"""
Author: Sari Haj Hussein
"""
from pyds import MassFunction
from pyuds import pyuds
m = MassFunction({
"a": 0.26,
"b": 0.26,
"c": 0.26,
"ab": 0.07,
"ac": 0.01,
"ad": 0.01,
"bc": 0.01,
"bd": 0.01,
"cd": 0.01,
"abcd": 0.1
})
print(pyuds.GH(m))
print(pyuds.GS(m))
print(pyuds.AU(m, False))
m = MassFunction([({'a'}, 0.26), ({'b'}, 0.26), ({'c'}, 0.26),
({'a', 'b'}, 0.07), ({'a', 'c'}, 0.01), ({'a', 'd'}, 0.01),
({'b', 'c'}, 0.01), ({'b', 'd'}, 0.01), ({'c', 'd'}, 0.01),
({'a', 'b', 'c', 'd'}, 0.1)])
print(pyuds.GH(m))
print(pyuds.GS(m))
print(pyuds.AU(m, False))
def time_q():
return measure_time(
MassFunction({(s, ): 1.0
for s in range(12)}).normalize().q)
# Combinations
from pyds import MassFunction
# For Discourse "put it down"
m1 = MassFunction({'k': 0.25, 'kt': 0.75})
m2 = MassFunction({'k': 1.0})
m3 = MassFunction({'k': 0.5, 't': 0.5})
m_dcr_putdown = m1.combine_conjunctive(m2, m3)
print("Putdown", m_dcr_putdown.bel())
# For Discourse put it in the bowl
m4 = MassFunction({'k': 0.5, 't': 0.21, 'kt': 0.27})
m5 = MassFunction({'t': 1.0})
m6 = MassFunction({'t': 1.0})
m_dcr_putin = m4.combine_conjunctive(m5, m6)
print("putin", m_dcr_putin)
# For Discoure pass it to me
#In washing dishes context
m7 = MassFunction({'k': 0.4, 't': 0.3, 'kt': 0.3})
m8_w = MassFunction({'k': 1.0})
m9_w = MassFunction({'k': 1.0})
m_dcr_pass_w = m7.combine_conjunctive(m8_w, m9_w)
print("pass -wash", m_dcr_pass_w)
#In cooking context
def time_from_bel():
bel = MassFunction({(s, ): 1.0 for s in range(10)}).normalize().bel()
return measure_time(MassFunction.from_bel, bel)
def time_from_pl():
pl = MassFunction({(s, ): 1.0 for s in range(10)}).normalize().pl()
return measure_time(MassFunction.from_pl, pl)
#n_bits = ecg_signal[3]
#ecg_signal = ecg_signal[4:]
return ev_dist
# Read prob distribution from MLII
ev_dist_MLII = read_ev_dist('ev_dist_MLII_Shanon')
# Read prob distribution from RR
ev_dist_RR = read_ev_dist('ev_dist_RR_Shanon')
# for each instance
final_output = list()
for i in range(0, len(ev_dist_RR)):
# Make the mass asignation
m1 = MassFunction(ev_dist_MLII[i])
m2 = MassFunction(ev_dist_RR[i])
# compute the combination with DS
#m1_2_disj = m1.combine_disjunctive(m2)
m1_2 = m1.combine_conjunctive(m2)
# Pignistic to make a decision
output = m1_2.pignistic()
# Keep output
output_array = [output['N'], output['S'], output['V'], output['F']]
#print(output_array)
max_val = max(output_array)
decision = output_array.index(max_val)
#print('Class: ' + str(decision) + ' val = ' + str(max_val))
def time_from_q():
q = MassFunction({(s, ): 1.0 for s in range(10)}).normalize().q()
return measure_time(MassFunction.from_q, q)
"""
Author: Sari Haj Hussein
"""
from pyds import MassFunction
from pyuds import pyuds
import cgi
import cgitb
formhtml = """Content-Type: text/html\n\n
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>pyuds Web Interface</title>
<script type="text/javascript">
function MM_uncertaintyMenu(targ,selObj,restore){
var verboseCheckBox = document.getElementById("verbose");
if (selObj.selectedIndex == 2)
verboseCheckBox.disabled = false;
else
verboseCheckBox.disabled = true;
}
</script>
</head>
<body>
<center>
<table>
<tr>
<td>
<p style="font-size:72px;font-weight:bold;margin:auto"><a href="https://sourceforge.net/projects/pyuds/" target="_blank" style="text-decoration:none;color:#000;outline:none">pyuds</a>
df = pd.DataFrame({
"m": ms,
"Bel": beliefs,
"Pl": plausibilities
},
index=hypotheses)
return df
########### EXERCICE 1
''' OMEGA = {Setosa (S), Versicolor (V), Virginica (I) } '''
# 1/ Modélisation des connaissances
m1 = MassFunction({'V': 0.6, 'SVI': 0.4})
m2 = MassFunction({'S': 0.1, 'SI': 0.5, 'SVI': 0.4})
m3 = MassFunction({'SVI': 1})
print("1/ Modélisation des connaissances :\n")
print('M1 : ' + str(m1))
print('M2 : ' + str(m2))
print('M3 : ' + str(m3) + "\n\n\n")
# 2/ Calcul des degrés de croyance et des degrés de plausibilité du premier expert
df_m1 = calcul_degres_croyances_plausibilite(m1)
print(
"2/ Calcul des degrés de croyance et des degrés de plausibilité du premier expert\n"
)
print(df_m1.to_markdown())
def test_frame(self):
self.assertEqual({'a', 'b', 'c', 'd'}, self.m1.frame())
self.assertEqual({'a', 'b', 'c'}, self.m2.frame())
self.assertEqual(set(), MassFunction().frame())
def test_norm(self):
self.assertEqual(0, self.m1.norm(self.m1))
self.assertEqual(0, self.m1.norm(self.m1, p=1))
m3 = MassFunction({'e': 1.0})
len_m1 = sum([v**2 for v in self.m1.values()])
self.assertEqual((1 + len_m1)**0.5, self.m1.norm(m3))
def test_multiple_dimensions(self):
md1 = MassFunction({(('a', 1), ('b', 2)): 0.8, (('a', 1), ): 0.2})
md2 = MassFunction({(('a', 1), ('b', 2), ('c', 1)): 1.0})
md12 = md1 & md2
self.assertAlmostEqual(0.2, md12[{('a', 1)}])
self.assertAlmostEqual(0.8, md12[{('a', 1), ('b', 2)}])
