namespace Tensorflow.Keras.Metrics;

public class Recall : Metric

{

Tensor _thresholds;

int _top_k;

int _class_id;

IVariableV1 true_positives;

IVariableV1 false_negatives;

bool _thresholds_distributed_evenly;

public Recall(float thresholds = 0.5f, int top_k = 1, int class_id = 0, string name = "recall", TF_DataType dtype = TF_DataType.TF_FLOAT)

: base(name: name, dtype: dtype)

{

_thresholds = constant_op.constant(new float[] { thresholds });

true_positives = add_weight("true_positives", shape: 1, initializer: tf.initializers.zeros_initializer());

false_negatives = add_weight("false_negatives", shape: 1, initializer: tf.initializers.zeros_initializer());

}

public override Tensor update_state(Tensor y_true, Tensor y_pred, Tensor sample_weight = null)

{

return metrics_utils.update_confusion_matrix_variables(

new Dictionary<string, IVariableV1>

{

{ "tp", true_positives },

{ "fn", false_negatives },

},

y_true,

y_pred,

thresholds: _thresholds,

thresholds_distributed_evenly: _thresholds_distributed_evenly,

top_k: _top_k,

class_id: _class_id,

sample_weight: sample_weight);

}

public override Tensor result()

{

var result = tf.divide(true_positives.AsTensor(), tf.add(true_positives, false_negatives));

return _thresholds.size == 1 ? result[0] : result;

}

public override void reset_states()

{

var num_thresholds = (int)_thresholds.size;

keras.backend.batch_set_value(

new List<(IVariableV1, NDArray)>

{

(true_positives, np.zeros(num_thresholds)),

(false_negatives, np.zeros(num_thresholds))

});

}

}