using System;

using System.Collections.Generic;

using System.Text;

using static Tensorflow.Python;

namespace Tensorflow.Clustering

{

/// <summary>

/// Creates the graph for k-means clustering.

/// </summary>

public class KMeans

{

public const string CLUSTERS_VAR_NAME = "clusters";

public const string SQUARED_EUCLIDEAN_DISTANCE = "squared_euclidean";

public const string COSINE_DISTANCE = "cosine";

public const string RANDOM_INIT = "random";

public const string KMEANS_PLUS_PLUS_INIT = "kmeans_plus_plus";

public const string KMC2_INIT = "kmc2";

Tensor[] _inputs;

int _num_clusters;

string _initial_clusters;

string _distance_metric;

bool _use_mini_batch;

int _mini_batch_steps_per_iteration;

int _random_seed;

int _kmeans_plus_plus_num_retries;

int _kmc2_chain_length;

public KMeans(Tensor inputs,

int num_clusters,

string initial_clusters = RANDOM_INIT,

string distance_metric = SQUARED_EUCLIDEAN_DISTANCE,

bool use_mini_batch = false,

int mini_batch_steps_per_iteration = 1,

int random_seed = 0,

int kmeans_plus_plus_num_retries = 2,

int kmc2_chain_length = 200)

{

_inputs = new Tensor[] { inputs };

_num_clusters = num_clusters;

_initial_clusters = initial_clusters;

_distance_metric = distance_metric;

_use_mini_batch = use_mini_batch;

_mini_batch_steps_per_iteration = mini_batch_steps_per_iteration;

_random_seed = random_seed;

_kmeans_plus_plus_num_retries = kmeans_plus_plus_num_retries;

_kmc2_chain_length = kmc2_chain_length;

}

public object training_graph()

{

var initial_clusters = _initial_clusters;

var num_clusters = ops.convert_to_tensor(_num_clusters);

var inputs = _inputs;

var vars = _create_variables(num_clusters);

var cluster_centers_var = vars[0];

var cluster_centers_initialized = vars[1];

var total_counts = vars[2];

var cluster_centers_updated = vars[3];

var update_in_steps = vars[4];

var init_op = new _InitializeClustersOpFactory(_inputs, num_clusters, initial_clusters, _distance_metric,

_random_seed, _kmeans_plus_plus_num_retries,

_kmc2_chain_length, cluster_centers_var, cluster_centers_updated,

cluster_centers_initialized).op();

throw new NotImplementedException("KMeans training_graph");

}

private RefVariable[] _create_variables(Tensor num_clusters)

{

var init_value = constant_op.constant(new float[0], dtype: TF_DataType.TF_FLOAT);

var cluster_centers = tf.Variable(init_value, name: CLUSTERS_VAR_NAME, validate_shape: false);

var cluster_centers_initialized = tf.Variable(false, dtype: TF_DataType.TF_BOOL, name: "initialized");

RefVariable update_in_steps = null;

if (_use_mini_batch && _mini_batch_steps_per_iteration > 1)

throw new NotImplementedException("KMeans._create_variables");

else

{

var cluster_centers_updated = cluster_centers;

var ones = array_ops.ones(new Tensor[] { num_clusters }, dtype: TF_DataType.TF_INT64);

var cluster_counts = _use_mini_batch ? tf.Variable(ones) : null;

return new RefVariable[]

{

cluster_centers,

cluster_centers_initialized,

cluster_counts,

cluster_centers_updated,

update_in_steps

};

}

}

}

}