def test_rule_notself(self):
ctx = Backward()
ctx.evaluate("""
!A => A
""")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
def scaled_estimate(self, x, T):
self.__init_storage(T)
for t in range(T):
fw, bw = Forward(self.A, self.B,
self.rho), Backward(self.A, self.B, self.rho)
P, _ = fw.scaled_evaluate(x), bw.scaled_evaluate(x, fw.C)
self.__scaled_update(fw.alpha, bw.beta, fw.C, x)
self.__store(np.log(fw.C).sum(), t)
def estimate(self, x, T):
self.__init_storage(T)
for t in range(T):
fw, bw = Forward(self.A, self.B,
self.rho), Backward(self.A, self.B, self.rho)
P, _ = fw.evaluate(x), bw.evaluate(x)
self.__update(fw.alpha, bw.beta, x)
self.__store(np.log(P), t)
def test_not(self):
ctx = Backward()
ans = ctx.evaluate("= a")
ans = ctx.evaluate("a")
self.assertEqual(ans, [True])
ans = ctx.evaluate("!a")
self.assertEqual(ans, [False])
ans = ctx.evaluate("!!a")
self.assertEqual(ans, [True])
ans = ctx.evaluate("!!(!a)")
self.assertEqual(ans, [False])
ans = ctx.evaluate("!b")
self.assertEqual(ans, [True])
def test_rule(self):
ctx = Backward()
ans = ctx.evaluate("a => b")
ans = ctx.evaluate("b => c")
ans = ctx.evaluate("= a")
ans = ctx.evaluate("a b c")
self.assertEqual(ans, [True, True, True])
ans = ctx.evaluate("a => d & e & f")
ans = ctx.evaluate("d e f")
self.assertEqual(ans, [True, True, True])
def test_or(self):
ctx = Backward()
ans = ctx.evaluate("= a c")
ans = ctx.evaluate("!a | c")
self.assertEqual(ans, [True])
ans = ctx.evaluate("a | !c")
self.assertEqual(ans, [True])
ans = ctx.evaluate("b | c")
self.assertEqual(ans, [True])
ans = ctx.evaluate("!a | !c")
self.assertEqual(ans, [False])
def test_atom(self):
ctx = Backward()
ans = ctx.evaluate("a")
self.assertEqual(ans, [False])
ans = ctx.evaluate("= a c")
ans = ctx.evaluate("a b c")
self.assertEqual(ans, [True, False, True])
ans = ctx.evaluate("=")
ans = ctx.evaluate("a b c")
self.assertEqual(ans, [False, False, False])
ans = ctx.evaluate("!a")
self.assertEqual(ans, [True])
def test_and(self):
ctx = Backward()
ans = ctx.evaluate("= a b c")
ans = ctx.evaluate("a & b & c")
self.assertEqual(ans, [True])
ans = ctx.evaluate("a & !b & c")
self.assertEqual(ans, [False])
ans = ctx.evaluate("a & !(!b & !c)")
self.assertEqual(ans, [True])
def test_xor(self):
ctx = Backward()
ans = ctx.read("a ^ b")
self.assertEqual(str(ans), "[xor((atom(a), atom(b)))]")
ans = ctx.read("!a ^ b")
self.assertEqual(str(ans), "[xor((not(atom(a)), atom(b)))]")
ans = ctx.read("a ^ !b")
self.assertEqual(str(ans), "[xor((atom(a), not(atom(b))))]")
ans = ctx.read("a ^ b | c")
self.assertEqual(str(ans), "[or((xor((atom(a), atom(b))), atom(c)))]")
def test_or(self):
ctx = Backward()
ans = ctx.read("a | b")
self.assertEqual(str(ans), "[or((atom(a), atom(b)))]")
ans = ctx.read("!a | b")
self.assertEqual(str(ans), "[or((not(atom(a)), atom(b)))]")
ans = ctx.read("a | !b")
self.assertEqual(str(ans), "[or((atom(a), not(atom(b))))]")
ans = ctx.read("a | b & c")
self.assertEqual(str(ans), "[and((or((atom(a), atom(b))), atom(c)))]")
def test_xor(self):
ctx = Backward()
ans = ctx.evaluate("= a c")
ans = ctx.evaluate("a ^ c")
self.assertEqual(ans, [False])
ans = ctx.evaluate("!a ^ c")
self.assertEqual(ans, [True])
ans = ctx.evaluate("a ^ !c")
self.assertEqual(ans, [True])
ans = ctx.evaluate("a ^ b")
self.assertEqual(ans, [True])
def test_rule_and(self):
ctx = Backward()
ans = ctx.evaluate("""
B => A
D & E => B
G & H => F
I & J => G
G => H
L & M => K
O & P => L & N
N => M
""")
ans = ctx.evaluate("= D E I J O P")
ans = ctx.evaluate("A F K P")
self.assertEqual(ans, [True, True, True, True])
ans = ctx.evaluate("= D E I J P")
ans = ctx.evaluate("A F K P")
self.assertEqual(ans, [True, True, False, True])
def test_rule_not1(self):
ctx = Backward()
ctx.evaluate("B & !C => A")
ctx.evaluate("=")
ans = ctx.evaluate("A")
self.assertEqual(ans, [False])
ctx.evaluate("= B")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
ctx.evaluate("= C")
ans = ctx.evaluate("A")
self.assertEqual(ans, [False])
ctx.evaluate("= B C")
ans = ctx.evaluate("A")
self.assertEqual(ans, [False])
def test_atom(self):
ctx = Backward()
ans = ctx.read("a")
self.assertEqual(str(ans), "[atom(a)]")
ans = ctx.read("a b c")
self.assertEqual(str(ans), "[atom(a), atom(b), atom(c)]")
def test_not(self):
ctx = Backward()
ans = ctx.read("!a")
self.assertEqual(str(ans), "[not(atom(a))]")
ans = ctx.read("!a !b")
self.assertEqual(str(ans), "[not(atom(a)), not(atom(b))]")
def test_rule_samerule(self):
ctx = Backward()
ctx.evaluate("""
B => A
C => A
""")
ctx.evaluate("= B")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
ctx.evaluate("= C")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
ctx.evaluate("= B C")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
def test_rule_xor(self):
ctx = Backward()
ctx.evaluate("""
B & C => A
D ^ E => B
B => C
""")
ctx.evaluate("=")
ans = ctx.evaluate("A")
self.assertEqual(ans, [False])
ctx.evaluate("= D")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
ctx.evaluate("= E")
ans = ctx.evaluate("A")
self.assertEqual(ans, [True])
ctx.evaluate("= D E")
ans = ctx.evaluate("A")
self.assertEqual(ans, [False])
num_iterations = 2
np.random.seed(1)
#track the cost
costs = []
print_cost = True
#boucle de 0 à nombre d'iterations:
for i in range (0,num_iterations):
#forward propagation l layers
annForward.forward_layers(X)
cost = annForward.compute_cost(Y)
caches = annForward.caches
parameters = annForward.parameters
# print(parameters)
AL = annForward.AL
annBackward = Backward(AL, Y, caches, parameters)
# #L_model_backward
annBackward.l_model_backward()
newParameters = annBackward.update_parameters()
# Print the cost every 100 training example
if print_cost and i % 1000 == 0:
print(cost)
#print (f"Cost after iteration {i}{cost}")
if print_cost and i % 1000 == 0:
costs.append(cost)
def test_empty(self):
ctx = Backward()
ans = ctx.read("")
self.assertEqual(str(ans), "[]")
If X croaks and X eats flies – Then X is a frog
If X chirps and X sings – Then X is a canary
If X is a frog – Then X is green
If X is a canary – Then X is yellow
And initial facts:
Fritz croaks
Fritz eats flies
Our goal is to decide wether Fritz is green.
"""
if __name__ == "__main__":
ctx = Backward()
# Defining our knowledge base
ctx.evaluate("croaks & eatsFlies => frog")
ctx.evaluate("chirps & sings => canary")
ctx.evaluate("frog => green")
ctx.evaluate("canary => yellow")
# Initial facts
# ctx.evaluate("= croaks eatsFlies")
# Let's bind some questions to ask the user whenever the value of the variable is unknown.
ctx.bind_question("croaks", "Does it croak?")
def test_rule_not2(self):
ctx = Backward()
ctx.evaluate("!A | A => B")
ctx.evaluate("A | !A => C")
ans = ctx.evaluate("B C")
self.assertEqual(ans, [True, True])
from forward import HMM, Forward
from backward import Backward
from math import log
from logsum import log_sum
f = Forward("dev.txt", "hmm-trans.txt", "hmm-emit.txt", "hmm-prior.txt")
b = Backward("dev.txt", "hmm-trans.txt", "hmm-emit.txt", "hmm-prior.txt")
dev_file = "dev.txt"
with open(dev_file, 'r') as file:
for line in file:
words = line.rstrip('\n\r').split(" ")
dev_a = list()
dev_b = list()
# calculate conditional probability
for i in range(len(words)):
t = i + 1
if t == 1:
f.initialize_alpha(words[0], dev_a)
else:
f.calculate_alpha(words[i], t, dev_a)
T = len(words)
t = T
for i in range(len(words)):
if t == T:
beta_T = [log(1) for x in range(b.hmm.N)]
dev_b.append(beta_T)
else:
b.calculate_beta(dev_b, words[t], t - 1, T - 1)
t -= 1