def test_validate_base1():
good = ['e', 'linear', 0.5, 1.5, 2, 10]
for value in good:
assert_equal(validate_base(value), value)
def test_validate_base2():
bad = ['nope', -0.5, 0, 1]
for value in bad:
with pytest.raises(InvalidBase):
validate_base(value)
def copypmf(d, base=None, mode='asis'):
"""
Returns a NumPy array of the distribution's pmf.
Parameters
----------
d : distribution
The distribution from which the pmf is copied.
base : float, 'linear', 'e', None
The desired base of the probabilities. If None, then the probabilities
maintain their current base.
mode : ['dense', 'sparse', 'asis']
A specification of how the pmf should be construted. 'dense' means
that the pmf should contain the entire sample space. 'sparse' means
the pmf should only contain nonnull probabilities. 'asis' means to
make a copy of the pmf, as it exists in the distribution.
Returns
-------
pmf : NumPy array
The pmf of the distribution.
"""
from dit.math import get_ops
from dit.params import validate_base
# Sanitize inputs, need numerical base for old base.
base_old = d.get_base(numerical=True)
if base is None:
base_new = base_old
else:
base_new = validate_base(base)
# Create ops instances.
ops_old = d.ops
ops_new = get_ops(base_new)
# Build the pmf
if mode == 'asis':
pmf = np.array(d.pmf, copy=True)
elif mode == 'dense':
pmf = np.array([d[o] for o in d.sample_space()], dtype=float)
elif mode == 'sparse':
pmf = np.array([p for p in d.pmf if not ops_old.is_null(p)],
dtype=float)
# Determine the conversion targets.
islog_old = d.is_log()
if base_new == 'linear':
islog_new = False
else:
islog_new = True
# Do the conversion!
if islog_old and islog_new:
# Convert from one log base to another.
## log_b(x) = log_b(a) * log_a(x)
pmf *= ops_new.log(base_old)
elif not islog_old and not islog_new:
# No conversion: from linear to linear.
pass
elif islog_old and not islog_new:
# Convert from log to linear.
## x = b**log_b(x)
pmf = base_old**pmf
else:
# Convert from linear to log.
## x = log_b(x)
pmf = ops_new.log(pmf)
return pmf
def copypmf(d, base=None, mode='asis'):
"""
Returns a NumPy array of the distribution's pmf.
Parameters
----------
d : distribution
The distribution from which the pmf is copied.
base : float, 'linear', 'e', None
The desired base of the probabilities. If None, then the probabilities
maintain their current base.
mode : ['dense', 'sparse', 'asis']
A specification of how the pmf should be construted. 'dense' means
that the pmf should contain the entire sample space. 'sparse' means
the pmf should only contain nonnull probabilities. 'asis' means to
make a copy of the pmf, as it exists in the distribution.
Returns
-------
pmf : NumPy array
The pmf of the distribution.
"""
from dit.math import get_ops
from dit.params import validate_base
# Sanitize inputs, need numerical base for old base.
base_old = d.get_base(numerical=True)
if base is None:
base_new = base_old
else:
base_new = validate_base(base)
# Create ops instances.
ops_old = d.ops
ops_new = get_ops(base_new)
# Build the pmf
if mode == 'asis':
pmf = np.array(d.pmf, copy=True)
elif mode == 'dense':
pmf = np.array([d[o] for o in d.sample_space()], dtype=float)
elif mode == 'sparse':
pmf = np.array([p for p in d.pmf if not ops_old.is_null(p)], dtype=float)
# Determine the conversion targets.
islog_old = d.is_log()
if base_new == 'linear':
islog_new = False
else:
islog_new = True
# Do the conversion!
if islog_old and islog_new:
# Convert from one log base to another.
## log_b(x) = log_b(a) * log_a(x)
pmf *= ops_new.log(base_old)
elif not islog_old and not islog_new:
# No conversion: from linear to linear.
pass
elif islog_old and not islog_new:
# Convert from log to linear.
## x = b**log_b(x)
pmf = base_old**pmf
else:
# Convert from linear to log.
## x = log_b(x)
pmf = ops_new.log(pmf)
return pmf
def test_validate_base1():
good = ['e', 'linear', 0.5, 1.5, 2, 10]
for value in good:
assert_equal(validate_base(value), value)
def test_validate_base2():
bad = ['nope', -0.5, 0, 1]
for value in bad:
with pytest.raises(InvalidBase):
validate_base(value)
