def manualWeight(self, data, target):
w = 0
X = np.zeros((np.size(data, 0) * 4, np.size(data, 1)))
Y = np.zeros(len(target) * 4)
for i in range(len(data)):
PlayType, Result = restorePlayType(target[i])
for j in range(sFunction(Result)):
X[w, :] = data[i, :]
Y[w] = target[i]
w += 1
return X, Y
def manualWeight(self, data, target):
w=0
X = np.zeros((np.size(data,0)*4, np.size(data,1)))
Y = np.zeros(len(target)*4)
for i in range(len(data)):
PlayType, Result = restorePlayType(target[i])
for j in range(sFunction(Result)):
X[w,:] = data[i,:]
Y[w] = target[i]
w += 1
return X, Y
def recommendationSingle(self, classType):
PlayType, Result = restorePlayType(classType)
if gFunction(Result) < 0.0001:
PlayType = self.switchPlayType(PlayType)
recommendationCalss = (PlayType - 1) * 10 + Result
return recommendationCalss
def recommendationSingle(self,classType):
PlayType, Result = restorePlayType(classType)
if gFunction(Result) < 0.0001:
PlayType = self.switchPlayType(PlayType)
recommendationCalss = (PlayType-1)*10 + Result
return recommendationCalss
