def assertTensorEqual(actual, expected, abs=False):
'''Takes an n-dimensional array 'expected' and runs several
tests on the tensor 'actual'. For the test description, see
assertTensorEqualCompleteDict.
'''
expected_dict = nested_list_to_dict(expected)
assertTensorEqualCompleteDict(actual, expected_dict, abs)
def setUp(self):
self.tensor = DictTensor(2)
# Note: this command actually puts 20 values in tensor!
self.tensor.update(nested_list_to_dict(svd_2d_test_matrix))
self.svd = self.tensor.svd(k=3)
self.incremental = self.tensor.incremental_svd(k=3, niter=200)
self.u, self.svals, self.v = self.svd.u, self.svd.svals, self.svd.v
def setUp(self):
self.tensor = DictTensor(2)
# Note: this command actually puts 20 values in tensor!
self.tensor.update(nested_list_to_dict(svd_2d_test_matrix))
self.svd = self.tensor.svd(k=3)
self.incremental = self.tensor.incremental_svd(k=3, niter=200)
self.u, self.svals, self.v = self.svd.u, self.svd.svals, self.svd.v
def setUp(self):
self.tensor = DictTensor(2)
self.tensor.update(nested_list_to_dict(
numpy.random.random_sample((10, 12))))
self.normalized_tensor = self.tensor.normalized()
self.svd = self.normalized_tensor.svd(k=3)
self.u, self.svals, self.v = self.svd.u, self.svd.svals, self.svd.v
def setUp(self):
self.tensor = DictTensor(2)
self.tensor.update(
nested_list_to_dict(numpy.random.random_sample((10, 12))))
self.normalized_tensor = self.tensor.normalized()
self.svd = self.normalized_tensor.svd(k=3)
self.u, self.svals, self.v = self.svd.u, self.svd.svals, self.svd.v
def testNorm(self):
norm_test = [[0,0,0],
[0,1,0],
[0,5.0,0]]
self.tensor.update(nested_list_to_dict(norm_test))
self.assertEqual(self.tensor.norm(), sqrt(26.0))
self.assertEqual(self.tensor.magnitude(), sqrt(26.0))
def test_delete(self):
self.tensor.update(
nones_removed(nested_list_to_dict(self.slice_testcase)))
assertTensorEqual(self.tensor, self.slice_testcase)
del self.tensor[0, 0]
assertTensorEqual(self.tensor, [[None, None, None], [None, 2, 3],
[4, None, None], [None, 5, None]])
def setUp(self):
self.tensor = DictTensor(2)
# Note: this command actually puts 20 values in tensor!
self.tensor.update(nested_list_to_dict(svd_2d_test_matrix))
self.svd = self.tensor.svd(k=3,
offset_for_row=offset_for_row,
offset_for_col=offset_for_col)
self.u, self.svals, self.v = self.svd.u, self.svd.svals, self.svd.v
def setUp(self):
self.tensor = DenseTensor(zeros((3, 4)))
self.tensor.update(nested_list_to_dict(self.data))
self.normalized = NormalizedView(self.tensor, mode=0)
self.randomtensor = DenseTensor(numpy.random.normal(size=(5, 8)))
self.randomnormal_0 = NormalizedView(self.randomtensor, mode=0)
self.randomnormal_1 = NormalizedView(self.randomtensor, mode=1)
def setUp(self):
self.tensor = DenseTensor(zeros((3, 4)))
self.tensor.update(nested_list_to_dict(self.data))
self.normalized = NormalizedView(self.tensor, mode=0)
self.randomtensor = DenseTensor(numpy.random.normal(size=(5, 8)))
self.randomnormal_0 = NormalizedView(self.randomtensor, mode=0)
self.randomnormal_1 = NormalizedView(self.randomtensor, mode=1)
def test_delete(self):
self.tensor.update(nones_removed(nested_list_to_dict(self.slice_testcase)))
assertTensorEqual(self.tensor, self.slice_testcase)
del self.tensor[0,0]
assertTensorEqual(self.tensor,
[[None, None, None],
[None, 2, 3 ],
[4, None, None],
[None, 5, None]])
def test_slice(self):
self.tensor.update(
nones_removed(nested_list_to_dict(self.slice_testcase)))
# Test end conditions: start index
# is included in slice, end index is not
slice = self.tensor[1:3, 0:2]
assertTensorEqual(slice, [[None, 2], [4, None]])
# Test that slicing on some dims correctly
# reduces the dimensionality of the tensor
slice = self.tensor[3, :]
assertTensorEqual(slice, [None, 5, None])
# Test the step parameter
slice = self.tensor[1:4:2, :]
assertTensorEqual(slice, [[None, 2, 3], [None, 5, None]])
def test_slice(self):
self.tensor.update(nones_removed(nested_list_to_dict(self.slice_testcase)))
# Test end conditions: start index
# is included in slice, end index is not
slice = self.tensor[1:3, 0:2]
assertTensorEqual(slice,
[[None, 2],
[4, None]])
# Test that slicing on some dims correctly
# reduces the dimensionality of the tensor
slice = self.tensor[3, :]
assertTensorEqual(slice, [None, 5, None])
# Test the step parameter
slice = self.tensor[1:4:2, :]
assertTensorEqual(slice,
[[None, 2, 3],
[None, 5, None]])
def setUp(self):
self.tensor = DictTensor(2)
# Note: this command actually puts 20 values in tensor!
self.tensor.update(nested_list_to_dict(svd_2d_test_matrix))
self.svd = self.tensor.svd(k=3, offset_for_row=offset_for_row, offset_for_col=offset_for_col)
self.u, self.svals, self.v = self.svd.u, self.svd.svals, self.svd.v
def tensor_from_nested_list(data, tensor_cls=DictTensor):
t = tensor_cls(2)
t.update(nested_list_to_dict(data))
return t
import numpy as np
import unittest
from nose.tools import eq_, raises
from math import sqrt
from csc.divisi.tensor import DictTensor
from csc.divisi.util import nested_list_to_dict
from tensor_util import assertTensorEqual, zeros_removed
data = np.array([[1, 2, 3, 4], [-1, 2, 3, 4], [0, 1, -1, 0]])
tensor = DictTensor(2)
tensor.update(zeros_removed(nested_list_to_dict(data)))
eq_(len(tensor), 10)
# For NumPy, "along an axis" means something different.
ms_data = data - data.mean(1)[:, np.newaxis]
ms_tensor = DictTensor(2)
ms_tensor.update(nested_list_to_dict(ms_data))
def test_means():
means = tensor.means()
eq_(len(means), 2)
assert np.allclose(means[0], [(1 + 2 + 3 + 4) / 4., (-1 + 2 + 3 + 4) / 4.,
(0 + 1 + -1 + 0) / 4.])
assert np.allclose(
means[1], [0, (2 + 2 + 1) / 3., (3 + 3 - 1) / 3., (4 + 4 + 0) / 3.])
def test_mean_subtracted():
mean_subtracted = tensor.mean_subtracted()
import numpy as np
import unittest
from nose.tools import eq_, raises
from math import sqrt
from csc.divisi.tensor import DictTensor
from csc.divisi.util import nested_list_to_dict
from tensor_util import assertTensorEqual, zeros_removed
data = np.array([[1, 2, 3, 4],
[-1,2, 3, 4],
[0, 1, -1,0]])
tensor = DictTensor(2)
tensor.update(zeros_removed(nested_list_to_dict(data)))
eq_(len(tensor), 10)
# For NumPy, "along an axis" means something different.
ms_data = data - data.mean(1)[:,np.newaxis]
ms_tensor = DictTensor(2)
ms_tensor.update(nested_list_to_dict(ms_data))
def test_means():
means = tensor.means()
eq_(len(means), 2)
assert np.allclose(means[0], [(1+2+3+4)/4., (-1+2+3+4)/4., (0+1+-1+0)/4.])
assert np.allclose(means[1], [0, (2+2+1)/3., (3+3-1)/3., (4+4+0)/3.])
def test_mean_subtracted():
mean_subtracted = tensor.mean_subtracted()
m = np.zeros(data.shape)
for (r, c), v in mean_subtracted.iteritems():
def testNorm(self):
norm_test = [[0, 0, 0], [0, 1, 0], [0, 5.0, 0]]
self.tensor.update(nested_list_to_dict(norm_test))
self.assertEqual(self.tensor.norm(), sqrt(26.0))
self.assertEqual(self.tensor.magnitude(), sqrt(26.0))
def tensor_from_nested_list(data, tensor_cls=DictTensor):
t = tensor_cls(2)
t.update(nested_list_to_dict(data))
return t
