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syntax fixes / function name fix

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pull/193/head
Andy Eschbacher 7 years ago
parent e28f00d98b
commit 92becac280
4 changed files with 54 additions and 31 deletions
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33
doc/02_moran.md
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@ -37,8 +37,10 @@ SELECT
aoi.quads,
aoi.significance,
c.num_cyclists_per_total_population
FROM cdb_crankshaft.CDB_AreasOfInterestLocal('SELECT * FROM commute_data'
'num_cyclists_per_total_population') As aoi
FROM
cdb_crankshaft.CDB_AreasOfInterestLocal(
'SELECT * FROM commute_data'
'num_cyclists_per_total_population') As aoi
JOIN commute_data As c
ON c.cartodb_id = aoi.rowid;
```
@ -71,8 +73,12 @@ A table with the following columns.
#### Examples
```sql
SELECT *
FROM cdb_crankshaft.CDB_AreasOfInterestGlobal('SELECT * FROM commute_data', 'num_cyclists_per_total_population')
SELECT
*
FROM
cdb_crankshaft.CDB_AreasOfInterestGlobal(
'SELECT * FROM commute_data',
'num_cyclists_per_total_population')
```
### CDB_AreasOfInterestLocalRate(subquery text, numerator_column text, denominator_column text)
@ -113,9 +119,11 @@ SELECT
aoi.quads,
aoi.significance,
c.cyclists_per_total_population
FROM cdb_crankshaft.CDB_AreasOfInterestLocalRate('SELECT * FROM commute_data'
'num_cyclists',
'total_population') As aoi
FROM
cdb_crankshaft.CDB_AreasOfInterestLocalRate(
'SELECT * FROM commute_data'
'num_cyclists',
'total_population') As aoi
JOIN commute_data As c
ON c.cartodb_id = aoi.rowid;
```
@ -149,10 +157,13 @@ A table with the following columns.
#### Examples
```sql
SELECT *
FROM cdb_crankshaft.CDB_AreasOfInterestGlobalRate('SELECT * FROM commute_data',
'num_cyclists',
'total_population')
SELECT
*
FROM
cdb_crankshaft.CDB_AreasOfInterestGlobalRate(
'SELECT * FROM commute_data',
'num_cyclists',
'total_population')
```
## Hotspot, Coldspot, and Outlier Functions

8
doc/04_markov.md
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@ -40,8 +40,12 @@ SELECT
m.trend_up,
m.trend_down,
m.volatility
FROM cdb_crankshaft.CDB_SpatialMarkovTrend('SELECT * FROM nyc_real_estate'
Array['m03y2009','m03y2010','m03y2011','m03y2012','m03y2013','m03y2014','m03y2015','m03y2016']) As m
FROM
cdb_crankshaft.CDB_SpatialMarkovTrend(
'SELECT * FROM nyc_real_estate'
Array['m03y2009', 'm03y2010', 'm03y2011',
'm03y2012', 'm03y2013', 'm03y2014',
'm03y2015','m03y2016']) As m
JOIN nyc_real_estate As c
ON c.cartodb_id = m.rowid;
```

36
doc/11_kmeans.md
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@ -1,8 +1,8 @@
## K-Means Functions
### CDB_KMeans(subquery text, no_clusters INTEGER)
### CDB_KMeans(subquery text, no_clusters integer)
This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and
This function attempts to find n clusters within the input data. It will return a table to CartoDB ids and
the number of the cluster each point in the input was assigend to.
@ -26,18 +26,20 @@ A table with the following columns.
#### Example Usage
```sql
SELECT
customers.*,
km.cluster_no
FROM cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3
WHERE customers.cartodb_id = km.cartodb_id
SELECT
customers.*,
km.cluster_no
FROM
cdb_crankshaft.CDB_Kmeans('SELECT * from customers' , 6) km, customers_3
WHERE
customers.cartodb_id = km.cartodb_id
```
### CDB_WeightedMean(subquery text, weight_column text, category_column text)
Function that computes the weighted centroid of a number of clusters by some weight column.
### Arguments
### Arguments
| Name | Type | Description |
|------|------|-------------|
@ -45,18 +47,24 @@ Function that computes the weighted centroid of a number of clusters by some wei
| weight\_column | TEXT | The name of the column to use as a weight |
| category\_column | TEXT | The name of the column to use as a category |
### Returns
### Returns
A table with the following columns.
| Column Name | Type | Description |
|-------------|------|-------------|
| the\_geom | GEOMETRY | A point for the weighted cluster center |
| class | INTEGER | The cluster class |
| class | INTEGER | The cluster class |
### Example Usage
### Example Usage
```sql
SELECT ST_TRANSFORM(the_geom, 3857) as the_geom_webmercator, class
FROM cdb_crankshaft.cdb_weighted_mean('SELECT *, customer_value FROM customers','customer_value','cluster_no')
```sql
SELECT
ST_Transform(m.the_geom, 3857) AS the_geom_webmercator,
m.class
FROM
cdb_crankshaft.cdb_WeightedMean(
'SELECT * FROM customers',
'customer_value',
'cluster_no') AS m
```

8
doc/12_segmentation.md
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@ -3,7 +3,7 @@
### CDB_CreateAndPredictSegment(query TEXT, variable_name TEXT, target_query TEXT)
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.
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.
#### Arguments
@ -34,12 +34,12 @@ A table with the following columns.
SELECT * from cdb_crankshaft.CDB_CreateAndPredictSegment(
'SELECT agg, median_rent::numeric, male_pop::numeric, female_pop::numeric FROM late_night_agg',
'agg',
'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny');
'SELECT row_number() OVER () As cartodb_id, median_rent, male_pop, female_pop FROM ml_learning_ny');
```
### CDB_CreateAndPredictSegment(target numeric[], train_features numeric[], prediction_features numeric[], prediction_ids numeric[])
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.
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.
#### Arguments
@ -76,7 +76,7 @@ WITH training As (
FROM late_night_agg),
target AS (
SELECT cdb_crankshaft.CDB_PyAgg(Array[median_rent, male_pop, female_pop]::Numeric[]) As features,
array_agg(cartodb_id) As cartodb_ids FROM late_night_agg)
array_agg(cartodb_id) As cartodb_ids FROM late_night_agg)
SELECT cdb_crankshaft.CDB_CreateAndPredictSegment(training.target, training.features, target.features, target.cartodb_ids)
FROM training, target;

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