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
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-1:000000000000 0:a3fd214e7555
1 {
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3 {
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12 "text/plain": [
13 "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=8, n_init=10,\n",
14 " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n",
15 " verbose=0)"
16 ]
17 },
18 "execution_count": 29,
19 "metadata": {},
20 "output_type": "execute_result"
21 }
22 ],
23 "source": [
24 "import sys\n",
25 "import json\n",
26 "import numpy as np\n",
27 "import sklearn.cluster\n",
28 "import pandas\n",
29 "\n",
30 "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",
31 "my_class = getattr(sklearn.cluster, \"KMeans\")\n",
32 "cluster_object = my_class()\n",
33 "\n",
34 "params = dict()\n",
35 "cluster_object.set_params(**params)\n"
36 ]
37 },
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103 "source": [
104 "\n",
105 "if 4 >= 4:\n",
106 " data_matrix = data.values[:, 1-1:1]\n",
107 " #print data_matrix\n",
108 "else:\n",
109 " data_matrix = data.values\n",
110 "\n",
111 "prediction = cluster_object.fit_predict( data_matrix )\n",
112 "print prediction, len(prediction), len(data_matrix)\n",
113 "\n",
114 "pred = pandas.DataFrame(prediction)\n",
115 "print pred\n",
116 "\n",
117 "#data[len(data.columns)] = prediction\n",
118 "#data.to_csv(path_or_buf = \"foo.tab\", sep=\"\\t\")\n"
119 ]
120 },
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677 "25 1 NaN NaN NaN NaN\n",
678 "26 1 NaN NaN NaN NaN\n",
679 "27 1 NaN NaN NaN NaN\n",
680 "28 1 NaN NaN NaN NaN\n",
681 "29 1 NaN NaN NaN NaN\n",
682 "30 1 NaN NaN NaN NaN\n",
683 "31 1 NaN NaN NaN NaN\n",
684 "32 1 NaN NaN NaN NaN\n",
685 "33 1 NaN NaN NaN NaN\n",
686 "34 1 NaN NaN NaN NaN\n",
687 "35 1 NaN NaN NaN NaN\n",
688 "36 1 NaN NaN NaN NaN\n",
689 "37 2 NaN NaN NaN NaN\n",
690 "38 2 NaN NaN NaN NaN\n",
691 "39 2 NaN NaN NaN NaN\n",
692 "40 2 NaN NaN NaN NaN\n",
693 "41 2 NaN NaN NaN NaN\n",
694 "42 2 NaN NaN NaN NaN\n",
695 "43 2 NaN NaN NaN NaN\n",
696 "44 2 NaN NaN NaN NaN\n",
697 "45 2 NaN NaN NaN NaN\n",
698 "46 2 NaN NaN NaN NaN\n",
699 "47 2 NaN NaN NaN NaN\n",
700 "\n",
701 "[96 rows x 5 columns]"
702 ]
703 },
704 "execution_count": 34,
705 "metadata": {},
706 "output_type": "execute_result"
707 }
708 ],
709 "source": [
710 "pandas.concat([data, pred], axis=0)"
711 ]
712 },
713 {
714 "cell_type": "code",
715 "execution_count": null,
716 "metadata": {
717 "collapsed": true
718 },
719 "outputs": [],
720 "source": []
721 }
722 ],
723 "metadata": {
724 "kernelspec": {
725 "display_name": "Python 2",
726 "language": "python",
727 "name": "python2"
728 },
729 "language_info": {
730 "codemirror_mode": {
731 "name": "ipython",
732 "version": 2
733 },
734 "file_extension": ".py",
735 "mimetype": "text/x-python",
736 "name": "python",
737 "nbconvert_exporter": "python",
738 "pygments_lexer": "ipython2",
739 "version": "2.7.10"
740 }
741 },
742 "nbformat": 4,
743 "nbformat_minor": 0
744 }