def training(a, b, problem, story, target, j, order, score, constraints):
#this function take the trips and creates positive and negative training
# instances from them
if j == 0:
j = -1
vec = [j, order, score, constraints]
vec.extend(makesets.eqvector(a, b, problem, story, target))
return vec
def training(trips, problem, story, target, sets):
#this function take the trips and creates positive and negative training instances from them
texamples = {x: ([], []) for x in ["+", "*", '/', '-', '=']}
for op, a, b in trips:
if op == '=':
vec = makesets.eqvector(a, b, problem, story, target, sets)
else:
vec = makesets.vector(a, b, problem, story, target)
texamples[op][0].append(vec)
return texamples
def training(trips, problem, story, target):
# this function take the trips and creates positive and negative training instances from them
texamples = {x: ([], []) for x in ["+", "*", "/", "-", "="]}
for op, a, b in trips:
if op == "=":
vec = makesets.eqvector(a, b, problem, story, target)
else:
vec = makesets.vector(a, b, problem, story, target)
texamples[op][0].append(vec)
return texamples
def compute(p,op,e,target,problem,story,order,sp=None):
if op == '==':
vec = [order].extend(makesets.eqvector(p,e,problem,story,target,sp))
op_label, op_acc, op_val = svm_predict([-1], [vec], glob ,'-q -b 1')
else:
vec = makesets.vector(p,e,problem,story,target)
op_label, op_acc, op_val = svm_predict([-1], [vec], multi ,'-q -b 1')
op_val=op_val[0]
if op == '+':
val = op_val[0]
if op == '-':
val = op_val[1]
if op == '*':
val = op_val[2]
if op == '/':
val = op_val[3]
if op == '=':
val = op_val[1]
c = makesets.combine(p[1],e[1],op)
return (val,c,op_val)