def updateResult(self):
inputColumn0 = self.tableWidget.cellWidget(0, 4).value() # number of legs
inputColumn1 = self.tableWidget.cellWidget(1, 4).value() # lives in water
inputColumn2 = self.tableWidget.cellWidget(2, 4).value() # can fly
inputColumn3 = self.tableWidget.cellWidget(3, 4).value() # has feathers
inputColumn4 = self.tableWidget.cellWidget(4, 4).value() # egg-laying
outputVector = self.linearNetwork.response([inputColumn0, inputColumn1, inputColumn2, inputColumn3, inputColumn4])
if self.checkBoxWinner.checkState() == Qt.Checked:
winner = LinearNetwork.winner(outputVector, self.spinBoxThreshold.value())
for i in xrange(len(outputVector)):
self.tableWidget.setCellWidget(5, i + 1, QLabel())
if winner != -1:
cellWinner = self.tableWidget.cellWidget(5, winner + 1)
cellWinner.setStyleSheet('background-color: red;')
self.labelNeuron.setText('Neuron ' + str(winner + 1))
self.labelType.setText('This is a ' + self.classNames[winner])
else:
self.labelNeuron.setText('(There is no winner.)')
self.labelType.setText('Thiss is something strange!')
for i in xrange(len(outputVector)):
self.tableWidget.cellWidget(5, i + 1).setText(str(outputVector[i]))
def __init__(self, parent=None):
super(Widget, self).__init__(parent)
buttonHelp1 = QPushButton('help')
buttonHelp1.setEnabled(False)
buttonHelp1.setToolTip('The table below, with the exception of the last row and column, describe the network\n' +
'in terms of the weight coefficient values. The value of a cell in the n-th row and m-th\n' +
'column is the weight of the n-th feature for the m-th animal class.\n\n' +
'As you can see, each animal class has its own neuron that recognizes it, and each of\n' +
'these neurons has inputs that receive individual feature values.\n\n' +
'The last column represents the input vector, and the last row contains the network\n' +
'output values.')
buttonHelp2 = QPushButton('help')
buttonHelp2.setEnabled(False)
buttonHelp2.setToolTip('If you show the winner, you will see the neuron whose response was the strongest.\n' +
'This neuron indicates the animal class the examined object will most probably fall into.\n' +
'The table below, with the exception of the last row and column, describe the network\n' +
'in terms of the weight coefficient values. The value of a cell in the n-th row and m-th\n' +
'column is the weight of the n-th feature for the m-th animal class.\n\n' +
'As you can see, each animal class has its own neuron that recognizes it, and each of\n' +
'these neurons has inputs that receive individual feature values.\n\n' +
'The last column represents the input vector, and the last row contains the network\n' +
'output values.')
hBoxLayout1 = QHBoxLayout()
hBoxLayout1.addWidget(QLabel('The neural network in this example uses five features to recognize\n' +
'three classes of animals. Its weight coefficients are predefined and shown in the table below.\n\n' +
'To test the network behavior, enter the input signals in the rightmost column and read the output\n' +
'values from the bottom row.'))
hBoxLayout1.addWidget(buttonHelp1)
self.checkBoxWinner = QCheckBox()
self.checkBoxWinner.setText('Show the winner')
hBoxLayout2 = QHBoxLayout()
hBoxLayout2.addWidget(self.checkBoxWinner)
hBoxLayout2.addWidget(buttonHelp2)
self.tableWidget = QTableWidget()
self.labelNeuron = QLabel('(There is no winner.)')
self.labelType = QLabel('This is something strange!')
self.spinBoxThreshold = QDoubleSpinBox()
self.spinBoxThreshold.setValue(5.0)
self.groupBox = QGroupBox('Show the winner')
gridLayout = QGridLayout()
gridLayout.addWidget(QLabel('And the winner is...'), 0, 0)
gridLayout.addWidget(self.labelNeuron, 0, 1)
gridLayout.addWidget(QLabel('Because of this, the network claims:'), 1, 0)
gridLayout.addWidget(self.labelType, 1, 1)
gridLayout.addWidget(QLabel('Threshold:'), 2, 0)
gridLayout.addWidget(self.spinBoxThreshold, 2, 1)
self.groupBox.setLayout(gridLayout)
self.groupBox.setVisible(False)
vBoxLayout = QVBoxLayout()
vBoxLayout.addLayout(hBoxLayout1)
vBoxLayout.addWidget(self.tableWidget)
vBoxLayout.addLayout(hBoxLayout2)
vBoxLayout.addWidget(self.groupBox)
self.setLayout(vBoxLayout)
self.setWindowTitle('Simple linear neural network (example2)')
self.classNames = ['mammal', 'bird', 'fish']
self.tableWidget.setColumnCount(5)
self.tableWidget.setRowCount(6)
self.tableWidget.verticalHeader().hide()
self.tableWidget.setHorizontalHeaderLabels(['Feature'] + self.classNames + ['Input vector'])
self.tableWidget.setCellWidget(0, 0, QLabel('number of legs'))
self.tableWidget.setCellWidget(1, 0, QLabel('lives in water'))
self.tableWidget.setCellWidget(2, 0, QLabel('can fly'))
self.tableWidget.setCellWidget(3, 0, QLabel('has feathers'))
self.tableWidget.setCellWidget(4, 0, QLabel('egg-laying'))
self.tableWidget.setCellWidget(5, 0, QLabel('Output'))
weights = [
[ 4, 0.01, 0.01, -1, -1.5 ],
[ 2, -1, 2, 2.5, 2 ],
[ -1, 3.5, 0.01, -2, 1.5 ]
]
for i in xrange(len(weights)):
for j in xrange(len(weights[i])):
self.tableWidget.setCellWidget(j, i + 1, QLabel(' ' + str(weights[i][j])))
for i in xrange(len(weights)):
self.tableWidget.setCellWidget(5, i + 1, QLabel(''))
for i in xrange(len(weights[0])):
doubleSpinBox = QDoubleSpinBox()
doubleSpinBox.setValue(0.0)
doubleSpinBox.setRange(-15.0, 15.0)
self.tableWidget.setCellWidget(i, 4, doubleSpinBox)
self.tableWidget.setCellWidget(5, 4, QPushButton('Calculate'))
self.linearNetwork = LinearNetwork(initialWeights=weights)
self.connect(self.checkBoxWinner, SIGNAL('stateChanged(int)'), self.visibleGrid)
self.connect(self.tableWidget.cellWidget(5, 4), SIGNAL('clicked()'), self.updateResult)
self.resize(600, 400)
