Merge branch 'develop' into update-segmentation
This commit is contained in:
Andy Eschbacher
+20
-3
@@ -23,9 +23,15 @@ REPLACEMENTS = -e 's/@@VERSION@@/$(EXTVERSION)/g'
|
||||
|
||||
$(DATA): $(SOURCES_DATA)
|
||||
$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > $@
|
||||
ifeq ($(PG_PARALLEL), 0)
|
||||
$(eval TMPFILE := $(shell mktemp /tmp/$@.XXXXXXXXXX))
|
||||
$(SED) -e 's/PARALLEL \= [A-Z]*,/''/g' -e 's/PARALLEL [A-Z]*/''/g' $@ > $(TMPFILE);
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||||
mv $(TMPFILE) $@
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||||
endif
|
||||
|
||||
|
||||
TEST_DIR = test
|
||||
REGRESS = $(notdir $(basename $(wildcard $(TEST_DIR)/sql/*test.sql)))
|
||||
REGRESS = $(sort $(notdir $(basename $(wildcard $(TEST_DIR)/sql/*test.sql))))
|
||||
REGRESS_OPTS = --inputdir='$(TEST_DIR)' --outputdir='$(TEST_DIR)'
|
||||
|
||||
PG_CONFIG = pg_config
|
||||
@@ -50,7 +56,18 @@ test: installcheck
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||||
release: ../../release/$(EXTENSION).control $(SOURCES_DATA)
|
||||
$(SED) $(REPLACEMENTS) $(SOURCES_DATA_DIR)/*.sql > ../../release/$(EXTENSION)--$(EXTVERSION).sql
|
||||
|
||||
# Install the current relese into the PostgreSQL extensions directory
|
||||
deploy:
|
||||
# If needed remove PARALLEL tags from the release files
|
||||
release_remove_parallel_deploy:
|
||||
ifeq ($(PG_PARALLEL), 0)
|
||||
for n in $(wildcard ../../release/*.sql); do \
|
||||
$(eval TMPFILE := $(shell mktemp /tmp/XXXXXXXXXX)) \
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||||
$(SED) -e 's/PARALLEL \= [A-Z]*,/''/g' -e 's/PARALLEL [A-Z]*/''/g' $$n > $(TMPFILE); \
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||||
mv $(TMPFILE) $$n; \
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||||
done
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||||
endif
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||||
|
||||
# Install the current release into the PostgreSQL extensions directory
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||||
deploy: release_remove_parallel_deploy
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||||
$(INSTALL_DATA) ../../release/$(EXTENSION).control '$(DESTDIR)$(datadir)/extension/'
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||||
$(INSTALL_DATA) ../../release/*.sql '$(DESTDIR)$(datadir)/extension/'
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||||
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||||
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@@ -1,5 +1,5 @@
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||||
comment = 'CartoDB Spatial Analysis extension'
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||||
default_version = '0.5.0'
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||||
default_version = '0.6.1'
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requires = 'plpythonu, postgis'
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||||
superuser = true
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schema = cdb_crankshaft
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@@ -2,11 +2,11 @@
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CREATE OR REPLACE FUNCTION cdb_crankshaft_version()
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RETURNS text AS $$
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SELECT '@@VERSION@@'::text;
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||||
$$ language 'sql' STABLE STRICT;
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||||
$$ language 'sql' IMMUTABLE STRICT PARALLEL SAFE;
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||||
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-- Internal identifier of the installed extension instence
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||||
-- e.g. 'dev' for current development version
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||||
CREATE OR REPLACE FUNCTION _cdb_crankshaft_internal_version()
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||||
RETURNS text AS $$
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||||
SELECT installed_version FROM pg_available_extensions where name='crankshaft' and pg_available_extensions IS NOT NULL;
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||||
$$ language 'sql' STABLE STRICT;
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||||
$$ language 'sql' STABLE STRICT PARALLEL SAFE;
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||||
|
||||
@@ -6,4 +6,4 @@ _cdb_random_seeds (seed_value INTEGER) RETURNS VOID
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AS $$
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||||
from crankshaft import random_seeds
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||||
random_seeds.set_random_seeds(seed_value)
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$$ LANGUAGE plpythonu;
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||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
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||||
|
||||
@@ -8,7 +8,7 @@ CREATE OR REPLACE FUNCTION
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||||
end if;
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||||
return array_cat(current_state,current_row) ;
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||||
END
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||||
$$ LANGUAGE plpgsql;
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||||
$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
-- Create aggregate if it did not exist
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||||
DO $$
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||||
@@ -24,6 +24,7 @@ BEGIN
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||||
CREATE AGGREGATE CDB_PyAgg(NUMERIC[]) (
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||||
SFUNC = CDB_PyAggS,
|
||||
STYPE = Numeric[],
|
||||
PARALLEL = SAFE,
|
||||
INITCOND = "{}"
|
||||
);
|
||||
END IF;
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||||
|
||||
@@ -34,7 +34,7 @@ AS $$
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||||
target_ids,
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||||
model_params)
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||||
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL RESTRICTED;
|
||||
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_CreateAndPredictSegment (
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||||
@@ -48,11 +48,11 @@ CREATE OR REPLACE FUNCTION
|
||||
min_samples_leaf INTEGER DEFAULT 1)
|
||||
RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
|
||||
AS $$
|
||||
from crankshaft.segmentation import Segmentation
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||||
seg = Segmentation()
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||||
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
|
||||
return seg.create_and_predict_segment(query,variable_name,target_table, model_params)
|
||||
$$ LANGUAGE plpythonu;
|
||||
from crankshaft.segmentation import Segmentation
|
||||
seg = Segmentation()
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||||
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
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||||
return seg.create_and_predict_segment(query,variable_name,target_table, model_params)
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||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_CreateAndPredictSegment (
|
||||
@@ -67,8 +67,8 @@ CREATE OR REPLACE FUNCTION
|
||||
min_samples_leaf INTEGER DEFAULT 1)
|
||||
RETURNS TABLE (cartodb_id TEXT, prediction NUMERIC, accuracy NUMERIC)
|
||||
AS $$
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||||
from crankshaft.segmentation import Segmentation
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||||
seg = Segmentation()
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||||
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
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||||
return seg.create_and_predict_segment(query, variable, feature_columns, target_query, model_params)
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||||
$$ LANGUAGE plpythonu;
|
||||
from crankshaft.segmentation import Segmentation
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||||
seg = Segmentation()
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||||
model_params = {'n_estimators': n_estimators, 'max_depth':max_depth, 'subsample' : subsample, 'learning_rate': learning_rate, 'min_samples_leaf' : min_samples_leaf}
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||||
return seg.create_and_predict_segment(query, variable, feature_columns, target_query, model_params)
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
@@ -27,7 +27,7 @@ BEGIN
|
||||
RETURN QUERY
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||||
SELECT g.* FROM t, s, CDB_Gravity(t_id, t_geom, t_weight, s_id, s_geom, s_pop, target, radius, minval) g;
|
||||
END;
|
||||
$$ language plpgsql;
|
||||
$$ language plpgsql VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_Gravity(
|
||||
IN t_id bigint[],
|
||||
@@ -112,4 +112,4 @@ BEGIN
|
||||
p.targ_id = target AND
|
||||
p.sourc_id = d.sourc_id;
|
||||
END;
|
||||
$$ language plpgsql;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -24,7 +24,7 @@ BEGIN
|
||||
RETURN output;
|
||||
END;
|
||||
$$
|
||||
language plpgsql IMMUTABLE;
|
||||
language plpgsql VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_SpatialInterpolation(
|
||||
IN geomin geometry[],
|
||||
@@ -141,4 +141,4 @@ BEGIN
|
||||
|
||||
END;
|
||||
$$
|
||||
language plpgsql IMMUTABLE;
|
||||
language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -167,7 +167,7 @@ BEGIN
|
||||
clipped_voro;
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||||
RETURN geomout;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
/** ----------------------------------------------------------------------------------------
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||||
* @function : FindCircle
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||||
@@ -181,6 +181,7 @@ $$ language plpgsql IMMUTABLE;
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||||
* or NULL if three points do not form a circle.
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||||
* @history : Simon Greener - Feb 2012 - Original coding.
|
||||
* Rafa de la Torre - Aug 2016 - Small fix for type checking
|
||||
* Raul Marin - Sept 2017 - Remove unnecessary NULL checks and set function categories
|
||||
* @copyright : Simon Greener @ 2012
|
||||
* Licensed under a Creative Commons Attribution-Share Alike 2.5 Australia License. (http://creativecommons.org/licenses/by-sa/2.5/au/)
|
||||
**/
|
||||
@@ -203,10 +204,6 @@ DECLARE
|
||||
v_dF NUMERIC;
|
||||
v_dG NUMERIC;
|
||||
BEGIN
|
||||
IF ( p_pt1 IS NULL OR p_pt2 IS NULL OR p_pt3 IS NULL ) THEN
|
||||
RAISE EXCEPTION 'All supplied points must be not null.';
|
||||
RETURN NULL;
|
||||
END IF;
|
||||
IF ( ST_GeometryType(p_pt1) <> 'ST_Point' OR
|
||||
ST_GeometryType(p_pt2) <> 'ST_Point' OR
|
||||
ST_GeometryType(p_pt3) <> 'ST_Point' ) THEN
|
||||
@@ -232,5 +229,5 @@ BEGIN
|
||||
RETURN ST_SetSRID(ST_MakePoint(v_CX, v_CY, v_radius),ST_Srid(p_pt1));
|
||||
END;
|
||||
$BODY$
|
||||
LANGUAGE plpgsql VOLATILE STRICT;
|
||||
LANGUAGE plpgsql IMMUTABLE STRICT PARALLEL SAFE;
|
||||
|
||||
|
||||
+12
-12
@@ -15,7 +15,7 @@ AS $$
|
||||
moran = Moran()
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||||
return moran.global_stat(subquery, column_name, w_type,
|
||||
num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Local (internal function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -34,7 +34,7 @@ AS $$
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran.local_stat(subquery, column_name, w_type,
|
||||
num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Local (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -52,7 +52,7 @@ AS $$
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col);
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I only for HH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -71,7 +71,7 @@ AS $$
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HH', 'HL');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I only for LL and LH (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -90,7 +90,7 @@ AS $$
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('LL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I only for LH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -109,7 +109,7 @@ AS $$
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocal(subquery, attr, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Global Rate (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -129,7 +129,7 @@ AS $$
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran.global_rate_stat(subquery, numerator, denominator, w_type,
|
||||
num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
|
||||
-- Moran's I Local Rate (internal function)
|
||||
@@ -150,7 +150,7 @@ AS $$
|
||||
moran = Moran()
|
||||
# TODO: use named parameters or a dictionary
|
||||
return moran.local_rate_stat(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Local Rate (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -170,7 +170,7 @@ AS $$
|
||||
SELECT moran, quads, significance, rowid, vals
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col);
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Local Rate only for HH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -191,7 +191,7 @@ AS $$
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HH', 'HL');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Local Rate only for LL and LH (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -212,7 +212,7 @@ AS $$
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('LL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Moran's I Local Rate only for LH and HL (public-facing function)
|
||||
CREATE OR REPLACE FUNCTION
|
||||
@@ -233,4 +233,4 @@ AS $$
|
||||
FROM cdb_crankshaft._CDB_AreasOfInterestLocalRate(subquery, numerator, denominator, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
WHERE quads IN ('HL', 'LH');
|
||||
|
||||
$$ LANGUAGE SQL;
|
||||
$$ LANGUAGE SQL VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
+54
-12
@@ -1,18 +1,58 @@
|
||||
-- Spatial k-means clustering
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_KMeans(query text, no_clusters integer, no_init integer default 20)
|
||||
RETURNS table (cartodb_id integer, cluster_no integer) as $$
|
||||
CREATE OR REPLACE FUNCTION CDB_KMeans(
|
||||
query TEXT,
|
||||
no_clusters INTEGER,
|
||||
no_init INTEGER DEFAULT 20
|
||||
)
|
||||
RETURNS TABLE(
|
||||
cartodb_id INTEGER,
|
||||
cluster_no INTEGER
|
||||
) AS $$
|
||||
|
||||
from crankshaft.clustering import Kmeans
|
||||
kmeans = Kmeans()
|
||||
return kmeans.spatial(query, no_clusters, no_init)
|
||||
from crankshaft.clustering import Kmeans
|
||||
kmeans = Kmeans()
|
||||
return kmeans.spatial(query, no_clusters, no_init)
|
||||
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- Non-spatial k-means clustering
|
||||
-- query: sql query to retrieve all the needed data
|
||||
-- colnames: text array of column names for doing the clustering analysis
|
||||
-- no_clusters: number of requested clusters
|
||||
-- standardize: whether to scale variables to a mean of zero and a standard
|
||||
-- deviation of 1
|
||||
-- id_colname: name of the id column
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_KMeansNonspatial(
|
||||
query TEXT,
|
||||
colnames TEXT[],
|
||||
no_clusters INTEGER,
|
||||
standardize BOOLEAN DEFAULT true,
|
||||
id_col TEXT DEFAULT 'cartodb_id'
|
||||
)
|
||||
RETURNS TABLE(
|
||||
cluster_label text,
|
||||
cluster_center json,
|
||||
silhouettes numeric,
|
||||
inertia numeric,
|
||||
rowid bigint
|
||||
) AS $$
|
||||
|
||||
from crankshaft.clustering import Kmeans
|
||||
kmeans = Kmeans()
|
||||
return kmeans.nonspatial(query, colnames, no_clusters,
|
||||
standardize=standardize,
|
||||
id_col=id_col)
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(state Numeric[],the_geom GEOMETRY(Point, 4326), weight NUMERIC)
|
||||
RETURNS Numeric[] AS
|
||||
$$
|
||||
CREATE OR REPLACE FUNCTION CDB_WeightedMeanS(
|
||||
state NUMERIC[],
|
||||
the_geom GEOMETRY(Point, 4326),
|
||||
weight NUMERIC
|
||||
)
|
||||
RETURNS Numeric[] AS $$
|
||||
DECLARE
|
||||
newX NUMERIC;
|
||||
newY NUMERIC;
|
||||
@@ -30,9 +70,10 @@ BEGIN
|
||||
RETURN Array[newX,newY,newW];
|
||||
|
||||
END
|
||||
$$ LANGUAGE plpgsql;
|
||||
$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state Numeric[])
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_WeightedMeanF(state NUMERIC[])
|
||||
RETURNS GEOMETRY AS
|
||||
$$
|
||||
BEGIN
|
||||
@@ -42,7 +83,7 @@ BEGIN
|
||||
RETURN ST_SETSRID(ST_MakePoint(state[1]/state[3], state[2]/state[3]),4326);
|
||||
END IF;
|
||||
END
|
||||
$$ LANGUAGE plpgsql;
|
||||
$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
-- Create aggregate if it did not exist
|
||||
DO $$
|
||||
@@ -59,6 +100,7 @@ BEGIN
|
||||
SFUNC = CDB_WeightedMeanS,
|
||||
FINALFUNC = CDB_WeightedMeanF,
|
||||
STYPE = Numeric[],
|
||||
PARALLEL = SAFE,
|
||||
INITCOND = "{0.0,0.0,0.0}"
|
||||
);
|
||||
END IF;
|
||||
|
||||
@@ -27,7 +27,7 @@ AS $$
|
||||
|
||||
## TODO: use named parameters or a dictionary
|
||||
return markov.spatial_trend(subquery, time_cols, num_classes, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- input table format: identical to above but in a predictable format
|
||||
-- Sample function call:
|
||||
|
||||
+34
-14
@@ -31,7 +31,7 @@ DECLARE
|
||||
sqr numeric;
|
||||
p geometry;
|
||||
BEGIN
|
||||
sqr := |/2;
|
||||
sqr := 0.5*(|/2.0);
|
||||
polygon := ST_Transform(polygon, 3857);
|
||||
|
||||
-- grid #0 cell size
|
||||
@@ -46,6 +46,7 @@ BEGIN
|
||||
SELECT array_agg(c) INTO cells FROM c1;
|
||||
|
||||
-- 1st guess: centroid
|
||||
best_c := polygon;
|
||||
best_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(Polygon));
|
||||
|
||||
-- looping the loop
|
||||
@@ -56,6 +57,7 @@ BEGIN
|
||||
EXIT WHEN i > n;
|
||||
|
||||
cell := cells[i];
|
||||
|
||||
i := i+1;
|
||||
|
||||
-- cell side size, it's square
|
||||
@@ -63,13 +65,14 @@ BEGIN
|
||||
|
||||
-- check distance
|
||||
test_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(cell));
|
||||
|
||||
IF test_d > best_d THEN
|
||||
best_d := test_d;
|
||||
best_c := cells[i];
|
||||
best_c := cell;
|
||||
END IF;
|
||||
|
||||
-- longest distance within the cell
|
||||
test_mx := test_d + (test_h/2 * sqr);
|
||||
test_mx := test_d + (test_h * sqr);
|
||||
|
||||
-- if the cell has no chance to contains the desired point, continue
|
||||
CONTINUE WHEN test_mx - best_d <= tolerance;
|
||||
@@ -91,33 +94,50 @@ BEGIN
|
||||
RETURN ST_transform(ST_Centroid(best_c), 4326);
|
||||
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
|
||||
|
||||
-- signed distance point to polygon with holes
|
||||
-- negative is the point is out the polygon
|
||||
-- rev 1. adding MULTIPOLYGON and GEOMETRYCOLLECTION support by @abelvm
|
||||
CREATE OR REPLACE FUNCTION _Signed_Dist(
|
||||
IN polygon geometry,
|
||||
IN point geometry
|
||||
)
|
||||
RETURNS numeric AS $$
|
||||
DECLARE
|
||||
pols geometry[];
|
||||
pol geometry;
|
||||
i integer;
|
||||
j integer;
|
||||
within integer;
|
||||
w integer;
|
||||
holes integer;
|
||||
dist numeric;
|
||||
d numeric;
|
||||
BEGIN
|
||||
dist := 1e999;
|
||||
SELECT LEAST(dist, ST_distance(point, ST_ExteriorRing(polygon))::numeric) INTO dist;
|
||||
SELECT CASE WHEN ST_Within(point,polygon) THEN 1 ELSE -1 END INTO within;
|
||||
SELECT ST_NumInteriorRings(polygon) INTO holes;
|
||||
IF holes > 0 THEN
|
||||
FOR i IN 1..holes
|
||||
LOOP
|
||||
SELECT LEAST(dist, ST_distance(point, ST_InteriorRingN(polygon, i))::numeric) INTO dist;
|
||||
END LOOP;
|
||||
END IF;
|
||||
WITH collection as (SELECT (ST_dump(polygon)).geom as geom) SELECT array_agg(geom) into pols FROM collection;
|
||||
FOR j in 1..array_length(pols, 1)
|
||||
LOOP
|
||||
pol := pols[j];
|
||||
d := dist;
|
||||
SELECT LEAST(dist, ST_distance(point, ST_ExteriorRing(pol))::numeric) INTO d;
|
||||
SELECT CASE WHEN ST_Within(point,pol) THEN 1 ELSE -1 END INTO w;
|
||||
SELECT ST_NumInteriorRings(pol) INTO holes;
|
||||
IF holes > 0 THEN
|
||||
FOR i IN 1..holes
|
||||
LOOP
|
||||
SELECT LEAST(d, ST_distance(point, ST_InteriorRingN(pol, i))::numeric) INTO d;
|
||||
END LOOP;
|
||||
END IF;
|
||||
IF d < dist THEN
|
||||
dist:= d;
|
||||
within := w;
|
||||
END IF;
|
||||
END LOOP;
|
||||
dist := dist * within::numeric;
|
||||
RETURN dist;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -64,4 +64,4 @@ BEGIN
|
||||
END LOOP;
|
||||
RETURN QUERY SELECT unnest(geotemp ) as geomout, unnest(coltemp ) as colout;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -40,4 +40,4 @@ BEGIN
|
||||
END LOOP;
|
||||
RETURN QUERY SELECT unnest(gs) as geomout, unnest(coltemp ) as colout;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -14,6 +14,6 @@ AS $$
|
||||
from crankshaft.clustering import Getis
|
||||
getis = Getis()
|
||||
return getis.getis_ord(subquery, column_name, w_type, num_ngbrs, permutations, geom_col, id_col)
|
||||
$$ LANGUAGE plpythonu;
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
-- TODO: make a version that accepts the values as arrays
|
||||
|
||||
@@ -9,7 +9,7 @@ BEGIN
|
||||
RETURN column_value > threshold;
|
||||
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE ;
|
||||
|
||||
-- Find outliers by a percentage above the threshold
|
||||
-- TODO: add symmetric option? `is_symmetric boolean DEFAULT false`
|
||||
@@ -38,7 +38,7 @@ BEGIN
|
||||
unnest(ids) As rowid;
|
||||
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
-- Find outliers above a given number of standard deviations from the mean
|
||||
|
||||
@@ -72,4 +72,4 @@ BEGIN
|
||||
SELECT unnest(out_vals) As is_outlier,
|
||||
unnest(ids) As rowid;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
$$ LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -139,8 +139,7 @@ BEGIN
|
||||
where final.bin is not null
|
||||
;
|
||||
END;
|
||||
$$ language plpgsql;
|
||||
|
||||
$$ language plpgsql VOLATILE PARALLEL RESTRICTED;
|
||||
|
||||
|
||||
-- =====================================================================
|
||||
@@ -205,4 +204,4 @@ BEGIN
|
||||
RETURN output;
|
||||
END;
|
||||
$$
|
||||
language plpgsql;
|
||||
language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
@@ -35,4 +35,4 @@ BEGIN
|
||||
INTO result;
|
||||
RETURN result;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
$$ LANGUAGE plpgsql STABLE PARALLEL SAFE;
|
||||
|
||||
@@ -0,0 +1,36 @@
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GWR(subquery text, dep_var text, ind_vars text[],
|
||||
bw numeric default null, fixed boolean default False,
|
||||
kernel text default 'bisquare', geom_col text default 'the_geom',
|
||||
id_col text default 'cartodb_id')
|
||||
RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON,
|
||||
filtered_t_vals JSON, predicted numeric,
|
||||
residuals numeric, r_squared numeric, bandwidth numeric,
|
||||
rowid bigint)
|
||||
AS $$
|
||||
|
||||
from crankshaft.regression import GWR
|
||||
|
||||
gwr = GWR()
|
||||
|
||||
return gwr.gwr(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
|
||||
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
|
||||
|
||||
CREATE OR REPLACE FUNCTION
|
||||
CDB_GWR_Predict(subquery text, dep_var text, ind_vars text[],
|
||||
bw numeric default null, fixed boolean default False,
|
||||
kernel text default 'bisquare',
|
||||
geom_col text default 'the_geom',
|
||||
id_col text default 'cartodb_id')
|
||||
RETURNS table(coeffs JSON, stand_errs JSON, t_vals JSON,
|
||||
r_squared numeric, predicted numeric, rowid bigint)
|
||||
AS $$
|
||||
|
||||
from crankshaft.regression import GWR
|
||||
gwr = GWR()
|
||||
|
||||
return gwr.gwr_predict(subquery, dep_var, ind_vars, bw, fixed, kernel, geom_col, id_col)
|
||||
|
||||
$$ LANGUAGE plpythonu VOLATILE PARALLEL UNSAFE;
|
||||
@@ -51,4 +51,4 @@ BEGIN
|
||||
RETURN ST_Collect(points);
|
||||
END;
|
||||
$$
|
||||
LANGUAGE plpgsql VOLATILE;
|
||||
LANGUAGE plpgsql VOLATILE PARALLEL RESTRICTED;
|
||||
|
||||
@@ -93,7 +93,7 @@ BEGIN
|
||||
|
||||
RETURN;
|
||||
END
|
||||
$$ LANGUAGE 'plpgsql' IMMUTABLE;
|
||||
$$ LANGUAGE 'plpgsql' IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
--
|
||||
-- Calculate the equal interval bins for a given column
|
||||
@@ -131,7 +131,7 @@ BEGIN
|
||||
END LOOP;
|
||||
RETURN reply;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
--
|
||||
-- Determine the Heads/Tails classifications from a numeric array
|
||||
@@ -178,7 +178,7 @@ BEGIN
|
||||
END LOOP;
|
||||
RETURN reply;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
--
|
||||
-- Determine the Jenks classifications from a numeric array
|
||||
@@ -299,7 +299,7 @@ BEGIN
|
||||
|
||||
RETURN (best_result)[2:array_upper(best_result, 1)];
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql VOLATILE PARALLEL RESTRICTED;
|
||||
|
||||
|
||||
|
||||
@@ -399,7 +399,7 @@ BEGIN
|
||||
RETURN array_prepend(gvf, reply);
|
||||
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE PARALLEL SAFE;
|
||||
|
||||
|
||||
--
|
||||
@@ -444,4 +444,4 @@ BEGIN
|
||||
END LOOP;
|
||||
RETURN reply;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
$$ language plpgsql IMMUTABLE STRICT PARALLEL SAFE;
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
-- Install dependencies
|
||||
CREATE EXTENSION plpythonu;
|
||||
CREATE EXTENSION postgis VERSION '2.2.2';
|
||||
CREATE EXTENSION postgis;
|
||||
-- Create role publicuser if it does not exist
|
||||
DO
|
||||
$$
|
||||
|
||||
@@ -1,10 +1,43 @@
|
||||
\pset format unaligned
|
||||
\set ECHO all
|
||||
SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
|
||||
-- spatial kmeans
|
||||
SELECT
|
||||
count(DISTINCT cluster_no) as clusters
|
||||
FROM
|
||||
cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
|
||||
clusters
|
||||
2
|
||||
(1 row)
|
||||
SELECT count(*) clusters from (select cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p;
|
||||
-- weighted mean
|
||||
SELECT
|
||||
count(*) clusters
|
||||
FROM (
|
||||
SELECT
|
||||
cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC),
|
||||
code
|
||||
FROM ppoints
|
||||
GROUP BY code
|
||||
) p;
|
||||
clusters
|
||||
52
|
||||
(1 row)
|
||||
-- nonspatial kmeans
|
||||
SELECT
|
||||
cluster_label::int in (0, 1) As cluster_label,
|
||||
cluster_center::json->>'col1' As cc_col1,
|
||||
cluster_center::json->>'col2' As cc_col2,
|
||||
silhouettes,
|
||||
inertia,
|
||||
rowid
|
||||
FROM cdb_crankshaft.CDB_KMeansNonspatial(
|
||||
'SELECT unnest(Array[1, 1, 10, 10]) As col1, ' ||
|
||||
'unnest(Array[100, 100, 2, 2]) As col2, ' ||
|
||||
'unnest(Array[1, 2, 3, 4]) As cartodb_id ',
|
||||
Array['col1', 'col2']::text[],
|
||||
2);
|
||||
cluster_label|cc_col1|cc_col2|silhouettes|inertia|rowid
|
||||
t|-1.0|1.0|1.0|0.0|1
|
||||
t|-1.0|1.0|1.0|0.0|2
|
||||
t|1.0|-1.0|1.0|0.0|3
|
||||
t|1.0|-1.0|1.0|0.0|4
|
||||
(4 rows)
|
||||
|
||||
@@ -2,6 +2,16 @@ SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
st_astext
|
||||
-------------------------------------------
|
||||
POINT(-3.67484492582767 40.4395084885993)
|
||||
POINT(-3.67484492582767 40.4394914243877)
|
||||
(1 row)
|
||||
|
||||
st_astext
|
||||
------------
|
||||
POINT(0 0)
|
||||
(1 row)
|
||||
|
||||
st_astext
|
||||
------------
|
||||
POINT(0 0)
|
||||
(1 row)
|
||||
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
-- test of Geographically Weighted Regression (GWR)
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
rowid|coeff_pctrural|std_errs_pctrural|t_vals_pctrural|predicted|residuals|r_squared|bandwidth
|
||||
13001|-0.0852|0.0220|-3.8678|8.8071|-0.6071|0.5218|90.0
|
||||
13027|-0.0719|0.0221|-3.2506|9.9673|-0.8673|0.5443|90.0
|
||||
13027|-0.0719|0.0221|-3.2506|9.9673|-0.8673|0.5443|90.0
|
||||
13039|-0.0959|0.0241|-3.9755|13.4802|0.0198|0.6269|90.0
|
||||
13231|-0.1383|0.0181|-7.6634|8.5520|0.7480|0.6337|90.0
|
||||
13293|-0.1207|0.0184|-6.5553|12.9930|-3.9930|0.6446|90.0
|
||||
13321|-0.0720|0.0204|-3.5337|8.2738|-1.9738|0.5573|90.0
|
||||
(7 rows)
|
||||
Vendored
+199
@@ -0,0 +1,199 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
--
|
||||
-- PostgreSQL database dump
|
||||
--
|
||||
-- Data from:
|
||||
-- https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/GData_utm.csv
|
||||
|
||||
CREATE TABLE g_utm_testing (
|
||||
cartodb_id bigint,
|
||||
the_geom geometry(Geometry, 2239),
|
||||
pctblack numeric,
|
||||
pctpov numeric,
|
||||
pctbach numeric,
|
||||
pctrural numeric,
|
||||
x numeric,
|
||||
y numeric,
|
||||
areakey int
|
||||
);
|
||||
|
||||
COPY g_utm_testing (cartodb_id, the_geom, pctblack, pctpov, pctbach, pctrural, x, y, areakey) FROM stdin;
|
||||
122 0101000020BF080000CDCCCCCC2AEB2A410000000043F74A41 34.4500000000000028 27.3000000000000007 8.59999999999999964 72.5999999999999943 882069.400000000023 3534470 13271
|
||||
9 0101000020BF0800009A999999786823410000000080684D41 0.349999999999999978 14.5999999999999996 8 96.5 635964.300000000047 3854592 13083
|
||||
30 0101000020BF0800009A9999990ACF294100000080E5174D41 9.89000000000000057 16.5 9.5 87.2000000000000028 845701.300000000047 3813323 13119
|
||||
121 0101000020BF08000000000000B67D2F4100000080AA6F4B41 14.0299999999999994 12.6999999999999993 7.59999999999999964 89.0999999999999943 1031899 3596117 13103
|
||||
139 0101000020BF080000CDCCCCCCFF4B2B410000008038A54A41 25.4600000000000009 22.5 11.0999999999999996 64.5999999999999943 894463.900000000023 3492465 13069
|
||||
78 0101000020BF08000066666666FB632741000000802BF44B41 34.0300000000000011 16.3000000000000007 10 63.6000000000000014 766461.699999999953 3663959 13171
|
||||
103 0101000020BF0800009A999999BEB62741000000809A594B41 58.7199999999999989 29.1999999999999993 10.0999999999999996 65.5999999999999943 777055.300000000047 3584821 13193
|
||||
104 0101000020BF0800009A999999E52C27410000008039874B41 43.2100000000000009 29.5 7.09999999999999964 100 759410.800000000047 3608179 13269
|
||||
160 0101000020BF0800009A999999A3DC2541000000004D544A41 27.4800000000000004 22.1000000000000014 8.19999999999999929 100 716369.800000000047 3451034 13201
|
||||
99 0101000020BF08000033333333DCC3294100000080D56E4B41 22.3599999999999994 18.3000000000000007 10.3000000000000007 57.8999999999999986 844270.099999999977 3595691 13023
|
||||
16 0101000020BF08000033333333C4532741000000004B164D41 0.28999999999999998 12.8000000000000007 8.59999999999999964 100 764386.099999999977 3812502 13085
|
||||
34 0101000020BF080000CDCCCCCCFE622441000000000FB94C41 14.3000000000000007 16.3000000000000007 6.79999999999999982 66.5 668031.400000000023 3764766 13233
|
||||
37 0101000020BF08000066666666423825410000008006AC4C41 3.93999999999999995 8.80000000000000071 7.59999999999999964 93.7000000000000028 695329.199999999953 3758093 13223
|
||||
47 0101000020BF08000066666666B195254100000080D0774C41 7.62999999999999989 6.59999999999999964 12 26.6999999999999993 707288.699999999953 3731361 13097
|
||||
108 0101000020BF080000333333339AFA264100000000173D4B41 34.0900000000000034 19.8999999999999986 8 100 752973.099999999977 3570222 13249
|
||||
75 0101000020BF0800009A9999992EEC29410000008048F74B41 42.3900000000000006 17.5 13.3000000000000007 42.7000000000000028 849431.300000000047 3665553 13009
|
||||
83 0101000020BF08000066666666C39D2D4100000080E3C54B41 41.509999999999998 27.8000000000000007 7.70000000000000018 53.7999999999999972 970465.699999999953 3640263 13165
|
||||
91 0101000020BF080000333333339939254100000000ABA74B41 25.4899999999999984 13.6999999999999993 13.5999999999999996 95.7999999999999972 695500.599999999977 3624790 13145
|
||||
131 0101000020BF080000CDCCCCCC2AEB2A410000000043F74A41 34.4500000000000028 27.3000000000000007 8.59999999999999964 72.5999999999999943 882069.400000000023 3534470 13271
|
||||
136 0101000020BF0800009A99999959562A4100000000D8DB4A41 31.3299999999999983 22 7.59999999999999964 47 863020.800000000047 3520432 13017
|
||||
140 0101000020BF080000CDCCCCCCFF4B2B410000008038A54A41 25.4600000000000009 22.5 11.0999999999999996 64.5999999999999943 894463.900000000023 3492465 13069
|
||||
45 0101000020BF080000333333338034294100000000B35D4C41 34.740000000000002 15 11 73 825920.099999999977 3717990 13211
|
||||
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|
||||
148 0101000020BF0800009A9999998461294100000080F19B4A41 26.6799999999999997 22.8999999999999986 14 51.1000000000000014 831682.300000000047 3487715 13277
|
||||
152 0101000020BF080000333333330E78254100000000C8734A41 44.0900000000000034 31.3999999999999986 9.40000000000000036 52.7999999999999972 703495.099999999977 3467152 13099
|
||||
149 0101000020BF0800009A9999997B192D4100000000DE904A41 11.6899999999999995 21.3000000000000007 6.29999999999999982 74.4000000000000057 953533.800000000047 3482044 13229
|
||||
150 0101000020BF08000000000000663B2F41000000806B7B4A41 25.5700000000000003 14.3000000000000007 19.8999999999999986 20.3000000000000007 1023411 3471063 13127
|
||||
151 0101000020BF08000033333333DBA22C4100000080C94B4A41 25.879999999999999 21.1000000000000014 10.4000000000000004 54.2000000000000028 938349.599999999977 3446675 13299
|
||||
153 0101000020BF0800009A999999173E2A410000008044724A41 11.6199999999999992 19.3000000000000007 7.5 66.2000000000000028 859915.800000000047 3466377 13019
|
||||
154 0101000020BF0800000000000082542B4100000000167D4A41 26.8599999999999994 26 6.40000000000000036 100 895553 3471916 13003
|
||||
155 0101000020BF080000333333336DBF264100000080A6824A41 51.6700000000000017 24.8000000000000007 9.40000000000000036 100 745398.599999999977 3474765 13007
|
||||
156 0101000020BF080000333333333D62274100000000F5594A41 47.9099999999999966 28.6999999999999993 7.79999999999999982 56.2000000000000028 766238.599999999977 3453930 13205
|
||||
157 0101000020BF080000CDCCCCCCB1E22D4100000080546D4A41 4.58000000000000007 18.1999999999999993 5.79999999999999982 100 979288.900000000023 3463849 13025
|
||||
162 0101000020BF080000333333331CB62B4100000000FA274A41 27.2899999999999991 26.3999999999999986 6.70000000000000018 58.6000000000000014 908046.099999999977 3428340 13065
|
||||
158 0101000020BF0800003333333310A129410000008057504A41 29.9400000000000013 22.3999999999999986 6.5 62 839816.099999999977 3449007 13075
|
||||
159 0101000020BF0800009A999999E7AD284100000000FD5D4A41 24.1600000000000001 22.8000000000000007 10 59.3999999999999986 808691.800000000047 3455994 13071
|
||||
163 0101000020BF0800009A99999998AA2A4100000080B63E4A41 26.5799999999999983 25.8999999999999986 5.40000000000000036 100 873804.300000000047 3439981 13173
|
||||
161 0101000020BF08000000000000C0C62E4100000080B63A4A41 20.1900000000000013 11.5 13.5 47.1000000000000014 1008480 3437933 13039
|
||||
172 0101000020BF080000000000005C43294100000000E31A4A41 41.4699999999999989 25.8999999999999986 9.09999999999999964 65.5999999999999943 827822 3421638 13027
|
||||
164 0101000020BF0800009A99999968602D4100000080A0304A41 27.0500000000000007 18.3000000000000007 6.40000000000000036 100 962612.300000000047 3432769 13049
|
||||
165 0101000020BF080000000000005C43294100000000E31A4A41 41.4699999999999989 25.8999999999999986 9.09999999999999964 65.5999999999999943 827822 3421638 13027
|
||||
167 0101000020BF0800003333333304592741000000803C1B4A41 31.5 22.3000000000000007 7.70000000000000018 55.3999999999999986 765058.099999999977 3421817 13131
|
||||
168 0101000020BF080000CDCCCCCCA85B264100000000341B4A41 39.4699999999999989 23.3000000000000007 11.6999999999999993 58 732628.400000000023 3421800 13087
|
||||
169 0101000020BF08000033333333DF7F254100000000991B4A41 32.740000000000002 29.1000000000000014 7.79999999999999982 69.4000000000000057 704495.599999999977 3422002 13253
|
||||
170 0101000020BF080000333333331A642A410000008058164A41 31.879999999999999 19.8999999999999986 16.3000000000000007 47.6000000000000014 864781.099999999977 3419313 13185
|
||||
171 0101000020BF080000000000001C5D2B4100000000DEF24941 11.4800000000000004 14.5999999999999996 4.70000000000000018 100 896654 3401148 13101
|
||||
\.
|
||||
|
||||
|
||||
--
|
||||
-- PostgreSQL database dump complete
|
||||
--
|
||||
@@ -1,6 +1,6 @@
|
||||
-- Install dependencies
|
||||
CREATE EXTENSION plpythonu;
|
||||
CREATE EXTENSION postgis VERSION '2.2.2';
|
||||
CREATE EXTENSION postgis;
|
||||
|
||||
-- Create role publicuser if it does not exist
|
||||
DO
|
||||
|
||||
@@ -1,6 +1,34 @@
|
||||
\pset format unaligned
|
||||
\set ECHO all
|
||||
|
||||
SELECT count(DISTINCT cluster_no) as clusters from cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
|
||||
-- spatial kmeans
|
||||
SELECT
|
||||
count(DISTINCT cluster_no) as clusters
|
||||
FROM
|
||||
cdb_crankshaft.cdb_kmeans('select * from ppoints', 2);
|
||||
|
||||
SELECT count(*) clusters from (select cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC), code from ppoints group by code) p;
|
||||
-- weighted mean
|
||||
SELECT
|
||||
count(*) clusters
|
||||
FROM (
|
||||
SELECT
|
||||
cdb_crankshaft.CDB_WeightedMean(the_geom, value::NUMERIC),
|
||||
code
|
||||
FROM ppoints
|
||||
GROUP BY code
|
||||
) p;
|
||||
|
||||
-- nonspatial kmeans
|
||||
SELECT
|
||||
cluster_label::int in (0, 1) As cluster_label,
|
||||
cluster_center::json->>'col1' As cc_col1,
|
||||
cluster_center::json->>'col2' As cc_col2,
|
||||
silhouettes,
|
||||
inertia,
|
||||
rowid
|
||||
FROM cdb_crankshaft.CDB_KMeansNonspatial(
|
||||
'SELECT unnest(Array[1, 1, 10, 10]) As col1, ' ||
|
||||
'unnest(Array[100, 100, 2, 2]) As col2, ' ||
|
||||
'unnest(Array[1, 2, 3, 4]) As cartodb_id ',
|
||||
Array['col1', 'col2']::text[],
|
||||
2);
|
||||
|
||||
@@ -5,3 +5,22 @@ with a as(
|
||||
select st_geomfromtext('POLYGON((-432540.453078056 4949775.20452642,-432329.947920966 4951361.232584,-431245.028163694 4952223.31516671,-429131.071033529 4951768.00415574,-424622.07505895 4952843.13503987,-423688.327170174 4953499.20752423,-424086.294349759 4954968.38274191,-423068.388925945 4954378.63345336,-423387.653225542 4953355.67417084,-420594.869840519 4953781.00230592,-416026.095299382 4951484.06849063,-412483.018546414 4951024.5410983,-410490.399661215 4954502.24032205,-408186.197521284 4956398.91417441,-407627.262358013 4959300.94633864,-406948.770061627 4959874.85407739,-404949.583326472 4959047.74518163,-402570.908447199 4953743.46829807,-400971.358683991 4952193.11680804,-403533.488084088 4949649.89857885,-406335.177028373 4950193.19571096,-407790.456731515 4952391.46015616,-412060.672398345 4950381.2389307,-410716.93482498 4949156.7509561,-408464.162289794 4943912.8940387,-409350.599394983 4942819.84896006,-408087.791091424 4942451.6711778,-407274.045613725 4940572.4807777,-404446.196589102 4939976.71501489,-402422.964843936 4940450.3670813,-401010.654464241 4939054.8061663,-397647.247369412 4940679.80737878,-395658.413346901 4940528.84765185,-395536.852462953 4938829.79565997,-394268.923462818 4938003.7277717,-393388.720249116 4934757.80596815,-392393.301362444 4934326.71675815,-392573.527618037 4932323.40974412,-393464.640141837 4931903.10653605,-393085.597275686 4931094.7353605,-398426.261165985 4929156.87541607,-398261.174361137 4926238.00816416,-394045.059966834 4925765.18668498,-392982.960705174 4926391.81893628,-393090.272694301 4927176.84692181,-391648.240010564 4924626.06386961,-391889.914625075 4923086.14787613,-394345.177314013 4923235.086036,-395550.878718795 4917812.79243978,-399009.463978251 4912927.7157945,-398948.794855767 4911941.91010796,-398092.636652078 4911806.57392519,-401991.601817112 4911722.9204501,-406225.972607907 4914505.47286319,-411104.994569885 4912569.26941163,-412925.513522316 4913030.3608866,-414630.148884835 4914436.69169949,-414207.691417276 4919205.78028405,-418306.141109809 4917994.9580478,-424184.700779621 4918938.12432889,-426816.961458921 4923664.37379373,-420956.324227126 4923381.98014807,-420186.661267781 4924286.48693378,-420943.411166194 4926812.76394433,-419779.45457046 4928527.43466337,-419768.767899344 4930681.94459216,-421911.668097113 4930432.40620397,-423482.386112205 4933451.28047252,-427272.814773717 4934151.56473242,-427144.908678797 4939731.77191996,-428982.125554848 4940522.84445172,-428986.133056516 4942437.17281266,-431237.792396792 4947309.68284815,-432476.889648814 4947791.74800037,-432540.453078056 4949775.20452642))', 3857) as g
|
||||
)
|
||||
SELECT st_astext(cdb_crankshaft.CDB_PIA(g)) from a;
|
||||
|
||||
-- square centered on 0,0 with sides of length 2
|
||||
-- expectation: point(0, 0)
|
||||
WITH square AS (
|
||||
SELECT 'SRID=4326;POLYGON((-1 1, 1 1, 1 -1, -1 -1, -1 1))'::geometry as g
|
||||
)
|
||||
SELECT ST_AsText(cdb_crankshaft.CDB_PIA(g))
|
||||
FROM square;
|
||||
|
||||
-- MultiPolygon test
|
||||
-- square centered on 0,0 with sides of length 2
|
||||
-- expectation: point(0, 0)
|
||||
WITH square AS (
|
||||
SELECT
|
||||
ST_Multi('SRID=4326;POLYGON((-1 1, 1 1, 1 -1, -1 -1, -1 1))'::geometry) as g
|
||||
)
|
||||
SELECT ST_AsText(cdb_crankshaft.CDB_PIA(g))
|
||||
FROM square
|
||||
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
\set VERBOSITY TERSE
|
||||
\pset format unaligned
|
||||
|
||||
--
|
||||
|
||||
@@ -0,0 +1,31 @@
|
||||
-- test of Geographically Weighted Regression (GWR)
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
\pset format unaligned
|
||||
\i test/fixtures/gwr_georgia.sql
|
||||
|
||||
SELECT
|
||||
rowid,
|
||||
round((coeffs->>'pctrural')::numeric, 4) As coeff_pctrural,
|
||||
round((stand_errs->>'pctrural')::numeric, 4) As std_errs_pctrural,
|
||||
round((t_vals->>'pctrural')::numeric, 4) As t_vals_pctrural,
|
||||
round(predicted, 4) As predicted,
|
||||
round(residuals, 4) As residuals,
|
||||
round(r_squared, 4) As r_squared,
|
||||
bandwidth As bandwidth
|
||||
FROM
|
||||
cdb_crankshaft.CDB_GWR('SELECT * FROM g_utm_testing', 'pctbach',
|
||||
Array['pctrural', 'pctpov', 'pctblack']::text[],
|
||||
90.0,
|
||||
False,
|
||||
'bisquare',
|
||||
'the_geom',
|
||||
'areakey')
|
||||
WHERE rowid in (13001, 13027, 13039, 13231, 13321, 13293)
|
||||
ORDER BY rowid ASC;
|
||||
|
||||
|
||||
-- comparison data from known calculated values in
|
||||
-- https://github.com/TaylorOshan/pysal/blob/1d6af33bda46b1d623f70912c56155064463383f/pysal/examples/georgia/georgia_BS_NN_listwise.csv
|
||||
-- Note: values output from this analysis were correct with 1% of the values in that table, possibly due to projection differences.
|
||||
|
||||
Reference in New Issue
Block a user