Mercurial > repos > bgruening > numeric_clustering
diff Untitled.ipynb @ 0:a3fd214e7555 draft default tip
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 | |
| children |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Untitled.ipynb Fri Jan 01 18:37:54 2016 -0500 @@ -0,0 +1,744 @@ +{ + "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", + "#data.to_csv(path_or_buf = \"foo.tab\", sep=\"\\t\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " <th>-67</th>\n", + " <th>0</th>\n", + " <th>56</th>\n", + " <th>58</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0 </th>\n", + " <td>NaN</td>\n", + " <td> -76</td>\n", + " <td> 0</td>\n", 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