Here are the examples of the python api numpy.array.mean taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat('table', 'value',
'knn', 5, 99, 'the_geom',
'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat('table', 'value',
'knn', 5, 99, 'the_geom',
'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat('table', 'value',
'knn', 5, 99, 'the_geom',
'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1235)
result = cc.moran('table', 'value',
'knn', 5, 99, 'the_geom', 'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat('table', 'value',
'knn', 5, 99, 'the_geom',
'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
plpy._define_result('select', data)
random_seeds.set_random_seeds(1235)
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
print 'result == None?', result == None
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)
3
View Complete Implementation : test_clustering_moran.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
Copyright BSD 3-Clause "New" or "Revised" License
Author : CartoDB
def test_moran(self):
"""Test Moran's I global"""
data = [{'id': d['id'],
'attr1': d['value'],
'neighbors': d['neighbors']} for d in self.neighbors_data]
random_seeds.set_random_seeds(1235)
moran = Moran(FakeDataProvider(data))
result = moran.global_stat('table', 'value',
'knn', 5, 99, 'the_geom',
'cartodb_id')
result_moran = result[0][0]
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
self.astertAlmostEqual(expected_moran, result_moran, delta=10e-2)