def predict(tree, d):
data = [item for item in d]
tag = [item for item in t]
while isinstance(tree, dict):
fName = tree.keys()[0]
findex = -1
for i in range(len(data)):
if ("feature" + str(i) == fName):
findex = i
break
if tag[findex] == 1: #discrete
try:
tree = tree[fName][data[findex]]
except:
rand = numpy.random.randint(0, 2)
if rand == 0:
return name1
else:
return name2
return 999
else: #numerical
minv = 0.0
maxv = 1.0
delta = (maxv - minv) / NforNumerical + 0.0001
for j in range(NforNumerical):
if (data[findex] >= minv + delta * j
and data[findex] < minv + delta * (j + 1)):
try:
tree = tree[fName][j]
except:
rand = numpy.random.randint(0, 2)
if rand == 0:
return name1
else:
return name2
return 999
break
datatmp = data[:findex]
datatmp.extend(data[findex + 1:])
data = datatmp
tagtmp = tag[:findex]
tagtmp.extend(tag[findex + 1:])
tag = tagtmp
return tree
def predict(tree,d):
data = [item for item in d]
tag = [item for item in t]
while isinstance(tree, dict):
fName = tree.keys()[0]
findex = -1
for i in range(len(data)):
if ("feature"+str(i) == fName):
findex = i
break
if tag[findex] == 1: #discrete
try:
tree = tree[fName][data[findex]]
except:
rand = numpy.random.randint(0,2)
if rand == 0:
return name1
else:
return name2
return 999
else: #numerical
minv = 0.0
maxv = 1.0
delta = (maxv - minv) / NforNumerical + 0.0001
for j in range(NforNumerical):
if (data[findex]>=minv+delta*j and data[findex]<minv+delta*(j+1)):
try:
tree = tree[fName][j]
except:
rand = numpy.random.randint(0,2)
if rand == 0:
return name1
else:
return name2
return 999
break
datatmp = data[:findex]
datatmp.extend(data[findex+1:])
data = datatmp
tagtmp = tag[:findex]
tagtmp.extend(tag[findex+1:])
tag = tagtmp
return tree
def predict(tree,d,t):
data = [item for item in d]
tag = [item for item in t]
while isinstance(tree, dict):
fName = tree.keys()[0]
findex = -1
for i in range(len(data)):
if ("feature"+str(i) == fName):
findex = i
break
if tag[findex] == 1: #discrete
try:
tree = tree[fName][data[findex]]
except:
rand = numpy.random.randint(0,2)
if rand == 0:
return name1
else:
return name2
return 999
else: #numerical
try:
if (data[findex]<=tree[fName][3]):
tree = tree[fName][0]
else:
tree = tree[fName][1]
except:
rand = numpy.random.randint(0,2)
if rand == 0:
return name1
else:
return name2
return 999
datatmp = data[:findex]
datatmp.extend(data[findex+1:])
data = datatmp
tagtmp = tag[:findex]
tagtmp.extend(tag[findex+1:])
tag = tagtmp
return tree
def predict(tree, d, t):
data = [item for item in d]
tag = [item for item in t]
while isinstance(tree, dict):
fName = tree.keys()[0]
findex = -1
for i in range(len(data)):
if ("feature" + str(i) == fName):
findex = i
break
if tag[findex] == 1: #discrete
try:
tree = tree[fName][data[findex]]
except:
rand = numpy.random.randint(0, 2)
if rand == 0:
return name1
else:
return name2
return 999
else: #numerical
try:
if (data[findex] <= tree[fName][3]):
tree = tree[fName][0]
else:
tree = tree[fName][1]
except:
rand = numpy.random.randint(0, 2)
if rand == 0:
return name1
else:
return name2
return 999
datatmp = data[:findex]
datatmp.extend(data[findex + 1:])
data = datatmp
tagtmp = tag[:findex]
tagtmp.extend(tag[findex + 1:])
tag = tagtmp
return tree