def f0(x):
return x in samplespace
def f1(x):
return x == 'dans' or x == 'en'
def f2(x):
return x == 'dans' or x == a_grave
f = [f0, f1, f2]
model = maxentropy.Model(f, samplespace, vectorized=False)
# Now set the desired feature expectations
K = [1.0, 0.3, 0.5]
model.verbose = True
# Fit the model
model.fit(K)
# Output the distribution
print("\nFitted model parameters are:\n" + str(model.params))
print("\nFitted distribution is:")
p = model.probdist()
for j in range(len(model.samplespace)):
x = model.samplespace[j]
def f0(x):
return x in samplespace
def f1(x):
return x == 'dans' or x == 'en'
def f2(x):
return x == 'dans' or x == a_grave
f = [f0, f1, f2]
model = maxentropy.Model(f, samplespace)
# Now set the desired feature expectations
K = [1.0, 0.3, 0.5]
model.verbose = True
# Fit the model
model.fit(K)
# Output the distribution
print("\nFitted model parameters are:\n" + str(model.params))
print("\nFitted distribution is:")
p = model.probdist()
for j in range(len(model.samplespace)):
x = model.samplespace[j]