Skip to content

Instantly share code, notes, and snippets.

@margusmartsepp

margusmartsepp/gist:4532104

Created January 14, 2013 18:21
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save margusmartsepp/4532104 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save margusmartsepp/4532104 to your computer and use it in GitHub Desktop.
Download ZIP
AI hw 3
Raw
gistfile1.java
21public static class ZooNNDataSet extends NNDataSet {
@Override
public void setTargetColumns() {
targetColumnNumbers = new ArrayList<Integer>();
int size = nds.get(0).size();
targetColumnNumbers.add(size - 1); // last column
targetColumnNumbers.add(size - 2); // last but one column
targetColumnNumbers.add(size - 3); // and the one before that
}
}
public static class ZooDataSetNumerizer implements Numerizer {
@Override
public Pair<List<Double>, List<Double>> numerize(Example e) {
List<Double> input = new ArrayList<Double>();
List<Double> desiredOutput = new ArrayList<Double>();
double a2 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a3 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a4 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a5 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a6 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a7 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a8 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a9 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a10 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a11 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a12 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a13 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a14 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a15 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a16 = Double.parseDouble(e.getAttributeValueAsString("a2"));
double a17 = Double.parseDouble(e.getAttributeValueAsString("a2"));
input.add(a2);
input.add(a3);
input.add(a4);
input.add(a5);
input.add(a6);
input.add(a7);
input.add(a8);
input.add(a9);
input.add(a10);
input.add(a11);
input.add(a12);
input.add(a13);
input.add(a14);
input.add(a15);
input.add(a16);
input.add(a17);
String plant_category_string = e.getAttributeValueAsString("a18");
desiredOutput = convertCategoryToListOfDoubles(plant_category_string);
Pair<List<Double>, List<Double>> io = new Pair<List<Double>, List<Double>>(
input, desiredOutput);
return io;
}
private List<Double> convertCategoryToListOfDoubles(
String zoo_category_string) {
if (zoo_category_string.equals("amphibian")) {
return Arrays.asList(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0);
} else if (zoo_category_string.equals("insect")) {
return Arrays.asList(0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0);
} else if (zoo_category_string.equals("mammal")) {
return Arrays.asList(0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0);
} else if (zoo_category_string.equals("bird")) {
return Arrays.asList(0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0);
} else if (zoo_category_string.equals("shellfish")) {
return Arrays.asList(0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0);
} else if (zoo_category_string.equals("fish")) {
return Arrays.asList(0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0);
} else if (zoo_category_string.equals("reptile")) {
return Arrays.asList(1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0);
} else {
throw new RuntimeException("invalid plant category");
}
}
@Override
public String denumerize(List<Double> outputValue) {
List<Double> rounded = new ArrayList<Double>();
for (Double d : outputValue) {
rounded.add(round(d));
}
if (rounded
.equals(Arrays.asList(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0))) {
return "amphibian";
} else if (rounded.equals(Arrays.asList(0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0))) {
return "insect";
} else if (rounded.equals(Arrays.asList(0.0, 0.0, 0.0, 0.0, 1.0,
0.0, 0.0))) {
return "mammal";
} else if (rounded.equals(Arrays.asList(0.0, 0.0, 0.0, 1.0, 0.0,
0.0, 0.0))) {
return "bird";
} else if (rounded.equals(Arrays.asList(0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0))) {
return "shellfish";
} else if (rounded.equals(Arrays.asList(0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0))) {
return "fish";
} else if (rounded.equals(Arrays.asList(1.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0))) {
return "reptile";
} else {
return "unknown";
}
}
private double round(Double d) {
if (d < 0) {
return 0.0;
}
if (d > 1) {
return 1.0;
} else {
return Math.round(d);
}
}
}
public static void backPropogation2() {
try {
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
Numerizer numerizer = new IrisDataSetNumerizer();
NNDataSet innds = new IrisNNDataSet();
innds.createExamplesFromDataSet(irisDataSet, numerizer);
innds.refreshDataset();
ArrayList<Integer> results = new ArrayList<Integer>();
for (int i = 3; i < 151; i++) {
DataSet ds1 = new DataSetFactory().fromFile("iris_" + i,
DataSetFactory.createIrisDataSetSpec(), ",");
NNDataSet nds1 = new IrisNNDataSet();
nds1.createExamplesFromDataSet(ds1, numerizer);
NNConfig config = new NNConfig();
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.setConfig(
FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6);
config.setConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS,
-2.0);
config.setConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS,
2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(
config);
ffnn.setTrainingScheme(new BackPropLearning(0.01, 0.95));
ffnn.trainOn(nds1, i);
nds1.refreshDataset();
int[] result = ffnn.testOnDataSet(innds);
results.add(result[0]);
}
System.out.println(results.toString());
} catch (Exception e) {
e.printStackTrace();
}
}
public static void backPropogation() throws Exception {
Numerizer numerizer = new ZooDataSetNumerizer();
DataSetSpecification spec = new DataSetSpecification();
spec.defineStringAttribute("a2", new String[] { "0", "1" });
spec.defineStringAttribute("a3", new String[] { "0", "1" });
spec.defineStringAttribute("a4", new String[] { "0", "1" });
spec.defineStringAttribute("a5", new String[] { "0", "1" });
spec.defineStringAttribute("a6", new String[] { "0", "1" });
spec.defineStringAttribute("a7", new String[] { "0", "1" });
spec.defineStringAttribute("a8", new String[] { "0", "1" });
spec.defineStringAttribute("a9", new String[] { "0", "1" });
spec.defineStringAttribute("a10", new String[] { "0", "1" });
spec.defineStringAttribute("a11", new String[] { "0", "1" });
spec.defineStringAttribute("a12", new String[] { "0", "1" });
spec.defineStringAttribute("a13", new String[] { "0", "1" });
spec.defineStringAttribute("a14", new String[] { "0", "2", "4", "5",
"6", "8" });
spec.defineStringAttribute("a15", new String[] { "0", "1" });
spec.defineStringAttribute("a16", new String[] { "0", "1" });
spec.defineStringAttribute("a17", new String[] { "0", "1" });
spec.defineStringAttribute("a18", new String[] { "amphibian", "bird",
"fish", "insect", "mammal", "reptile", "shellfish" });
DataSet ds2 = new DataSetFactory().fromFile("zoo_101", spec, ",");
NNDataSet nds2 = new ZooNNDataSet();
nds2.createExamplesFromDataSet(ds2, numerizer);
nds2.refreshDataset();
ArrayList<Integer> results = new ArrayList<Integer>();
for (int i = 7; i < 102; i++) {
DataSet ds1 = new DataSetFactory().fromFile("zoo_" + i, spec, ",");
NNDataSet nds1 = new ZooNNDataSet();
nds1.createExamplesFromDataSet(ds1, numerizer);
NNConfig config = new NNConfig();
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 17);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 16);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS,
20);
config.setConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS,
-0.001);
config.setConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS,
0.001);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.setTrainingScheme(new BackPropLearning(0.01, 0.95));
ffnn.trainOn(nds1, 100);
nds1.refreshDataset();
int[] result = ffnn.testOnDataSet(nds2);
results.add(result[0]);
}
System.out.println(results.toString());
}
public static void decisionTree2() throws Exception {
DataSetSpecification spec = new DataSetSpecification();
spec.defineStringAttribute("a1", new String[] { "4", "5", "6", "7" });
spec.defineStringAttribute("a2", new String[] { "2", "3", "4" });
spec.defineStringAttribute("a3", new String[] { "1", "3", "4", "5", "6" });
spec.defineStringAttribute("a4", new String[] { "0", "1", "2" });
spec.defineStringAttribute("a5", new String[] { "Floor[\"setosa\"]", "Floor[\"versicolor\"]", "Floor[\"virginica\"]" });
DataSet ds2 = new DataSetFactory().fromFile("iris_150", spec, ",");
DecisionTreeLearner learner2 = new DecisionTreeLearner();
learner2.train(ds2);
System.out.println(learner2.getDecisionTree());
for (int i = 3; i < 150; i++) {
DataSet ds1 = new DataSetFactory().fromFile("iris_" + i, spec, ",");
DecisionTreeLearner learner = new DecisionTreeLearner();
learner.train(ds1);
int[] result = learner.test(ds2);
System.out.print("" + result[0] + ", ");
}
}
public static void decisionTree() throws Exception {
DataSetSpecification spec = new DataSetSpecification();
spec.defineStringAttribute("a2", new String[] { "0", "1" });
spec.defineStringAttribute("a3", new String[] { "0", "1" });
spec.defineStringAttribute("a4", new String[] { "0", "1" });
spec.defineStringAttribute("a5", new String[] { "0", "1" });
spec.defineStringAttribute("a6", new String[] { "0", "1" });
spec.defineStringAttribute("a7", new String[] { "0", "1" });
spec.defineStringAttribute("a8", new String[] { "0", "1" });
spec.defineStringAttribute("a9", new String[] { "0", "1" });
spec.defineStringAttribute("a10", new String[] { "0", "1" });
spec.defineStringAttribute("a11", new String[] { "0", "1" });
spec.defineStringAttribute("a12", new String[] { "0", "1" });
spec.defineStringAttribute("a13", new String[] { "0", "1" });
spec.defineStringAttribute("a14", new String[] { "0", "2", "4", "5",
"6", "8" });
spec.defineStringAttribute("a15", new String[] { "0", "1" });
spec.defineStringAttribute("a16", new String[] { "0", "1" });
spec.defineStringAttribute("a17", new String[] { "0", "1" });
spec.defineStringAttribute("a18", new String[] { "amphibian", "bird",
"fish", "insect", "mammal", "reptile", "shellfish" });
DataSet ds2 = new DataSetFactory().fromFile("zoo_101", spec, ",");
for (int i = 7; i < 102; i++) {
DataSet ds1 = new DataSetFactory().fromFile("zoo_" + i, spec, ",");
DecisionTreeLearner learner = new DecisionTreeLearner();
learner.train(ds1);
int[] result = learner.test(ds2);
System.out.print("" + result[0] + ", ");
}
}
Raw
gistfile2.java
21private DecisionTree decisionTreeLearning(DataSet ds,
List<String> attributeNames, ConstantDecisonTree defaultTree) {
if (ds.size() == 0) {
return defaultTree;
}
if (allExamplesHaveSameClassification(ds)) {
return new ConstantDecisonTree(ds.getExample(0).targetValue());
}
if (attributeNames.size() == 0) {
return majorityValue(ds);
}
String chosenAttribute = chooseAttribute(ds, attributeNames);
DecisionTree tree = new DecisionTree(chosenAttribute);
ConstantDecisonTree m = majorityValue(ds);
List<String> values = ds.getPossibleAttributeValues(chosenAttribute);
for (String v : values) {
DataSet filtered = ds.matchingDataSet(chosenAttribute, v);
List<String> newAttribs = Util.removeFrom(attributeNames,
chosenAttribute);
DecisionTree subTree = decisionTreeLearning(filtered, newAttribs, m);
tree.addNode(v, subTree);
}
return tree;
}
Raw
gistfile3.txt
rs = {{26, 25, 39, 56, 31, 93, 90, 100, 56, 118, 120, 117, 135, 123,
132, 121, 128, 121, 122, 124, 128, 120, 140, 124, 124, 131, 132,
124, 115, 139, 124, 136, 127, 136, 141, 126, 136, 144, 138, 123,
139, 144, 144, 129, 137, 144, 133, 148, 140, 147, 145, 144, 147,
145, 139, 145, 145, 145, 144, 145, 146, 145, 144, 146, 145, 146,
137, 143, 143, 143, 142, 147, 145, 147, 146, 147, 145, 146, 147,
146, 145, 146, 145, 148, 143, 146, 146, 145, 146, 145, 148, 146,
140, 145, 147, 148, 147, 145, 148, 146, 145, 148, 146, 145, 148,
147, 146, 147, 146, 147, 146, 146, 145, 144, 146, 145, 147, 146,
146, 145, 144, 146, 146, 147, 147, 145, 148, 145, 147, 147, 147,
146, 146, 147, 146, 147, 147, 146, 148, 146, 147, 147, 147, 148,
146, 146, 146, 148}, {19, 13, 53, 49, 60, 66, 61, 62, 71, 123,
116, 120, 111, 114, 132, 117, 120, 136, 121, 127, 128, 119, 123,
118, 125, 129, 113, 136, 131, 134, 112, 144, 124, 133, 134, 139,
128, 144, 131, 117, 144, 136, 143, 131, 130, 144, 140, 139, 143,
146, 141, 144, 147, 146, 141, 144, 136, 145, 147, 144, 147, 144,
145, 146, 143, 145, 131, 144, 140, 140, 142, 146, 144, 148, 146,
145, 143, 146, 147, 147, 146, 146, 145, 146, 145, 145, 147, 145,
147, 145, 146, 146, 141, 145, 146, 147, 148, 145, 148, 146, 145,
148, 146, 145, 148, 147, 145, 147, 145, 146, 145, 146, 145, 144,
146, 146, 147, 148, 145, 146, 144, 146, 146, 147, 145, 146, 148,
146, 147, 147, 146, 147, 146, 148, 147, 146, 146, 147, 146, 147,
147, 146, 146, 147, 147, 147, 147, 146}, {84, 51, 15, 42, 101,
114, 99, 109, 112, 84, 123, 126, 125, 117, 124, 117, 115, 122,
123, 117, 122, 124, 127, 131, 123, 118, 131, 133, 117, 120, 135,
143, 129, 138, 136, 143, 124, 140, 141, 123, 139, 142, 144, 131,
141, 124, 143, 145, 141, 143, 146, 144, 145, 145, 144, 147, 146,
145, 144, 143, 146, 143, 146, 146, 143, 146, 131, 146, 142, 138,
141, 146, 145, 146, 147, 146, 142, 147, 145, 147, 145, 145, 145,
146, 146, 145, 146, 146, 148, 145, 148, 145, 142, 145, 148, 146,
148, 145, 145, 146, 148, 144, 146, 145, 147, 146, 145, 146, 146,
148, 145, 146, 145, 145, 144, 145, 148, 147, 145, 146, 146, 146,
145, 146, 145, 146, 148, 144, 148, 147, 147, 146, 146, 147, 147,
147, 146, 147, 146, 147, 147, 146, 146, 147, 147, 146, 146,
147}, {52, 81, 82, 15, 58, 75, 84, 124, 109, 101, 124, 117, 123,
112, 123, 121, 109, 123, 125, 129, 124, 122, 129, 125, 124, 124,
124, 146, 126, 134, 138, 130, 130, 131, 131, 139, 142, 139, 138,
123, 143, 138, 144, 136, 142, 142, 142, 144, 144, 146, 142, 144,
143, 148, 142, 146, 144, 145, 143, 144, 144, 145, 143, 145, 145,
146, 132, 144, 146, 140, 141, 144, 144, 147, 146, 148, 146, 146,
148, 147, 145, 144, 145, 146, 143, 145, 145, 144, 147, 145, 145,
146, 141, 145, 147, 148, 148, 145, 147, 146, 145, 147, 146, 145,
145, 147, 146, 147, 146, 148, 146, 146, 145, 144, 144, 145, 147,
146, 145, 146, 144, 145, 146, 147, 145, 147, 148, 145, 146, 147,
146, 147, 147, 147, 146, 146, 147, 146, 146, 147, 147, 147, 147,
147, 146, 146, 147, 146}, {33, 50, 41, 25, 29, 102, 82, 128, 98,
113, 129, 101, 125, 128, 134, 121, 124, 140, 130, 134, 118, 123,
132, 121, 131, 130, 138, 139, 126, 131, 137, 138, 127, 131, 122,
135, 126, 140, 145, 121, 136, 134, 144, 131, 143, 142, 145, 147,
141, 147, 133, 140, 147, 146, 146, 146, 146, 145, 145, 133, 147,
143, 144, 144, 147, 145, 135, 144, 145, 144, 142, 146, 144, 146,
146, 146, 143, 147, 147, 146, 146, 146, 145, 144, 143, 145, 145,
145, 148, 144, 146, 146, 143, 145, 148, 146, 148, 145, 147, 145,
147, 144, 146, 145, 147, 147, 146, 145, 146, 148, 146, 145, 146,
145, 146, 145, 147, 148, 146, 146, 145, 145, 145, 146, 146, 146,
148, 145, 147, 147, 147, 147, 147, 148, 147, 147, 146, 146, 147,
147, 146, 146, 147, 146, 146, 147, 147, 147}, {73, 72, 61, 4,
103, 70, 80, 82, 109, 125, 106, 120, 119, 125, 121, 120, 128,
140, 122, 127, 116, 123, 132, 133, 123, 120, 129, 133, 125, 134,
122, 127, 122, 138, 141, 142, 134, 138, 121, 127, 140, 131, 144,
123, 144, 146, 139, 142, 141, 144, 143, 142, 141, 145, 143, 147,
146, 146, 147, 145, 144, 144, 144, 145, 148, 144, 128, 142, 145,
142, 142, 146, 146, 146, 145, 147, 144, 144, 146, 147, 144, 146,
145, 146, 146, 145, 146, 145, 148, 147, 147, 146, 142, 145, 145,
147, 148, 145, 148, 145, 148, 143, 146, 145, 145, 146, 145, 146,
146, 148, 146, 145, 145, 145, 145, 146, 147, 146, 146, 147, 145,
146, 146, 147, 146, 147, 148, 144, 147, 147, 148, 148, 146, 147,
147, 146, 145, 146, 146, 147, 146, 146, 146, 147, 147, 146, 146,
145},
{38, 50, 4, 37, 40, 80, 82, 92, 97, 116, 125, 123, 127, 113, 125,
122, 114, 130, 124, 123, 126, 120, 131, 123, 125, 120, 118, 83,
126, 128, 131, 141, 125, 138, 128, 141, 129, 132, 132, 126, 142,
133, 144, 130, 146, 146, 146, 145, 142, 139, 143, 144, 134, 146,
141, 144, 144, 146, 146, 144, 145, 144, 146, 147, 146, 146, 128,
141, 143, 142, 142, 146, 145, 147, 144, 148, 146, 147, 146, 146,
145, 145, 145, 144, 145, 145, 147, 145, 148, 145, 145, 145, 143,
145, 145, 146, 148, 147, 148, 146, 146, 146, 147, 145, 148, 147,
146, 146, 146, 148, 146, 146, 145, 145, 144, 145, 147, 146, 145,
146, 145, 146, 145, 146, 145, 146, 148, 146, 145, 147, 146, 146,
146, 147, 146, 147, 146, 146, 147, 146, 146, 147, 147, 147, 147,
146, 146, 147},
{50, 2, 57, 64, 119, 105, 101, 105, 123, 124, 114, 126, 127, 114,
133, 125, 109, 125, 119, 126, 126, 117, 134, 124, 124, 126, 121,
104, 130, 139, 124, 125, 127, 136, 144, 132, 132, 136, 144, 122,
134, 122, 147, 120, 136, 147, 142, 146, 131, 145, 141, 144, 144,
144, 145, 147, 144, 144, 144, 144, 144, 144, 147, 146, 148, 145,
134, 143, 140, 140, 142, 146, 146, 148, 144, 146, 145, 147, 148,
147, 144, 146, 145, 146, 143, 145, 145, 145, 148, 145, 145, 146,
140, 145, 148, 144, 147, 145, 148, 146, 146, 147, 146, 145, 147,
147, 145, 146, 146, 145, 146, 145, 146, 145, 145, 145, 147, 146,
146, 146, 146, 146, 145, 145, 146, 147, 148, 144, 147, 146, 147,
145, 145, 147, 147, 146, 146, 146, 146, 147, 147, 146, 147, 147,
147, 145, 146, 147},
{50, 50, 59, 71, 54, 108, 82, 75, 98, 91, 126, 120, 114, 126, 125,
117, 118, 113, 115, 138, 124, 122, 122, 121, 124, 115, 138, 143,
123, 121, 139, 138, 114, 128, 126, 139, 134, 129, 143, 128, 146,
142, 144, 134, 142, 145, 128, 145, 143, 147, 142, 145, 148, 146,
145, 145, 145, 145, 147, 145, 142, 145, 145, 144, 147, 146, 131,
143, 143, 140, 141, 146, 145, 148, 146, 147, 143, 147, 148, 147,
146, 146, 145, 148, 143, 145, 146, 145, 147, 145, 145, 146, 140,
145, 145, 146, 148, 145, 148, 146, 145, 143, 148, 145, 148, 147,
146, 146, 145, 148, 146, 146, 145, 145, 144, 145, 147, 146, 145,
146, 144, 146, 144, 146, 146, 147, 148, 145, 147, 147, 145, 148,
147, 147, 147, 147, 146, 146, 146, 147, 146, 146, 147, 147, 147,
146, 147, 147},
{3, 46, 75, 59, 99, 97, 100, 100, 108, 91, 110, 106, 118, 131,
121, 121, 109, 118, 116, 119, 114, 118, 124, 130, 119, 132, 133,
138, 132, 123, 130, 119, 123, 138, 134, 130, 120, 138, 124, 124,
129, 140, 143, 129, 138, 144, 143, 142, 137, 138, 144, 143, 146,
144, 145, 147, 144, 144, 145, 134, 146, 144, 148, 144, 146, 145,
134, 145, 140, 144, 141, 147, 144, 148, 146, 147, 145, 147, 145,
147, 143, 146, 145, 146, 143, 144, 146, 145, 146, 144, 145, 146,
142, 145, 145, 147, 148, 145, 148, 145, 146, 145, 146, 145, 145,
148, 145, 147, 146, 148, 146, 146, 145, 145, 144, 145, 147, 146,
145, 146, 146, 146, 145, 146, 146, 147, 148, 145, 148, 147, 148,
147, 147, 147, 146, 148, 146, 147, 146, 147, 147, 146, 147, 147,
146, 147, 146, 147}
}/150;
r = Mean[#] & /@ Transpose[rs]
kl = ListInterpolation[r, InterpolationOrder -> 0][x]
kr = Fit[Transpose[{Range[1, Length[r]], r}], {1, x^2, Sqrt[x],
Log[E, x], x}, x]
u = Show[ListPlot[{r // N}, Filling -> Axis,
PlotRange -> {0.0, 1.01}],
Plot[{kl, kr}, {x, 0, 150 - 3}, PlotPoints -> 200,
PlotRange -> {0, 1}, Filling -> {1 -> {2}}]]
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment