def testLogisticExecution(self):
arr = tensor.random.logistic(.5, 1.0, 100, chunk_size=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.logistic(.5, 1.0, 100, chunk_size=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
np.testing.assert_equal(
res,
np.random.RandomState(0).logistic(.5, 1.0, 10))
def testZipfExecute(self):
arr = tensor.random.zipf(1.1, 100, chunk_size=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.zipf(1.1, 100, chunk_size=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(res,
np.random.RandomState(0).zipf(1.1, 10)))
def testStandardNormalExecute(self):
arr = tensor.random.standard_normal(100, chunks=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.standard_normal(100, chunks=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(res,
np.random.RandomState(0).standard_normal(10)))
def testRandnExecution(self):
arr = tensor.random.randn(10, 20, chunk_size=3)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (10, 20))
arr = tensor.random.randn(10, 20, chunk_size=5).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(res,
np.random.RandomState(0).randn(5, 5)))
def testBetaExecute(self):
arr = tensor.random.beta(1, 2, chunk_size=2).tiles()
arr.chunks[0].op._state = State(np.random.RandomState(0))
self.assertEqual(
self.executor.execute_tensor(arr)[0],
np.random.RandomState(0).beta(1, 2))
arr = tensor.random.beta([1, 2], [3, 4], chunk_size=2).tiles()
arr.chunks[0].op._state = State(np.random.RandomState(0))
self.assertTrue(
np.array_equal(
self.executor.execute_tensor(arr)[0],
np.random.RandomState(0).beta([1, 2], [3, 4])))
arr = tensor.random.beta([[2, 3]],
from_ndarray([[4, 6], [5, 2]], chunk_size=2),
chunk_size=1,
size=(3, 2, 2)).tiles()
for c in arr.chunks:
c.op._state = State(np.random.RandomState(0))
res = self.executor.execute_tensor(arr, concat=True)[0]
self.assertEqual(res[0, 0, 0], np.random.RandomState(0).beta(2, 4))
self.assertEqual(res[0, 0, 1], np.random.RandomState(0).beta(3, 6))
self.assertEqual(res[0, 1, 0], np.random.RandomState(0).beta(2, 5))
self.assertEqual(res[0, 1, 1], np.random.RandomState(0).beta(3, 2))
arr = tensor.random.RandomState(0).beta([[3, 4]], [[1], [2]],
chunk_size=1)
tensor.random.seed(0)
arr2 = tensor.random.beta([[3, 4]], [[1], [2]], chunk_size=1)
self.assertTrue(
np.array_equal(
self.executor.execute_tensor(arr, concat=True)[0],
self.executor.execute_tensor(arr2, concat=True)[0]))
def testLogseriesExecution(self):
arr = tensor.random.logseries(.5, 100, chunks=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.logseries(.5, 100, chunks=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(res,
np.random.RandomState(0).logseries(.5, 10)))
def testRandomIntegersExecution(self):
arr = tensor.random.random_integers(0, 10, size=(10, 20), chunk_size=3)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (10, 20))
arr = tensor.random.random_integers(0, 10, size=(10, 20),
chunk_size=5).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
np.testing.assert_equal(
res,
np.random.RandomState(0).random_integers(0, 10, size=(5, 5)))
def testTriangularExecute(self):
arr = tensor.random.triangular(.1, .2, .3, 100, chunks=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.triangular(.1, .2, .3, 100, chunks=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).triangular(.1, .2, .3, 10)))
def testNegativeBinomialExecution(self):
arr = tensor.random.negative_binomial(5, 1.0, 100, chunks=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.negative_binomial(5, 1.0, 100, chunks=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).negative_binomial(5, 1.0, 10)))
def testDirichletExecute(self):
arr = tensor.random.dirichlet((10, 5, 3), 100, chunks=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, 3))
arr = tensor.random.dirichlet((10, 5, 3), 100, chunks=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).dirichlet((10, 5, 3), 10)))
def testHypergeometricExecution(self):
arr = tensor.random.hypergeometric(10, 20, 15, 100, chunk_size=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.hypergeometric(10, 20, 15, 100,
chunk_size=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).hypergeometric(10, 20, 15, 10)))
def testNoncentralChisquareExecution(self):
arr = tensor.random.noncentral_chisquare(.5, 1.0, 100, chunk_size=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, ))
arr = tensor.random.noncentral_chisquare(.5, 1.0, 100,
chunk_size=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).noncentral_chisquare(.5, 1.0,
10)))
def testMultinomialExecution(self):
arr = tensor.random.multinomial(10, [.2, .5, .3], 100, chunk_size=10)
self.assertEqual(arr.shape, (100, 3))
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, 3))
arr = tensor.random.multinomial(10, [.2, .5, .3], 100,
chunk_size=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).multinomial(10, [.2, .5, .3],
10)))
def testMultivariateNormalExecution(self):
arr = tensor.random.multivariate_normal([1, 2], [[1, 0], [0, 1]],
100,
chunk_size=10)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (100, 2))
arr = tensor.random.multivariate_normal([1, 2], [[1, 0], [0, 1]],
100,
chunk_size=10).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).multivariate_normal(
[1, 2], [[1, 0], [0, 1]], 10)))
def testChoiceExecution(self):
arr = tensor.random.choice(5, size=3, chunk_size=1)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (3, ))
arr = tensor.random.choice(5, size=(15, ), chunk_size=5).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(res,
np.random.RandomState(0).choice(5, size=(5, ))))
arr = tensor.random.choice([1, 4, 9], size=3, chunk_size=1)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (3, ))
arr = tensor.random.choice([1, 4, 9], size=(15, ),
chunk_size=5).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).choice([1, 4, 9], size=(5, ))))
with self.assertRaises(ValueError):
tensor.random.choice([1, 3, 4],
size=5,
replace=False,
chunk_size=2)
arr = tensor.random.choice([1, 4, 9],
size=3,
replace=False,
chunk_size=1)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (3, ))
arr = tensor.random.choice([1, 4, 9],
size=(3, ),
replace=False,
chunk_size=1).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).choice([1, 4, 9],
size=(1, ),
replace=False)))
arr = tensor.random.choice([1, 4, 9],
size=3,
p=[.2, .5, .3],
chunk_size=1)
self.assertEqual(
self.executor.execute_tensor(arr, concat=True)[0].shape, (3, ))
arr = tensor.random.choice([1, 4, 9],
size=(15, ),
chunk_size=5,
p=[.2, .5, .3]).tiles()
for chunk in arr.chunks:
chunk.op._state = State(np.random.RandomState(0))
for res in self.executor.execute_tensor(arr):
self.assertTrue(
np.array_equal(
res,
np.random.RandomState(0).choice([1, 4, 9],
size=(5, ),
p=[.2, .5, .3])))
