def test_get_value(self):
m = numpy.ndarray((3, 3))
# [[ 0 1 2 ] [[ 0 2 4 ]
# [ 3 4 5 ] -> should -> [ 2 4 6 ]
# [ 6 7 8 ]] [ 4 6 8 ]]
m.reshape((9,))[:] = list(range(9))
c = Covariance.from_matrix(3, c_type='d', init=m)
# Test matrix upper triangle locations
nose.tools.assert_equal(c[0,0], 0)
nose.tools.assert_equal(c[0,1], 2)
nose.tools.assert_equal(c[0,2], 4)
nose.tools.assert_equal(c[1,1], 4)
nose.tools.assert_equal(c[1,2], 6)
nose.tools.assert_equal(c[2,2], 8)
nose.tools.assert_equal(c[0,1], c[1,0])
nose.tools.assert_equal(c[0,2], c[2,0])
nose.tools.assert_equal(c[1,2], c[2,1])
c = Covariance.from_matrix(3, c_type='f', init=m)
# Test matrix upper triangle locations
nose.tools.assert_equal(c[0,0], 0)
nose.tools.assert_equal(c[0,1], 2)
nose.tools.assert_equal(c[0,2], 4)
nose.tools.assert_equal(c[1,1], 4)
nose.tools.assert_equal(c[1,2], 6)
nose.tools.assert_equal(c[2,2], 8)
nose.tools.assert_equal(c[0,1], c[1,0])
nose.tools.assert_equal(c[0,2], c[2,0])
nose.tools.assert_equal(c[1,2], c[2,1])
def test_get_value(self):
m = numpy.ndarray((3, 3))
# [[ 0 1 2 ] [[ 0 2 4 ]
# [ 3 4 5 ] -> should -> [ 2 4 6 ]
# [ 6 7 8 ]] [ 4 6 8 ]]
m.reshape((9, ))[:] = list(range(9))
c = Covariance.from_matrix(3, c_type='d', init=m)
# Test matrix upper triangle locations
nose.tools.assert_equal(c[0, 0], 0)
nose.tools.assert_equal(c[0, 1], 2)
nose.tools.assert_equal(c[0, 2], 4)
nose.tools.assert_equal(c[1, 1], 4)
nose.tools.assert_equal(c[1, 2], 6)
nose.tools.assert_equal(c[2, 2], 8)
nose.tools.assert_equal(c[0, 1], c[1, 0])
nose.tools.assert_equal(c[0, 2], c[2, 0])
nose.tools.assert_equal(c[1, 2], c[2, 1])
c = Covariance.from_matrix(3, c_type='f', init=m)
# Test matrix upper triangle locations
nose.tools.assert_equal(c[0, 0], 0)
nose.tools.assert_equal(c[0, 1], 2)
nose.tools.assert_equal(c[0, 2], 4)
nose.tools.assert_equal(c[1, 1], 4)
nose.tools.assert_equal(c[1, 2], 6)
nose.tools.assert_equal(c[2, 2], 8)
nose.tools.assert_equal(c[0, 1], c[1, 0])
nose.tools.assert_equal(c[0, 2], c[2, 0])
nose.tools.assert_equal(c[1, 2], c[2, 1])
def test_set(self):
m = numpy.ndarray((3, 3))
# [[ 0 1 2 ] [[ 0 2 4 ]
# [ 3 4 5 ] -> should become -> [ 2 4 6 ]
# [ 6 7 8 ]] [ 4 6 8 ]]
m.reshape((9,))[:] = list(range(9))
c = Covariance.from_matrix(3, c_type='d', init=m)
# modify some locations
c[0,1] = 1
c[2,2] = 3
nose.tools.assert_equal(c[0,0], 0)
nose.tools.assert_equal(c[0,1], 1)
nose.tools.assert_equal(c[0,2], 4)
nose.tools.assert_equal(c[1,1], 4)
nose.tools.assert_equal(c[1,2], 6)
nose.tools.assert_equal(c[2,2], 3)
nose.tools.assert_equal(c[0,1], c[1,0])
nose.tools.assert_equal(c[0,2], c[2,0])
nose.tools.assert_equal(c[1,2], c[2,1])
# Set in upper triangle and see it reflect in lower
c[0, 2] = 10.1
nose.tools.assert_equal(c[2, 0], 10.1)
# Change something in lower triangle and see it reflected in upper
c[2, 1] = 20.2
nose.tools.assert_equal(c[1, 2], 20.2)
# FLOAT
c = Covariance.from_matrix(3, c_type='f', init=m)
# modify some locations
c[0,1] = 1
c[2,2] = 3
nose.tools.assert_equal(c[0,0], 0)
nose.tools.assert_equal(c[0,1], 1)
nose.tools.assert_equal(c[0,2], 4)
nose.tools.assert_equal(c[1,1], 4)
nose.tools.assert_equal(c[1,2], 6)
nose.tools.assert_equal(c[2,2], 3)
nose.tools.assert_equal(c[0,1], c[1,0])
nose.tools.assert_equal(c[0,2], c[2,0])
nose.tools.assert_equal(c[1,2], c[2,1])
# Set in upper triangle and see it reflect in lower
c[0, 2] = 10.1
nose.tools.assert_almost_equal(c[2, 0], 10.1, 6)
# Change something in lower triangle and see it reflected in upper
c[2, 1] = 20.2
nose.tools.assert_almost_equal(c[1, 2], 20.2, 5)
def test_set(self):
m = numpy.ndarray((3, 3))
# [[ 0 1 2 ] [[ 0 2 4 ]
# [ 3 4 5 ] -> should become -> [ 2 4 6 ]
# [ 6 7 8 ]] [ 4 6 8 ]]
m.reshape((9, ))[:] = list(range(9))
c = Covariance.from_matrix(3, c_type='d', init=m)
# modify some locations
c[0, 1] = 1
c[2, 2] = 3
nose.tools.assert_equal(c[0, 0], 0)
nose.tools.assert_equal(c[0, 1], 1)
nose.tools.assert_equal(c[0, 2], 4)
nose.tools.assert_equal(c[1, 1], 4)
nose.tools.assert_equal(c[1, 2], 6)
nose.tools.assert_equal(c[2, 2], 3)
nose.tools.assert_equal(c[0, 1], c[1, 0])
nose.tools.assert_equal(c[0, 2], c[2, 0])
nose.tools.assert_equal(c[1, 2], c[2, 1])
# Set in upper triangle and see it reflect in lower
c[0, 2] = 10.1
nose.tools.assert_equal(c[2, 0], 10.1)
# Change something in lower triangle and see it reflected in upper
c[2, 1] = 20.2
nose.tools.assert_equal(c[1, 2], 20.2)
# FLOAT
c = Covariance.from_matrix(3, c_type='f', init=m)
# modify some locations
c[0, 1] = 1
c[2, 2] = 3
nose.tools.assert_equal(c[0, 0], 0)
nose.tools.assert_equal(c[0, 1], 1)
nose.tools.assert_equal(c[0, 2], 4)
nose.tools.assert_equal(c[1, 1], 4)
nose.tools.assert_equal(c[1, 2], 6)
nose.tools.assert_equal(c[2, 2], 3)
nose.tools.assert_equal(c[0, 1], c[1, 0])
nose.tools.assert_equal(c[0, 2], c[2, 0])
nose.tools.assert_equal(c[1, 2], c[2, 1])
# Set in upper triangle and see it reflect in lower
c[0, 2] = 10.1
nose.tools.assert_almost_equal(c[2, 0], 10.1, 6)
# Change something in lower triangle and see it reflected in upper
c[2, 1] = 20.2
nose.tools.assert_almost_equal(c[1, 2], 20.2, 5)
def test_new_matrix(self):
a = EigenArray(2, 2, type='d')
m = a.get_matrix()
m[:] = 1.
c = Covariance.from_matrix(2, 'd', m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Type casting should be handled
a = EigenArray(2, 2, type='f')
m = a.get_matrix()
m[:] = 1.
c = Covariance.from_matrix(2, 'd', m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Any other numpy array of the correct shape should be acceptable
m = numpy.ndarray((2, 2))
m[:] = 3.
c = Covariance.from_matrix(2, 'f', init=m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Diagonally congruent values should be averages when initializing with
# matrix
m = numpy.eye(3, dtype=numpy.double)
m[0,2] = 2.
m_expected = m.copy()
m_expected[0,2] = 1.
m_expected[2,0] = 1.
c = Covariance.from_matrix(3, init=m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m_expected)
def test_new_matrix(self):
a = EigenArray(2, 2, type='d')
m = a.get_matrix()
m[:] = 1.
c = Covariance.from_matrix(2, 'd', m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Type casting should be handled
a = EigenArray(2, 2, type='f')
m = a.get_matrix()
m[:] = 1.
c = Covariance.from_matrix(2, 'd', m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Any other numpy array of the correct shape should be acceptable
m = numpy.ndarray((2, 2))
m[:] = 3.
c = Covariance.from_matrix(2, 'f', init=m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m)
# Diagonally congruent values should be averages when initializing with
# matrix
m = numpy.eye(3, dtype=numpy.double)
m[0, 2] = 2.
m_expected = m.copy()
m_expected[0, 2] = 1.
m_expected[2, 0] = 1.
c = Covariance.from_matrix(3, init=m)
m_out = c.to_matrix()
print('input matrix:\n', m)
print('output matrix:\n', m_out)
numpy.testing.assert_array_equal(m_out, m_expected)
def test_set_covar(self):
f = Feature(ctype='d')
#nose.tools.assert_equal(f.covariance, Covariance.new_covar())
expected = [[1, 2], [3, 4]]
c = Covariance.from_matrix(2, 'd', expected)
f.covariance = c
#nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
#nose.tools.assert_equal(f.covariance, c)
# And for floats...
f = Feature(ctype='f')
#nose.tools.assert_equal(f.covariance, Covariance())
expected = [[1, 2], [3, 4]]
c = Covariance.from_matrix(2, 'f', expected)
f.covariance = c
#nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
def test_set_covar(self):
f = Feature(ctype='d')
#nose.tools.assert_equal(f.covariance, Covariance.new_covar())
expected = [[1, 2],
[3, 4]]
c = Covariance.from_matrix(2, 'd', expected)
f.covariance = c
#nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
#nose.tools.assert_equal(f.covariance, c)
# And for floats...
f = Feature(ctype='f')
#nose.tools.assert_equal(f.covariance, Covariance())
expected = [[1, 2],
[3, 4]]
c = Covariance.from_matrix(2, 'f', expected)
f.covariance = c
#nose.tools.assert_equal(f.covariance, c)
# Should also work if we just give it the raw iterable
f.covariance = expected
