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public static Sum ( Matrix m, VectorType t ) : |
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| m | Matrix | Input Matrix. |
| t | VectorType | Row or Column sum. |
| return | ||
/// <summary>Estimates.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="type">(Optional) the type.</param>
public void Estimate(Matrix X, VectorType type = VectorType.Row)
{
int n = type == VectorType.Row ? X.Rows : X.Cols;
int s = type == VectorType.Row ? X.Cols : X.Rows;
Mu = X.Sum(type) / n;
Sigma = Matrix.Zeros(s);
for (int i = 0; i < n; i++)
{
var x = X[i, type] - Mu;
Sigma += x.Outer(x);
}
Sigma *= (1d / (n - 1d));
}
/// <summary>Estimates.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="type">(Optional) the type.</param>
public void Estimate(Matrix X, VectorType type = VectorType.Row)
{
var n = type == VectorType.Row ? X.Rows : X.Cols;
var s = type == VectorType.Row ? X.Cols : X.Rows;
this.Mu = X.Sum(type) / n;
this.Sigma = Matrix.Zeros(s);
for (var i = 0; i < n; i++)
{
var x = X[i, type] - this.Mu;
this.Sigma += x.Outer(x);
}
this.Sigma *= 1d / (n - 1d);
}
/// <summary>Generates.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="k">The int to process.</param>
public void Generate(Matrix X, int k)
{
int n = X.Rows;
int d = X.Cols;
/***********************
* initialize parameters
***********************/
// convergence params
var log_probability = 0d;
var probability_difference = double.MaxValue;
var mu_difference = double.MaxValue;
// initialize centers with KMeans
KMeans kmeans = new KMeans();
var asgn = kmeans.Generate(X, k, new EuclidianDistance());
// tentative centers
var mu_k = kmeans.Centers;
// initial covariances (stored as diag(cov) 1 of k)
var sg_k = new Matrix(k, d);
for (int i = 0; i < k; i++)
{
var indices = asgn.Select((a, b) => new Tuple<int, int>(a, b)).Where(t => t.Item1 == i).Select(t => t.Item2);
var matrix = X.Slice(indices, VectorType.Row);
sg_k[i] = matrix.CovarianceDiag();
}
// mixing coefficient
var pi_k = asgn
.OrderBy(i => i)
.GroupBy(j => j)
.Select(g => (double)g.Count() / (double)asgn.Length)
.ToVector();
int max_iter = 100;
do
{
/***********************
* Expectation Step
***********************/
// responsibilty matrix: how much is gaussian k responsible for this point x
var z_nk = new Matrix(n, k);
for (int i = 0; i < n; i++)
{
// pi_j * N(x_n | mu_j, sigma_j)
for (int j = 0; j < k; j++)
z_nk[i, j] = pi_k[j] * Normal(X[i], mu_k[j], sg_k[j]);
var dn = z_nk[i].Sum();
if(dn == 0)
Console.WriteLine("Uh oh....");
z_nk[i].Each(z => z / dn);
}
/***********************
* Maximization Step
***********************/
var N_k = z_nk.Sum(VectorType.Row);
var mu_k_new = new Matrix(mu_k.Rows, mu_k.Cols);
for (int i = 0; i < k; i++)
{
var sum = Vector.Zeros(d);
for (int j = 0; j < n; j++)
sum += z_nk[j, i] * X[j];
mu_k_new[i] = sum / N_k[i];
}
var sg_k_new = new Matrix(k, d);
for (int i = 0; i < k; i++)
{
var sum = Vector.Zeros(d);
for (int j = 0; j < n; j++)
sum += z_nk[j, i] * (X[j] - mu_k_new[i]).Each(s => s * s);
sg_k_new[i] = sum / N_k[i];
}
var pi_k_new = N_k / n;
/***********************
* Convergence Check
***********************/
var new_log_prob = 0d;
for (int i = 0; i < n; i++)
{
var acc = 0d;
// pi_j * N(x_n | mu_j, sigma_j)
for (int j = 0; j < k; j++)
acc += pi_k[j] * Normal(X[i], mu_k[j], sg_k[j]);
new_log_prob += System.Math.Log(acc, System.Math.E);
}
// log likelihood differences
probability_difference = System.Math.Abs(log_probability - new_log_prob);
Console.WriteLine("Log Likelihoods (Total Points: {0}, k={1}, d={2})\nO: {3}\nN: {4}\nDifference: {5}\n", n, k, d, log_probability, new_log_prob, probability_difference);
log_probability = new_log_prob;
// centers differences
mu_difference = mu_k.GetRows()
.Zip(mu_k_new.GetRows(), (v1, v2) => new { V1 = v1, V2 = v2 })
.Sum(a => (a.V1 - a.V2).Norm());
Console.WriteLine("Centers:\nO: {0}\nN: {1}\nDifference: {2}\n", mu_k, mu_k_new, mu_difference);
mu_k = mu_k_new;
// covariance differences
double diff = sg_k.GetRows()
.Zip(sg_k_new.GetRows(), (v1, v2) => new { V1 = v1, V2 = v2 })
.Sum(a => (a.V1 - a.V2).Norm());
Console.WriteLine("Covariance:\nO: {0}\nN: {1}\nDifference: {2}\n", sg_k, sg_k_new, diff);
sg_k = sg_k_new;
// mixing differences
diff = (pi_k - pi_k_new).Each(s => System.Math.Abs(s)).Sum();
Console.WriteLine("Mixing Coeffs:\nO: {0}\nN: {1}\nDifference: {2}\n", pi_k, pi_k_new, diff);
pi_k = pi_k_new;
Console.WriteLine("-------------------------------------------------------------");
} while (probability_difference > .0000000001 && mu_difference > .0000000001 && --max_iter >= 0);
}
/// <summary>
/// Computes the sum of either the rows or columns of a matrix and returns a vector.
/// </summary>
/// <param name="m">Matrix.</param>
/// <returns>Vector Sum.</returns>
public static double Sum(this Matrix m)
{
return(Matrix.Sum(m));
}
public static double Sum(Matrix m, int i, VectorType t)
{
return(Matrix.Sum(m, i, t));
}
/// <summary>
/// Computes the sum of either the rows
/// or columns of a matrix and returns
/// a vector
/// </summary>
/// <param name="t">Row or Column sum</param>
/// <returns>Vector Sum</returns>
public static Vector Sum(this Matrix m, VectorType t)
{
return(Matrix.Sum(m, t));
}
private static string classify0(Vector inX, Matrix dataset, List<string> labels, int k)
{
Console.WriteLine("Input");
Console.WriteLine(inX.ToString());
Console.WriteLine("Data");
Console.WriteLine(dataset);
Console.WriteLine("Labels");
labels.ForEach(s => Console.Write(s+" "));
// Create difference matrix with same dimensions as the dataset
var diffMatrix = new Matrix(dataset.Rows,dataset.Cols);
for (int i = 0; i < dataset.Rows; i++)
{
diffMatrix[i]=inX;
}
diffMatrix = diffMatrix - dataset;
Console.WriteLine("Diff Matrix");
Console.WriteLine(diffMatrix.ToString());
// Square all the items
for (int i = 0; i < diffMatrix.Rows; i++)
{
var v = diffMatrix[i];
v.Each((d)=>Math.Pow(d,2.0));
diffMatrix[i] = v;
}
// Sum of each row and then square root
var sqDistances = diffMatrix.Sum(VectorType.Col); //Why is this a Column Summation?
sqDistances.Each(d => Math.Sqrt(d));
Console.WriteLine("Squared distances");
Console.WriteLine(sqDistances.ToString());
// get the sorted indices
var sortedIndices = Vector.SortOrder(sqDistances).Reverse().ToVector();
Console.WriteLine("Sorted Indices");
Console.WriteLine(sortedIndices);
var classCount = new Dictionary<string, double>();
// Now compare the first 'k' items that are closest
for (int i = 0; i < k; i++)
{
var votelabel = labels[(int)sortedIndices[i]];
if (!classCount.ContainsKey(votelabel))
{
classCount.Add(votelabel,1.0);
}
else
{
classCount[votelabel] += 1; // Increment count
}
}
// Order by the values descending and return the first Key
var orderedCount = classCount.OrderByDescending(kvp => kvp.Value).ToArray();
return orderedCount.First().Key;
}
