def test_norm_diff(self):
"""norm_diff should calculate per-element rms difference"""
m = array([[1.0,2,3],[4,5,6], [7,8,9]])
m2 = array([[1.0,1,4],[2,6,-1],[8,6,-5]])
#matrix should not be different from itself
self.assertEqual(norm_diff(m,m), 0.0)
self.assertEqual(norm_diff(m2,m2), 0.0)
#difference should be same either direction
self.assertEqual(norm_diff(m,m2), sqrt(257.0)/9)
self.assertEqual(norm_diff(m2,m), sqrt(257.0)/9)
def test_heuristics(p_range=None, num_to_do=71, heuristics=None):
if p_range is None:
p_range = [0.6]
if heuristics is None:
heuristics = [
'fixNegsDiag', 'fixNegsEven', 'fixNegsReflect',
'fixNegsConstrainedOpt'
]
num_heuristics = len(heuristics)
print '\t'.join(['p'] + heuristics)
for p in p_range:
result = zeros((num_to_do, num_heuristics), Float64)
has_nonzero = 0
i = 0
while i < num_to_do:
curr_row = result[i]
random_p = Probs.random(DnaPairs, p)
q = random_p.toRates()
if not q.hasNegOffDiags():
continue
has_nonzero += 1
#print "P:"
#print random_p._data
#print "Q:"
#print q._data
i += 1
for j, h in enumerate(heuristics):
#print "HEURISTIC: ", h
q_corr = getattr(q, h)()
#print "CORRECTED Q: "
#print q_corr._data
p_corr = expm(q_corr._data)(t=1)
#print "CORRECTED P:"
#print p_corr
dist = norm_diff(p_corr, random_p._data)
#print "DISTANCE: ", dist
curr_row[j] = dist
averages = average(result)
print p, '\t', '\t'.join(map(str, averages))
def test_heuristics(p_range=None, num_to_do=71, heuristics=None):
if p_range is None:
p_range = [0.6]
if heuristics is None:
heuristics = ['fixNegsDiag', 'fixNegsEven', 'fixNegsReflect', 'fixNegsConstrainedOpt']
num_heuristics = len(heuristics)
print '\t'.join(['p'] + heuristics)
for p in p_range:
result = zeros((num_to_do, num_heuristics), Float64)
has_nonzero = 0
i = 0
while i < num_to_do:
curr_row = result[i]
random_p = Probs.random(DnaPairs, p)
q = random_p.toRates()
if not q.hasNegOffDiags():
continue
has_nonzero += 1
#print "P:"
#print random_p._data
#print "Q:"
#print q._data
i += 1
for j, h in enumerate(heuristics):
#print "HEURISTIC: ", h
q_corr = getattr(q, h)()
#print "CORRECTED Q: "
#print q_corr._data
p_corr = expm(q_corr._data)(t=1)
#print "CORRECTED P:"
#print p_corr
dist = norm_diff(p_corr, random_p._data)
#print "DISTANCE: ", dist
curr_row[j] = dist
averages = average(result)
print p, '\t', '\t'.join(map(str, averages))
