public static float [,] covariance(
float [][] data)
{
data = ToolsCollection.Transpose(data);
int column_count = data.Length;
float [,] covariance_array = new float [column_count, column_count];
float [] means = new float [column_count];
for (int index_column = 0; index_column < column_count; index_column++)
{
means[index_column] = ToolsMathStatistics.Mean(data[index_column]);
}
for (int index_column = 0; index_column < column_count; index_column++)
{
covariance_array[index_column, index_column] = ToolsMathCollectionFloat
.covariance(data[index_column], data[index_column], means[index_column], means[index_column]);
}
for (int index_row = 0; index_row < column_count; index_row++)
{
for (int index_column = index_row + 1; index_column < column_count; index_column++)
{
float covariance = ToolsMathCollectionFloat
.covariance(data[index_row], data[index_column], means[index_row], means[index_column]);
covariance_array[index_row, index_column] = covariance;
covariance_array[index_column, index_row] = covariance;
}
}
return(covariance_array);
}
// //cov(xi, xj) = mean(xi.*xj) - mean(xi)*mean(xj);
public static float covariance(
float [] array_0,
float [] array_1)
{
float mean_0 = ToolsMathStatistics.Mean(array_0);
float mean_1 = ToolsMathStatistics.Mean(array_1);
float mean_product = MeanProduct(array_0, array_1);
return(mean_product - (mean_0 * mean_1));
}
public static float [] means_row(
float [][] array)
{
float [] means = new float [array.Length];
for (int index_row = 0; index_row < array.Length; index_row++)
{
means[index_row] = ToolsMathStatistics.Mean(array[index_row]);
}
return(means);
}
