fixes syntax errors
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@ -11,7 +11,7 @@ $$ LANGUAGE plpythonu;
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-- Non-spatial k-means clustering
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-- query: sql query to retrieve all the needed data
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CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial(query TEXT, col_names TEXT[], no_clusters INTEGER, id_col TEXT DEFAULT 'cartodb_id')
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CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial(query TEXT, colnames TEXT[], num_clusters INTEGER, id_col TEXT DEFAULT 'cartodb_id')
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RETURNS TABLE(cluster_label text, cluster_center text, rowid bigint) AS $$
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from crankshaft.clustering import kmeans_nonspatial
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@ -38,7 +38,7 @@ def kmeans_nonspatial(query, colnames, num_clusters=5, id_col='cartodb_id'):
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num_clusters (int): number of clusters (greater than zero)
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id_col (string): name of the input id_column
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"""
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import numpy as np
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id_colname = 'rowids'
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full_query = '''
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@ -55,13 +55,14 @@ def kmeans_nonspatial(query, colnames, num_clusters=5, id_col='cartodb_id'):
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plpy.notice('query: %s' % full_query)
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# fill array with values for kmeans clustering
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data = np.array([d[c] for c in d if c != 'id_colname'],
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dtype=float).T
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cluster_columns = np.array([data[0][c] for c in data.colnames()
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if c != 'id_colname'],
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dtype=float).T
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except plpy.SPIError, err:
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plpy.error('KMeans cluster failed: %s' % err)
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kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(data)
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kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(cluster_columns)
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# zip(ids, labels, means)
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return zip(kmeans.labels_, map(str, kmeans.cluster_centers_),
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d[0]['rowids'])
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data[0]['rowids'])
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