class Widget(QWidget):
def __init__(self, parent=None):
super(Widget, self).__init__(parent)
label = QLabel()
label.setText('The neuron that you will be examining will divide the objects you will show it into the ones\n' +
'that it likes and the ones it dislikes\n' +
'Let\'s assume that be recognized objects are flowers\n')
# groupbox1
groupBox1 = QGroupBox()
groupBox1.setTitle('Feature weight')
groupBox1Label1 = QLabel()
groupBox1Label1.setText('Here you enter the neuron\'s weight coefficients for the individual object features. Positve\n' +
'coefficient means approval to the attribute, and negative one will indicate that the given\n' +
'attribute should be disliked by the neuron.')
groupBox1Label2 = QLabel()
groupBox1Label2.setText('Now, enter if you want the neuron to like the flower that is:')
self.spinBoxWeightFragment = QDoubleSpinBox()
self.spinBoxWeightFragment.setValue(1.0)
self.spinBoxWeightFragment.setRange(-100.0, 100.0)
self.spinBoxWeightColorful = QDoubleSpinBox()
self.spinBoxWeightColorful.setValue(2.0)
self.spinBoxWeightColorful.setRange(-100.0, 100.0)
groupBox1HLayout = QHBoxLayout()
groupBox1HLayout.addWidget(QLabel('Fragment:'))
groupBox1HLayout.addWidget(self.spinBoxWeightFragment)
groupBox1HLayout.addWidget(QLabel('Colorful:'))
groupBox1HLayout.addWidget(self.spinBoxWeightColorful)
groupBox1VLayout = QVBoxLayout()
groupBox1VLayout.addWidget(groupBox1Label1)
groupBox1VLayout.addWidget(groupBox1Label2)
groupBox1VLayout.addLayout(groupBox1HLayout)
groupBox1.setLayout(groupBox1VLayout)
# groupbox2
groupBox2 = QGroupBox()
groupBox2.setTitle('Evaluated object')
self.spinBoxObjectFragment = QDoubleSpinBox()
self.spinBoxObjectFragment.setRange(-100.0, 100.0)
self.spinBoxObjectColorful = QDoubleSpinBox()
self.spinBoxObjectColorful.setRange(-100.0, 100.0)
groupBox2HLayout = QHBoxLayout()
groupBox2HLayout.addWidget(QLabel('Fragment'))
groupBox2HLayout.addWidget(self.spinBoxObjectFragment)
groupBox2HLayout.addWidget(QLabel('Colorful'))
groupBox2HLayout.addWidget(self.spinBoxObjectColorful)
groupBox2VLayout = QVBoxLayout()
groupBox2VLayout.addWidget(QLabel('After setting the weight, yout can perform experiments. Enter if the flower is:'))
groupBox2VLayout.addLayout(groupBox2HLayout)
groupBox2.setLayout(groupBox2VLayout)
# groupbox3
groupBox3 = QGroupBox()
groupBox3.setTitle('Neuron\'s response')
pushbuttonGroup3 = QPushButton('Recalculate!')
self.output = QLabel()
self.attitude = QLabel()
groupBox3HLayout1 = QHBoxLayout()
groupBox3HLayout1.addWidget(QLabel('The neuron\'s response value is:'))
groupBox3HLayout1.addWidget(self.output)
groupBox3HLayout2 = QHBoxLayout()
groupBox3HLayout2.addWidget(QLabel('It means that the neuron\'s attitude against the flower is:'))
groupBox3HLayout2.addWidget(self.attitude)
groupBox3HLayout2.addWidget(pushbuttonGroup3)
groupBox3VLayout = QVBoxLayout()
groupBox3VLayout.addLayout(groupBox3HLayout1)
groupBox3VLayout.addLayout(groupBox3HLayout2)
groupBox3.setLayout(groupBox3VLayout)
layout = QVBoxLayout()
layout.addWidget(label)
layout.addWidget(groupBox1)
layout.addWidget(groupBox2)
layout.addWidget(groupBox3)
self.setLayout(layout)
self.setWindowTitle('Single neuron examination (example 01a)')
self.connect(self.spinBoxWeightFragment, SIGNAL('valueChanged(double)'), self.evaluateObject)
self.connect(self.spinBoxWeightColorful, SIGNAL('valueChanged(double)'), self.evaluateObject)
self.connect(self.spinBoxObjectFragment, SIGNAL('valueChanged(double)'), self.evaluateObject)
self.connect(self.spinBoxObjectColorful, SIGNAL('valueChanged(double)'), self.evaluateObject)
self.connect(pushbuttonGroup3, SIGNAL('clicked()'), self.evaluateObject)
self.neuron = Neuron(2)
def evaluateObject(self):
self.neuron.weights[0] = self.spinBoxWeightFragment.value()
self.neuron.weights[1] = self.spinBoxWeightColorful.value()
signals = [self.spinBoxObjectFragment.value(), self.spinBoxObjectColorful.value()]
response = self.neuron.response(signals)
strength = self.neuron.memoryTraceStrength(Neuron.StrenghtNormManhattan)
attitude = ''
palette = QPalette()
if abs(response) < 0.2 * strength:
print 'response ', response, '\t0.2 * strength ', 0.2 * strength, '\tstrenght ', strength, '\tindiffrent'
attitude = 'indiffrent'
palette.setColor(QPalette.WindowText, Qt.darkCyan)
elif response < 0:
print 'responce ', response, '\tstrength ', strength, '\tnegative'
attitude = 'negative'
palette.setColor(QPalette.WindowText, Qt.blue)
else:
print 'responce ', response, '\tstrength ', strength, '\tpositive'
attitude = 'positive'
palette.setColor(QPalette.WindowText, Qt.red)
self.output.setText(str(response))
self.attitude.setText(attitude)
self.attitude.setPalette(palette)
class Widget(QWidget):
def __init__(self, parent=None):
super(Widget, self).__init__(parent)
groupBox1 = QGroupBox()
groupBox1.setTitle('Feature weights (model object)')
self.w1 = QDoubleSpinBox()
self.w1.setRange(-10.0, 10.0)
self.w1.setValue(5.0)
self.w2 = QDoubleSpinBox()
self.w2.setRange(-10.0, 10.0)
self.w2.setValue(5.0)
buttonHelpGroupBox1 = QPushButton('help')
buttonHelpGroupBox1.setToolTip('Here you enter the neuron\'s weight coefficients for the individual object features.\n' +
'Positive coefficient means approval to the feature, and negative one will indicate that\n' +
'the given feature should be disliked by the neuron.\n\n' +
'Now the only important thing will be the numeric values of the weights and input signals.\n' +
'However, if you want, you can still imagine that the examined objects are flowers - as\n' +
'in the previous program. The w(1) weight will then indicate the fragrance intensity of\n' +
'the flower, and the w(2) weight - its colour intensity.\n\n' +
'You can also think about this as the location of the "model object".')
buttonHelpGroupBox1.setEnabled(False)
hBoxLayoutGroupBox1 = QHBoxLayout()
hBoxLayoutGroupBox1.addWidget(QLabel('w(1) = '))
hBoxLayoutGroupBox1.addWidget(self.w1)
hBoxLayoutGroupBox1.addWidget(QLabel('w(2) = '))
hBoxLayoutGroupBox1.addWidget(self.w2)
hBoxLayoutGroupBox1.addWidget(buttonHelpGroupBox1)
groupBox1.setLayout(hBoxLayoutGroupBox1)
groupBox2 = QGroupBox()
groupBox2.setTitle('Evaluated object')
self.x1 = QDoubleSpinBox()
self.x1.setRange(-10.0, 10.0)
self.x1.setValue(-5.0)
self.x2 = QDoubleSpinBox()
self.x2.setRange(-10.0, 10.0)
self.x2.setValue(-5.0)
buttonHelpGroupBox2 = QPushButton('help')
buttonHelpGroupBox2.setToolTip('Here you can enter the feature values of the evaluated object. As above, you are\n' +
'free to imagine that it is a flower whose fragrance intensity is x(1) and colour intensity\n' +
'is x(2).a')
buttonHelpGroupBox2.setEnabled(False)
hBoxLayoutGroupBox2 = QHBoxLayout()
hBoxLayoutGroupBox2.addWidget(QLabel('x(1) = '))
hBoxLayoutGroupBox2.addWidget(self.x1)
hBoxLayoutGroupBox2.addWidget(QLabel('x(2) = '))
hBoxLayoutGroupBox2.addWidget(self.x2)
hBoxLayoutGroupBox2.addWidget(buttonHelpGroupBox2)
groupBox2.setLayout(hBoxLayoutGroupBox2)
groupBox3 = QGroupBox()
groupBox3.setTitle('Graph')
self.colorResponse = QLabel('Evaluated object')
buttonHelpGroupBox3 = QPushButton('help')
buttonHelpGroupBox3.setToolTip('On your left, you see a signal graph. The blue point represents the location of the\n' +
'model object. The other point is the location of the evaluated object. Its color, shown\n' +
'on the chart\'s key, corresponds to the neuron\'s attitude towards the evaluated\n' +
'object.\n\n' +
'Click the graph with the right mouse button to set the model object location. The left\n' +
'mouse button sets the evaluated object. You can also perform dragging to move the\n' +
'objects smoothly.')
buttonHelpGroupBox3.setEnabled(False)
hBoxLayoutGroupBox3 = QHBoxLayout()
hBoxLayoutGroupBox3.addWidget(self.colorResponse)
hBoxLayoutGroupBox3.addWidget(buttonHelpGroupBox3)
vBoxLayoutGroupBox3 = QVBoxLayout()
vBoxLayoutGroupBox3.addWidget(QLabel('Key'))
vBoxLayoutGroupBox3.addWidget(QLabel('Model object'))
vBoxLayoutGroupBox3.addLayout(hBoxLayoutGroupBox3)
groupBox3.setLayout(vBoxLayoutGroupBox3)
self.response = QLabel('0')
hBoxLayoutResponse = QHBoxLayout()
hBoxLayoutResponse.addWidget(QLabel('Neuron\'s response: '))
hBoxLayoutResponse.addWidget(self.response)
vBoxlayout = QVBoxLayout()
vBoxlayout.addWidget(QLabel('The neuron that you will be examining will divide the objects you\n' +
'will show it into the ones that it likes and the ones id dislikes.'))
vBoxlayout.addWidget(groupBox1)
vBoxlayout.addWidget(groupBox2)
vBoxlayout.addLayout(hBoxLayoutResponse)
vBoxlayout.addWidget(groupBox3)
self.figure = plot.figure()
self.canvas = FigureCanvas(self.figure)
self.customePlot()
hBoxLayout = QHBoxLayout()
hBoxLayout.addWidget(self.canvas)
hBoxLayout.addLayout(vBoxlayout)
self.neuron = Neuron(2)
self.evaluatedObject()
self.setLayout(hBoxLayout)
self.setWindowTitle('Single neuron examination with visualization (example 01b)')
self.connect(self.w1, SIGNAL('valueChanged(double)'), self.evaluatedObject)
self.connect(self.w2, SIGNAL('valueChanged(double)'), self.evaluatedObject)
self.connect(self.x1, SIGNAL('valueChanged(double)'), self.evaluatedObject)
self.connect(self.x2, SIGNAL('valueChanged(double)'), self.evaluatedObject)
def evaluatedObject(self):
self.figure.clear()
self.customePlot()
ax = self.figure.add_subplot(111)
valueW1 = self.w1.value()
valueW2 = self.w2.value()
self.neuron.weights[0] = valueW1
self.neuron.weights[1] = valueW2
plot.scatter(valueW1, valueW2, color='k')
plot.plot([0.0, valueW1],[0.0, valueW2], color='k')
plot.plot([0.0, valueW1], [valueW2, valueW2], 'r--', linewidth=2)
plot.plot([valueW1, valueW1], [0.0, valueW2], 'r--', linewidth=2)
valueX1 = self.x1.value()
valueX2 = self.x2.value()
inputs = [valueX1, valueX2]
response = self.neuron.response(inputs)
strength = self.neuron.memoryTraceStrength(Neuron.StrenghtNormEuclidean)
palette = QPalette()
if abs(response) < 0.2 * strength:
palette.setColor(QPalette.WindowText, Qt.darkCyan)
elif response < 0:
palette.setColor(QPalette.WindowText, Qt.blue)
else:
palette.setColor(QPalette.WindowText, Qt.red)
self.colorResponse.setPalette(palette)
self.response.setPalette(palette)
self.response.setText(str(response))
plot.scatter(valueX1, valueX2, color='k')
plot.plot([0.0, valueX1],[0.0, valueX2], color='k')
plot.plot([0.0, valueX1], [valueX2, valueX2], 'b--', linewidth=2)
plot.plot([valueX1, valueX1], [0.0, valueX2], 'b--', linewidth=2)
self.canvas.draw()
def customePlot(self):
plot.xlim(-10.0, 10.0)
plot.ylim(-10.0, 10.0)
ax = plot.subplot()
ax.grid(True, which='both')
# y axis
ax.spines['bottom'].set_position('center')
# x axis
ax.spines['left'].set_position('center')
class Widget(QWidget):
def __init__(self, parent=None):
super(Widget, self).__init__(parent)
self.widgetInputCount = inputcountselectionpanel.Widget()
self.widgetExperiment = experimentpanel.Widget()
self.stackedWidget = QStackedWidget()
self.stackedWidget.addWidget(self.widgetInputCount)
self.stackedWidget.addWidget(self.widgetExperiment)
self.buttonBack = QPushButton('Back')
self.buttonBack.setEnabled(False)
self.buttonNext = QPushButton('Next')
hboxLayoutButton = QHBoxLayout()
hboxLayoutButton.addStretch(1)
hboxLayoutButton.addWidget(self.buttonBack)
hboxLayoutButton.addWidget(self.buttonNext)
vboxLayout = QVBoxLayout()
vboxLayout.addWidget(self.stackedWidget)
vboxLayout.addLayout(hboxLayoutButton)
self.setWindowTitle('Examination of a single neuron with multiple inputs (example 01c)')
self.setLayout(vboxLayout)
self.connect(self.buttonNext, SIGNAL('clicked()'), self.nextWidget)
self.connect(self.buttonBack, SIGNAL('clicked()'), self.backWidget)
self.connect(self.widgetExperiment.buttonRecalculate, SIGNAL('clicked()'), self.updateResult)
def nextWidget(self):
self.buttonNext.setEnabled(False)
self.buttonBack.setEnabled(True)
self.stackedWidget.setCurrentIndex(1)
self.widgetExperiment.tableWidget.clear()
self.widgetExperiment.memoryEdit.clear()
self.widgetExperiment.signalEdit.clear()
self.widgetExperiment.outputEdit.clear()
self.widgetExperiment.tableWidget.setRowCount(self.widgetInputCount.spinInput.value())
self.widgetExperiment.tableWidget.setColumnCount(3)
self.widgetExperiment.tableWidget.setHorizontalHeaderLabels(['i', 'w(i)', 'x(i)'])
self.neuron = Neuron(self.widgetInputCount.spinInput.value())
for i in xrange(len(self.neuron.weights)):
spinBox1 = QDoubleSpinBox()
spinBox1.setRange(-100.0, 100.0)
spinBox2 = QDoubleSpinBox()
spinBox2.setRange(-100.0, 100.0)
self.connect(spinBox1, SIGNAL('valueChanged(double)'), self.updateResult)
self.connect(spinBox2, SIGNAL('valueChanged(double)'), self.updateResult)
self.widgetExperiment.tableWidget.setCellWidget(i, 0, QLabel(str(i + 1)))
self.widgetExperiment.tableWidget.setCellWidget(i, 1, spinBox1)
self.widgetExperiment.tableWidget.setCellWidget(i, 2, spinBox2)
def backWidget(self):
self.buttonNext.setEnabled(True)
self.buttonBack.setEnabled(False)
self.stackedWidget.setCurrentIndex(0)
def updateResult(self):
for i in xrange(len(self.neuron.weights)):
self.neuron.weights[i] = self.widgetExperiment.tableWidget.cellWidget(i, 1).value()
inputs = [self.widgetExperiment.tableWidget.cellWidget(i, 2).value() for i in xrange(len(self.neuron.weights))]
response = self.neuron.response(inputs)
signalStrength = Neuron.strength(inputs, Neuron.StrenghtNormEuclidean)
memStrength = self.neuron.memoryTraceStrength(Neuron.StrenghtNormEuclidean)
self.widgetExperiment.signalEdit.setText(str(signalStrength))
self.widgetExperiment.memoryEdit.setText(str(memStrength))
self.widgetExperiment.outputEdit.setText(str(response))
