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Updating Fern and fixing a couple small links (#2210)
Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Divye Gala (https://github.com/divyegala) URL: #2210
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fern/build_docs.sh

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@@ -52,8 +52,22 @@ require_node_22() {
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require_node_22
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fern_config_version() {
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python3 - "${SCRIPT_DIR}/fern.config.json" <<'PY'
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import json
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import sys
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with open(sys.argv[1], encoding="utf-8") as f:
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print(json.load(f).get("version", "*"))
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PY
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}
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FERN_VERSION="$(fern_config_version)"
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if [[ -n "${FERN_CLI:-}" ]]; then
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FERN_CMD=("${FERN_CLI}")
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elif [[ "${FERN_VERSION}" != "*" ]]; then
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FERN_CMD=("npx" "--yes" "fern-api@${FERN_VERSION}")
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elif command -v fern >/dev/null 2>&1; then
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FERN_CMD=("fern")
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else

fern/docs.yml

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@@ -30,6 +30,22 @@ redirects:
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destination: "/cuvs/developer-guide/coding-guidelines/cpp-guidelines"
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- source: "/cuvs/developer-guide/guidelines/python-guidelines"
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destination: "/cuvs/developer-guide/coding-guidelines/python-guidelines"
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- source: "/cuvs/user-guide/compatibility"
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destination: "/cuvs/user-guide/field-guide/compatibility"
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- source: "/cuvs/user-guide/integration-patterns"
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destination: "/cuvs/user-guide/field-guide/integration-patterns"
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- source: "/cuvs/user-guide/advanced-topics"
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destination: "/cuvs/user-guide/field-guide"
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- source: "/cuvs/user-guide/advanced-topics/jit-compilation"
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destination: "/cuvs/user-guide/field-guide/jit-compilation"
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- source: "/cuvs/user-guide/advanced-topics/udf-usage"
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destination: "/cuvs/user-guide/field-guide/udf-usage"
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- source: "/cuvs/developer-guide/advanced-topics/jit-compilation"
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destination: "/cuvs/user-guide/field-guide/jit-compilation"
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- source: "/cuvs/developer-guide/abi-stability"
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destination: "/cuvs/developer-guide/advanced-topics/abi-stability"
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- source: "/cuvs/developer-guide/link-time-optimization"
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destination: "/cuvs/developer-guide/advanced-topics/link-time-optimization"
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layout:
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searchbar-placement: "header"
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page-width: "1376px"
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- page: "Wiki-all Dataset"
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hidden: true
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path: "./pages/cuvs_bench/wiki_all_dataset.md"
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- page: "Compatibility"
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path: "./pages/user_guide/abi_stability.md"
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- page: "Integration Patterns"
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path: "./pages/user_guide/integration_patterns.md"
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- section: "Advanced Topics"
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path: "./pages/advanced_topics.md"
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- section: "Field Guide"
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path: "./pages/field_guide.md"
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contents:
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- page: "Compatibility"
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path: "./pages/user_guide/abi_stability.md"
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- page: "Integration Patterns"
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path: "./pages/user_guide/integration_patterns.md"
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- page: "JIT Compilation"
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path: "./pages/jit_compilation.md"
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- page: "UDF Usage"
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path: "./pages/java_guidelines.md"
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- page: "Python Guidelines"
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path: "./pages/python_guidelines.md"
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- page: "ABI Stability"
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path: "./developer_guide/abi_stability.md"
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- page: "Link-time Optimization"
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path: "./pages/jit_lto_guide.md"
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- section: "Advanced Topics"
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contents:
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- page: "ABI Stability"
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path: "./developer_guide/abi_stability.md"
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- page: "Link-time Optimization"
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path: "./pages/jit_lto_guide.md"
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- page: "Contributing"
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path: "./pages/contributing.md"
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- section: "API Reference"

fern/fern.config.json

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{
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"organization": "nvidia",
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"version": "*"
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"version": "5.44.3"
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}

fern/pages/advanced_topics.md

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fern/pages/api_guide.md

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@@ -6,7 +6,7 @@ slug: user-guide/api-guides
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Use these pages to find task-focused NVIDIA cuVS API examples for clustering, vector indexing, preprocessing, and supporting routines.
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NVIDIA cuVS is written in C++ at its core and wrapped by a stable C API layer. The Python, Java, Rust, and Go bindings use that C layer so they can share the same ABI boundary; see [Compatibility](/user-guide/compatibility) for why that matters. These API guides are intended for general use and include examples for supported programming languages where possible, but some guides document C++ concepts explicitly because all NVIDIA cuVS algorithm implementations are C++ at the core.
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NVIDIA cuVS is written in C++ at its core and wrapped by a stable C API layer. The Python, Java, Rust, and Go bindings use that C layer so they can share the same ABI boundary; see [Compatibility](/user-guide/field-guide/compatibility) for why that matters. These API guides are intended for general use and include examples for supported programming languages where possible, but some guides document C++ concepts explicitly because all NVIDIA cuVS algorithm implementations are C++ at the core.
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## Common Types
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fern/pages/c_api/c-api-cluster-kmeans.md

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@@ -63,7 +63,7 @@ struct cuvsKMeansParams {
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| `tol` | `double` | Relative tolerance with regards to inertia to declare convergence. |
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| `n_init` | `int` | Number of instance k-means algorithm will be run with different seeds. |
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| `oversampling_factor` | `double` | Oversampling factor for use in the k-means\|\| algorithm |
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| `batch_samples` | `int` | batch_samples and batch_centroids are used to tile 1NN computation which is useful to optimize/control the memory footprint Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0 then don't tile the centroids |
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| `batch_samples` | `int` | batch_samples and batch_centroids are used to tile 1NN computation which is useful to optimize/control the memory footprint<br />Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0 then don't tile the centroids |
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| `batch_centroids` | `int` | if 0 then batch_centroids = n_clusters |
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| `inertia_check` | `bool` | Deprecated, ignored. Kept for ABI compatibility. |
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| `hierarchical` | `bool` | Whether to use hierarchical (balanced) kmeans or not |
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| `tol` | `double` | Relative tolerance with regards to inertia to declare convergence. |
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| `n_init` | `int` | Number of instance k-means algorithm will be run with different seeds. |
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| `oversampling_factor` | `double` | Oversampling factor for use in the k-means\|\| algorithm |
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| `batch_samples` | `int` | batch_samples and batch_centroids are used to tile 1NN computation which is useful to optimize/control the memory footprint Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0 then don't tile the centroids |
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| `batch_samples` | `int` | batch_samples and batch_centroids are used to tile 1NN computation which is useful to optimize/control the memory footprint<br />Default tile is [batch_samples x n_clusters] i.e. when batch_centroids is 0 then don't tile the centroids |
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| `batch_centroids` | `int` | if 0 then batch_centroids = n_clusters |
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| `hierarchical` | `bool` | Whether to use hierarchical (balanced) kmeans or not |
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| `hierarchical_n_iters` | `int` | For hierarchical k-means , defines the number of training iterations |
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Allocate KMeans params, and populate with default values
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansParamsCreate(cuvsKMeansParams_t* params);
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cuvsError_t cuvsKMeansParamsCreate(cuvsKMeansParams_t* params);
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```
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**Note:** In cuVS 26.08 (next ABI major version) this signature will be<br />replaced by cuvsKMeansParamsCreate_v2.
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeansparamsdestroy"></a>
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### cuvsKMeansParamsDestroy
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De-allocate KMeans params
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansParamsDestroy(cuvsKMeansParams_t params);
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cuvsError_t cuvsKMeansParamsDestroy(cuvsKMeansParams_t params);
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```
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**Note:** In cuVS 26.08 (next ABI major version) this signature will be<br />replaced by cuvsKMeansParamsDestroy_v2.
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeansparamscreate-v2"></a>
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### cuvsKMeansParamsCreate_v2
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Allocate KMeans params
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansParamsCreate_v2(cuvsKMeansParams_v2_t* params);
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cuvsError_t cuvsKMeansParamsCreate_v2(cuvsKMeansParams_v2_t* params);
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```
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Mirrors cuvsKMeansParamsCreate but operates on cuvsKMeansParams_v2. Will become the unsuffixed cuvsKMeansParamsCreate in cuVS 26.08.
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeansparamsdestroy-v2"></a>
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### cuvsKMeansParamsDestroy_v2
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De-allocate KMeans params allocated by cuvsKMeansParamsCreate_v2.
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansParamsDestroy_v2(cuvsKMeansParams_v2_t params);
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cuvsError_t cuvsKMeansParamsDestroy_v2(cuvsKMeansParams_v2_t params);
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```
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**Parameters**
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeanstype"></a>
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### cuvsKMeansType
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Find clusters with k-means algorithm.
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansFit(cuvsResources_t res,
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cuvsError_t cuvsKMeansFit(cuvsResources_t res,
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cuvsKMeansParams_t params,
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DLManagedTensor* X,
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DLManagedTensor* sample_weight,
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeansfit-v2"></a>
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### cuvsKMeansFit_v2
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Find clusters with k-means algorithm (v2 params layout).
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansFit_v2(cuvsResources_t res,
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cuvsError_t cuvsKMeansFit_v2(cuvsResources_t res,
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cuvsKMeansParams_v2_t params,
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DLManagedTensor* X,
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DLManagedTensor* sample_weight,
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeanspredict"></a>
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### cuvsKMeansPredict
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Predict the closest cluster each sample in X belongs to.
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansPredict(cuvsResources_t res,
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cuvsError_t cuvsKMeansPredict(cuvsResources_t res,
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cuvsKMeansParams_t params,
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DLManagedTensor* X,
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DLManagedTensor* sample_weight,
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeanspredict-v2"></a>
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### cuvsKMeansPredict_v2
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Predict the closest cluster each sample in X belongs to (v2 params layout).
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansPredict_v2(cuvsResources_t res,
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cuvsError_t cuvsKMeansPredict_v2(cuvsResources_t res,
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cuvsKMeansParams_v2_t params,
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DLManagedTensor* X,
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DLManagedTensor* sample_weight,
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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<a id="cuvskmeansclustercost"></a>
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### cuvsKMeansClusterCost
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Compute cluster cost
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```c
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CUVS_EXPORT cuvsError_t cuvsKMeansClusterCost(cuvsResources_t res,
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cuvsError_t cuvsKMeansClusterCost(cuvsResources_t res,
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DLManagedTensor* X,
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DLManagedTensor* centroids,
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double* cost);
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**Returns**
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[`CUVS_EXPORT cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)
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[`cuvsError_t`](/api-reference/c-api-core-c-api#cuvserror-t)

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