Python prediction Examples

Example #1

def click():
	output.delete(0.0, tk.END)
	input_var = prediction(var)
	output.insert(tk.END, input_var)

Example #2

File: ica.py Project: tulsyanp/tcd-ai-group-project

# 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)

Example #3

File: app.py Project: tyrodeveloper/Flask-Project-To-Identify-Tweets-for-Real-and-fake-disaster

def getAccuracy():
    accuracy = prediction()
    return render_template('wordcloud.html',
                           name=accuracy,
                           url='static/images/nlp.png')

Example #4

    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)

Example #5

# 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)