def learn(self, Xtrain, ytrain):
self.features = Xtrain.shape[1]
if not self.usecolumnones:
self.features -= 1
Xtrain = Xtrain[:,0:self.features]
zeroindex = ytrain == 0
self.priozero = float(sum(zeroindex)/Xtrain.shape[0])
self.prioone = 1 - self.priozero
classzero = Xtrain[zeroindex,:]
classone = Xtrain[-zeroindex,:]
self.meanstdev = np.empty((2,2,self.features))
for f in xrange(self.features):
data = classzero[:,f]
self.meanstdev[0,0,f] = utils.mean(data)
self.meanstdev[0,1,f] = utils.stdev(data)
data = classone[:,f]
self.meanstdev[1,0,f] = utils.mean(data)
self.meanstdev[1,1,f] = utils.stdev(data)
def learn(self, Xtrain, ytrain):
# Separate by class
separated = {}
for tt in range(Xtrain.shape[0]):
inputv = Xtrain[tt]
outputy = ytrain[tt]
if outputy not in separated:
separated[outputy] = []
separated[outputy].append(inputv)
for classValue, instances in separated.iteritems():
summ = [(utils.mean(attribute), utils.stdev(attribute)) for attribute in zip(*instances)]
del summ[-1]
self.summaries[classValue] = summ
def divide(self,ds): dividedDS = [(utils.mean(x), utils.stdev(x)) for x in zip(*ds)] del dividedDS[-1] return dividedDS
def end_game(self):
self.mean_a = mean(self.a1)
self.mean_a2 = mean(self.a2)
self.mean_t1 = mean(self.time1)
self.mean_t2 = mean(self.time2)
self.mean_tC = mean(self.trap_body_c)
