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- /**
- * Copyright 2021 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
- /*!
- * \file cluster.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_CLUSTER_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_CLUSTER_H_
-
- #include "graph/operator_reg.h"
- #include "graph/operator.h"
-
- namespace ge {
- /**
- * @brief Perform k-means clustering on a data matrix. \n
-
- * @par Inputs:
- * Three required inputs and one optional inputs, including:
- * @li x: A 2D tensor of data type float32.
- * @li y: A 2D tensor of data type float32.
- * @li sum_square_x: An optional 2D tensor of data type float32.
- * @li sum_square_y: A 2D tensor of data type float32. \n
-
- * @par Attributes:
- * use_actual_distance: Indicates whether to calculate the complete distance. \n
-
- * @par Outputs:
- * @li segment_sum: A tensor of data type float32.
- * @li segment_count: A tensor of data type float32.
- * @li k_mean_total_sum: A tensor of data type float32.
- */
- REG_OP(KMeansCentroids)
- .INPUT(x, TensorType({DT_FLOAT}))
- .INPUT(y, TensorType({DT_FLOAT}))
- .INPUT(sum_square_y, TensorType({DT_FLOAT}))
- .OPTIONAL_INPUT(sum_square_x, TensorType({DT_FLOAT}))
- .OUTPUT(segment_sum, TensorType({DT_FLOAT}))
- .OUTPUT(segment_count, TensorType({DT_FLOAT}))
- .OUTPUT(kmean_total_sum, TensorType({DT_FLOAT}))
- .ATTR(use_actual_distance, Bool, false)
- .OP_END_FACTORY_REG(KMeansCentroids)
- } // namespace ge
-
- #endif // OPS_BUILT_IN_OP_PROTO_INC_CLUSTER_H_
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