def click(): output.delete(0.0, tk.END) input_var = prediction(var) output.insert(tk.END, input_var)
# split into a training and testing set
X_train, X_test, y_train, y_test = split_data(X, y)
# compute ICA
n_components = 150
ica, eigenfaces = dimensionality_reduction_ICA(n_components, X_train, height,
width)
X_train_ica, X_test_ica = train_text_transform_Model(ica, X_train, X_test)
# Training a SVM classification model
clf = classification_svc(X_train_ica, y_train)
# Quantitative evaluation of the model quality on the test set
y_pred = prediction(clf, X_test_ica)
# printing classification report
print_report(y_test, y_pred, target_names, n_classes)
# printing images
prediction_titles = [
title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
]
plot_images(X_test, prediction_titles, height, width)
# plot eigenfaces
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_images(eigenfaces, eigenface_titles, height, width)
def getAccuracy():
accuracy = prediction()
return render_template('wordcloud.html',
name=accuracy,
url='static/images/nlp.png')
dataset)
# split into a training and testing set
X_train, X_test, y_train, y_test = split_data(X, y)
# compute LDA
n_components = 150
lda, pca = dimensionality_reduction_LDA(n_components, X_train, y_train)
X_train_lda, X_test_lda = train_text_transform_LDA(lda, pca, X_train, X_test)
# Training a SVM classification model
clf = classification_svc(X_train_lda, y_train)
# Quantitative evaluation of the model quality on the test set
y_pred = prediction(clf, X_test_lda)
# printing classification report
print_report(y_test, y_pred, target_names, n_classes)
# printing images
prediction_titles = [
title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
]
plot_images(X_test, prediction_titles, height, width)
# plot fisherfaces
fisherface_titles = ["fisherface %d" % i for i in range(4)]
plot_images_lda(pca, lda, fisherface_titles, height, width)
# split into a training and testing set
X_train, X_test, y_train, y_test = split_data(X, y)
# compute NMF
n_components = 150
nmf, eigenfaces = dimensionality_reduction_NMF(n_components, X_train, height,
width)
X_train_nmf, X_test_nmf = train_text_transform_Model(nmf, X_train, X_test)
# Training a SVM classification model
clf = classification_svc(X_train_nmf, y_train)
# Quantitative evaluation of the model quality on the test set
y_pred = prediction(clf, X_test_nmf)
# printing classification report
print_report(y_test, y_pred, target_names, n_classes)
# printing images
prediction_titles = [
title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
]
plot_images(X_test, prediction_titles, height, width)
# plot eigenfaces
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_images(eigenfaces, eigenface_titles, height, width)