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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-means (MADlib v1.11+)\n",
"Demonstrates k-means including new array input in 1.10 and new array unnest function in 1.11."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The sql extension is already loaded. To reload it, use:\n",
" %reload_ext sql\n"
]
}
],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'Connected: gpdbchina@madlib'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Greenplum 4.3.10.0\n",
"%sql postgresql://gpdbchina@10.194.10.68:61000/madlib\n",
" \n",
"# PostgreSQL local\n",
"#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
"\n",
"# Greenplum 4.2.3.0\n",
"#%sql postgresql://gpdbchina@10.194.10.68:55000/madlib"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
" <td>MADlib version: 1.11-dev, git revision: rel/v1.10.0-26-ga3d54be, cmake configuration time: Thu Apr 27 01:01:30 UTC 2017, build type: Release, build system: Linux-2.6.18-238.27.1.el5.hotfix.bz516490, C compiler: gcc 4.4.0, C++ compiler: g++ 4.4.0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'MADlib version: 1.11-dev, git revision: rel/v1.10.0-26-ga3d54be, cmake configuration time: Thu Apr 27 01:01:30 UTC 2017, build type: Release, build system: Linux-2.6.18-238.27.1.el5.hotfix.bz516490, C compiler: gcc 4.4.0, C++ compiler: g++ 4.4.0',)]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select madlib.version();\n",
"#%sql select version();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Prepare some input data:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"Done.\n",
"10 rows affected.\n",
"10 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>pid</th>\n",
" <th>points</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>[14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>[13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>[13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>[14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>[13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>[14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>[14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>[14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>[14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>[13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, [14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]),\n",
" (2, [13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]),\n",
" (3, [13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]),\n",
" (4, [14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]),\n",
" (5, [13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]),\n",
" (6, [14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]),\n",
" (7, [14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]),\n",
" (8, [14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]),\n",
" (9, [14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]),\n",
" (10, [13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0])]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS km_sample;\n",
"\n",
"CREATE TABLE km_sample(pid int, points double precision[]);\n",
"\n",
"INSERT INTO km_sample VALUES\n",
"(1, '{14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065}'),\n",
"(2, '{13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050}'),\n",
"(3, '{13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185}'),\n",
"(4, '{14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480}'),\n",
"(5, '{13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735}'),\n",
"(6, '{14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450}'),\n",
"(7, '{14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290}'),\n",
"(8, '{14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295}'),\n",
"(9, '{14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045}'),\n",
"(10, '{13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045}');\n",
"\n",
"SELECT * FROM km_sample ORDER BY pid;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Run k-means clustering using kmeans++ with centroid seeding:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>centroids</th>\n",
" <th>cluster_variance</th>\n",
" <th>objective_fn</th>\n",
" <th>frac_reassigned</th>\n",
" <th>num_iterations</th>\n",
" </tr>\n",
" <tr>\n",
" <td>[[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0], [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]]</td>\n",
" <td>[90512.324426408, 60672.638245208]</td>\n",
" <td>151184.962672</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[([[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0], [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]], [90512.324426408, 60672.638245208], 151184.962671616, 0.0, 2)]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS km_result;\n",
"\n",
"-- Run kmeans algorithm\n",
"CREATE TABLE km_result AS\n",
"SELECT * FROM madlib.kmeanspp( 'km_sample', -- Table of source data\n",
" 'points', -- Column containing point co-ordinates \n",
" 2, -- Number of centroids to calculate\n",
" 'madlib.squared_dist_norm2', -- Distance function\n",
" 'madlib.avg', -- Aggregate function\n",
" 20, -- Number of iterations\n",
" 0.001 -- Fraction of centroids reassigned to keep iterating \n",
" );\n",
"\n",
"SELECT * FROM km_result;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Calculate the simplified silhouette coefficient:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>simple_silhouette</th>\n",
" </tr>\n",
" <tr>\n",
" <td>0.689788048829</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(0.68978804882941,)]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM madlib.simple_silhouette( 'km_sample', -- Input points table\n",
" 'points', -- Column containing points\n",
" (SELECT centroids FROM km_result), -- Centroids\n",
" 'madlib.dist_norm2' -- Distance function\n",
" );"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Find the cluster assignment for each point:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>pid</th>\n",
" <th>points</th>\n",
" <th>cluster_id</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>[14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>[13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>[13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>[14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>[13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>[14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>[14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>[14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>[14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>[13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0]</td>\n",
" <td>0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, [14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0], 0),\n",
" (2, [13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0], 0),\n",
" (3, [13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0], 1),\n",
" (4, [14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0], 1),\n",
" (5, [13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0], 0),\n",
" (6, [14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0], 1),\n",
" (7, [14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0], 1),\n",
" (8, [14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0], 1),\n",
" (9, [14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0], 0),\n",
" (10, [13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0], 0)]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT data.*, (madlib.closest_column(centroids, points)).column_id as cluster_id\n",
"FROM km_sample as data, km_result\n",
"ORDER BY data.pid;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Unnest the cluster centroids 2-D array to get a set of 1-D centroid arrays"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"2 rows affected.\n",
"2 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>unnest_row_id</th>\n",
" <th>unnest_result</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>[14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, [13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]),\n",
" (2, [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0])]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS km_centroids_unnest;\n",
"\n",
"-- Run unnest function\n",
"CREATE TABLE km_centroids_unnest AS\n",
"SELECT (madlib.array_unnest_2d_to_1d(centroids)).*\n",
"FROM km_result;\n",
"\n",
"SELECT * FROM km_centroids_unnest ORDER BY 1;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the ID column returned by array_unnest_2d_to_1d() is not the same as the cluster ID assigned by k-means. See below to display the cluster IDs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Display cluster ID\n",
"Create cluster IDs for 1-D centroid arrays so that cluster ID for any centroid can be matched to the cluster assignment for the data points:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>unnest_row_id</th>\n",
" <th>unnest_result</th>\n",
" <th>cluster_id</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>[14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]</td>\n",
" <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, [13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0], 0),\n",
" (2, [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0], 1)]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT cent.*, (madlib.closest_column(centroids, unnest_result)).column_id as cluster_id\n",
"FROM km_centroids_unnest as cent, km_result\n",
"ORDER BY cent.unnest_row_id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Array input"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"Done.\n",
"10 rows affected.\n",
"10 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>pid</th>\n",
" <th>p1</th>\n",
" <th>p2</th>\n",
" <th>p3</th>\n",
" <th>p4</th>\n",
" <th>p5</th>\n",
" <th>p6</th>\n",
" <th>p7</th>\n",
" <th>p8</th>\n",
" <th>p9</th>\n",
" <th>p10</th>\n",
" <th>p11</th>\n",
" <th>p12</th>\n",
" <th>p13</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>14.23</td>\n",
" <td>1.71</td>\n",
" <td>2.43</td>\n",
" <td>15.6</td>\n",
" <td>127.0</td>\n",
" <td>2.8</td>\n",
" <td>3.06</td>\n",
" <td>0.28</td>\n",
" <td>2.29</td>\n",
" <td>5.64</td>\n",
" <td>1.04</td>\n",
" <td>3.92</td>\n",
" <td>1065.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>13.2</td>\n",
" <td>1.78</td>\n",
" <td>2.14</td>\n",
" <td>11.2</td>\n",
" <td>1.0</td>\n",
" <td>2.65</td>\n",
" <td>2.76</td>\n",
" <td>0.26</td>\n",
" <td>1.28</td>\n",
" <td>4.38</td>\n",
" <td>1.05</td>\n",
" <td>3.49</td>\n",
" <td>1050.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>13.16</td>\n",
" <td>2.36</td>\n",
" <td>2.67</td>\n",
" <td>18.6</td>\n",
" <td>101.0</td>\n",
" <td>2.8</td>\n",
" <td>3.24</td>\n",
" <td>0.3</td>\n",
" <td>2.81</td>\n",
" <td>5.6799</td>\n",
" <td>1.03</td>\n",
" <td>3.17</td>\n",
" <td>1185.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>14.37</td>\n",
" <td>1.95</td>\n",
" <td>2.5</td>\n",
" <td>16.8</td>\n",
" <td>113.0</td>\n",
" <td>3.85</td>\n",
" <td>3.49</td>\n",
" <td>0.24</td>\n",
" <td>2.18</td>\n",
" <td>7.8</td>\n",
" <td>0.86</td>\n",
" <td>3.45</td>\n",
" <td>1480.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>13.24</td>\n",
" <td>2.59</td>\n",
" <td>2.87</td>\n",
" <td>21.0</td>\n",
" <td>118.0</td>\n",
" <td>2.8</td>\n",
" <td>2.69</td>\n",
" <td>0.39</td>\n",
" <td>1.82</td>\n",
" <td>4.32</td>\n",
" <td>1.04</td>\n",
" <td>2.93</td>\n",
" <td>735.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>14.2</td>\n",
" <td>1.76</td>\n",
" <td>2.45</td>\n",
" <td>15.2</td>\n",
" <td>112.0</td>\n",
" <td>3.27</td>\n",
" <td>3.39</td>\n",
" <td>0.34</td>\n",
" <td>1.97</td>\n",
" <td>6.75</td>\n",
" <td>1.05</td>\n",
" <td>2.85</td>\n",
" <td>1450.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>14.39</td>\n",
" <td>1.87</td>\n",
" <td>2.45</td>\n",
" <td>14.6</td>\n",
" <td>96.0</td>\n",
" <td>2.5</td>\n",
" <td>2.52</td>\n",
" <td>0.3</td>\n",
" <td>1.98</td>\n",
" <td>5.25</td>\n",
" <td>1.02</td>\n",
" <td>3.58</td>\n",
" <td>1290.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>14.06</td>\n",
" <td>2.15</td>\n",
" <td>2.61</td>\n",
" <td>17.6</td>\n",
" <td>121.0</td>\n",
" <td>2.6</td>\n",
" <td>2.51</td>\n",
" <td>0.31</td>\n",
" <td>1.25</td>\n",
" <td>5.05</td>\n",
" <td>1.06</td>\n",
" <td>3.58</td>\n",
" <td>1295.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>14.83</td>\n",
" <td>1.64</td>\n",
" <td>2.17</td>\n",
" <td>14.0</td>\n",
" <td>97.0</td>\n",
" <td>2.8</td>\n",
" <td>2.98</td>\n",
" <td>0.29</td>\n",
" <td>1.98</td>\n",
" <td>5.2</td>\n",
" <td>1.08</td>\n",
" <td>2.85</td>\n",
" <td>1045.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>13.86</td>\n",
" <td>1.35</td>\n",
" <td>2.27</td>\n",
" <td>16.0</td>\n",
" <td>98.0</td>\n",
" <td>2.98</td>\n",
" <td>3.15</td>\n",
" <td>0.22</td>\n",
" <td>1.85</td>\n",
" <td>7.2199</td>\n",
" <td>1.01</td>\n",
" <td>3.55</td>\n",
" <td>1045.0</td>\n",
" </tr>\n",
"</table>"
],
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"[(1, 14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0),\n",
" (2, 13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0),\n",
" (3, 13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0),\n",
" (4, 14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0),\n",
" (5, 13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0),\n",
" (6, 14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0),\n",
" (7, 14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0),\n",
" (8, 14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0),\n",
" (9, 14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0),\n",
" (10, 13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0)]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS km_arrayin CASCADE;\n",
"\n",
"CREATE TABLE km_arrayin(pid int, \n",
" p1 float, \n",
" p2 float, \n",
" p3 float,\n",
" p4 float, \n",
" p5 float, \n",
" p6 float,\n",
" p7 float, \n",
" p8 float, \n",
" p9 float,\n",
" p10 float, \n",
" p11 float, \n",
" p12 float,\n",
" p13 float);\n",
"\n",
"INSERT INTO km_arrayin VALUES\n",
"(1, 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065),\n",
"(2, 13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050),\n",
"(3, 13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185),\n",
"(4, 14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480),\n",
"(5, 13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735),\n",
"(6, 14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450),\n",
"(7, 14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290),\n",
"(8, 14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295),\n",
"(9, 14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045),\n",
"(10, 13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045);\n",
"\n",
"SELECT * FROM km_arrayin ORDER BY pid;"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"10 rows affected.\n"
]
},
{
"data": {
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" <tr>\n",
" <th>pid</th>\n",
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" <th>p4</th>\n",
" <th>p5</th>\n",
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" <th>p13</th>\n",
" <th>cluster_id</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>14.23</td>\n",
" <td>1.71</td>\n",
" <td>2.43</td>\n",
" <td>15.6</td>\n",
" <td>127.0</td>\n",
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" <td>1065.0</td>\n",
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" </tr>\n",
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" <td>2</td>\n",
" <td>13.2</td>\n",
" <td>1.78</td>\n",
" <td>2.14</td>\n",
" <td>11.2</td>\n",
" <td>1.0</td>\n",
" <td>2.65</td>\n",
" <td>2.76</td>\n",
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" <tr>\n",
" <td>3</td>\n",
" <td>13.16</td>\n",
" <td>2.36</td>\n",
" <td>2.67</td>\n",
" <td>18.6</td>\n",
" <td>101.0</td>\n",
" <td>2.8</td>\n",
" <td>3.24</td>\n",
" <td>0.3</td>\n",
" <td>2.81</td>\n",
" <td>5.6799</td>\n",
" <td>1.03</td>\n",
" <td>3.17</td>\n",
" <td>1185.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>14.37</td>\n",
" <td>1.95</td>\n",
" <td>2.5</td>\n",
" <td>16.8</td>\n",
" <td>113.0</td>\n",
" <td>3.85</td>\n",
" <td>3.49</td>\n",
" <td>0.24</td>\n",
" <td>2.18</td>\n",
" <td>7.8</td>\n",
" <td>0.86</td>\n",
" <td>3.45</td>\n",
" <td>1480.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>13.24</td>\n",
" <td>2.59</td>\n",
" <td>2.87</td>\n",
" <td>21.0</td>\n",
" <td>118.0</td>\n",
" <td>2.8</td>\n",
" <td>2.69</td>\n",
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" <td>1.82</td>\n",
" <td>4.32</td>\n",
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" <td>2.93</td>\n",
" <td>735.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>14.2</td>\n",
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" <td>15.2</td>\n",
" <td>112.0</td>\n",
" <td>3.27</td>\n",
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" <td>1.97</td>\n",
" <td>6.75</td>\n",
" <td>1.05</td>\n",
" <td>2.85</td>\n",
" <td>1450.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>14.39</td>\n",
" <td>1.87</td>\n",
" <td>2.45</td>\n",
" <td>14.6</td>\n",
" <td>96.0</td>\n",
" <td>2.5</td>\n",
" <td>2.52</td>\n",
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" <tr>\n",
" <td>8</td>\n",
" <td>14.06</td>\n",
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" <td>2.61</td>\n",
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" <td>121.0</td>\n",
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" <td>14.83</td>\n",
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" <td>14.0</td>\n",
" <td>97.0</td>\n",
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" <td>1.08</td>\n",
" <td>2.85</td>\n",
" <td>1045.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>13.86</td>\n",
" <td>1.35</td>\n",
" <td>2.27</td>\n",
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" <td>98.0</td>\n",
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" (2, 13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0, 1),\n",
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" (5, 13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0, 1),\n",
" (6, 14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0, 0),\n",
" (7, 14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0, 0),\n",
" (8, 14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0, 0),\n",
" (9, 14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0, 1),\n",
" (10, 13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0, 1)]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS km_result;\n",
"\n",
"-- Run kmeans algorithm\n",
"CREATE TABLE km_result AS\n",
"SELECT * FROM madlib.kmeans_random('km_arrayin', \n",
" 'ARRAY[p1, p2, p3, p4, p5, p6, \n",
" p7, p8, p9, p10, p11, p12, p13]', \n",
" 2,\n",
" 'madlib.squared_dist_norm2',\n",
" 'madlib.avg', \n",
" 20, \n",
" 0.001);\n",
"\n",
"-- Get point assignment\n",
"SELECT data.*, (madlib.closest_column(centroids, \n",
" ARRAY[p1, p2, p3, p4, p5, p6, \n",
" p7, p8, p9, p10, p11, p12, p13])).column_id as cluster_id\n",
"FROM km_arrayin as data, km_result\n",
"ORDER BY data.pid;"
]
}
],
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