<filename>examples/undocumented/python/graphical/interactive_gp_demo.py
# This software is distributed under BSD 3-clause license (see LICENSE file).
#
# Authors: <NAME>, <NAME>

"""
Shogun Gaussian processes demo based on interactive SVM demo by Christian \
Widmer and <NAME> which itself is based on PyQT Demo by <NAME>

Work to be done on parameter (e.g. kernel width) optimization.
"""
import sys, os, csv
import scipy as SP
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from numpy import *

import matplotlib
from matplotlib.colorbar import make_axes, Colorbar
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure

from shogun import *
import shogun as sg
import util

class Form(QMainWindow):
    def __init__(self, parent=None):
        super(Form, self).__init__(parent)
        self.setWindowTitle('SHOGUN interactive demo')

        self.series_list_model = QStandardItemModel()

        self.create_menu()
        self.create_main_frame()
        self.create_status_bar()
        self.create_toy_data()

        self.on_show()

    def on_show(self):
        self.axes.clear()
        self.axes.plot(self.x, self.y, 'ro')
        self.axes.set_xlim((self.xmin,self.xmax))
        self.axes.set_ylim((self.ymin,self.ymax))
        self.axes.grid(True)
        self.canvas.draw()
        self.fill_series_list(self.get_stats())


    def on_about(self):
        msg = __doc__
        QMessageBox.about(self, "About the demo", msg.strip())


    def fill_series_list(self, names):
        self.series_list_model.clear()

        for name in names:
            item = QStandardItem(name)
            item.setCheckState(Qt.Unchecked)
            item.setCheckable(False)
            self.series_list_model.appendRow(item)


    def onclick(self, event):
        print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(event.button, event.x, event.y, event.xdata, event.ydata)
        x=SP.append(self.x, event.xdata)
        self.y=SP.append(self.y, event.ydata)
        self.x= x[:,SP.newaxis]
        self.on_show()
        self.status_text.setText("New data point: x=%f, y=%f"%(event.xdata, event.ydata))

    def create_menu(self):
        self.file_menu = self.menuBar().addMenu("&File")

        #load_action = self.create_action("&Load file",
        #    shortcut="Ctrl+L", slot=self.load_file, tip="Load a file")
        quit_action = self.create_action("&Quit", slot=self.close,
            shortcut="Ctrl+Q", tip="Close the application")

        #self.add_actions(self.file_menu,
        #    (load_action, None, quit_action))

        self.help_menu = self.menuBar().addMenu("&Help")
        about_action = self.create_action("&About",
            shortcut='F1', slot=self.on_about,
            tip='About the demo')

        self.add_actions(self.help_menu, (about_action,))

    def clear_data(self):
        self.x=SP.array([])
        self.y=SP.array([])
        self.xmin=-5
        self.xmax=5
        self.ymin=-5
        self.ymax=5
        self.on_show()
        self.status_text.setText("Data cleared")

    def enable_widgets(self):
        kernel_name = self.kernel_combo.currentText()
        if kernel_name == "Linear":
            self.sigma.setDisabled(True)
            self.degree.setDisabled(True)
        elif kernel_name == "Polynomial":
            self.sigma.setDisabled(True)
            self.degree.setEnabled(True)
        elif kernel_name == "Gaussian":
            self.sigma.setEnabled(True)
            self.degree.setDisabled(True)

    def get_stats(self):
        num_train = len(self.x)
        str_train = "num training points: %i" % num_train
        str_test = "num training points: %s" % self.nTest.text()

        return (str_train, str_test)

    def create_toy_data(self):
        #0. generate Toy-Data; just samples from a superposition of a sin + linear trend
        x = SP.arange(self.xmin,self.xmax,(self.xmax-self.xmin)/100.0)

        C = 2       #offset
        b = 0

        y  = b*x + C + float(self.sine_amplitude.text())*SP.sin(float(self.sine_freq.text())*x)
        #    dy = b   +     1*SP.cos(x)
        y += float(self.noise_level.text())*random.randn(y.shape[0])

        self.y=y-y.mean()
        self.x= x[:,SP.newaxis]
        self.on_show()

    def learn_kernel_width(self):
        root=ModelSelectionParameters();
        c1=ModelSelectionParameters("inference_method", inf);
        root.append_child(c1);

        c2 = ModelSelectionParameters("scale");
        c1.append_child(c2);
        c2.build_values(0.01, 4.0, R_LINEAR);
        c3 = ModelSelectionParameters("likelihood_model", likelihood);
        c1.append_child(c3);

        c4=ModelSelectionParameters("sigma");
        c3.append_child(c4);
        c4.build_values(0.001, 4.0, R_LINEAR);
        c5 =ModelSelectionParameters("kernel", SECF);
        c1.append_child(c5);

        c6 =ModelSelectionParameters("width");
        c5.append_child(c6);
        c6.build_values(0.001, 4.0, R_LINEAR);

        crit = GradientCriterion();

        grad = machine_evaluation(
                machine=gp, features=feat_train, labels=labels,
                evaluation_criterion=crit,
                differentiable_function=inf)

        gp.print_modsel_params();

        root.print_tree();

        grad_search=GradientModelSelection(root, grad);

        best_combination=grad_search.select_model(1);

        self.sigma.setText("1.0")
        self.plot_gp()

    def plot_gp(self):
        feat_train = RealFeatures(self.x.T)
        labels = RegressionLabels(self.y)
        #[x,y]=self.data.get_data()
        #feat_train=RealFeatures(x.T)
        #labels=RegressionLabels(y)
        n_dimensions = 1

        kernel_name = self.kernel_combo.currentText()
        print "current kernel is %s" % (kernel_name)

        #new interface with likelihood parametres being decoupled from the covaraince function
        likelihood = GaussianLikelihood()
        #covar_parms = SP.log([2])
        #hyperparams = {'covar':covar_parms,'lik':SP.log([1])}

        # construct covariance function
        width=float(self.sigma.text())
        degree=int(self.degree.text())
        if kernel_name == "Linear":
            gk = LinearKernel(feat_train, feat_train)
            gk.set_normalizer(IdentityKernelNormalizer())
        elif kernel_name == "Polynomial":
            gk = sg.kernel("PolyKernel", degree=degree)
            gk.init(train, train)
            gk.set_normalizer(IdentityKernelNormalizer())
        elif kernel_name == "Gaussian":
            gk = GaussianKernel(feat_train, feat_train, width)
        #SECF = GaussianKernel(feat_train, feat_train, width)
        #covar = SECF
        zmean = ZeroMean();
        inf = ExactInferenceMethod(gk, feat_train, zmean, labels, likelihood);

        inf.get_negative_marginal_likelihood()
        # location of unispaced predictions
        x_test = array([linspace(self.xmin,self.xmax, self.nTest.text())])
        feat_test=RealFeatures(x_test)

        gp = GaussianProcessRegression(inf)
        gp.train()
        covariance = gp.get_variance_vector(feat_test)
        predictions = gp.get_mean_vector(feat_test)

        #print "x_test"
        #print feat_test.get_feature_matrix()
        #print "mean predictions"
        #print predictions.get_labels()
        #print "covariances"
        #print covariance.get_labels()

        self.status_text.setText("Negative Log Marginal Likelihood = %f"%(inf.get_negative_marginal_likelihood()))


        self.axes.clear()
        self.axes.grid(True)
        self.axes.set_xlim((self.xmin,self.xmax))
        self.axes.set_ylim((self.ymin,self.ymax))
        self.axes.hold(True)
        x_test=feat_test.get_feature_matrix()[0]
        self.axes.plot(x_test, predictions, 'b-x')
        #self.axes.plot(x_test, labels.get_labels(), 'ro')
        self.axes.plot(self.x, self.y, 'ro')
        #self.axes.plot(feat_test.get_feature_matrix()[0], predictions.get_labels()-3*sqrt(covariance.get_labels()))
        #self.axes.plot(feat_test.get_feature_matrix()[0], predictions.get_labels()+3*sqrt(covariance.get_labels()))
        upper = predictions+3*sqrt(covariance)
        lower = predictions-3*sqrt(covariance)
        self.axes.fill_between(x_test, lower, upper, color='grey')
        self.axes.hold(False)
        self.canvas.draw()
        self.fill_series_list(self.get_stats())


    def create_main_frame(self):
        self.xmin=-5
        self.xmax=5
        self.ymin=-5
        self.ymax=5

        self.main_frame = QWidget()

        plot_frame = QWidget()

        self.dpi = 100
        self.fig = Figure((6.0, 6.0), dpi=self.dpi)
        self.canvas = FigureCanvas(self.fig)
        self.canvas.setParent(self.main_frame)

        cid = self.canvas.mpl_connect('button_press_event', self.onclick)
        self.axes = self.fig.add_subplot(111)
        self.cax = None
        #self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)

        self.kernel_combo = QComboBox()
        self.kernel_combo.insertItem(-1, "Gaussian")
        self.kernel_combo.insertItem(-1, "Polynomial")
        self.kernel_combo.insertItem(-1, "Linear")
        self.kernel_combo.maximumSize = QSize(300, 50)
        self.connect(self.kernel_combo, SIGNAL("currentIndexChanged(QString)"), self.enable_widgets)

        log_label = QLabel("Data points")
        self.series_list_view = QListView()
        self.series_list_view.setModel(self.series_list_model)

        self.sine_freq = QLineEdit()
        self.sine_freq.setText("1.0")
        self.sine_amplitude = QLineEdit()
        self.sine_amplitude.setText("1.0")
        self.sigma = QLineEdit()
        self.sigma.setText("1.2")
        self.degree = QLineEdit()
        self.degree.setText("2")
        self.noise_level = QLineEdit()
        self.noise_level.setText("1")
        self.nTest = QLineEdit()
        self.nTest.setText("100")

        spins_hbox = QHBoxLayout()
        spins_hbox.addWidget(QLabel('Sine data setting:     '))
        spins_hbox.addWidget(QLabel('Sine Freq.'))
        spins_hbox.addWidget(self.sine_freq)
        spins_hbox.addWidget(QLabel('Sine Amplitude'))
        spins_hbox.addWidget(self.sine_amplitude)
        spins_hbox.addWidget(QLabel('Noise Level'))
        spins_hbox.addWidget(self.noise_level)
        spins_hbox.addStretch(1)

        spins_hbox2 = QHBoxLayout()
        spins_hbox2.addWidget(QLabel('Kernel Setting:      '))
        spins_hbox2.addWidget(QLabel('Type'))
        spins_hbox2.addWidget(self.kernel_combo)
        spins_hbox2.addWidget(QLabel("Width"))
        spins_hbox2.addWidget(self.sigma)
        spins_hbox2.addWidget(QLabel("Degree"))
        spins_hbox2.addWidget(self.degree)
        spins_hbox2.addStretch(1)

        spins_hbox3 = QHBoxLayout()
        spins_hbox3.addWidget(QLabel('Test Setting:     '))
        spins_hbox3.addWidget(QLabel('Number of test points'))
        spins_hbox3.addWidget(self.nTest)
        spins_hbox3.addStretch(1)

        self.show_button = QPushButton("&Train GP")
        self.connect(self.show_button, SIGNAL('clicked()'), self.plot_gp)

        self.gen_sine_data_button = QPushButton("&Generate Sine Data")
        self.connect(self.gen_sine_data_button, SIGNAL('clicked()'), self.create_toy_data)

        self.clear_data_button = QPushButton("&Clear")
        self.connect(self.clear_data_button, SIGNAL('clicked()'), self.clear_data)

        self.learn_kernel_button = QPushButton("&Learn Kernel Width and train GP")
        self.connect(self.learn_kernel_button, SIGNAL('clicked()'), self.learn_kernel_width)

        left_vbox = QVBoxLayout()
        left_vbox.addWidget(self.canvas)
        #left_vbox.addWidget(self.mpl_toolbar)

        right0_vbox = QVBoxLayout()
        right0_vbox.addWidget(QLabel("Data Points"))
        right0_vbox.addWidget(self.series_list_view)
        #right0_vbox.addWidget(self.legend_cb)
        right0_vbox.addStretch(1)

        right2_vbox = QVBoxLayout()
        right2_vbox.addWidget(QLabel("Settings"))
        right2_vbox.addWidget(self.gen_sine_data_button)
        right2_vbox.addWidget(self.clear_data_button)
        right2_vbox.addWidget(self.show_button)
        #right2_vbox.addWidget(self.learn_kernel_button)
        right2_vbox.addLayout(spins_hbox)
        right2_vbox.addLayout(spins_hbox2)
        right2_vbox.addLayout(spins_hbox3)
        right2_vbox.addStretch(1)

        right_vbox = QHBoxLayout()
        right_vbox.addLayout(right0_vbox)
        right_vbox.addLayout(right2_vbox)

        hbox = QVBoxLayout()
        hbox.addLayout(left_vbox)
        hbox.addLayout(right_vbox)
        self.main_frame.setLayout(hbox)

        self.setCentralWidget(self.main_frame)
        self.enable_widgets()



    def create_status_bar(self):
        self.status_text = QLabel("")
        self.statusBar().addWidget(self.status_text, 1)

    def add_actions(self, target, actions):
        for action in actions:
            if action is None:
                target.addSeparator()
            else:
                target.addAction(action)

    def create_action(  self, text, slot=None, shortcut=None,
                        icon=None, tip=None, checkable=False,
                        signal="triggered()"):
        action = QAction(text, self)
        if icon is not None:
            action.setIcon(QIcon(":/%s.png" % icon))
        if shortcut is not None:
            action.setShortcut(shortcut)
        if tip is not None:
            action.setToolTip(tip)
            action.setStatusTip(tip)
        if slot is not None:
            self.connect(action, SIGNAL(signal), slot)
        if checkable:
            action.setCheckable(True)
        return action

def main():
    app = QApplication(sys.argv)
    form = Form()
    form.show()
    app.exec_()


if __name__ == "__main__":
    main()

<filename>examples/undocumented/python/graphical/interactive_gp_demo.py
# This software is distributed under BSD 3-clause license (see LICENSE file).
#
# Authors: <NAME>, <NAME>

"""
Shogun Gaussian processes demo based on interactive SVM demo by Christian \
Widmer and <NAME> which itself is based on PyQT Demo by <NAME>

Work to be done on parameter (e.g. kernel width) optimization.
"""
import sys, os, csv
import scipy as SP
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from numpy import *

import matplotlib
from matplotlib.colorbar import make_axes, Colorbar
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure

from shogun import *
import shogun as sg
import util

class Form(QMainWindow):
    def __init__(self, parent=None):
        super(Form, self).__init__(parent)
        self.setWindowTitle('SHOGUN interactive demo')

        self.series_list_model = QStandardItemModel()

        self.create_menu()
        self.create_main_frame()
        self.create_status_bar()
        self.create_toy_data()

        self.on_show()

    def on_show(self):
        self.axes.clear()
        self.axes.plot(self.x, self.y, 'ro')
        self.axes.set_xlim((self.xmin,self.xmax))
        self.axes.set_ylim((self.ymin,self.ymax))
        self.axes.grid(True)
        self.canvas.draw()
        self.fill_series_list(self.get_stats())


    def on_about(self):
        msg = __doc__
        QMessageBox.about(self, "About the demo", msg.strip())


    def fill_series_list(self, names):
        self.series_list_model.clear()

        for name in names:
            item = QStandardItem(name)
            item.setCheckState(Qt.Unchecked)
            item.setCheckable(False)
            self.series_list_model.appendRow(item)


    def onclick(self, event):
        print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%(event.button, event.x, event.y, event.xdata, event.ydata)
        x=SP.append(self.x, event.xdata)
        self.y=SP.append(self.y, event.ydata)
        self.x= x[:,SP.newaxis]
        self.on_show()
        self.status_text.setText("New data point: x=%f, y=%f"%(event.xdata, event.ydata))

    def create_menu(self):
        self.file_menu = self.menuBar().addMenu("&File")

        #load_action = self.create_action("&Load file",
        #    shortcut="Ctrl+L", slot=self.load_file, tip="Load a file")
        quit_action = self.create_action("&Quit", slot=self.close,
            shortcut="Ctrl+Q", tip="Close the application")

        #self.add_actions(self.file_menu,
        #    (load_action, None, quit_action))

        self.help_menu = self.menuBar().addMenu("&Help")
        about_action = self.create_action("&About",
            shortcut='F1', slot=self.on_about,
            tip='About the demo')

        self.add_actions(self.help_menu, (about_action,))

    def clear_data(self):
        self.x=SP.array([])
        self.y=SP.array([])
        self.xmin=-5
        self.xmax=5
        self.ymin=-5
        self.ymax=5
        self.on_show()
        self.status_text.setText("Data cleared")

    def enable_widgets(self):
        kernel_name = self.kernel_combo.currentText()
        if kernel_name == "Linear":
            self.sigma.setDisabled(True)
            self.degree.setDisabled(True)
        elif kernel_name == "Polynomial":
            self.sigma.setDisabled(True)
            self.degree.setEnabled(True)
        elif kernel_name == "Gaussian":
            self.sigma.setEnabled(True)
            self.degree.setDisabled(True)

    def get_stats(self):
        num_train = len(self.x)
        str_train = "num training points: %i" % num_train
        str_test = "num training points: %s" % self.nTest.text()

        return (str_train, str_test)

    def create_toy_data(self):
        #0. generate Toy-Data; just samples from a superposition of a sin + linear trend
        x = SP.arange(self.xmin,self.xmax,(self.xmax-self.xmin)/100.0)

        C = 2       #offset
        b = 0

        y  = b*x + C + float(self.sine_amplitude.text())*SP.sin(float(self.sine_freq.text())*x)
        #    dy = b   +     1*SP.cos(x)
        y += float(self.noise_level.text())*random.randn(y.shape[0])

        self.y=y-y.mean()
        self.x= x[:,SP.newaxis]
        self.on_show()

    def learn_kernel_width(self):
        root=ModelSelectionParameters();
        c1=ModelSelectionParameters("inference_method", inf);
        root.append_child(c1);

        c2 = ModelSelectionParameters("scale");
        c1.append_child(c2);
        c2.build_values(0.01, 4.0, R_LINEAR);
        c3 = ModelSelectionParameters("likelihood_model", likelihood);
        c1.append_child(c3);

        c4=ModelSelectionParameters("sigma");
        c3.append_child(c4);
        c4.build_values(0.001, 4.0, R_LINEAR);
        c5 =ModelSelectionParameters("kernel", SECF);
        c1.append_child(c5);

        c6 =ModelSelectionParameters("width");
        c5.append_child(c6);
        c6.build_values(0.001, 4.0, R_LINEAR);

        crit = GradientCriterion();

        grad = machine_evaluation(
                machine=gp, features=feat_train, labels=labels,
                evaluation_criterion=crit,
                differentiable_function=inf)

        gp.print_modsel_params();

        root.print_tree();

        grad_search=GradientModelSelection(root, grad);

        best_combination=grad_search.select_model(1);

        self.sigma.setText("1.0")
        self.plot_gp()

    def plot_gp(self):
        feat_train = RealFeatures(self.x.T)
        labels = RegressionLabels(self.y)
        #[x,y]=self.data.get_data()
        #feat_train=RealFeatures(x.T)
        #labels=RegressionLabels(y)
        n_dimensions = 1

        kernel_name = self.kernel_combo.currentText()
        print "current kernel is %s" % (kernel_name)

        #new interface with likelihood parametres being decoupled from the covaraince function
        likelihood = GaussianLikelihood()
        #covar_parms = SP.log([2])
        #hyperparams = {'covar':covar_parms,'lik':SP.log([1])}

        # construct covariance function
        width=float(self.sigma.text())
        degree=int(self.degree.text())
        if kernel_name == "Linear":
            gk = LinearKernel(feat_train, feat_train)
            gk.set_normalizer(IdentityKernelNormalizer())
        elif kernel_name == "Polynomial":
            gk = sg.kernel("PolyKernel", degree=degree)
            gk.init(train, train)
            gk.set_normalizer(IdentityKernelNormalizer())
        elif kernel_name == "Gaussian":
            gk = GaussianKernel(feat_train, feat_train, width)
        #SECF = GaussianKernel(feat_train, feat_train, width)
        #covar = SECF
        zmean = ZeroMean();
        inf = ExactInferenceMethod(gk, feat_train, zmean, labels, likelihood);

        inf.get_negative_marginal_likelihood()
        # location of unispaced predictions
        x_test = array([linspace(self.xmin,self.xmax, self.nTest.text())])
        feat_test=RealFeatures(x_test)

        gp = GaussianProcessRegression(inf)
        gp.train()
        covariance = gp.get_variance_vector(feat_test)
        predictions = gp.get_mean_vector(feat_test)

        #print "x_test"
        #print feat_test.get_feature_matrix()
        #print "mean predictions"
        #print predictions.get_labels()
        #print "covariances"
        #print covariance.get_labels()

        self.status_text.setText("Negative Log Marginal Likelihood = %f"%(inf.get_negative_marginal_likelihood()))


        self.axes.clear()
        self.axes.grid(True)
        self.axes.set_xlim((self.xmin,self.xmax))
        self.axes.set_ylim((self.ymin,self.ymax))
        self.axes.hold(True)
        x_test=feat_test.get_feature_matrix()[0]
        self.axes.plot(x_test, predictions, 'b-x')
        #self.axes.plot(x_test, labels.get_labels(), 'ro')
        self.axes.plot(self.x, self.y, 'ro')
        #self.axes.plot(feat_test.get_feature_matrix()[0], predictions.get_labels()-3*sqrt(covariance.get_labels()))
        #self.axes.plot(feat_test.get_feature_matrix()[0], predictions.get_labels()+3*sqrt(covariance.get_labels()))
        upper = predictions+3*sqrt(covariance)
        lower = predictions-3*sqrt(covariance)
        self.axes.fill_between(x_test, lower, upper, color='grey')
        self.axes.hold(False)
        self.canvas.draw()
        self.fill_series_list(self.get_stats())


    def create_main_frame(self):
        self.xmin=-5
        self.xmax=5
        self.ymin=-5
        self.ymax=5

        self.main_frame = QWidget()

        plot_frame = QWidget()

        self.dpi = 100
        self.fig = Figure((6.0, 6.0), dpi=self.dpi)
        self.canvas = FigureCanvas(self.fig)
        self.canvas.setParent(self.main_frame)

        cid = self.canvas.mpl_connect('button_press_event', self.onclick)
        self.axes = self.fig.add_subplot(111)
        self.cax = None
        #self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame)

        self.kernel_combo = QComboBox()
        self.kernel_combo.insertItem(-1, "Gaussian")
        self.kernel_combo.insertItem(-1, "Polynomial")
        self.kernel_combo.insertItem(-1, "Linear")
        self.kernel_combo.maximumSize = QSize(300, 50)
        self.connect(self.kernel_combo, SIGNAL("currentIndexChanged(QString)"), self.enable_widgets)

        log_label = QLabel("Data points")
        self.series_list_view = QListView()
        self.series_list_view.setModel(self.series_list_model)

        self.sine_freq = QLineEdit()
        self.sine_freq.setText("1.0")
        self.sine_amplitude = QLineEdit()
        self.sine_amplitude.setText("1.0")
        self.sigma = QLineEdit()
        self.sigma.setText("1.2")
        self.degree = QLineEdit()
        self.degree.setText("2")
        self.noise_level = QLineEdit()
        self.noise_level.setText("1")
        self.nTest = QLineEdit()
        self.nTest.setText("100")

        spins_hbox = QHBoxLayout()
        spins_hbox.addWidget(QLabel('Sine data setting:     '))
        spins_hbox.addWidget(QLabel('Sine Freq.'))
        spins_hbox.addWidget(self.sine_freq)
        spins_hbox.addWidget(QLabel('Sine Amplitude'))
        spins_hbox.addWidget(self.sine_amplitude)
        spins_hbox.addWidget(QLabel('Noise Level'))
        spins_hbox.addWidget(self.noise_level)
        spins_hbox.addStretch(1)

        spins_hbox2 = QHBoxLayout()
        spins_hbox2.addWidget(QLabel('Kernel Setting:      '))
        spins_hbox2.addWidget(QLabel('Type'))
        spins_hbox2.addWidget(self.kernel_combo)
        spins_hbox2.addWidget(QLabel("Width"))
        spins_hbox2.addWidget(self.sigma)
        spins_hbox2.addWidget(QLabel("Degree"))
        spins_hbox2.addWidget(self.degree)
        spins_hbox2.addStretch(1)

        spins_hbox3 = QHBoxLayout()
        spins_hbox3.addWidget(QLabel('Test Setting:     '))
        spins_hbox3.addWidget(QLabel('Number of test points'))
        spins_hbox3.addWidget(self.nTest)
        spins_hbox3.addStretch(1)

        self.show_button = QPushButton("&Train GP")
        self.connect(self.show_button, SIGNAL('clicked()'), self.plot_gp)

        self.gen_sine_data_button = QPushButton("&Generate Sine Data")
        self.connect(self.gen_sine_data_button, SIGNAL('clicked()'), self.create_toy_data)

        self.clear_data_button = QPushButton("&Clear")
        self.connect(self.clear_data_button, SIGNAL('clicked()'), self.clear_data)

        self.learn_kernel_button = QPushButton("&Learn Kernel Width and train GP")
        self.connect(self.learn_kernel_button, SIGNAL('clicked()'), self.learn_kernel_width)

        left_vbox = QVBoxLayout()
        left_vbox.addWidget(self.canvas)
        #left_vbox.addWidget(self.mpl_toolbar)

        right0_vbox = QVBoxLayout()
        right0_vbox.addWidget(QLabel("Data Points"))
        right0_vbox.addWidget(self.series_list_view)
        #right0_vbox.addWidget(self.legend_cb)
        right0_vbox.addStretch(1)

        right2_vbox = QVBoxLayout()
        right2_vbox.addWidget(QLabel("Settings"))
        right2_vbox.addWidget(self.gen_sine_data_button)
        right2_vbox.addWidget(self.clear_data_button)
        right2_vbox.addWidget(self.show_button)
        #right2_vbox.addWidget(self.learn_kernel_button)
        right2_vbox.addLayout(spins_hbox)
        right2_vbox.addLayout(spins_hbox2)
        right2_vbox.addLayout(spins_hbox3)
        right2_vbox.addStretch(1)

        right_vbox = QHBoxLayout()
        right_vbox.addLayout(right0_vbox)
        right_vbox.addLayout(right2_vbox)

        hbox = QVBoxLayout()
        hbox.addLayout(left_vbox)
        hbox.addLayout(right_vbox)
        self.main_frame.setLayout(hbox)

        self.setCentralWidget(self.main_frame)
        self.enable_widgets()



    def create_status_bar(self):
        self.status_text = QLabel("")
        self.statusBar().addWidget(self.status_text, 1)

    def add_actions(self, target, actions):
        for action in actions:
            if action is None:
                target.addSeparator()
            else:
                target.addAction(action)

    def create_action(  self, text, slot=None, shortcut=None,
                        icon=None, tip=None, checkable=False,
                        signal="triggered()"):
        action = QAction(text, self)
        if icon is not None:
            action.setIcon(QIcon(":/%s.png" % icon))
        if shortcut is not None:
            action.setShortcut(shortcut)
        if tip is not None:
            action.setToolTip(tip)
            action.setStatusTip(tip)
        if slot is not None:
            self.connect(action, SIGNAL(signal), slot)
        if checkable:
            action.setCheckable(True)
        return action

def main():
    app = QApplication(sys.argv)
    form = Form()
    form.show()
    app.exec_()


if __name__ == "__main__":
    main()

# This software is distributed under BSD 3-clause license (see LICENSE file). # # Authors: <NAME>, <NAME> Shogun Gaussian processes demo based on interactive SVM demo by Christian \ Widmer and <NAME> which itself is based on PyQT Demo by <NAME>  Work to be done on parameter (e.g. kernel width) optimization. #load_action = self.create_action("&Load file", #    shortcut="Ctrl+L", slot=self.load_file, tip="Load a file") #self.add_actions(self.file_menu, #    (load_action, None, quit_action)) #0. generate Toy-Data; just samples from a superposition of a sin + linear trend #offset #    dy = b   +     1*SP.cos(x) #[x,y]=self.data.get_data() #feat_train=RealFeatures(x.T) #labels=RegressionLabels(y) #new interface with likelihood parametres being decoupled from the covaraince function #covar_parms = SP.log([2]) #hyperparams = {'covar':covar_parms,'lik':SP.log([1])} # construct covariance function #SECF = GaussianKernel(feat_train, feat_train, width) #covar = SECF # location of unispaced predictions #print "x_test" #print feat_test.get_feature_matrix() #print "mean predictions" #print predictions.get_labels() #print "covariances" #print covariance.get_labels() #self.axes.plot(x_test, labels.get_labels(), 'ro') #self.axes.plot(feat_test.get_feature_matrix()[0], predictions.get_labels()-3*sqrt(covariance.get_labels())) #self.axes.plot(feat_test.get_feature_matrix()[0], predictions.get_labels()+3*sqrt(covariance.get_labels())) #self.mpl_toolbar = NavigationToolbar(self.canvas, self.main_frame) #left_vbox.addWidget(self.mpl_toolbar) #right0_vbox.addWidget(self.legend_cb) #right2_vbox.addWidget(self.learn_kernel_button)