]<<" ";
}
std::cout<<"]"<<std::endl;
}
std::cout<<"]"<<std::endl;
}
double relu(double x)
{
......@@ -11,7 +42,6 @@ double relu(double x)
else return 0;
}
double softmax(double* tab, int size, int indice)
{
double S = 0;
......@@ -21,8 +51,7 @@ double softmax(double* tab, int size, int indice)
return exp(tab[indice])/S;
}
double ann_predict(std::vector<real> feature){
int ann_predict(std::vector<real> feature){
double layer1_node[N0];
double layer2_node[N1];
double output_layer_node[N2];
......@@ -49,7 +78,7 @@ double ann_predict(std::vector<real> feature){
}
layer2_node[i]+= B1[i];
layer2_node[i] = relu(layer2_node[i]);
// std::cout << layer1_node[i] << std::endl;
// std::cout << layer2_node[i] << std::endl;
}
// output layer
for(int i = 0; i<N2; ++i)
......@@ -66,8 +95,8 @@ double ann_predict(std::vector<real> feature){
double predict = 0;
double best_predict = predict;
int best_index = 0;
for (int i = 0; i < N2; ++i) {
predict = softmax(output_layer_node,N2, 0);
for (int i = 1; i < N2; ++i) {
predict = softmax(output_layer_node,N2, i);
// std::cout << predict<< std::endl;
if (predict>best_predict)
......@@ -80,12 +109,6 @@ double ann_predict(std::vector<real> feature){
return best_index;
}
std::istream& operator>>(std::istream& str, CSVRow& data)
{
data.readNextRow(str);
return str;
}
double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value)
{
double valid = 0;
......@@ -93,43 +116,59 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu
else
{
for (int i = 0; i < truth_value.size(); ++i) {
// std::cout << predicted_value[i] << " " << truth_value[i] << std::endl;
if (predicted_value[i] == truth_value[i]) valid++;
}
// std::cout << truth_value.size() << " " << predicted_value.size() << std::endl;
// std::cout << "valid number is " << valid << std::endl;
return valid/double (truth_value.size());
}
}
void build_confusion_matrix(std::vector<int>& truth_value, std::vector<int>& predicted_value, std::vector<std::vector<int>> &confusion_matrix)
{
for (int i = 0; i < truth_value.size(); ++i) {
confusion_matrix[predicted_value[i]][truth_value[i]]++;
}
}
int main(){
std::ifstream file("../../DATASETS/path_test_files_ANN.csv");
CSVRow files_name_row;
CSVRow style_name_row;
CSVRow accuracy_SKLearn;
std::vector<std::vector<int>> confusion_matrix_sklearn(M, std::vector<int>(M, 0));
std::vector<std::vector<int>> our_confusion_matrix(M, std::vector<int>(M, 0));
file >> files_name_row;
file >> style_name_row;
file >> accuracy_SKLearn;
get_confusion_matrix_in_csv_file(file,confusion_matrix_sklearn);
file.close();
std::vector<int> style_test_set(style_name_row.size());
std::vector<int> style_predict;
transform_to_index(style_name_row,style_test_set);
// std::cout << "size of test set: " << files_name_row.size() << std::endl;
// std::cout << "size of test set : " << style_name_row.size() << std::endl;
for (int i = 0; i < files_name_row.size(); ++i) {
if (!files_name_row[i].empty()) {
// std::cout <<style_test_set[i]<< std::endl;
std::filesystem::path fileName = files_name_row[i];
auto bins = compute_features_for(fileName);
auto featuring = bins.begin()->second;
std::vector<real> featuring;
auto featuring_avg = bins[FTYPE::BINAVG];
auto featuring_std = bins[FTYPE::BINSTDEV];
featuring.insert(featuring.cbegin(), featuring_avg.begin(), featuring_avg.end());
featuring.insert(featuring.cend(), featuring_std.begin(), featuring_std.end());
style_predict.push_back(ann_predict(featuring));
// std::cout << "step " << i << " cart_predict(feature) : " << cart_predict(featuring) << std::endl;
// std::cout << "featuring size " << featuring.size() << std::endl;
} else style_predict.push_back(-1);
}
build_confusion_matrix(style_test_set,style_predict,our_confusion_matrix);
std::cout << "The confusion matrix given by SKlearn is : " << std::endl;
print_confusion_matrix(confusion_matrix_sklearn);
std::cout << "The confusion matrix given by our code is : " << std::endl;
print_confusion_matrix(our_confusion_matrix);
std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl;
std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl;
return 0;
......
ANN/src/ann_cpp_music_style.h
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CART/Python/CropRec_main.py
View file @ 8379effb
......@@ -60,12 +60,24 @@ print(confusion_matrix(Y_test, Y_pred, labels=classes))
accuracy = (Y_pred == Y_test).mean()
print('Test Accuracy : %.3f'%accuracy)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
M = confusion_matrix(Y_test, Y_pred, labels=classes)
with open('../../DATASETS/path_test_files_CART.csv', 'w') as f:
# using csv.writer method from CSV package
write = csv.writer(f)
write.writerow(files_test)
write.writerow(Y_test)
write.writerow([accuracy])
for i in range(M.shape[0]):
write.writerow(M[i])
with open('../../DATASETS/path_train_files_CART.csv', 'w') as f:
# using csv.writer method from CSV package
......@@ -73,16 +85,6 @@ with open('../../DATASETS/path_train_files_CART.csv', 'w') as f:
write.writerow(files_train)
write.writerow(Y_train)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
......
CART/Python/Music Style
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CART/src/main.cpp
View file @ 8379effb
......@@ -3,7 +3,7 @@
#include <iostream>
#include <fstream>
//using namespace std;
int M = 10;
std::istream& operator>>(std::istream& str, CSVRow& data)
{
......@@ -11,6 +11,30 @@ std::istream& operator>>(std::istream& str, CSVRow& data)
return str;
}
void get_confusion_matrix_in_csv_file(std::ifstream &file, std::vector<std::vector<int>> &confusion_matrix_sklearn)
{
CSVRow line;
for (int i = 0; i < M; ++i) {
file >> line;
for (int j = 0; j < M; ++j) {
confusion_matrix_sklearn[i][j] = (std::stoi(line[j]));
}
}
}
void print_confusion_matrix(std::vector<std::vector<int>> &confusion_matrix)
{
std::cout<<"["<<std::endl;
for (int i = 0; i < M; ++i) {
std::cout<<"[ ";
for (int j = 0; j < M; ++j) {
std::cout<<confusion_matrix[i][j]<<" ";
}
std::cout<<"]"<<std::endl;
}
std::cout<<"]"<<std::endl;
}
double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value)
{
double valid = 0;
......@@ -18,46 +42,58 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu
else
{
for (int i = 0; i < truth_value.size(); ++i) {
// std::cout << predicted_value[i] << " " << truth_value[i] << std::endl;
if (predicted_value[i] == truth_value[i]) valid++;
}
// std::cout << truth_value.size() << " " << predicted_value.size() << std::endl;
// std::cout << "valid number is " << valid << std::endl;
return valid/double (truth_value.size());
}
}
void build_confusion_matrix(std::vector<int>& truth_value, std::vector<int>& predicted_value, std::vector<std::vector<int>> &confusion_matrix)
{
for (int i = 0; i < truth_value.size(); ++i) {
confusion_matrix[predicted_value[i]][truth_value[i]]++;
}
}
int main(){
// for crop recommendation
// std::vector<double> feature{90,42,43,20.87974371,82.00274423,6.502985292000001,202.9355362};
// cout << "cart_predict(feature) : " << cart_predict(feature) << endl;
std::ifstream file("../../DATASETS/path_test_files_CART.csv");
CSVRow files_name_row;
CSVRow style_name_row;
CSVRow accuracy_SKLearn;
std::vector<std::vector<int>> confusion_matrix_sklearn(M, std::vector<int>(M, 0));
std::vector<std::vector<int>> our_confusion_matrix(M, std::vector<int>(M, 0));
file >> files_name_row;
file >> style_name_row;
file >> accuracy_SKLearn;
get_confusion_matrix_in_csv_file(file,confusion_matrix_sklearn);
file.close();
std::vector<int> style_test_set(style_name_row.size());
std::vector<int> style_predict;
transform_to_index(style_name_row,style_test_set);
// std::cout << "size of test set: " << files_name_row.size() << std::endl;
// std::cout << "size of test set : " << style_name_row.size() << std::endl;
for (int i = 0; i < files_name_row.size(); ++i) {
if (!files_name_row[i].empty()) {
// std::cout <<style_test_set[i]<< std::endl;
std::filesystem::path fileName = files_name_row[i];
auto bins = compute_features_for(fileName);
auto featuring = bins.begin()->second;
std::vector<real> featuring;
auto featuring_avg = bins[FTYPE::BINAVG];
auto featuring_std = bins[FTYPE::BINSTDEV];
featuring.insert(featuring.cbegin(), featuring_avg.begin(), featuring_avg.end());
featuring.insert(featuring.cend(), featuring_std.begin(), featuring_std.end());
style_predict.push_back(cart_predict(featuring));
// std::cout << "step " << i << " cart_predict(feature) : " << cart_predict(featuring) << std::endl;
} else style_predict.push_back(-1);
}
build_confusion_matrix(style_test_set,style_predict,our_confusion_matrix);
std::cout << "The confusion matrix given by SKlearn is : " << std::endl;
print_confusion_matrix(confusion_matrix_sklearn);
std::cout << "The confusion matrix given by our code is : " << std::endl;
print_confusion_matrix(our_confusion_matrix);
std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl;
std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl;
return 0;
......
DATASETS/path_test_files_ANN.csv
View file @ 8379effb
../../DATASETS/Music/jazz/jazz.00021.au,../../DATASETS/Music/pop/pop.00037.au,../../DATASETS/Music/pop/pop.00040.au,../../DATASETS/Music/metal/metal.00060.au,../../DATASETS/Music/hiphop/hiphop.00011.au,../../DATASETS/Music/metal/metal.00078.au,../../DATASETS/Music/metal/metal.00026.au,../../DATASETS/Music/jazz/jazz.00013.au,../../DATASETS/Music/reggae/reggae.00059.au,../../DATASETS/Music/classical/classical.00036.au,../../DATASETS/Music/reggae/reggae.00011.au,../../DATASETS/Music/blues/blues.00076.au,../../DATASETS/Music/metal/metal.00036.au,../../DATASETS/Music/rock/rock.00073.au,../../DATASETS/Music/rock/rock.00038.au,../../DATASETS/Music/reggae/reggae.00099.au,../../DATASETS/Music/country/country.00080.au,../../DATASETS/Music/reggae/reggae.00083.au,../../DATASETS/Music/pop/pop.00061.au,../../DATASETS/Music/disco/disco.00019.au,../../DATASETS/Music/jazz/jazz.00049.au,../../DATASETS/Music/classical/classical.00074.au,../../DATASETS/Music/disco/disco.00071.au,../../DATASETS/Music/jazz/jazz.00027.au,../../DATASETS/Music/country/country.00010.au,../../DATASETS/Music/country/country.00035.au,../../DATASETS/Music/classical/classical.00001.au,../../DATASETS/Music/rock/rock.00086.au,../../DATASETS/Music/rock/rock.00002.au,../../DATASETS/Music/rock/rock.00047.au,../../DATASETS/Music/disco/disco.00046.au,../../DATASETS/Music/classical/classical.00039.au,../../DATASETS/Music/metal/metal.00021.au,../../DATASETS/Music/hiphop/hiphop.00099.au,../../DATASETS/Music/disco/disco.00070.au,../../DATASETS/Music/classical/classical.00098.au,../../DATASETS/Music/metal/metal.00087.au,../../DATASETS/Music/jazz/jazz.00084.au,../../DATASETS/Music/rock/rock.00001.au,../../DATASETS/Music/blues/blues.00059.au,../../DATASETS/Music/disco/disco.00028.au,../../DATASETS/Music/blues/blues.00096.au,../../DATASETS/Music/disco/disco.00012.au,../../DATASETS/Music/rock/rock.00074.au,../../DATASETS/Music/country/country.00099.au,../../DATASETS/Music/country/country.00077.au,../../DATASETS/Music/rock/rock.00024.au,../../DATASETS/Music/metal/metal.00001.au,../../DATASETS/Music/hiphop/hiphop.00039.au,../../DATASETS/Music/reggae/reggae.00037.au,../../DATASETS/Music/jazz/jazz.00070.au,../../DATASETS/Music/reggae/reggae.00079.au,../../DATASETS/Music/country/country.00061.au,../../DATASETS/Music/jazz/jazz.00078.au,../../DATASETS/Music/blues/blues.00023.au,../../DATASETS/Music/blues/blues.00030.au,../../DATASETS/Music/metal/metal.00017.au,../../DATASETS/Music/blues/blues.00010.au,../../DATASETS/Music/country/country.00021.au,../../DATASETS/Music/reggae/reggae.00020.au,../../DATASETS/Music/country/country.00096.au,../../DATASETS/Music/blues/blues.00054.au,../../DATASETS/Music/jazz/jazz.00042.au,../../DATASETS/Music/country/country.00009.au,../../DATASETS/Music/metal/metal.00004.au,../../DATASETS/Music/metal/metal.00092.au,../../DATASETS/Music/metal/metal.00062.au,../../DATASETS/Music/reggae/reggae.00066.au,../../DATASETS/Music/blues/blues.00070.au,../../DATASETS/Music/jazz/jazz.00043.au,../../DATASETS/Music/classical/classical.00007.au,../../DATASETS/Music/hiphop/hiphop.00093.au,../../DATASETS/Music/jazz/jazz.00090.au,../../DATASETS/Music/pop/pop.00041.au,../../DATASETS/Music/country/country.00092.au,../../DATASETS/Music/country/country.00089.au,../../DATASETS/Music/metal/metal.00052.au,../../DATASETS/Music/blues/blues.00039.au,../../DATASETS/Music/jazz/jazz.00089.au,../../DATASETS/Music/disco/disco.00007.au,../../DATASETS/Music/metal/metal.00079.au,../../DATASETS/Music/blues/blues.00066.au,../../DATASETS/Music/country/country.00075.au,../../DATASETS/Music/blues/blues.00067.au,../../DATASETS/Music/disco/disco.00018.au,../../DATASETS/Music/jazz/jazz.00048.au,../../DATASETS/Music/rock/rock.00098.au,../../DATASETS/Music/pop/pop.00014.au,../../DATASETS/Music/pop/pop.00053.au,../../DATASETS/Music/disco/disco.00027.au,../../DATASETS/Music/disco/disco.00082.au,../../DATASETS/Music/hiphop/hiphop.00051.au,../../DATASETS/Music/jazz/jazz.00022.au,../../DATASETS/Music/country/country.00018.au,../../DATASETS/Music/pop/pop.00087.au,../../DATASETS/Music/hiphop/hiphop.00036.au,../../DATASETS/Music/pop/pop.00064.au,../../DATASETS/Music/blues/blues.00088.au,../../DATASETS/Music/blues/blues.00063.au,../../DATASETS/Music/reggae/reggae.00026.au,../../DATASETS/Music/pop/pop.00016.au,../../DATASETS/Music/disco/disco.00051.au,../../DATASETS/Music/rock/rock.00036.au,../../DATASETS/Music/country/country.00056.au,../../DATASETS/Music/metal/metal.00035.au,../../DATASETS/Music/metal/metal.00044.au,../../DATASETS/Music/jazz/jazz.00054.au,../../DATASETS/Music/rock/rock.00059.au,../../DATASETS/Music/classical/classical.00068.au,../../DATASETS/Music/rock/rock.00017.au,../../DATASETS/Music/jazz/jazz.00028.au,../../DATASETS/Music/reggae/reggae.00023.au,../../DATASETS/Music/rock/rock.00085.au,../../DATASETS/Music/reggae/reggae.00016.au,../../DATASETS/Music/blues/blues.00086.au,../../DATASETS/Music/hiphop/hiphop.00032.au,../../DATASETS/Music/classical/classical.00084.au,../../DATASETS/Music/r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DATASETS/path_test_files_CART.csv
View file @ 8379effb
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DATASETS/path_test_files_SVM.csv
View file @ 8379effb
../../DATASETS/Music/jazz/jazz.00021.au,../../DATASETS/Music/pop/pop.00037.au,../../DATASETS/Music/pop/pop.00040.au,../../DATASETS/Music/metal/metal.00060.au,../../DATASETS/Music/hiphop/hiphop.00011.au,../../DATASETS/Music/metal/metal.00078.au,../../DATASETS/Music/metal/metal.00026.au,../../DATASETS/Music/jazz/jazz.00013.au,../../DATASETS/Music/reggae/reggae.00059.au,../../DATASETS/Music/classical/classical.00036.au,../../DATASETS/Music/reggae/reggae.00011.au,../../DATASETS/Music/blues/blues.00076.au,../../DATASETS/Music/metal/metal.00036.au,../../DATASETS/Music/rock/rock.00073.au,../../DATASETS/Music/rock/rock.00038.au,../../DATASETS/Music/reggae/reggae.00099.au,../../DATASETS/Music/country/country.00080.au,../../DATASETS/Music/reggae/reggae.00083.au,../../DATASETS/Music/pop/pop.00061.au,../../DATASETS/Music/disco/disco.00019.au,../../DATASETS/Music/jazz/jazz.00049.au,../../DATASETS/Music/classical/classical.00074.au,../../DATASETS/Music/disco/disco.00071.au,../../DATASETS/Music/jazz/jazz.00027.au,../../DATASETS/Music/country/country.00010.au,../../DATASETS/Music/country/country.00035.au,../../DATASETS/Music/classical/classical.00001.au,../../DATASETS/Music/rock/rock.00086.au,../../DATASETS/Music/rock/rock.00002.au,../../DATASETS/Music/rock/rock.00047.au,../../DATASETS/Music/disco/disco.00046.au,../../DATASETS/Music/classical/classical.00039.au,../../DATASETS/Music/metal/metal.00021.au,../../DATASETS/Music/hiphop/hiphop.00099.au,../../DATASETS/Music/disco/disco.00070.au,../../DATASETS/Music/classical/classical.00098.au,../../DATASETS/Music/metal/metal.00087.au,../../DATASETS/Music/jazz/jazz.00084.au,../../DATASETS/Music/rock/rock.00001.au,../../DATASETS/Music/blues/blues.00059.au,../../DATASETS/Music/disco/disco.00028.au,../../DATASETS/Music/blues/blues.00096.au,../../DATASETS/Music/disco/disco.00012.au,../../DATASETS/Music/rock/rock.00074.au,../../DATASETS/Music/country/country.00099.au,../../DATASETS/Music/country/country.00077.au,../../DATASETS/Music/rock/rock.00024.au,../../DATASETS/Music/metal/metal.00001.au,../../DATASETS/Music/hiphop/hiphop.00039.au,../../DATASETS/Music/reggae/reggae.00037.au,../../DATASETS/Music/jazz/jazz.00070.au,../../DATASETS/Music/reggae/reggae.00079.au,../../DATASETS/Music/country/country.00061.au,../../DATASETS/Music/jazz/jazz.00078.au,../../DATASETS/Music/blues/blues.00023.au,../../DATASETS/Music/blues/blues.00030.au,../../DATASETS/Music/metal/metal.00017.au,../../DATASETS/Music/blues/blues.00010.au,../../DATASETS/Music/country/country.00021.au,../../DATASETS/Music/reggae/reggae.00020.au,../../DATASETS/Music/country/country.00096.au,../../DATASETS/Music/blues/blues.00054.au,../../DATASETS/Music/jazz/jazz.00042.au,../../DATASETS/Music/country/country.00009.au,../../DATASETS/Music/metal/metal.00004.au,../../DATASETS/Music/metal/metal.00092.au,../../DATASETS/Music/metal/metal.00062.au,../../DATASETS/Music/reggae/reggae.00066.au,../../DATASETS/Music/blues/blues.00070.au,../../DATASETS/Music/jazz/jazz.00043.au,../../DATASETS/Music/classical/classical.00007.au,../../DATASETS/Music/hiphop/hiphop.00093.au,../../DATASETS/Music/jazz/jazz.00090.au,../../DATASETS/Music/pop/pop.00041.au,../../DATASETS/Music/country/country.00092.au,../../DATASETS/Music/country/country.00089.au,../../DATASETS/Music/metal/metal.00052.au,../../DATASETS/Music/blues/blues.00039.au,../../DATASETS/Music/jazz/jazz.00089.au,../../DATASETS/Music/disco/disco.00007.au,../../DATASETS/Music/metal/metal.00079.au,../../DATASETS/Music/blues/blues.00066.au,../../DATASETS/Music/country/country.00075.au,../../DATASETS/Music/blues/blues.00067.au,../../DATASETS/Music/disco/disco.00018.au,../../DATASETS/Music/jazz/jazz.00048.au,../../DATASETS/Music/rock/rock.00098.au,../../DATASETS/Music/pop/pop.00014.au,../../DATASETS/Music/pop/pop.00053.au,../../DATASETS/Music/disco/disco.00027.au,../../DATASETS/Music/disco/disco.00082.au,../../DATASETS/Music/hiphop/hiphop.00051.au,../../DATASETS/Music/jazz/jazz.00022.au,../../DATASETS/Music/country/country.00018.au,../../DATASETS/Music/pop/pop.00087.au,../../DATASETS/Music/hiphop/hiphop.00036.au,../../DATASETS/Music/pop/pop.00064.au,../../DATASETS/Music/blues/blues.00088.au,../../DATASETS/Music/blues/blues.00063.au,../../DATASETS/Music/reggae/reggae.00026.au,../../DATASETS/Music/pop/pop.00016.au,../../DATASETS/Music/disco/disco.00051.au,../../DATASETS/Music/rock/rock.00036.au,../../DATASETS/Music/country/country.00056.au,../../DATASETS/Music/metal/metal.00035.au,../../DATASETS/Music/metal/metal.00044.au,../../DATASETS/Music/jazz/jazz.00054.au,../../DATASETS/Music/rock/rock.00059.au,../../DATASETS/Music/classical/classical.00068.au,../../DATASETS/Music/rock/rock.00017.au,../../DATASETS/Music/jazz/jazz.00028.au,../../DATASETS/Music/reggae/reggae.00023.au,../../DATASETS/Music/rock/rock.00085.au,../../DATASETS/Music/reggae/reggae.00016.au,../../DATASETS/Music/blues/blues.00086.au,../../DATASETS/Music/hiphop/hiphop.00032.au,../../DATASETS/Music/classical/classical.00084.au,../../DATASETS/Music/rock/rock.00078.au,../../DATASETS/Music/jazz/jazz.00034.au,../../DATASETS/Music/country/country.00094.au,../../DATASETS/Music/reggae/reggae.00092.au,../../DATASETS/Music/hiphop/hiphop.00025.au,../../DATASETS/Music/pop/pop.00013.au,../../DATASETS/Music/country/country.00060.au,../../DATASETS/Music/country/country.00037.au,../../DATASETS/Music/jazz/jazz.00059.au,../../DATASETS/Music/jazz/jazz.00083.au,../../DATASETS/Music/hiphop/hiphop.00045.au,../../DATASETS/Music/reggae/reggae.00067.au,../../DATASETS/Music/reggae/reggae.00000.au,../../DATASETS/Music/jazz/jazz.00099.au,../../DATASETS/Music/reggae/reggae.00049.au,../../DATASETS/Music/country/country.00065.au,../../DATASETS/Music/rock/rock.00095.au,../../DATASETS/Music/jazz/jazz.00029.au,../../DATASETS/Music/blues/blues.00055.au,../../DATASETS/Music/classical/classical.00020.au,../../DATASETS/Music/country/country.00015.au,../../DATASETS/Music/blues/blues.00025.au,../../DATASETS/Music/blues/blues.00072.au,../../DATASETS/Music/blues/blues.00044.au,../../DATASETS/Music/country/country.00047.au,../../DATASETS/Music/pop/pop.00021.au,../../DATASETS/Music/country/country.00081.au,../../DATASETS/Music/reggae/reggae.00093.au,../../DATASETS/Music/rock/rock.00014.au,../../DATASETS/Music/reggae/reggae.00010.au,../../DATASETS/Music/country/country.00044.au,../../DATASETS/Music/reggae/reggae.00022.au,../../DATASETS/Music/disco/disco.00021.au,../../DATASETS/Music/metal/metal.00043.au,../../DATASETS/Music/classical/classical.00058.au,../../DATASETS/Music/rock/rock.00077.au,../../DATASETS/Music/hiphop/hiphop.00029.au,../../DATASETS/Music/rock/rock.00041.au,../../DATASETS/Music/hiphop/hiphop.00062.au,../../DATASETS/Music/disco/disco.00009.au,../../DATASETS/Music/metal/metal.00097.au,../../DATASETS/Music/blues/blues.00060.au,../../DATASETS/Music/reggae/reggae.00084.au,../../DATASETS/Music/jazz/jazz.00095.au,../../DATASETS/Music/pop/pop.00067.au,../../DATASETS/Music/metal/metal.00049.au,../../DATASETS/Music/metal/metal.00050.au,../../DATASETS/Music/reggae/reggae.00065.au,../../DATASETS/Music/metal/metal.00068.au,../../DATASETS/Music/country/country.00098.au,../../DATASETS/Music/metal/metal.00089.au,../../DATASETS/Music/disco/disco.00014.au,../../DATASETS/Music/disco/disco.00010.au,../../DATASETS/Music/disco/disco.00061.au,../../DATASETS/Music/hiphop/hiphop.00079.au,../../DATASETS/Music/classical/classical.00010.au,../../DATASETS/Music/rock/rock.00089.au,../../DATASETS/Music/hiphop/hiphop.00086.au,../../DATASETS/Music/disco/disco.00063.au,../../DATASETS/Music/country/country.00054.au,../../DATASETS/Music/country/country.00059.au,../../DATASETS/Music/reggae/reggae.00002.au,../../DATASETS/Music/metal/metal.00077.au,../../DATASETS/Music/hiphop/hiphop.00094.au,../../DATASETS/Music/metal/metal.00070.au,../../DATASETS/Music/disco/disco.00077.au,../../DATASETS/Music/jazz/jazz.00026.au,../../DATASETS/Music/reggae/reggae.00045.au,../../DATASETS/Music/classical/classical.00037.au,../../DATASETS/Music/disco/disco.00055.au,../../DATASETS/Music/disco/disco.00065.au,../../DATASETS/Music/rock/rock.00042.au,../../DATASETS/Music/pop/pop.00049.au,../../DATASETS/Music/rock/rock.00048.au,../../DATASETS/Music/reggae/reggae.00029.au,../../DATASETS/Music/metal/metal.00056.au,../../DATASETS/Music/classical/classical.00099.au,../../DATASETS/Music/country/country.00013.au,../../DATASETS/Music/hiphop/hiphop.00008.au,../../DATASETS/Music/disco/disco.00032.au,../../DATASETS/Music/country/country.00008.au,../../DATASETS/Music/metal/metal.00013.au,../../DATASETS/Music/blues/blues.00078.au,../../DATASETS/Music/blues/blues.00029.au,../../DATASETS/Music/jazz/jazz.00035.au,../../DATASETS/Music/metal/metal.00095.au,../../DATASETS/Music/jazz/jazz.00057.au,../../DATASETS/Music/reggae/reggae.00036.au,../../DATASETS/Music/jazz/jazz.00096.au,../../DATASETS/Music/classical/classical.00065.au,../../DATASETS/Music/rock/rock.00018.au,../../DATASETS/Music/hiphop/hiphop.00095.au,../../DATASETS/Music/reggae/reggae.00024.au,../../DATASETS/Music/blues/blues.00065.au,../../DATASETS/Music/classical/classical.00041.au,../../DATASETS/Music/rock/rock.00025.au,../../DATASETS/Music/reggae/reggae.00027.au,../../DATASETS/Music/metal/metal.00055.au,../../DATASETS/Music/disco/disco.00031.au,../../DATASETS/Music/metal/metal.00064.au,../../DATASETS/Music/country/country.00049.au,../../DATASETS/Music/rock/rock.00007.au,../../DATASETS/Music/pop/pop.00008.au,../../DATASETS/Music/disco/disco.00005.au,../../DATASETS/Music/pop/pop.00034.au,../../DATASETS/Music/rock/rock.00075.au,../../DATASETS/Music/blues/blues.00049.au,../../DATASETS/Music/reggae/reggae.00096.au,../../DATASETS/Music/blues/blues.00002.au,../../DATASETS/Music/jazz/jazz.00044.au,../../DATASETS/Music/disco/disco.00050.au,../../DATASETS/Music/rock/rock.00004.au,../../DATASETS/Music/jazz/jazz.00036.au,../../DATASETS/Music/disco/disco.00044.au,../../DATASETS/Music/rock/rock.00094.au,../../DATASETS/Music/hiphop/hiphop.00081.au,../../DATASETS/Music/jazz/jazz.00075.au,../../DATASETS/Music/blues/blues.00033.au,../../DATASETS/Music/blues/blues.00031.au,../../DATASETS/Music/country/country.00031.au,../../DATASETS/Music/rock/rock.00063.au,../../DATASETS/Music/classical/classical.00092.au,../../DATASETS/Music/disco/disco.00033.au,../../DATASETS/Music/blues/blues.00003.au,../../DATASETS/Music/country/country.00004.au,../../DATASETS/Music/jazz/jazz.00014.au,../../DATASETS/Music/pop/pop.00099.au,../../DATASETS/Music/disco/disco.00006.au,../../DATASETS/Music/classical/classical.00009.au,../../DATASETS/Music/hiphop/hiphop.00030.au,../../DATASETS/Music/blues/blues.00077.au,../../DATASETS/Music/blues/blues.00084.au,../../DATASETS/Music/country/country.00086.au,../../DATASETS/Music/blues/blues.00082.au,../../DATASETS/Music/rock/rock.00091.au,../../DATASETS/Music/pop/pop.00089.au,../../DATASETS/Music/reggae/reggae.00094.au,../../DATASETS/Music/disco/disco.00098.au,../../DATASETS/Music/disco/disco.00023.au,../../DATASETS/Music/jazz/jazz.00019.au,../../DATASETS/Music/rock/rock.00016.au,../../DATASETS/Music/rock/rock.00022.au,../../DATASETS/Music/blues/blues.00005.au,../../DATASETS/Music/pop/pop.00031.au,../../DATASETS/Music/hiphop/hiphop.00065.au,../../DATASETS/Music/blues/blues.00097.au,../../DATASETS/Music/country/country.00066.au,../../DATASETS/Music/disco/disco.00057.au,../../DATASETS/Music/reggae/reggae.00068.au,../../DATASETS/Music/pop/pop.00098.au,../../DATASETS/Music/disco/disco.00080.au,../../DATASETS/Music/metal/metal.00031.au,../../DATASETS/Music/disco/disco.00081.au,../../DATASETS/Music/hiphop/hiphop.00090.au,../../DATASETS/Music/classical/classical.00018.au,../../DATASETS/Music/rock/rock.00000.au,../../DATASETS/Music/country/country.00050.au,../../DATASETS/Music/jazz/jazz.00023.au,../../DATASETS/Music/blues/blues.00009.au,../../DATASETS/Music/classical/classical.00096.au,../../DATASETS/Music/metal/metal.00003.au,../../DATASETS/Music/blues/blues.00081.au,../../DATASETS/Music/pop/pop.00083.au,../../DATASETS/Music/jazz/jazz.00087.au,../../DATASETS/Music/pop/pop.00097.au,../../DATASETS/Music/country/country.00039.au,../../DATASETS/Music/country/country.00090.au,../../DATASETS/Music/country/country.00011.au,../../DATASETS/Music/pop/pop.00017.au,../../DATASETS/Music/disco/disco.00059.au,../../DATASETS/Music/hiphop/hiphop.00049.au,../../DATASETS/Music/country/country.00027.au,../../DATASETS/Music/rock/rock.00050.au,../../DATASETS/Music/rock/rock.00046.au,../../DATASETS/Music/pop/pop.00096.au,../../DATASETS/Music/jazz/jazz.00001.au,../../DATASETS/Music/hiphop/hiphop.00064.au,../../DATASETS/Music/disco/disco.00062.au,../../DATASETS/Music/hiphop/hiphop.00068.au,../../DATASETS/Music/rock/rock.00035.au,../../DATASETS/Music/hiphop/hiphop.00028.au,../../DATASETS/Music/blues/blues.00007.au,../../DATASETS/Music/classical/classical.00055.au,../../DATASETS/Music/jazz/jazz.00041.au,../../DATASETS/Music/hiphop/hiphop.00040.au,../../DATASETS/Music/hiphop/hiphop.00082.au,../../DATASETS/Music/hiphop/hiphop.00022.au,../../DATASETS/Music/pop/pop.00078.au,../../DATASETS/Music/rock/rock.00049.au,../../DATASETS/Music/disco/disco.00034.au,../../DATASETS/Music/jazz/jazz.00076.au,../../DATASETS/Music/rock/rock.00034.au,../../DATASETS/Music/jazz/jazz.00067.au,../../DATASETS/Music/jazz/jazz.00094.au,../../DATASETS/Music/jazz/jazz.00030.au,../../DATASETS/Music/jazz/jazz.00081.au,../../DATASETS/Music/pop/pop.00007.au,../../DATASETS/Music/hiphop/hiphop.00048.au,../../DATASETS/Music/hiphop/hiphop.00053.au,../../DATASETS/Music/country/country.00028.au,../../DATASETS/Music/disco/disco.00052.au,../../DATASETS/Music/pop/pop.00028.au,../../DATASETS/Music/country/country.00012.au,../../DATASETS/Music/blues/blues.00079.au,../../DATASETS/Music/classical/classical.00048.au,../../DATASETS/Music/disco/disco.00002.au,../../DATASETS/Music/metal/metal.00028.au,../../DATASETS/Music/pop/pop.00077.au,../../DATASETS/Music/jazz/jazz.00006.au,../../DATASETS/Music/disco/disco.00042.au,../../DATASETS/Music/hiphop/hiphop.00085.au,../../DATASETS/Music/pop/pop.00011.au,../../DATASETS/Music/classical/classical.00033.au
jazz, pop, pop, metal, hiphop, metal, metal, jazz, reggae, classical, reggae, blues, metal, rock, rock, reggae, country, reggae, pop, disco, jazz, classical, disco, jazz, country, country, classical, rock, rock, rock, disco, classical, metal, hiphop, disco, classical, metal, jazz, rock, blues, disco, blues, disco, rock, country, country, rock, metal, hiphop, reggae, jazz, reggae, country, jazz, blues, blues, metal, blues, country, reggae, country, blues, jazz, country, metal, metal, metal, reggae, blues, jazz, classical, hiphop, jazz, pop, country, country, metal, blues, jazz, disco, metal, blues, country, blues, disco, jazz, rock, pop, pop, disco, disco, hiphop, jazz, country, pop, hiphop, pop, blues, blues, reggae, pop, disco, rock, country, metal, metal, jazz, rock, classical, rock, jazz, reggae, rock, reggae, blues, hiphop, classical, rock, jazz, country, reggae, hiphop, pop, country, country, jazz, jazz, hiphop, reggae, reggae, jazz, reggae, country, rock, jazz, blues, classical, country, blues, blues, blues, country, pop, country, reggae, rock, reggae, country, reggae, disco, metal, classical, rock, hiphop, rock, hiphop, disco, metal, blues, reggae, jazz, pop, metal, metal, reggae, metal, country, metal, disco, disco, disco, hiphop, classical, rock, hiphop, disco, country, country, reggae, metal, hiphop, metal, disco, jazz, reggae, classical, disco, disco, rock, pop, rock, reggae, metal, classical, country, hiphop, disco, country, metal, blues, blues, jazz, metal, jazz, reggae, jazz, classical, rock, hiphop, reggae, blues, classical, rock, reggae, metal, disco, metal, country, rock, pop, disco, pop, rock, blues, reggae, blues, jazz, disco, rock, jazz, disco, rock, hiphop, jazz, blues, blues, country, rock, classical, disco, blues, country, jazz, pop, disco, classical, hiphop, blues, blues, country, blues, rock, pop, reggae, disco, disco, jazz, rock, rock, blues, pop, hiphop, blues, country, disco, reggae, pop, disco, metal, disco, hiphop, classical, rock, country, jazz, blues, classical, metal, blues, pop, jazz, pop, country, country, country, pop, disco, hiphop, country, rock, rock, pop, jazz, hiphop, disco, hiphop, rock, hiphop, blues, classical, jazz, hiphop, hiphop, hiphop, pop, rock, disco, jazz, rock, jazz, jazz, jazz, jazz, pop, hiphop, hiphop, country, disco, pop, country, blues, classical, disco, metal, pop, jazz, disco, hiphop, pop, classical
0.5575757575757576
16,0,2,2,0,3,0,2,5,6
0,20,1,0,0,0,0,1,0,0
0,0,19,4,0,3,4,3,0,6
2,1,0,23,2,0,1,2,4,3
2,0,1,2,15,1,0,1,8,0
1,9,1,0,0,28,0,0,0,1
0,0,0,0,0,0,29,0,0,2
0,3,3,2,1,1,1,14,3,0
1,0,5,2,5,0,0,0,12,4
2,1,8,6,3,1,4,1,3,8
SVM/python/start_svm.py
View file @ 8379effb
......@@ -46,12 +46,32 @@ print(confusion_matrix(Y_test, Y_pred, labels=classes))
accuracy = (Y_pred == Y_test).mean()
print('Test Accuracy : %.3f' % accuracy)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
M = confusion_matrix(Y_test, Y_pred, labels=classes)
# with open('../../DATASETS/test_features_SVM.csv', 'w') as f:
# # using csv.writer method from CSV package
# write = csv.writer(f)
# for i in range(X_test.shape[0]):
# write.writerow(X_test[i])
with open('../../DATASETS/path_test_files_SVM.csv', 'w') as f:
# using csv.writer method from CSV package
write = csv.writer(f)
write.writerow(files_test)
write.writerow(Y_test)
write.writerow([accuracy])
for i in range(M.shape[0]):
write.writerow(M[i])
with open('../../DATASETS/path_train_files_SVM.csv', 'w') as f:
# using csv.writer method from CSV package
......@@ -59,20 +79,12 @@ with open('../../DATASETS/path_train_files_SVM.csv', 'w') as f:
write.writerow(files_train)
write.writerow(Y_train)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("My predictions")
plt.show()
Y_pred = clf.predict(X_test)
ConfusionMatrixDisplay.from_predictions(Y_test, Y_pred, display_labels=classes)
plt.title("SKLEARN")
plt.show()
# linearsvc coefs
H = clf.named_steps['linearsvc'].coef_
b = clf.named_steps['linearsvc'].intercept_
mean = clf.named_steps['standardscaler'].mean_
var = clf.named_steps['standardscaler'].var_
print("les classes : ",clf.named_steps['linearsvc'].classes_)
print("mean : ", mean, "var : ", var)
print("coef.shape : ", H.shape, "coefs : ", H, "b.shape : ", b.shape, "b : ", b)
......@@ -121,7 +133,7 @@ def gen_h_code():
out_str += "{}".format(var[-1])
out_str += "};\n"
out_str += "double svm_predict(std::vector<double> feature);\n"
out_str += "int svm_predict(std::vector<double> feature);\n"
# cpp_file = open("../src/svm_cpp.h", "w") # for crop-recommendation
cpp_file = open("../src/svm_cpp_music_style.h", "w") # for music style
cpp_file.write(out_str)
......
SVM/src/svm_cpp_music_style.cpp
View file @ 8379effb
......@@ -4,6 +4,36 @@
#include <cmath>
#include <fstream>
std::istream& operator>>(std::istream& str, CSVRow& data)
{
data.readNextRow(str);
return str;
}
void get_confusion_matrix_in_csv_file(std::ifstream &file, std::vector<std::vector<int>> &confusion_matrix_sklearn)
{
CSVRow line;
for (int i = 0; i < M; ++i) {
file >> line;
for (int j = 0; j < M; ++j) {
confusion_matrix_sklearn[i][j] = (std::stoi(line[j]));
}
}
}
void print_confusion_matrix(std::vector<std::vector<int>> &confusion_matrix)
{
std::cout<<"["<<std::endl;
for (int i = 0; i < M; ++i) {
std::cout<<"[ ";
for (int j = 0; j < M; ++j) {
std::cout<<confusion_matrix[i][j]<<" ";
}
std::cout<<"]"<<std::endl;
}
std::cout<<"]"<<std::endl;
}
int svm_predict(std::vector<real> feature){
double predict;
double best_predict;
......@@ -17,7 +47,7 @@ int svm_predict(std::vector<real> feature){
}
predict += B[i];
best_predict = predict;
for(int i = 0; i<M; ++i)
for(int i = 1; i<M; ++i)
{
predict = 0;
for(int j = 0; j<N; ++j)
......@@ -25,7 +55,6 @@ int svm_predict(std::vector<real> feature){
predict += H[i][j]*((feature[j]-MEAN[j])/sqrt(VAR[j]));
}
predict+= B[i];
// std::cout << "predict : "<< predict << std::endl;
if (predict>best_predict)
{
best_predict = predict;
......@@ -35,12 +64,6 @@ int svm_predict(std::vector<real> feature){
return indice;
}
std::istream& operator>>(std::istream& str, CSVRow& data)
{
data.readNextRow(str);
return str;
}
double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_value)
{
double valid = 0;
......@@ -48,43 +71,57 @@ double evaluate (std::vector<int>& truth_value, std::vector<int>& predicted_valu
else
{
for (int i = 0; i < truth_value.size(); ++i) {
// std::cout << predicted_value[i] << " " << truth_value[i] << std::endl;
if (predicted_value[i] == truth_value[i]) valid++;
}
// std::cout << truth_value.size() << " " << predicted_value.size() << std::endl;
// std::cout << "valid number is " << valid << std::endl;
return valid/double (truth_value.size());
}
}
void build_confusion_matrix(std::vector<int>& truth_value, std::vector<int>& predicted_value, std::vector<std::vector<int>> &confusion_matrix)
{
for (int i = 0; i < truth_value.size(); ++i) {
confusion_matrix[predicted_value[i]][truth_value[i]]++;
}
}
int main(){
std::ifstream file("../../DATASETS/path_test_files_SVM.csv");
CSVRow files_name_row;
CSVRow style_name_row;
CSVRow accuracy_SKLearn;
std::vector<std::vector<int>> confusion_matrix_sklearn(M, std::vector<int>(M, 0));
std::vector<std::vector<int>> our_confusion_matrix(M, std::vector<int>(M, 0));
file >> files_name_row;
file >> style_name_row;
file >> accuracy_SKLearn;
get_confusion_matrix_in_csv_file(file,confusion_matrix_sklearn);
file.close();
std::vector<int> style_test_set(style_name_row.size());
std::vector<int> style_predict;
transform_to_index(style_name_row,style_test_set);
// std::cout << "size of test set: " << files_name_row.size() << std::endl;
// std::cout << "size of test set : " << style_name_row.size() << std::endl;
for (int i = 0; i < files_name_row.size(); ++i) {
if (!files_name_row[i].empty()) {
// std::cout <<style_test_set[i]<< std::endl;
std::filesystem::path fileName = files_name_row[i];
auto bins = compute_features_for(fileName);
auto featuring = bins.begin()->second;
std::vector<real> featuring;
auto featuring_avg = bins[FTYPE::BINAVG];
auto featuring_std = bins[FTYPE::BINSTDEV];
featuring.insert(featuring.cbegin(), featuring_avg.begin(), featuring_avg.end());
featuring.insert(featuring.cend(), featuring_std.begin(), featuring_std.end());
style_predict.push_back(svm_predict(featuring));
// std::cout << "step " << i << " cart_predict(feature) : " << cart_predict(featuring) << std::endl;
} else style_predict.push_back(-1);
}
build_confusion_matrix(style_test_set,style_predict,our_confusion_matrix);
std::cout << "The confusion matrix given by SKlearn is : " << std::endl;
print_confusion_matrix(confusion_matrix_sklearn);
std::cout << "The confusion matrix given by our code is : " << std::endl;
print_confusion_matrix(our_confusion_matrix);
std::cout << "The accuracy given by our code is : " << evaluate(style_test_set, style_predict) << std::endl;
std::cout << "The accuracy given by SKLearn is : " << accuracy_SKLearn[0] << std::endl;
return 0;
......
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