syntax fixes / function name fix
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@ -37,8 +37,10 @@ SELECT
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aoi.quads,
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aoi.significance,
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c.num_cyclists_per_total_population
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FROM cdb_crankshaft.CDB_AreasOfInterestLocal('SELECT * FROM commute_data'
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'num_cyclists_per_total_population') As aoi
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FROM
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cdb_crankshaft.CDB_AreasOfInterestLocal(
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'SELECT * FROM commute_data'
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'num_cyclists_per_total_population') As aoi
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JOIN commute_data As c
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ON c.cartodb_id = aoi.rowid;
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```
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@ -71,8 +73,12 @@ A table with the following columns.
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#### Examples
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```sql
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SELECT *
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FROM cdb_crankshaft.CDB_AreasOfInterestGlobal('SELECT * FROM commute_data', 'num_cyclists_per_total_population')
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SELECT
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*
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FROM
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cdb_crankshaft.CDB_AreasOfInterestGlobal(
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'SELECT * FROM commute_data',
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'num_cyclists_per_total_population')
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```
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### CDB_AreasOfInterestLocalRate(subquery text, numerator_column text, denominator_column text)
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@ -113,9 +119,11 @@ SELECT
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aoi.quads,
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aoi.significance,
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c.cyclists_per_total_population
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FROM cdb_crankshaft.CDB_AreasOfInterestLocalRate('SELECT * FROM commute_data'
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'num_cyclists',
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'total_population') As aoi
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FROM
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cdb_crankshaft.CDB_AreasOfInterestLocalRate(
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'SELECT * FROM commute_data'
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'num_cyclists',
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'total_population') As aoi
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JOIN commute_data As c
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ON c.cartodb_id = aoi.rowid;
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```
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@ -149,10 +157,13 @@ A table with the following columns.
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#### Examples
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```sql
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SELECT *
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FROM cdb_crankshaft.CDB_AreasOfInterestGlobalRate('SELECT * FROM commute_data',
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'num_cyclists',
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'total_population')
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SELECT
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*
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FROM
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cdb_crankshaft.CDB_AreasOfInterestGlobalRate(
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'SELECT * FROM commute_data',
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'num_cyclists',
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'total_population')
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```
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## Hotspot, Coldspot, and Outlier Functions
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@ -40,8 +40,12 @@ SELECT
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m.trend_up,
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m.trend_down,
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m.volatility
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FROM cdb_crankshaft.CDB_SpatialMarkovTrend('SELECT * FROM nyc_real_estate'
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Array['m03y2009','m03y2010','m03y2011','m03y2012','m03y2013','m03y2014','m03y2015','m03y2016']) As m
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FROM
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cdb_crankshaft.CDB_SpatialMarkovTrend(
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'SELECT * FROM nyc_real_estate'
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Array['m03y2009', 'm03y2010', 'm03y2011',
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'm03y2012', 'm03y2013', 'm03y2014',
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'm03y2015','m03y2016']) As m
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JOIN nyc_real_estate As c
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ON c.cartodb_id = m.rowid;
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```
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@ -1,8 +1,8 @@
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## K-Means Functions
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### CDB_KMeans(subquery text, no_clusters INTEGER)
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### CDB_KMeans(subquery text, no_clusters integer)
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This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and
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This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and
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the number of the cluster each point in the input was assigend to.
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@ -26,18 +26,20 @@ A table with the following columns.
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#### Example Usage
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```sql
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SELECT
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customers.*,
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km.cluster_no
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FROM cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3
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WHERE customers.cartodb_id = km.cartodb_id
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SELECT
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customers.*,
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km.cluster_no
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FROM
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cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3
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WHERE
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customers.cartodb_id = km.cartodb_id
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```
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### CDB_WeightedMean(subquery text, weight_column text, category_column text)
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Function that computes the weighted centroid of a number of clusters by some weight column.
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### Arguments
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### Arguments
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| Name | Type | Description |
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|------|------|-------------|
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@ -45,18 +47,24 @@ Function that computes the weighted centroid of a number of clusters by some wei
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| weight\_column | TEXT | The name of the column to use as a weight |
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| category\_column | TEXT | The name of the column to use as a category |
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### Returns
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### Returns
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A table with the following columns.
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| Column Name | Type | Description |
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|-------------|------|-------------|
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| the\_geom | GEOMETRY | A point for the weighted cluster center |
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| class | INTEGER | The cluster class |
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| class | INTEGER | The cluster class |
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### Example Usage
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### Example Usage
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```sql
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SELECT ST_TRANSFORM(the_geom, 3857) as the_geom_webmercator, class
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FROM cdb_crankshaft.cdb_weighted_mean('SELECT *, customer_value FROM customers','customer_value','cluster_no')
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```sql
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SELECT
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ST_Transform(m.the_geom, 3857) AS the_geom_webmercator,
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m.class
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FROM
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cdb_crankshaft.cdb_WeightedMean(
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'SELECT * FROM customers',
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'customer_value',
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'cluster_no') AS m
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```
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@ -3,7 +3,7 @@
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### CDB_CreateAndPredictSegment(query TEXT, variable_name TEXT, target_query TEXT)
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This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.
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This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.
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#### Arguments
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@ -34,12 +34,12 @@ A table with the following columns.
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SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment(
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'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric FROM late_night_agg',
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'agg',
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'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny');
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'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny');
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```
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### CDB_CreateAndPredictSegment(target numeric[], train_features numeric[], prediction_features numeric[], prediction_ids numeric[])
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This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.
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This function trains a [Gradient Boosting](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) model to attempt to predict the target data and then generates predictions for new data.
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#### Arguments
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@ -76,7 +76,7 @@ WITH training As (
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FROM late_night_agg),
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target AS (
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SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features,
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array_agg(cartodb_id) As cartodb_ids FROM late_night_agg)
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array_agg(cartodb_id) As cartodb_ids FROM late_night_agg)
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SELECT cdb_crankshaft.CDB_CreateAndPredictSegment(training.target, training.features, target.features, target.cartodb_ids)
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FROM training, target;
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