@Override
public Chart getChart() {
// Create Chart
Chart chart = new Chart(800, 600);
// generates linear data
Collection<Date> xData = new ArrayList<Date>();
Collection<Number> yData = new ArrayList<Number>();
DateFormat sdf = new SimpleDateFormat("dd.MM.yyyy");
Date date = null;
for (int i = 1; i <= 10; i++) {
try {
date = sdf.parse(i + ".10.2008");
} catch (ParseException e) {
e.printStackTrace();
}
xData.add(date);
yData.add(Math.random() * i);
}
// Customize Chart
chart.setChartTitle("LineChart03");
chart.setXAxisTitle("X");
chart.setYAxisTitle("Y");
chart.getStyleManager().setPlotBackgroundColor(ChartColor.getAWTColor(ChartColor.GREY));
chart.getStyleManager().setPlotGridLinesColor(new Color(255, 255, 255));
chart.getStyleManager().setChartBackgroundColor(Color.WHITE);
chart.getStyleManager().setLegendBackgroundColor(Color.PINK);
chart.getStyleManager().setChartFontColor(Color.MAGENTA);
chart.getStyleManager().setChartTitleBoxBackgroundColor(new Color(0, 222, 0));
chart.getStyleManager().setChartTitleBoxVisible(true);
chart.getStyleManager().setChartTitleBoxBorderColor(Color.BLACK);
chart.getStyleManager().setPlotGridLinesVisible(false);
chart.getStyleManager().setAxisTickPadding(20);
chart.getStyleManager().setAxisTickMarkLength(15);
chart.getStyleManager().setPlotPadding(20);
chart.getStyleManager().setChartTitleFont(new Font(Font.MONOSPACED, Font.BOLD, 24));
chart.getStyleManager().setLegendFont(new Font(Font.SERIF, Font.PLAIN, 18));
chart.getStyleManager().setLegendPosition(LegendPosition.InsideSE);
chart.getStyleManager().setAxisTitleFont(new Font(Font.SANS_SERIF, Font.ITALIC, 18));
chart.getStyleManager().setAxisTickLabelsFont(new Font(Font.SERIF, Font.PLAIN, 11));
chart.getStyleManager().setDatePattern("dd-MMM");
chart.getStyleManager().setNormalDecimalPattern("#0.000");
chart.getStyleManager().setLocale(Locale.GERMAN);
Series series = chart.addDateSeries("Fake Data", xData, yData);
series.setLineColor(SeriesColor.BLUE);
series.setMarkerColor(Color.ORANGE);
series.setMarker(SeriesMarker.CIRCLE);
series.setLineStyle(SeriesLineStyle.SOLID);
return chart;
}
@Override
public Chart getChart() {
yData = getRandomData(5);
// Create Chart
Chart chart = new Chart(500, 400);
chart.setChartTitle("Sample Real-time Chart");
chart.setXAxisTitle("X");
chart.setYAxisTitle("Y");
chart.addSeries(SERIES_NAME, null, yData);
return chart;
}
@Override
public Chart getChart() {
// Create Chart
Chart chart = new Chart(800, 600);
// Customize Chart
chart.setChartTitle("LineChart05");
chart.setXAxisTitle("X");
chart.setYAxisTitle("Y");
chart.getStyleManager().setLegendPosition(LegendPosition.InsideSW);
double[] xData = new double[] {0.0, 1.0, 2.0, 3.0, 4.0, 5, 6};
double[] yData = new double[] {106, 44, 26, 10, 7.5, 3.4, .88};
double[] yData2 = new double[] {102, 49, 23.6, 11.3, 5.4, 2.6, 1.25};
Series series = chart.addSeries("A", xData, yData);
series.setLineStyle(SeriesLineStyle.NONE);
series.setMarker(SeriesMarker.DIAMOND);
series.setMarkerColor(Color.BLACK);
Series series2 = chart.addSeries("B", xData, yData2);
series2.setMarker(SeriesMarker.NONE);
series2.setLineStyle(SeriesLineStyle.DASH_DASH);
series2.setLineColor(Color.BLACK);
chart.getStyleManager().setYAxisLogarithmic(true);
chart.getStyleManager().setYAxisMin(0.01);
chart.getStyleManager().setYAxisMax(1000);
chart.getStyleManager().setXAxisMin(2);
chart.getStyleManager().setXAxisMax(7);
return chart;
}
private void go() throws IOException {
// initialize the ahahNode
double readPeriod = 1E-5; // increase the learning rate from default
double writePeriod = 1E-5;
int numBias = 15;
int numInputs = 70; // max number of inputs/spikes
MemristorType memristorType = MemristorType.AgChalc;
AHaH21Circuit ahahNodeCircuit =
new AHaH21CircuitBuilder()
.numInputs(numInputs)
.numBiasInputs(numBias)
.readPeriod(readPeriod)
.writePeriod(writePeriod)
.memristorType(memristorType)
.build();
// initialize the feature factory. This code converts the raw data to a spiking representation.
BreastCancerSpikeEncoder breastCancerSpikeEncoder = new BreastCancerSpikeEncoder();
SpikeConverter spikeConverter =
new SpikeConverter(); // converts the spike code produced by the feature factory to one that
// can be used by the circuit.
// train the classifier--->
List<BreastCancer> breastCancerTrainingData = BreastCancerDAO.selectTrainData();
System.out.println("Learning...");
for (BreastCancer breastCancer : breastCancerTrainingData) {
String[] topicTrueLabels = new String[1];
topicTrueLabels[0] = breastCancer.getCellClass() + "";
long[] spikes = breastCancerSpikeEncoder.encode(breastCancer); // get the spike representation
Set<Integer> convertedSpikes = spikeConverter.convert(spikes);
int superviseSignal = 0;
if (breastCancer.getCellClass() == 2) {
superviseSignal = 1;
} else {
superviseSignal = -1;
}
System.out.println("Spike Pattern Length = " + convertedSpikes.size());
// System.out.println("Spikes = " + Arrays.toString(convertedSpikes.toArray()));
double y = ahahNodeCircuit.update(convertedSpikes, superviseSignal);
}
System.out.println("Spike Pattern Space = " + breastCancerSpikeEncoder.getSpikePatternSpace());
// Test the classifier
List<BreastCancer> breastCancerTestData = BreastCancerDAO.selectTestData();
System.out.println("Testing...");
Set<String> labels =
new HashSet<String>(); // these are the labels we are evaluating the performance of
labels.add("2");
labels.add("4");
// we are testing the performance for a range of confidence values, from 0 to 1.
int numSteps = 100;
double[] confidenceThresholds = new double[numSteps];
double inc = .061 / numSteps;
for (int i = 0; i < confidenceThresholds.length; i++) {
confidenceThresholds[i] = i * inc;
}
// create evaluators for keeping track of performance
ClassificationEvaluator[] evaluators = new ClassificationEvaluator[numSteps];
for (int i = 0; i < evaluators.length; i++) {
evaluators[i] = new ClassificationEvaluator(labels);
}
// run through the test set
for (BreastCancer breastCancer : breastCancerTestData) {
long[] spikes = breastCancerSpikeEncoder.encode(breastCancer);
Set<Integer> convertedSpikes = spikeConverter.convert(spikes);
double y =
ahahNodeCircuit.update(convertedSpikes, 0); // NOTE: no supervised labels are passed here.
// true labels for evaluation.
Set<String> trueLabels = new HashSet<String>();
trueLabels.add(breastCancer.getCellClass() + "");
for (int i = 0; i < evaluators.length; i++) {
List<String> labelOutput = new ArrayList<String>();
if (y > confidenceThresholds[i]) {
labelOutput.add("2");
} else if (y < -confidenceThresholds[i]) {
labelOutput.add("4");
}
evaluators[i].update(trueLabels, labelOutput);
}
}
// plot results---->
double maxF1 = 0;
int cIdx = 0;
double[] accuracy = new double[numSteps];
double[] precision = new double[numSteps];
double[] recall = new double[numSteps];
double[] f1 = new double[numSteps];
for (int i = 0; i < numSteps; i++) {
accuracy[i] = evaluators[i].getAccuracyMicroAve();
precision[i] = evaluators[i].getPrecisionMicroAve();
recall[i] = evaluators[i].getRecallMicroAve();
f1[i] = evaluators[i].getF1MicroAve();
if (f1[i] > maxF1) { // get the best confidence threshold
maxF1 = f1[i];
cIdx = i;
}
}
Chart chart = new Chart(300, 300, ChartTheme.Matlab);
// chart.setChartTitle("Wisconsin Breast Cancer Benchmark - F1=" + df.format(maxF1) + "
// w/ConfidenceThreshold=" + df.format(confidenceThresholds[cIdx]));
chart.setChartTitle("Breast Cancer - Circuit");
chart.setXAxisTitle("Confidence Threshold");
chart.setYAxisTitle("Score");
chart.getStyleManager().setLegendPosition(LegendPosition.InsideSW);
Series accuracy_series = chart.addSeries("Accuracy", confidenceThresholds, accuracy);
accuracy_series.setMarker(SeriesMarker.NONE);
Series precision_series = chart.addSeries("Precision", confidenceThresholds, precision);
precision_series.setMarker(SeriesMarker.NONE);
Series recall_series = chart.addSeries("Recall", confidenceThresholds, recall);
recall_series.setMarker(SeriesMarker.NONE);
Series f1_series = chart.addSeries("F1", confidenceThresholds, f1);
f1_series.setMarker(SeriesMarker.NONE);
BitmapEncoder.savePNGWithDPI(chart, "./PLOS_AHAH/Figures/Breast_Cancer_Circuit.png", 300);
new SwingWrapper(chart).displayChart();
}