private static IList <Cluster> SplitPointsIntoClusters(IList <Point> points, int clustersCount, bool normalize)
{
var observations = points.Select(p => p.Coordinates).ToArray();
var kmeans = new KMeans(clustersCount)
{
ParallelOptions = { MaxDegreeOfParallelism = 1 }
};
if (normalize)
{
Normalization.Detect(observations);
observations = Normalization.Apply(observations);
}
var initialClustersIndices = kmeans.Learn(observations).Decide(observations);
var distinctIndices = initialClustersIndices.Distinct();
var clusters =
distinctIndices.Select(
clusterIndex =>
points.Where((p, index) =>
initialClustersIndices[index] == clusterIndex).ToList())
.Select((newClusterPoints, index) =>
new Cluster(newClusterPoints, kmeans, index)).ToList();
return(clusters);
}
private DataTable NormalizeData(DataTable dataTable)
{
//Normalization of data
Normalization normalization = new Normalization(dataTable);
mean = new double[dataTable.Columns.Count];
rmsd = new double[dataTable.Columns.Count];
for (int i = 0; i < dataTable.Columns.Count; i++)
{
mean[i] = normalization[i].Mean;
rmsd[i] = normalization[i].StandardDeviation;
}
return(normalization.Apply(dataTable));
}
public void ApplyTest3()
{
// Suppose we have a data table relating the age of
// a person and its categorical classification, as
// in "child", "adult" or "elder".
// The Normalization filter is able to transform
// numerical data into Z-Scores, subtracting the
// mean for each variable and dividing by their
// standard deviation.
// Create the aforementioned sample table
DataTable table = new DataTable("Sample data");
table.Columns.Add("Age", typeof(double));
table.Columns.Add("Label", typeof(string));
// age label
table.Rows.Add(10, "child");
table.Rows.Add(07, "child");
table.Rows.Add(04, "child");
table.Rows.Add(21, "adult");
table.Rows.Add(27, "adult");
table.Rows.Add(12, "child");
table.Rows.Add(79, "elder");
table.Rows.Add(40, "adult");
table.Rows.Add(30, "adult");
// The filter will ignore non-real (continuous) data
Normalization normalization = new Normalization(table);
double mean = normalization["Age"].Mean; // 25.55
double sdev = normalization["Age"].StandardDeviation; // 23.29
// Now we can process another table at once:
DataTable result = normalization.Apply(table);
// The result will be a table with the same columns, but
// in which any column named "Age" will have been normalized
// using the previously detected mean and standard deviation:
// Accord.Controls.DataGridBox.Show(result);
Assert.AreEqual(25.555555555555557, mean);
Assert.AreEqual(23.297591673342072, sdev);
}
public void ApplyTest()
{
DataTable input = new DataTable("Sample data");
input.Columns.Add("x", typeof(double));
input.Columns.Add("y", typeof(double));
input.Rows.Add(0.0, 0);
input.Rows.Add(0.2, -20);
input.Rows.Add(0.8, -80);
input.Rows.Add(1.0, -100);
DataTable expected = new DataTable("Sample data");
expected.Columns.Add("x", typeof(double));
expected.Columns.Add("y", typeof(double));
expected.Rows.Add(-1.0502, 1.0502);
expected.Rows.Add(-0.6301, 0.6301);
expected.Rows.Add(0.6301, -0.6301);
expected.Rows.Add(1.0502, -1.0502);
Normalization target = new Normalization("x", "y");
target.Detect(input);
DataTable actual = target.Apply(input);
for (int i = 0; i < actual.Rows.Count; i++)
{
double ex = (double)expected.Rows[i][0];
double ey = (double)expected.Rows[i][1];
double ax = (double)actual.Rows[i][0];
double ay = (double)actual.Rows[i][1];
Assert.AreEqual(ex, ax, 0.001);
Assert.AreEqual(ey, ay, 0.001);
}
}
public void ApplyTest2()
{
string colName = "(test ['a'])";
DataTable input = new DataTable("Sample data");
input.Columns.Add(colName, typeof(double));
input.Rows.Add(-2);
input.Rows.Add(-1);
input.Rows.Add(0);
input.Rows.Add(1);
input.Rows.Add(2);
DataTable expected = new DataTable("Sample data");
expected.Columns.Add(colName, typeof(double));
expected.Rows.Add(-1.2649110640673518);
expected.Rows.Add(-0.63245553203367588);
expected.Rows.Add(0);
expected.Rows.Add(0.63245553203367588);
expected.Rows.Add(1.2649110640673518);
Normalization target = new Normalization(colName);
target.Detect(input);
DataTable actual = target.Apply(input);
for (int i = 0; i < actual.Rows.Count; i++)
{
double ex = (double)expected.Rows[i][0];
double ax = (double)actual.Rows[i][0];
Assert.AreEqual(ex, ax, 0.001);
}
}
public void ApplyTest()
{
DataTable input = new DataTable("Sample data");
input.Columns.Add("x", typeof(double));
input.Columns.Add("y", typeof(double));
input.Rows.Add(0.0, 0);
input.Rows.Add(0.2, -20);
input.Rows.Add(0.8, -80);
input.Rows.Add(1.0, -100);
DataTable expected = new DataTable("Sample data");
expected.Columns.Add("x", typeof(double));
expected.Columns.Add("y", typeof(double));
expected.Rows.Add(-1.0502, 1.0502);
expected.Rows.Add(-0.6301, 0.6301);
expected.Rows.Add(0.6301, -0.6301);
expected.Rows.Add(1.0502, -1.0502);
Normalization target = new Normalization("x", "y");
target.Detect(input);
DataTable actual = target.Apply(input);
for (int i = 0; i < actual.Rows.Count; i++)
{
double ex = (double)expected.Rows[i][0];
double ey = (double)expected.Rows[i][1];
double ax = (double)actual.Rows[i][0];
double ay = (double)actual.Rows[i][1];
Assert.AreEqual(ex, ax, 0.001);
Assert.AreEqual(ey, ay, 0.001);
}
}
public override void Transform(ref List <double[]> data)
{
Normalization Normalizer = new Normalization();
data = new List <double[]>(Normalizer.Apply(data.ToArray()));
}
public void ApplyTest3()
{
// Suppose we have a data table relating the age of
// a person and its categorical classification, as
// in "child", "adult" or "elder".
// The Normalization filter is able to transform
// numerical data into Z-Scores, subtracting the
// mean for each variable and dividing by their
// standard deviation.
// Create the aforementioned sample table
DataTable table = new DataTable("Sample data");
table.Columns.Add("Age", typeof(double));
table.Columns.Add("Label", typeof(string));
// age label
table.Rows.Add(10, "child");
table.Rows.Add(07, "child");
table.Rows.Add(04, "child");
table.Rows.Add(21, "adult");
table.Rows.Add(27, "adult");
table.Rows.Add(12, "child");
table.Rows.Add(79, "elder");
table.Rows.Add(40, "adult");
table.Rows.Add(30, "adult");
// The filter will ignore non-real (continuous) data
Normalization normalization = new Normalization(table);
double mean = normalization["Age"].Mean; // 25.55
double sdev = normalization["Age"].StandardDeviation; // 23.29
// Now we can process another table at once:
DataTable result = normalization.Apply(table);
// The result will be a table with the same columns, but
// in which any column named "Age" will have been normalized
// using the previously detected mean and standard deviation:
// DataGridBox.Show(result);
Assert.AreEqual(25.555555555555557, mean);
Assert.AreEqual(23.297591673342072, sdev);
}
public void ApplyTest2()
{
string colName = "(test ['a'])";
DataTable input = new DataTable("Sample data");
input.Columns.Add(colName, typeof(double));
input.Rows.Add(-2);
input.Rows.Add(-1);
input.Rows.Add(0);
input.Rows.Add(1);
input.Rows.Add(2);
DataTable expected = new DataTable("Sample data");
expected.Columns.Add(colName, typeof(double));
expected.Rows.Add(-1.2649110640673518);
expected.Rows.Add(-0.63245553203367588);
expected.Rows.Add(0);
expected.Rows.Add(0.63245553203367588);
expected.Rows.Add(1.2649110640673518);
Normalization target = new Normalization(colName);
target.Detect(input);
DataTable actual = target.Apply(input);
for (int i = 0; i < actual.Rows.Count; i++)
{
double ex = (double)expected.Rows[i][0];
double ax = (double)actual.Rows[i][0];
Assert.AreEqual(ex, ax, 0.001);
}
}
