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        "value": "\nCreate z, an array with shape (100, 2) containing a mixture of samples\nfrom three multivariate normal distributions.\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "rng = np.random.default_rng()\na = rng.multivariate_normal([0, 6], [[2, 1], [1, 1.5]], size=45)\nb = rng.multivariate_normal([2, 0], [[1, -1], [-1, 3]], size=30)\nc = rng.multivariate_normal([6, 4], [[5, 0], [0, 1.2]], size=25)\nz = np.concatenate((a, b, c))\nrng.shuffle(z)\n",
        "execution_status": "success"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nCompute three clusters.\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "centroid, label = kmeans2(z, 3, minit='points')\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "centroid\n",
        "execution_status": "failure"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nHow many points are in each cluster?\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "counts = np.bincount(label)\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "counts\n",
        "execution_status": "failure"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nPlot the clusters.\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "w0 = z[label == 0]\nw1 = z[label == 1]\nw2 = z[label == 2]\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "plt.plot(w0[:, 0], w0[:, 1], 'o', alpha=0.5, label='cluster 0')\nplt.plot(w1[:, 0], w1[:, 1], 'd', alpha=0.5, label='cluster 1')\nplt.plot(w2[:, 0], w2[:, 1], 's', alpha=0.5, label='cluster 2')\nplt.plot(centroid[:, 0], centroid[:, 1], 'k*', label='centroids')\nplt.axis('equal')\nplt.legend(shadow=True)\n",
        "execution_status": "failure"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "plt.show()\n",
        "execution_status": "success"
      },
      {
        "__type": "Figure",
        "__tag": 4024,
        "value": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "scipy",
          "version": "1.17.1",
          "kind": "assets",
          "path": "fig-aeedf57f8694fa1b.png"
        }
      }
    ],
    "title": [],
    "level": 0,
    "target": null
  },
  "see_also": [
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "kmeans",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.cluster.vq:kmeans"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    }
  ],
  "signature": {
    "__type": "SignatureNode",
    "__tag": 4029,
    "kind": "function",
    "parameters": [
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "data",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "k",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "iter",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "10"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "thresh",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "1e-05"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "minit",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "random"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "missing",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "warn"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "check_finite",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "True"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "rng",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "KEYWORD_ONLY",
        "default": "None"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "seed",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "KEYWORD_ONLY",
        "default": "None"
      }
    ],
    "return_annotation": {
      "__type": "Empty",
      "__tag": 4031
    },
    "target_name": "kmeans2"
  },
  "references": [
    ".. [1] D. Arthur and S. Vassilvitskii, \"k-means++: the advantages of",
    "   careful seeding\", Proceedings of the Eighteenth Annual ACM-SIAM Symposium",
    "   on Discrete Algorithms, 2007."
  ],
  "qa": "scipy.cluster.vq:kmeans2",
  "arbitrary": [],
  "local_refs": [
    "centroid",
    "check_finite",
    "data",
    "iter",
    "k",
    "label",
    "minit",
    "missing",
    "rng",
    "thresh"
  ]
}