示例#1
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def strategy01(X_train, labels_train, X_test, labels_test):
    '''
    Estrategia número 1 para el primer clasificador
    '''

    print('\nEjecutando la estrategia número 1 del primer clasificador...')

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 1: Cleaning de los datos
    #   > Training: 5040 x 82
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 2: Normalización Mean-Std de los datos
    X_train, a, b = normalize(X_train)

    # Paso 3: Selección de características
    # Acá se utilizó el criterio de fisher
    #   > Training: 5040 x 50
    s_sfs = sfs(X_train, labels_train, n_features=50, method="fisher")
    X_train = X_train[:, s_sfs]

    # Paso 4: PCA
    #         > Training: 5040 x 50
    X_train, _, A, Xm, _ = pca(X_train, n_components=50)

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 1: Clean
    X_test = X_test * a + b  # Paso 2: Normalizacion
    X_test = X_test[:, s_sfs]  # Paso 3: SFS
    X_test = np.matmul(X_test - Xm, A)  # Paso 4: PCA

    return classifier_tests(X_train, labels_train, X_test, labels_test)
示例#2
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def strategy02(X_train, labels_train, X_test, labels_test):
    '''
    Estrategia número 2 para el primer clasificador
    '''

    print('\nEjecutando la estrategia número 2 del primer clasificador...')

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 1: Clean
    #         > Training: 5040 x 82
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 2: PCA de 70 componentes
    #         > Training: 5040 x 70
    X_train, _, A, Xm, _ = pca(X_train, n_components=70)

    # Paso 3: Normalizacion
    #         > Training: 5040 x 70
    X_train, a, b = normalize(X_train)

    # Paso 4: SFS
    #         > Training: 5040 x 20
    s_sfs = sfs(X_train, labels_train, n_features=20, method="fisher")
    X_train = X_train[:, s_sfs]

    # *** DEFINCION DE DATOS PARA EL TESTING ***
    X_test = X_test[:, s_clean]  # Paso 1: Clean
    X_test = np.matmul(X_test - Xm, A)  # Paso 2: PCA
    X_test = X_test * a + b  # Paso 3: Normalizacion
    X_test = X_test[:, s_sfs]  # Paso 4: SFS

    return classifier_tests(X_train, labels_train, X_test, labels_test)
示例#3
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def strategy04(X_train, labels_train, X_test, labels_test):
    '''
    Estrategia número 4 para el primer clasificador
    '''

    print('\nEjecutando la estrategia número 4 del primer clasificador...')

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 1: Cleaning de los datos
    #   > Training: 5040 x 82
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 2: Normalización Mean-Std de los datos
    X_train, a, b = normalize(X_train)

    # Paso 3: Selección de características
    # Acá se utilizó el criterio de fisher
    #   > Training: 5040 x 26
    s_sfs = sfs(X_train, labels_train, n_features=26, method="fisher")
    X_train = X_train[:, s_sfs]

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 3: clean
    X_test = X_test * a + b  # Paso 4: normalizacion
    X_test = X_test[:, s_sfs]  # Paso 5: SFS

    return classifier_tests(X_train, labels_train, X_test, labels_test)
def strategy01(X_train, labels_train, X_test, labels_test, groups):
    '''
    Estrategia número 1 para el segundo clasificador
    '''

    print('\nEjecutando la estrategia número 1 del segundo clasificador...')

    # Paso 3: Cleaning de los datos
    #   > Training: 8000 x 250
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 4: Normalización Mean-Std de los datos
    X_train, a, b = normalize(X_train)

    # Paso 5: Selección de características
    # Acá se utilizó el criterio de fisher
    #   > Training: 8000 x 50
    s_sfs = sfs(X_train, labels_train, n_features=50, method="fisher")
    X_train = X_train[:, s_sfs]

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 3: clean
    X_test = X_test * a + b  # Paso 4: normalizacion
    X_test = X_test[:, s_sfs]  # Paso 5: SFS

    return classifier_tests(X_train, labels_train, X_test, labels_test, groups)
def WinnerStrategy(X_train, labels_train, X_test, labels_test, groups):
    '''
    Estrategia Número 1 con redes neuronales,
    Reescrita para poder obtener estadísticas
    '''

    # Paso 3: Cleaning de los datos
    #   > Training: 8000 x 250
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 4: Normalización Mean-Std de los datos
    X_train, a, b = normalize(X_train)

    # Paso 5: Selección de características
    # Acá se utilizó el criterio de fisher
    #   > Training: 8000 x 50
    s_sfs = sfs(X_train, labels_train, n_features=50, method="fisher")
    X_train = X_train[:, s_sfs]

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 3: clean
    X_test = X_test * a + b  # Paso 4: normalizacion
    X_test = X_test[:, s_sfs]  # Paso 5: SFS

    classifier = MLPClassifier(alpha=1, max_iter=1000, random_state=2)

    results = {}

    Y_pred = np.array([])
    labels_test = np.array([])

    # Probamos con los valores de testing
    classifier.fit(X_train, labels_train)

    for sample in groups['test']:
        patch_data = np.array([])
        for patch in groups['test'][sample]:

            features = X_test[groups['test'][sample][patch], :].reshape(1, -1)
            patch_data = np.append(patch_data, classifier.predict(features)[0])

        # Clase clasificada
        Y_pred = np.append(Y_pred, stats.mode(patch_data)[0][0])

        labels_test = np.append(labels_test, get_class_by_name(sample))

    results['Accuracy'] = performance(Y_pred, labels_test) * 100
    results['Y_pred'] = Y_pred
    results['labels_test'] = labels_test

    return results
def strategy05(X_train, labels_train, X_test, labels_test, groups):
    '''
    Estrategia número 5 para el segundo clasificador
    '''

    print('\nEjecutando la estrategia número 5 del segundo clasificador...')

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 1: Clean
    #         > Training: 5040 x 82
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 2: PCA
    #         > Training: 5040 x 82
    X_train, _, A1, Xm1, _ = pca(X_train, n_components=X_train.shape[1])

    # Paso 3: Normalizacion
    #         > Training: 5040 x 82
    X_train, a, b = normalize(X_train)

    # Paso 4: SFS
    #         > Training: 5040 x 80
    s_sfs = sfs(X_train, labels_train, n_features=80, method="fisher")
    X_train = X_train[:, s_sfs]
    X_train_sfs80 = X_train.copy()

    # Paso 5: PCA
    #         > Training: 5040 x 10
    X_train, _, A2, Xm2, _ = pca(X_train, n_components=10)

    #Paso 6: SFS
    #	> Trainning: 5040 x 20
    X_train = np.concatenate((X_train, X_train_sfs80), axis=1)
    s_sfs2 = sfs(X_train, labels_train, n_features=20, method="fisher")
    X_train = X_train[:, s_sfs2]

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 1: clean
    X_test = np.matmul(X_test - Xm1, A1)  # Paso 2: PCA
    X_test = X_test * a + b  # Paso 3: normalizacion
    X_test = X_test[:, s_sfs]  # Paso 4: SFS
    X_test_sfs80 = X_test.copy()
    X_test = np.matmul(X_test - Xm2, A2)  # Paso 5: PCA
    X_test = np.concatenate((X_test, X_test_sfs80), axis=1)
    X_test = X_test[:, s_sfs2]  # Paso 6: SFS

    # *** ENTRENAMIENTO CON DATOS DE TRAINING Y PRUEBA CON DATOS DE TESTING ***

    return classifier_tests(X_train, labels_train, X_test, labels_test, groups)
示例#7
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def WinnerStrategy(X_train, labels_train, X_test, labels_test):
    '''
    Estrategia Número 1 con redes neuronales,
    Reescrita para poder obtener estadísticas
    '''

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 1: Cleaning de los datos
    #   > Training: 5040 x 82
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 2: Normalización Mean-Std de los datos
    X_train, a, b = normalize(X_train)

    # Paso 3: Selección de características
    # Acá se utilizó el criterio de fisher
    #   > Training: 5040 x 50
    s_sfs = sfs(X_train, labels_train, n_features=50, method="fisher")
    X_train = X_train[:, s_sfs]

    # Paso 4: PCA
    #         > Training: 5040 x 50
    X_train, _, A, Xm, _ = pca(X_train, n_components=50)

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 1: Clean
    X_test = X_test * a + b  # Paso 2: Normalizacion
    X_test = X_test[:, s_sfs]  # Paso 3: SFS
    X_test = np.matmul(X_test - Xm, A)  # Paso 4: PCA

    classifier = MLPClassifier(alpha=1, max_iter=1000, random_state=2)

    results = {}

    # Clasificamos las muestras de Testing
    classifier.fit(X_train, labels_train)
    Y_pred = classifier.predict(X_test)
    accuracy = performance(Y_pred, labels_test)

    results['Accuracy'] = accuracy * 100
    results['Y_pred'] = Y_pred
    results['labels_test'] = labels_test

    return results
def strategy03(X_train, labels_train, X_test, labels_test, groups):
    '''
    Estrategia número 3 para el segundo clasificador
    '''

    print('\nEjecutando la estrategia número 3 del segundo clasificador...')

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 1: Clean
    #         > Training: 5040 x 82
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 2: Normalizacion
    #         > Training: 5040 x 82
    X_train, a, b = normalize(X_train)

    # Paso 3: SFS
    #         > Training: 5040 x 80
    s_sfs = sfs(X_train, labels_train, n_features=80, method="fisher")
    X_train = X_train[:, s_sfs]

    # Paso 4: PCA
    #         > Training: 5040 x 20
    X_train, _, A, Xm, _ = pca(X_train, n_components=20)

    # Paso 5: SFS
    #	      > Training: 5040 x 15
    s_sfs2 = sfs(X_train, labels_train, n_features=15, method="fisher")
    X_train = X_train[:, s_sfs2]

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 1: Clean
    X_test = X_test * a + b  # Paso 2: Normalizacion
    X_test = X_test[:, s_sfs]  # Paso 3: SFS
    X_test = np.matmul(X_test - Xm, A)  # Paso 4: PCA
    X_test = X_test[:, s_sfs2]  # Paso 5: SFS

    # *** ENTRENAMIENTO CON DATOS DE TRAINING Y PRUEBA CON DATOS DE TESTING ***

    return classifier_tests(X_train, labels_train, X_test, labels_test, groups)
示例#9
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#         > Testing :  53 x 1589
X_train, X_test, d_train, d_test = train_test_split(X,
                                                    d,
                                                    test_size=0.2,
                                                    shuffle=False)

# *** DEFINCION DE DATOS PARA EL TRAINING ***

# Paso 2-Training: Clean
#         > Training: 211 x 387
s_clean = clean(X_train)
X_train = X_train[:, s_clean]

# Paso 3-Training: Normalizacion
#         > Training: 211 x 387
X_train, a, b = normalize(X_train)

# Paso 4-Training: SFS
#         > Training: 211 x 40
s_sfs = sfs(X_train, d_train, n_features=40, method="fisher", show=True)
X_train = X_train[:, s_sfs]

# Paso 5-Training: PCA
#         > Training: 211 x 10
X_train, _, A, Xm, _ = pca(X_train, n_components=10)

# *** DEFINCION DE DATOS PARA EL TESTING ***

X_test = X_test[:, s_clean]  # Paso 2: clean
X_test = X_test * a + b  # Paso 3: normalizacion
X_test = X_test[:, s_sfs]  # Paso 4: SFS
示例#10
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data = loadmat("realdata")
features = data["features"]
classes = data["classes"].squeeze()

# %%
from pybalu.data_selection import stratify
from pybalu.feature_transformation import normalize

# Training and Testing data (90% training, 10% testing)
idx_train, idx_test = stratify(classes, .90)
f_train = features[idx_train]
c_train = classes[idx_train]
f_test = features[idx_test]
c_test = classes[idx_test]

f_train_norm, a, b = normalize(f_train)
f_test_norm = f_test * a + b

# %%
from pybalu.feature_selection import sfs

N_FEATURES = 15

selected_feats = sfs(f_train_norm, c_train, n_features=N_FEATURES,
                     method="fisher", show=True)


# %%
from pybalu.classification import structure
from pybalu.performance_eval import performance
from sklearn.neighbors import KNeighborsClassifier
示例#11
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def main():
    '''
    Desarrollo de el flujo del programa de reconocimiento de paredes rayadas

    La metodología utilizada es la que se describe en:
    https://github.com/domingomery/patrones/blob/master/clases/Cap03_Seleccion_de_Caracteristicas/presentations/PAT03_GeneralSchema.pdf

    La estructura del código se basó en el código de ejemplo de la actividad en clases:
    https://github.com/domingomery/patrones/tree/master/clases/Cap03_Seleccion_de_Caracteristicas/ejercicios/PCA_SFS
    '''

    # Paso 1: Extracción de características
    #   > 4000 imágenes de training rayadas
    #   > 4000 imágenes de training no rayadas
    #   > 1000 imágenes de testing rayadas
    #   > 1000 imágenes de testing no rayadas
    #   > 357 características por imagen
    features = FeatureExtractor(classes=CLASSES)

    # Paso 2: Definición de datos training - testing
    #   > Training: 8000 x 357
    #   > Testing: 2000 x 357
    X_train, labels_train = features['feature_values_train'], features[
        'labels_train']
    X_test, labels_test = features['feature_values_test'], features[
        'labels_test']

    X_train, labels_train = np.array(X_train), np.array(labels_train)
    X_test, labels_test = np.array(X_test), np.array(labels_test)

    # *** DEFINCION DE DATOS PARA EL TRAINING ***

    # Paso 3: Cleaning de los datos
    #   > Training: 8000 x 250
    s_clean = clean(X_train)
    X_train = X_train[:, s_clean]

    # Paso 4: Normalización Mean-Std de los datos
    X_train, a, b = normalize(X_train)

    # Paso 5: Selección de características
    # Acá se utilizó el criterio de fisher
    #   > Training: 8000 x 50
    s_sfs = sfs(X_train, labels_train, n_features=50)
    X_train = X_train[:, s_sfs]

    # *** DEFINCION DE DATOS PARA EL TESTING ***

    X_test = X_test[:, s_clean]  # Paso 3: clean
    X_test = X_test * a + b  # Paso 4: normalizacion
    X_test = X_test[:, s_sfs]  # Paso 5: SFS

    # *** ENTRENAMIENTO CON DATOS DE TRAINING Y PRUEBA CON DATOS DE TESTING ***

    knn = KNN(n_neighbors=3)
    knn.fit(X_train, labels_train)
    Y_pred = knn.predict(X_test)

    # *** Estadísticas y desempeño del clasificador ***
    accuracy = performance(Y_pred, labels_test)
    print("Accuracy = " + str(accuracy))
    confusionMatrix(Y_pred, labels_test)

    printChoosenFeatures = True
    if printChoosenFeatures:
        feature_names = np.array(features['feature_names'])
        feature_names = feature_names[s_sfs]
        print('Las features seleccionadas por el sistema son:')
        for name in feature_names:
            print(name, end=' -- ')

    # *** Guardado de las variables para el reconocedor externo ***
    with open('data/reconocedor.json', 'w') as file:
        file.write(
            json.dumps({
                's_clean': s_clean.tolist(),
                'a': a.tolist(),
                'b': b.tolist(),
                's_sfs': s_sfs.tolist()
            }))
示例#12
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文件: main.py 项目: pipeton8/patrones
print('Training Subset:')
X_train = np.concatenate((X0_train, X1_train), axis=0)
d0_train = np.zeros([X0_train.shape[0], 1], dtype=int)
d1_train = np.ones([X1_train.shape[0], 1], dtype=int)
d_train = np.concatenate((d0_train, d1_train), axis=0)
print('Original extracted features: ' + str(X_train.shape[1]) + '(' +
      str(X_train.shape[0]) + ' samples)')

# Training: Cleaning
sclean = clean(X_train, show=True)
X_train_clean = X_train[:, sclean]
print('           cleaned features: ' + str(X_train_clean.shape[1]) + '(' +
      str(X_train_clean.shape[0]) + ' samples)')

# Training: Normalization
X_train_norm, a, b = normalize(X_train_clean)
print('        normalized features: ' + str(X_train_norm.shape[1]) + '(' +
      str(X_train_norm.shape[0]) + ' samples)')

# Training: Feature selection
ssfs = sfs(X_train_norm, d_train, n_features=20, method="fisher", show=True)
X_train_sfs = X_train_norm[:, ssfs]
print('          selected features: ' + str(X_train_sfs.shape[1]) + '(' +
      str(X_train_sfs.shape[0]) + ' samples)')

# Testing dataset
print('Testing Subset:')
X_test = np.concatenate((X0_test, X1_test), axis=0)
d0_test = np.zeros([X0_test.shape[0], 1], dtype=int)
d1_test = np.ones([X1_test.shape[0], 1], dtype=int)
d_test = np.concatenate((d0_test, d1_test), axis=0)
示例#13
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def clean_norm(X):
    sclean = clean(X, show=True)
    X = X[:, sclean]
    X, a, b = normalize(X)
    return X, sclean, a, b