{
  "__type": "IngestedDoc",
  "__tag": 4010,
  "_content": {
    "Notes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "This function is similar to the MATLAB function "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "clusterdata"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Strong",
              "__tag": 4048,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "Array API Standard Support"
                }
              ]
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "CrossRef",
              "__tag": 4002,
              "value": "fclusterdata",
              "reference": {
                "__type": "RefInfo",
                "__tag": 4000,
                "module": null,
                "version": null,
                "kind": "local",
                "path": "fclusterdata"
              },
              "kind": "local"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "SCIPY_ARRAY_API=1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported."
            }
          ]
        },
        {
          "__type": "Code",
          "__tag": 4050,
          "value": "====================  ====================  ====================\nLibrary               CPU                   GPU\n====================  ====================  ====================\nNumPy                 ✅                     n/a                 \nCuPy                  n/a                   ⛔                   \nPyTorch               ✅                     ⛔                   \nJAX                   ⚠️ no JIT             ⛔                   \nDask                  ⚠️ computes graph     n/a                 \n====================  ====================  ====================",
          "execution_status": null
        },
        {
          "__type": "Blockquote",
          "__tag": 4059,
          "children": [
            {
              "__type": "Paragraph",
              "__tag": 4045,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "See "
                },
                {
                  "__type": "InlineRole",
                  "__tag": 4003,
                  "value": "dev-arrayapi",
                  "domain": null,
                  "role": "ref",
                  "inventory": null
                },
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": " for more information."
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Warns": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Raises": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Yields": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Methods": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Returns": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "fclusterdata",
              "annotation": "ndarray",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "A vector of length n. T[i] is the flat cluster number to which original observation i belongs."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Cluster observation data using a given metric."
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Receives": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Warnings": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Attributes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Parameters": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "X",
              "annotation": "(N, M) ndarray",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "N by M data matrix with N observations in M dimensions."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "t",
              "annotation": "scalar",
              "desc": [
                {
                  "__type": "DefList",
                  "__tag": 4033,
                  "children": [
                    {
                      "__type": "DefListItem",
                      "__tag": 4037,
                      "dt": {
                        "__type": "Paragraph",
                        "__tag": 4045,
                        "children": [
                          {
                            "__type": "Text",
                            "__tag": 4046,
                            "value": "For criteria 'inconsistent', 'distance' or 'monocrit',"
                          }
                        ]
                      },
                      "dd": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "this is the threshold to apply when forming flat clusters."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "DefListItem",
                      "__tag": 4037,
                      "dt": {
                        "__type": "Paragraph",
                        "__tag": 4045,
                        "children": [
                          {
                            "__type": "Text",
                            "__tag": 4046,
                            "value": "For 'maxclust' or 'maxclust_monocrit' criteria,"
                          }
                        ]
                      },
                      "dd": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": "this would be max number of clusters requested."
                            }
                          ]
                        }
                      ]
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "criterion",
              "annotation": "str, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Specifies the criterion for forming flat clusters. Valid values are 'inconsistent' (default), 'distance', or 'maxclust' cluster formation algorithms. See "
                    },
                    {
                      "__type": "CrossRef",
                      "__tag": 4002,
                      "value": "fcluster",
                      "reference": {
                        "__type": "LocalRef",
                        "__tag": 4022,
                        "kind": "module",
                        "path": "scipy.cluster.hierarchy:fcluster"
                      },
                      "kind": "module"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " for descriptions."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "metric",
              "annotation": "str or function, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "The distance metric for calculating pairwise distances. See "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "distance.pdist"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " for descriptions and linkage to verify compatibility with the linkage method."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "depth",
              "annotation": "int, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "The maximum depth for the inconsistency calculation. See "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "inconsistent",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " for more information."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "method",
              "annotation": "str, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "The linkage method to use (single, complete, average, weighted, median centroid, ward). See "
                    },
                    {
                      "__type": "InlineRole",
                      "__tag": 4003,
                      "value": "linkage",
                      "domain": null,
                      "role": null,
                      "inventory": null
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " for more information. Default is \"single\"."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "R",
              "annotation": "ndarray, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "The inconsistency matrix. It will be computed if necessary if it is not passed."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Extended Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Clusters the original observations in the n-by-m data matrix X (n observations in m dimensions), using the euclidean distance metric to calculate distances between original observations, performs hierarchical clustering using the single linkage algorithm, and forms flat clusters using the inconsistency method with "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "t"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " as the cut-off threshold."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "A 1-D array "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "T"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " of length "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "n"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is returned. "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "T[i]"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " is the index of the flat cluster to which the original observation "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "i"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " belongs."
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Other Parameters": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    }
  },
  "_ordered_sections": [
    "Summary",
    "Extended Summary",
    "Parameters",
    "Attributes",
    "Methods",
    "Returns",
    "Yields",
    "Receives",
    "Other Parameters",
    "Raises",
    "Warns",
    "Warnings",
    "Notes"
  ],
  "item_file": "/scipy/cluster/hierarchy.py",
  "item_line": 2703,
  "item_type": "function",
  "aliases": [
    "scipy.cluster.hierarchy.fclusterdata"
  ],
  "example_section_data": {
    "__type": "Section",
    "__tag": 4015,
    "children": [
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "from scipy.cluster.hierarchy import fclusterdata\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nThis is a convenience method that abstracts all the steps to perform in a\ntypical SciPy's hierarchical clustering workflow.\n\n* Transform the input data into a condensed matrix with\n  `scipy.spatial.distance.pdist`.\n\n* Apply a clustering method.\n\n* Obtain flat clusters at a user defined distance threshold ``t`` using\n  `scipy.cluster.hierarchy.fcluster`.\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "X = [[0, 0], [0, 1], [1, 0],\n     [0, 4], [0, 3], [1, 4],\n     [4, 0], [3, 0], [4, 1],\n     [4, 4], [3, 4], [4, 3]]\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "fclusterdata(X, t=1)\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nThe output here (for the dataset ``X``, distance threshold ``t``, and the\ndefault settings) is four clusters with three data points each."
      }
    ],
    "title": [],
    "level": 0,
    "target": null
  },
  "see_also": [
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.spatial.distance.pdist",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.spatial.distance:pdist"
        },
        "kind": "module"
      },
      "descriptions": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "pairwise distance metrics"
            }
          ]
        }
      ],
      "type": "func"
    }
  ],
  "signature": {
    "__type": "SignatureNode",
    "__tag": 4029,
    "kind": "function",
    "parameters": [
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "X",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "t",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "criterion",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "inconsistent"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "metric",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "euclidean"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "depth",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "2"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "method",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "single"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "R",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "None"
      }
    ],
    "return_annotation": {
      "__type": "Empty",
      "__tag": 4031
    },
    "target_name": "fclusterdata"
  },
  "references": null,
  "qa": "scipy.cluster.hierarchy:fclusterdata",
  "arbitrary": [],
  "local_refs": [
    "R",
    "X",
    "criterion",
    "depth",
    "fclusterdata",
    "method",
    "metric",
    "t"
  ]
}