Mercurial > repos > bgruening > numeric_clustering
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/numeric_clustering commit bafd56379ff227fb81f8cd61d708ebc39814da54
| author | bgruening |
|---|---|
| date | Fri, 01 Jan 2016 18:37:54 -0500 |
| parents | |
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{ "cells": [ { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=8, n_init=10,\n", " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "import json\n", "import numpy as np\n", "import sklearn.cluster\n", "import pandas\n", "\n", "data = pandas.read_csv(\"/home/bag/projects/code/galaxytools/tools/numeric_clustering/test-data/numeric_values.tabular\", sep='\\t', header=0, index_col=None, parse_dates=True, encoding=None )\n", "my_class = getattr(sklearn.cluster, \"KMeans\")\n", "cluster_object = my_class()\n", "\n", "params = dict()\n", "cluster_object.set_params(**params)\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 0 0 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1\n", " 2 2 2 2 2 2 2 2 2 2 2] 48 48\n", " 0\n", "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "5 0\n", "6 0\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 3\n", "13 3\n", "14 3\n", "15 3\n", "16 3\n", "17 3\n", "18 3\n", "19 3\n", "20 3\n", "21 3\n", "22 3\n", "23 3\n", "24 3\n", "25 1\n", "26 1\n", "27 1\n", "28 1\n", "29 1\n", "30 1\n", "31 1\n", "32 1\n", "33 1\n", "34 1\n", "35 1\n", "36 1\n", "37 2\n", "38 2\n", "39 2\n", "40 2\n", "41 2\n", "42 2\n", "43 2\n", "44 2\n", "45 2\n", "46 2\n", "47 2\n" ] } ], "source": [ "\n", "if 4 >= 4:\n", " data_matrix = data.values[:, 1-1:1]\n", " #print data_matrix\n", "else:\n", " data_matrix = data.values\n", "\n", "prediction = cluster_object.fit_predict( data_matrix )\n", "print prediction, len(prediction), len(data_matrix)\n", "\n", "pred = pandas.DataFrame(prediction)\n", "print pred\n", "\n", "#data[len(data.columns)] = prediction\n", 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