def test_math(self):
if self.isWindows:
skip('windows does not support c99 complex')
import sys
import numpypy as np
rAlmostEqual = self.rAlmostEqual
for t, testcases in (
('complex128', self.testcases128),
#('complex64', self.testcases64),
):
complex_ = np.dtype(t).type
for id, fn, ar, ai, er, ei, flags in testcases:
arg = complex_(complex(ar, ai))
expected = (er, ei)
if fn.startswith('acos'):
fn = 'arc' + fn[1:]
elif fn.startswith('asin'):
fn = 'arc' + fn[1:]
elif fn.startswith('atan'):
fn = 'arc' + fn[1:]
elif fn in ('rect', 'polar'):
continue
function = getattr(np, fn)
_actual = function(arg)
actual = (_actual.real, _actual.imag)
if 'ignore-real-sign' in flags:
actual = (abs(actual[0]), actual[1])
expected = (abs(expected[0]), expected[1])
if 'ignore-imag-sign' in flags:
actual = (actual[0], abs(actual[1]))
expected = (expected[0], abs(expected[1]))
# for the real part of the log function, we allow an
# absolute error of up to 2e-15.
if fn in ('log', 'log10'):
real_abs_err = 2e-15
else:
real_abs_err = 5e-323
error_message = (
'%s: %s(%r(%r, %r))\n'
'Expected: complex(%r, %r)\n'
'Received: complex(%r, %r)\n'
) % (id, fn, complex_, ar, ai,
expected[0], expected[1],
actual[0], actual[1])
# since rAlmostEqual is a wrapped function,
# convert arguments to avoid boxed values
rAlmostEqual(float(expected[0]), float(actual[0]),
abs_err=real_abs_err, msg=error_message)
rAlmostEqual(float(expected[1]), float(actual[1]),
msg=error_message)
sys.stderr.write('.')
sys.stderr.write('\n')
def rAlmostEqual(a, b, rel_err=2e-15, abs_err=5e-323, msg=''):
"""Fail if the two floating-point numbers are not almost equal.
Determine whether floating-point values a and b are equal to within
a (small) rounding error. The default values for rel_err and
abs_err are chosen to be suitable for platforms where a float is
represented by an IEEE 754 double. They allow an error of between
9 and 19 ulps.
"""
# special values testing
if isnan(a):
if isnan(b):
return True,''
raise AssertionError(msg + '%r should be nan' % (b,))
if isinf(a):
if a == b:
return True,''
raise AssertionError(msg + 'finite result where infinity expected: '+ \
'expected %r, got %r' % (a, b))
# if both a and b are zero, check whether they have the same sign
# (in theory there are examples where it would be legitimate for a
# and b to have opposite signs; in practice these hardly ever
# occur).
if not a and not b:
# only check it if we are running on top of CPython >= 2.6
if sys.version_info >= (2, 6) and copysign(1., a) != copysign(1., b):
raise AssertionError( msg + \
'zero has wrong sign: expected %r, got %r' % (a, b))
# if a-b overflows, or b is infinite, return False. Again, in
# theory there are examples where a is within a few ulps of the
# max representable float, and then b could legitimately be
# infinite. In practice these examples are rare.
try:
absolute_error = abs(b-a)
except OverflowError:
pass
else:
# test passes if either the absolute error or the relative
# error is sufficiently small. The defaults amount to an
# error of between 9 ulps and 19 ulps on an IEEE-754 compliant
# machine.
if absolute_error <= max(abs_err, rel_err * abs(a)):
return True,''
raise AssertionError(msg + \
'%r and %r are not sufficiently close, %g > %g' %\
(a, b, absolute_error, max(abs_err, rel_err*abs(a))))
def elbo_did_not_converge(elbo, last_elbo, num_iter=0,
criterion=0.001, min_iter=0, max_iter=20):
"""Accepts two elbo doubles.
Also accepts the number of iterations already performed in this loop.
Also accepts convergence criterion:
(elbo - last_elbo) < criterion # True to stop
Finally, accepts
Returns boolean.
Figures out whether the elbo is sufficiently smaller than
last_elbo.
"""
if num_iter < min_iter:
return True
if num_iter >= max_iter:
return False
if elbo == INITIAL_ELBO or last_elbo == INITIAL_ELBO:
return True
else:
# todo: do a criterion convergence test
if np.abs(elbo - last_elbo) < criterion:
return False
else:
return True
def lm_recalculate_eta_sigma(eta, y, phi1, phi2):
"""
Accepts eta (K+J)-size vector,
also y (a D-size vector of reals),
also two phi D-size vectors of NxK matrices.
Returns new sigma squared update (a double).
ηnew ← (E[ATA])-1 E[A]Ty
σ2new ← (1/D) {yTy - yTE[A]ηnew}
(Note that A is the D X (K + J) matrix whose rows are the vectors ZdT for document and comment concatenated.)
(Also note that the dth row of E[A] is φd, and E[ATA] = Σd E[ZdZdT] .)
(Also, note that E[Z] = φ := (1/N)Σnφn, and E[ZdZdT] = (1/N2)(ΣnΣm!=nφd,nφd,mT + Σndiag{φd,n})
"""
ensure(len(phi1) == len(phi2))
D = len(phi1)
Nd,K = phi1[0].shape
Nc,J = phi2[0].shape
Ndc, KJ = (Nd+Nc,K+J)
#print 'e_a...'
E_A = np.zeros((D, KJ))
for d in xrange(D):
E_A[d,:] = calculate_EZ_from_small_phis(phi1[d], phi2[d])
#print 'inverse...'
E_ATA_inverse = calculate_E_ATA_inverse_from_small_phis(phi1, phi2)
#print 'new eta...'
#new_eta = matrix_multiply(matrix_multiply(E_ATA_inverse, E_A.T), y)
new_eta = np.dot(np.dot(E_ATA_inverse, E_A.T), y)
if np.sum(np.abs(new_eta)) > (KJ * KJ * 5):
print 'ETA is GOING CRAZY {0}'.format(eta)
print 'aborting the update!!!'
else:
eta[:] = new_eta
# todo: don't do this later
# keep sigma squared fix
#import pdb; pdb.set_trace()
#new_sigma_squared = (1.0 / D) * (np.dot(y, y) - np.dot(np.dot(np.dot(np.dot(y, E_A), E_ATA_inverse), E_A.T), y))
new_sigma_squared = 1.0
return new_sigma_squared
def mean_absolute_error(y_true, y_pred):
return np.mean(np.abs(y_pred - y_true))
def test_basic(self):
import sys
from numpypy import (dtype, add, array, dtype,
subtract as sub, multiply, divide, negative, absolute as abs,
floor_divide, real, imag, sign)
from numpypy import (equal, not_equal, greater, greater_equal, less,
less_equal, isnan)
assert real(4.0) == 4.0
assert imag(0.0) == 0.0
a = array([complex(3.0, 4.0)])
b = a.real
b[0] = 1024
assert a[0].real == 1024
assert b.dtype == dtype(float)
a = array(complex(3.0, 4.0))
b = a.real
assert b == array(3)
assert a.imag == array(4)
a.real = 1024
a.imag = 2048
assert a.real == 1024 and a.imag == 2048
assert b.dtype == dtype(float)
a = array(4.0)
b = a.imag
assert b == 0
assert b.dtype == dtype(float)
exc = raises(TypeError, 'a.imag = 1024')
assert str(exc.value).startswith("array does not have imaginary")
exc = raises(ValueError, 'a.real = [1, 3]')
assert str(exc.value) == \
"could not broadcast input array from shape (2) into shape ()"
a = array('abc')
assert str(a.real) == 'abc'
assert str(a.imag) == ''
for t in 'complex64', 'complex128', 'clongdouble':
complex_ = dtype(t).type
O = complex(0, 0)
c0 = complex_(complex(2.5, 0))
c1 = complex_(complex(1, 2))
c2 = complex_(complex(3, 4))
c3 = complex_(complex(-3, -3))
assert equal(c0, 2.5)
assert equal(c1, complex_(complex(1, 2)))
assert equal(c1, complex(1, 2))
assert equal(c1, c1)
assert not_equal(c1, c2)
assert not equal(c1, c2)
assert less(c1, c2)
assert less_equal(c1, c2)
assert less_equal(c1, c1)
assert not less(c1, c1)
assert greater(c2, c1)
assert greater_equal(c2, c1)
assert not greater(c1, c2)
assert add(c1, c2) == complex_(complex(4, 6))
assert add(c1, c2) == complex(4, 6)
assert sub(c0, c0) == sub(c1, c1) == 0
assert sub(c1, c2) == complex(-2, -2)
assert negative(complex(1,1)) == complex(-1, -1)
assert negative(complex(0, 0)) == 0
assert multiply(1, c1) == c1
assert multiply(2, c2) == complex(6, 8)
assert multiply(c1, c2) == complex(-5, 10)
assert divide(c0, 1) == c0
assert divide(c2, -1) == negative(c2)
assert divide(c1, complex(0, 1)) == complex(2, -1)
n = divide(c1, O)
assert repr(n.real) == 'inf'
assert repr(n.imag).startswith('inf') #can be inf*j or infj
assert divide(c0, c0) == 1
res = divide(c2, c1)
assert abs(res.real-2.2) < 0.001
assert abs(res.imag+0.4) < 0.001
assert floor_divide(c0, c0) == complex(1, 0)
assert isnan(floor_divide(c0, complex(0, 0)).real)
assert floor_divide(c0, complex(0, 0)).imag == 0.0
assert abs(c0) == 2.5
assert abs(c2) == 5
assert sign(complex(0, 0)) == 0
assert sign(complex(-42, 0)) == -1
assert sign(complex(42, 0)) == 1
assert sign(complex(-42, 2)) == -1
assert sign(complex(42, 2)) == 1
assert sign(complex(-42, -3)) == -1
assert sign(complex(42, -3)) == 1
assert sign(complex(0, -42)) == -1
assert sign(complex(0, 42)) == 1
inf_c = complex_(complex(float('inf'), 0.))
assert repr(abs(inf_c)) == 'inf'
assert repr(abs(complex(float('nan'), float('nan')))) == 'nan'
# numpy actually raises an AttributeError,
# but numpypy raises a TypeError
if '__pypy__' in sys.builtin_module_names:
exct, excm = TypeError, 'readonly attribute'
else:
exct, excm = AttributeError, 'is not writable'
exc = raises(exct, 'c2.real = 10.')
assert excm in exc.value[0]
exc = raises(exct, 'c2.imag = 10.')
assert excm in exc.value[0]
assert(real(c2) == 3.0)
assert(imag(c2) == 4.0)
def test_basic(self):
from numpypy import (complex128, complex64, add, array, dtype,
subtract as sub, multiply, divide, negative, absolute as abs,
floor_divide, real, imag, sign)
from numpypy import (equal, not_equal, greater, greater_equal, less,
less_equal, isnan)
complex_dtypes = [complex64, complex128]
try:
from numpypy import clongfloat
complex_dtypes.append(clongfloat)
except:
pass
assert real(4.0) == 4.0
assert imag(0.0) == 0.0
a = array([complex(3.0, 4.0)])
b = a.real
b[0] = 1024
assert a[0].real == 1024
assert b.dtype == dtype(float)
a = array(complex(3.0, 4.0))
b = a.real
assert b == array(3)
assert a.imag == array(4)
a.real = 1024
a.imag = 2048
assert a.real == 1024 and a.imag == 2048
assert b.dtype == dtype(float)
a = array(4.0)
b = a.imag
assert b == 0
assert b.dtype == dtype(float)
exc = raises(TypeError, 'a.imag = 1024')
assert str(exc.value).startswith("array does not have imaginary")
exc = raises(ValueError, 'a.real = [1, 3]')
assert str(exc.value) == \
"could not broadcast input array from shape (2) into shape ()"
a = array('abc')
assert str(a.real) == 'abc'
# numpy imag for flexible types returns self
assert str(a.imag) == 'abc'
for complex_ in complex_dtypes:
O = complex(0, 0)
c0 = complex_(complex(2.5, 0))
c1 = complex_(complex(1, 2))
c2 = complex_(complex(3, 4))
c3 = complex_(complex(-3, -3))
assert equal(c0, 2.5)
assert equal(c1, complex_(complex(1, 2)))
assert equal(c1, complex(1, 2))
assert equal(c1, c1)
assert not_equal(c1, c2)
assert not equal(c1, c2)
assert less(c1, c2)
assert less_equal(c1, c2)
assert less_equal(c1, c1)
assert not less(c1, c1)
assert greater(c2, c1)
assert greater_equal(c2, c1)
assert not greater(c1, c2)
assert add(c1, c2) == complex_(complex(4, 6))
assert add(c1, c2) == complex(4, 6)
assert sub(c0, c0) == sub(c1, c1) == 0
assert sub(c1, c2) == complex(-2, -2)
assert negative(complex(1,1)) == complex(-1, -1)
assert negative(complex(0, 0)) == 0
assert multiply(1, c1) == c1
assert multiply(2, c2) == complex(6, 8)
assert multiply(c1, c2) == complex(-5, 10)
assert divide(c0, 1) == c0
assert divide(c2, -1) == negative(c2)
assert divide(c1, complex(0, 1)) == complex(2, -1)
n = divide(c1, O)
assert repr(n.real) == 'inf'
assert repr(n.imag).startswith('inf') #can be inf*j or infj
assert divide(c0, c0) == 1
res = divide(c2, c1)
assert abs(res.real-2.2) < 0.001
assert abs(res.imag+0.4) < 0.001
assert floor_divide(c0, c0) == complex(1, 0)
assert isnan(floor_divide(c0, complex(0, 0)).real)
assert floor_divide(c0, complex(0, 0)).imag == 0.0
assert abs(c0) == 2.5
assert abs(c2) == 5
assert sign(complex(0, 0)) == 0
assert sign(complex(-42, 0)) == -1
assert sign(complex(42, 0)) == 1
assert sign(complex(-42, 2)) == -1
assert sign(complex(42, 2)) == 1
assert sign(complex(-42, -3)) == -1
assert sign(complex(42, -3)) == 1
assert sign(complex(0, -42)) == -1
assert sign(complex(0, 42)) == 1
inf_c = complex_(complex(float('inf'), 0.))
assert repr(abs(inf_c)) == 'inf'
assert repr(abs(complex(float('nan'), float('nan')))) == 'nan'
# numpy actually raises an AttributeError,
# but numpypy raises a TypeError
exc = raises((TypeError, AttributeError), 'c2.real = 10.')
assert str(exc.value) == "readonly attribute"
exc = raises((TypeError, AttributeError), 'c2.imag = 10.')
assert str(exc.value) == "readonly attribute"
assert(real(c2) == 3.0)
assert(imag(c2) == 4.0)